Effects of Behavioral Coaching on Exercise Behavior and Adherence … · 2020. 4. 29. · EXERCISE...

EXERCISE ADHERENCE 1

Effects of Behavioral Coaching on Exercise Behavior and Adherence

Jessica R. Mias1

1 Department of Behavior Analysis, Simmons University

Author Note

Jessica R. Mias https://orcid.org/0000-0002-8753-0308

This manuscript was completed in partial fulfillment of the requirements for the degree of

Doctor of Philosophy in Behavior Analysis at Simmons University in Boston, Massachusetts.

Correspondence concerning this manuscript should be addressed to Jessica R. Mias,

Department of Behavior Analysis, Simmons University, Boston, Massachusetts 02115. Email:

[email protected].

mailto:[email protected]


Effects of Behavioral Coaching on Exercise Behavior and

Adherence

Dissertation

Presented in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy in the

College of Natural, Behavioral, and Health Sciences

By

Jessica R. Mias

Simmons University

April 23rd, 2020

Dissertation Committee Signatures:

Russell W. Maguire, Ph.D., BCBA-D, LABA

Raymond G. Miltenberger, Ph.D., BCBA-D

4/23/2020

Approved by:

Gretchen A. Dittrich, Ph.D., BCBA-D, LABA


Abstract

Optimal health outcomes are positively correlated with regular exercise, yet nearly one-quarter

of adults in the United States reportedly do not participate in physical activity during their free

time. The purpose of the current study was to evaluate the effects of gradually faded behavioral

coaching for increasing both physical activity frequency and duration during the study, and to

evaluate maintenance of treatment effects post-intervention. Participants were divided into two

groups, matched according to age and body mass index. One group received behavioral coaching

once per week for the duration of the intervention and the other group experienced gradual

fading of behavioral coaching. Results showed an increase in frequency and duration of exercise

for participants in both groups from baseline to intervention. A statistically significant difference

was found in the duration of activity between groups; the continuous coaching group decreased

the duration of physical activity more than the faded coaching group in the follow-up period,

however no statistically significant difference was found in frequency between groups during

that period. Additionally, no statistically significant difference was found between the faded

coaching group and continuous coaching group from post-intervention to follow-up, suggesting

that the 12-week faded coaching and continuous coaching interventions were equally effective

for maintaining physical activity behavior. Results showed individuals in both groups decreased

physical activity post-intervention to follow-up. Implications of the results and future research

are discussed.

Keywords: behavioral coaching, goal setting, increasing physical activity, self-

monitoring, exercise adherence, physical activity, maintenance of physical activity


Effects of Behavioral Coaching on Exercise Behavior and Adherence

The prevalence of overweight and obesity in the adult population is increasing (Hedley et

al., 2004). In the period between 1999-2014 in the United States, there was a significant increase

in the prevalence of obesity for adults (Ogden et al., 2015). In the United States between 2011

and 2014, one-third of adults met the criteria for obesity (National Center for Health Statistics,

2015), and nearly three-quarters of adults were overweight including, obese (Centers for Disease

Control [CDC], 2015), with 65% of adults being overweight or obese (National Center for

Chronic Disease Prevention and Health Promotion [NCCDPHP], 2017). Obesity is on the rise

across all sociodemographic groups; however, there appears to be a greater increase in obesity

for women and African Americans (Wyatt et al., 2006).

The most common method for classifying individuals as overweight or obese is through

determining a person’s body mass index1 (BMI; Van Itallie, 1985). There are three main

categories of BMI including healthy, overweight, and obese (Wyatt et al., 2006). An individual is

considered of healthy weight with a BMI between 18.9-24.9, and is considered overweight when

BMI is between 25.0-29.9 (CDC, 2016). A person is considered obese when BMI exceeds 30.0

(CDC, 2016). There are further staging classifications of obesity with three stages (Wyatt et al.,

2006). In stage I, a person has a BMI between 30.0-34.9. When a person is in stage II obesity,

BMI is between 35.0-39.9, and in stage III obesity the individual has a BMI that exceeds 40.0

(Wyatt et al., 2006).

Complications of Overweight and Obesity

1 Body mass index (BMI) is a height-to-weight ratio that is commonly used as an indicator of body fat and to

categorize weight (CDC, 2016). BMI is calculated by dividing weight in kilograms by square of height (i.e., 𝑘𝑔

ℎ𝑒𝑖𝑔ℎ𝑡2 ).


Overweight, obesity, and physical inactivity are associated with a variety of health

conditions that decrease quality of life (Wyatt et al., 2006). There are myriad complications and

increased challenges facing individuals who are overweight or obese, including both medical and

social consequences. Health complications arising from being overweight and obese include

increased risk of diabetes (Must et al., 1999; Van Itallie, 1985), hypertension (Rahmouni et al.,

2004), asthma, high cholesterol, and overall poorer health quality than individuals of healthy

weight (Mokdad et al., 2003). Additional deleterious effects of overweight and obesity include

increased stroke risk, increased cancer risk, osteoarthritis, as well as liver and gallbladder disease

(Kopelman, 2007). As BMI approaches 30.0 or higher, reductions in life expectancy increase,

though it is difficult to directly link increased BMI to increased risk of death (Wyatt et al., 2006).

Previous research suggested that individuals with higher levels of obesity are at increased

mortality risk (Flegal et al., 2005; Fontaine et al., 2003).

In addition to the medical complications associated with overweight and obesity, there

are social consequences (Wyatt et al., 2006). Individuals who are overweight or obese are less

likely to marry and to be hired by prospective employers, and may be more likely to receive

poorer healthcare treatment than individuals of a healthy weight (Puhl, & Heuer, 2010; Wyatt et

al., 2006). Individuals who are obese may be more likely to face discrimination from healthcare

practitioners and have less access to preventive screenings (Puhl & Heuer, 2010). There may also

be a link between overweight and obesity and depression; individuals who are overweight or

obese for many years reportedly are at higher risk for mild depression (Dankel et al., 2016).

Healthcare costs for individuals who are overweight and obese are higher compared to

costs for individuals with BMI in the healthy range (Wee et al., 2005). Increased healthcare costs

take the form of higher medication costs, increased office visits, as well as inpatient and


emergency hospital services (Wee et al., 2005). Hospitalization and prescription medication costs

account for the majority of increased expenses for individuals who are overweight and obese

(Wee et al., 2005). With the increase in overweight and obese individuals in the United States,

recommendations for physical activity have been set forth by national agencies (i.e., American

Heart Association and U.S. Department of Health and Human Services).

Definition and Recommendations for Physical Activity

Physical activity is defined as bodily movements of the skeletal muscles resulting in low-

to high-energy expenditure (Caspersen et al., 1985). The amount of energy expenditure, or

caloric expenditure, during physical activity is determined by the intensity, duration, and

frequency of muscle contractions during the activity (Caspersen et al., 1985). Physical activity is

a broad category that includes movement for occupational and leisure activities, exercise

(Caspersen et al., 1985), and transportation purposes (Owen et al., 2004). Exercise is defined as a

subsection of physical activity that is completed to improve physical fitness and is planned,

repetitive, and structured (Caspersen et al., 1985). There are five health-related components of

physical fitness, including muscular endurance, muscular strength, flexibility, cardiorespiratory

endurance, and body composition (Caspersen et al., 1985).

This paper will focus on three main categories of exercise activity, including aerobic,

muscular, and flexibility (American College of Sports Medicine [ACSM], 2018). Aerobic

activities involve performing large muscle, dynamic activities for long durations (ACSM, 2018),

and include activities such as walking, running, and bicycling. Muscular activities build muscle

strength, power, and endurance, and include activities such as resistance training with weights or

body weight resistance (ACSM, 2018). Some examples of muscular activities include push-ups,


squats, leg presses, chest presses, bicep curls, and deadlifts (ACSM, 2018). Flexibility exercises

focus on improving range of motion of muscles and tendons throughout the body (ACSM, 2018).

Additionally, there are different classifications of intensity of exercise based on

metabolic equivalents, or energy expenditure, ranging from light to vigorous (ACSM, 2018).

Examples of light-intensity exercise include walking slowly, making a bed, ironing, preparing

food, and fishing (ACSM, 2018). Moderate-intensity physical activity examples include walking

at a brisk pace, washing a car, vacuuming, stacking wood, dancing, and playing tennis (ACSM,

2018). Vigorous-intensity physical activity includes jogging and running, hiking, shoveling,

carrying heavy loads, bicycling, playing basketball and soccer, and swimming (ACSM, 2018).

The American Heart Association (AHA, 2016) recommends adults participate in at least

30 minutes of moderate-intensity cardiovascular activity, five times per week, totaling 150

minutes per week. When walking to meet the guideline of moderate-to-vigorous activity,

individuals should walk at a rate of more than 100 steps per minute (Marshall et al, 2009). The

AHA (2016) also recommends that adults walk at least 10,000 steps a day. Additionally, for

optimal health maintenance, adults should regularly engage in strength-based exercises targeting

all muscle groups at least two days per week (U.S. Department of Health and Human Services

[USDHHS], 2008). Individuals who have been sedentary and have other cardiovascular

conditions should only begin exercise programs under medical supervision to monitor the effects

of exercise and blood pressure during exercise, and should begin with walking (Pescatello et al.,

2004).

Health Benefits of Physical Activity

Individuals who engage in regular physical activity are at a decreased risk of chronic

diseases and premature death (Warburton et al., 2006). Engaging in regular physical activity


results in improvements in bodily systems and processes, including the vascular and

inflammatory systems, blood pressure, cholesterol levels, and glucose control (Warburton et al.,

2006). Additionally, regular exercise is correlated with lower resting heart rate (Martin &

Dubbert, 1982) and blood pressure (Pescatello et al., 2004). Lower resting heart rate may be

linked to decreased risk of death due to myocardial infarctions (Fox et al., 2007). Research also

suggested that engaging in physical activity may assist with glucose management for individuals

with non-insulin-dependent diabetes mellitus (Leung et al., 2007; Martin & Dubbert, 1982b).

Additionally, engaging in regular physical activity may be correlated with reductions in self-

reported depressive symptoms for individuals who are overweight or obese (Dankel et al., 2016).

Another benefit of regular exercise is that of improved quality of sleep and longer sleep

durations, decreases in daytime sleepiness, and shortened sleep latency (Kredlow et al., 2015).

Research findings suggested that engaging in moderate-intensity aerobic physical activity

for between 150-250 minutes per week may lead to moderate weight loss (Donnelly et al., 2009).

However, increasing physical activity for individuals who are overweight and obese as a

standalone weight loss intervention will likely not be enough to lead to dramatic weight loss,

which would need to be combined with caloric restriction (Donnelly et al., 2009; Martin &

Dubbert, 1982). There does appear to be an inverse relationship between weight and caloric

expenditure. That is, when caloric expenditure increases through exercise and exceeds caloric

intake, weight measures decrease (Slentz et al., 2004).

Different types of physical activity have different health benefits. For individuals who

engage in resistance training and strengthening activities on a regular basis, weight measures

may not change, as resistance training increases muscle mass, and muscle mass weighs more

than adipose tissue (Donnelly et al., 2009). Despite limited changes in body weight with


resistance training, there are health benefits gained from resistance training, including

improvements in cardiovascular systems, decreases in adipose tissue, and improvements in

glucose homeostasis (Donnelly et al., 2009). Fagard (2006) conducted a review of existing

literature on the effects of aerobic and muscular exercise and found that both aerobic and

muscular exercise are associated with decreases in blood pressure, though the extant research is

more supportive of this effect for aerobic exercise over muscular exercise. One benefit specific

to engaging in weight-bearing activities is the preservation of bone mass, which is especially

important for women as they age (Kohrt et al., 2004).

Bone mass is developed through high intensity resistance training and exercise in

childhood, and these gains are often maintained through adulthood (Kohrt et al., 2004). During

adulthood, decreases in bone mass are observed, most notably in women, which leads to

osteoporosis (Kohrt et al., 2004). Building bone mass in adulthood is challenging, and the effects

of exercise on bone mass in adulthood end when the exercise program ends (Kohrt et al., 2004).

Due to the short-term nature of bone mass gains during exercise in adulthood, it is important for

adults to continue to engage in exercise to promote bone health and prevent osteoporosis (Kohrt

et al., 2004). However, it may not be necessary to engage in high-intensity activities to reap

health benefits.

Some research suggested that engaging in regular light-to-moderate physical activity has

similar health benefits as moderately vigorous physical activity (Siegel et al., 1995). This is

important because it suggests that regular walking, which is low-impact and has a lower risk of

injury than more high-impact forms of physical activity, may be a viable option for improving

health outcomes for individuals who are beginning exercise programs. Additionally, individuals


who are overweight and obese tend to prefer walking as the primary method of physical activity

(Siegel et al., 1995).

Physical Inactivity

Physical inactivity can be defined as engaging in less than 30 minutes of physical activity

three times a week (Pratt et al., 2000). There is a link between overweight and obesity and

limited physical activity (Coleman et al., 1999), and engaging in regular activity reduces the

risks associated with overweight and obesity (Sallis & Owen, 1999).

Prevalence of Inactivity

Despite recommendations and public health guidelines for physical activity, it is

estimated that almost one quarter of adults in the United States do not participate in physical

activity during their free time (USDHHS, 2015). It is also estimated that at least 40% of adults

aged 35 and older do not engage in 30 or more minutes of moderate physical activity five or

more days per week, or 20 or more minutes of vigorous physical activity three times per week

(NCCDPHP, 2017).

Problems Associated with Chronic Inactivity

Research suggested that individuals who have higher body weight tend to be more

sedentary than individuals of lower body weights (Sherwood & Jeffrey, 2000; Siegel et al.,

1995). There may be a positive correlation between physical inactivity, smoking, and unhealthy

eating (Sherwood & Jeffrey, 2000), each of which lead to additional health risks.

Research has shown that there is a link between inactivity and the development of Type 2

diabetes, and this risk is increased when BMI is high (Warburton et al., 2006). Research

suggested that there is a link between breast and colon cancers and physical inactivity

(Warburton et al., 2006). Also, there is a link between hypertension, blood pressure that is equal


to or exceeds 140/90 mm Hg, and physical inactivity (Pescatello et al., 2004). Hypertension is

related to other cardiovascular diseases including stroke, coronary heart disease, heart failure,

and renal disease (Pescatello et al., 2004).

In addition to the health complications of physical inactivity directly experienced by

individuals, there are also direct economic costs associated with physical inactivity. It is

estimated that between 1%-2.6% of national health care costs are related to physical inactivity

(Pratt et al., 2000; Pratt et al., 2014). Pratt and colleagues (2000) conducted a cross-sectional

investigation of medical expenses using a survey of national data on health care expenditures of

20,000 respondents over the age of 15 years. Respondents were interviewed, completed a

questionnaire, and medical providers were surveyed. Researchers found that physically inactive

persons incurred higher annual medical costs in the form of hospital visits, doctor visits, and

medication costs than persons who were physically active (Pratt et al., 2000).

Measuring and Assessing Physical Activity

Dependent Variables

Dependent variables in physical activity interventions often include frequency, duration,

and intensity of exercise (Martin & Dubbert, 1987), as well as changes in BMI. Additionally,

heart rate as a dependent variable provides information about the intensity of exercise (Martin &

Dubbert, 1987). Heart rate is used as an indicator of cardiovascular stress during physical activity

and exercise (Strath et al., 2013).

Measures of Changes in Anthropometrics

Physical fitness tests include anthropometry measures that determine height, body

weight, and body composition. Height measures are taken while a person stands barefoot on a


stadiometer (ACSM, 2018). Weight measures are obtained while a person is wearing minimal

clothing using an electronic scale (ACSM, 2018).

BMI is used to estimate weight relative to height, and provides a standardized method

for classifying weight. However, BMI does not consider body composition, including lean

muscle mass, adipose tissue, and bone (Ogden et al., 2015; Wyatt et al., 2006). The relationship

between BMI and body fat varies by race, gender, and sex (Ogden et al., 2015). Males and

younger individuals tend to have a lower percentage of body fat with a given BMI than older

individuals and females with the same BMI (Wyatt et al., 2006). When assessing changes in BMI

as a result of physical activity interventions, changes in BMI may not be observed (Donaldson &

Normand, 2009), as the distinction between lean muscle mass, adipose tissue, and bone is not

made using BMI (ACSM, 2018). For direct measures of body fat, it is advisable to compare lean

tissue and healthy ranges of fat to determine body composition (Drenowatz et al., 2016).

One method for quantifying body fat is completed through circumference measurements

at eight sites on the body, including abdomen, arm, hips/buttocks, calf, forearm, hips/thighs, mid-

thigh, and waist (ACSM, 2018). When circumference measurements are used, an inelastic tape

measure is wrapped around each area of the body, and the circumference of each region is

recorded (ACSM, 2018). Circumference measurements are then repeated for reliability purposes

(ACSM, 2018). Another body composition measure used in physical fitness assessments is

obtained through skinfold measurements (ACSM, 2018). When skinfold measurements are taken

with calipers, estimates of body fat can be made by measuring the thickness of folds of skin in

nine specific places on the body including, abdomen, triceps, biceps, chest, medial calf, sternum,

scapula, iliac crest, and thigh (ACSM, 2018). During skinfold tests, calipers are placed on each

area of the body, causing the skin to pinch and collect in the caliper, and the width of the fold is


measured (ACSM, 2018). When taking skinfold measurements, all measurements are taken on

the right side of the body with the calipers directly contacting the skin (ACSM, 2018). Duplicate

measures are taken at all nine skinfold sites and repeated again if the measurements are not

within 1-2 mm (ACSM, 2018).

Measures of Health and Fitness

During participation in physical activity interventions, one way to determine the

effectiveness of the intervention is by measuring changes in specific health-related variables.

Engaging in regular physical activity for an extended period of time results in changes in health

and fitness measures (Pescatello et al., 2004). For example, prolonged participation in endurance

activities leads to reductions in blood pressure and heart rate (ACSM, 2018; Pescatello et al.,

2015). Measuring cardiorespiratory fitness provides information about functioning of the

respiratory system, cardiovascular system, and the musculoskeletal system (ACSM, 2018). Heart

rate can be measured using a heart rate monitor, or by placing an index and middle finger over

the artery on the wrist and counting heart beats for one minute, or 30 seconds and then

multiplying by 2 (ACSM, 2018). Resting heart rate refers to number of beats per minute while at

rest, and the healthy range is 60-80 beats per minute (LeWine, 2018). Recovery heart rate, or

heart rate taken 1 minute post-exercise, decreases as cardiorespiratory fitness improves (ACSM,

2018). Resting blood pressure is measured using a blood pressure cuff and stethoscope, after the

participant has been seated and inactive for 5-minutes (ACSM, 2018). Blood pressure is

considered in the healthy range when measurements are <120/<80 mm Hg (ACSM, 2018).

Ratings of perceived exertion provide information about a person’s exercise tolerance

and about the limits of appropriate levels of exercise (ACSM, 2018). Ratings of perceived

exertion can be established by using the Borg Rating of Perceived Exertion Scale (Borg, 1998),


which involves having participants rate their overall feelings of exertion while engaging in

physical activity. Scores on the Borg Rating of Perceived Exertion Scale range from 6-20, with 6

representing no exertion and 20 representing maximal exertion (Borg, 1998).

Measures of endurance and fitness can occur via field tests, in which participants are

asked to walk or run for a specific duration or distance (ASCM, 2018). The Rockport One-Mile

Fitness Walking Test is a field test in which participants walk one mile as quickly as possible,

and the total time required to perform the task is recorded. With increases in fitness, the total

duration to walk one mile should decrease (ACSM, 2018). Heart rate is taken in the final minute

of the mile of the Rockport One-Mile Fitness Walking Test to provide a measure of fitness

(ACSM, 2018). For the Rockport One-Mile Fitness Walking Test, as cardiorespiratory fitness

increases from participating in regular physical activity, measures of heart rate and blood

pressure should decrease (ACSM, 2018).

Measures of Physical Activity

There are subjective and objective methods for measuring the frequency, duration, and

intensity of physical activity.

Subjective Measurement. Subjective methods rely on self-report measures (Sallis &

Owen, 1999). Self-reporting requires individuals to report physically active behavior, which can

be completed via surveys asking people to recall activity in a specified time period, or by having

people record activity once it has ended (Sallis & Owen, 1999). Self-report measures are

inexpensive and easy to administer (Sallis & Owen, 1999). Some limitations of self-report

measures include low reliability, accuracy, and validity between self-reported data and

objectively recorded data (Sallis & Owen, 1999; Troiano et al., 2007). There are differences

when individuals self-report physical activity and when objective measurements taken from


accelerometers are used, with self-reporting potentially being higher than what is measured by

the device (Jakicic et al., 1995). Consequently, if self-reporting is used as a method for

evaluating treatment effects, an objective measure of physical activity should also be used (Sallis

& Owen, 1999; Troiano et al., 2007).

Objective Measurement. Objective measurement of physical activity is considered the

gold standard of measurement (Sallis & Owen, 1999). Objective measurements of physical

activity and exercise behavior have been made more accessible and convenient with the

development of activity trackers. Pedometers are one form of inexpensive activity trackers that

count steps taken (Tudor-Locke et al., 2002). Pedometers are limited by their ability to only

measure vertical movement from the waist and below, and do not account for changes in

elevation while walking (Marshall et al., 2009). Another limitation of pedometers is that they

tend to underestimate physical activity, as only movement of the hip or hand is recorded,

depending on where the device is worn (Van Camp & Hayes, 2012). Different pedometers may

yield different step counts (Butte et al., 2012). Consequently, assessment of the reliability of

pedometers is important. One method for assessing reliability of pedometers is to have a person

wear the activity tracker and measure steps taken on a treadmill at different speeds by manually

counting the steps and comparing those counts to the device counts (Kooiman et al., 2015).

Heart rate monitors worn in contact with the skin can measure electrical activity of the

heart and can provide information about levels of physical activity when worn throughout the

day (Van Camp & Hayes, 2012). Heart rate monitors can be used to estimate the intensity of

physical activity (Sallis & Owen, 1999). While heart rate monitors are useful for recording and

measuring heart rate, there are other variables that impact an individual’s heart rate, including

body temperature, weight, body position, and medications (American Heart Association, 2015),


and heart rate monitors may be less sensitive to distinguishing between light-and moderate-

intensity levels of activity (Sallis & Owen, 1999). Larson and colleagues (2011) compared the

use of pedometers and heart rate monitors to measure physical activity levels of children, with

the goal of determining the correspondence of both devices for measuring activity levels. Results

revealed some correspondence between the pedometer and the heart rate monitor and both

devices recorded increases in activity level (Larson et al., 2011). One major difference noted by

the authors was that the heart rate monitor showed consistently higher activity levels across all of

the conditions, whereas the pedometer showed step counts that were more variable (Larson et al.,

2011). The implication of these findings is that heart rate monitors may be the favored tool for

objectively and accurately measuring physical activity (Larson et al., 2011).

Accelerometers are more sophisticated and expensive devices that contain sensors that

measure the intensity and duration of physical activity, approximate steps taken, and measure

sedentary behavior (Tudor-Locke et al., 2002). Accelerometers detect movement in three planes

including, vertical, forward and backward, and side to side (Sylvia et al., 2014). Tudor-Locke

and colleagues (2002) conducted a study in which they compared the simultaneous use of an

accelerometer and pedometer to determine if using the less expensive pedometer would be a

viable alternative to using a more expensive accelerometer in large-scale public health initiatives

aimed at increasing physical activity. Participants in their study wore the pedometer and

accelerometer concurrently on the waist of their clothing for seven days while they were

instructed to engage in typical daily activities. Results showed a small correlation between steps

measured by the pedometer and activity measured by the accelerometer. However, for exact step

counts, there was less agreement between devices. The authors suggested one reason for the

difference was that the pedometer used in the study may have under-recorded steps taken at


lower speeds due to the lack of detection of movement in more than one plane (Tudor-Locke et

al., 2002). Results showed that the accelerometer was better able to capture physical activity than

the pedometer (Tudor-Locke et al., 2002). Combining technology used in different types of

activity trackers may be most effective for objectively measuring physical activity. There are

devices that have combined accelerometer and heart rate monitor, and these devices have better

accuracy for measuring multiple dimensions of physical activity than either device does

independently (Butte et al., 2012).

Evaluating Treatment Effectiveness

Daily physical activity levels are typically highly variable in physical activity

interventions (Valbuena et al., 2017). This variability is likely due to the nature of measuring

behavior in naturalistic conditions in which participants experience changing demands and

opportunities for physical activity (Valbuena et al., 2017). Due to high variability, evaluation of

treatment effects can be challenging. Consequently, evaluating mean and median weekly

changes in levels of physical activity may allow for more useful analysis of these data (Valbuena

et al., 2017).

Interventions for Increasing Physical Activity

Commercially Available Interventions

Commercially available fitness interventions, though being effective for improving

physical fitness and increasing activity for some individuals (Kessler et al., 2012; Walker et al.,

2016), do not include an analysis of specific factors influencing exercise and sedentary behavior,

and are not always individually tailored (Ba & Wing, 2013; Doshi et al., 2003). For example,

Schneider and colleagues (2006) conducted a study to increase daily step counts for obese adults,

and to assess the effects of a gradually increasing step count goal on daily step counts. During


the intervention phase, all participants were given the same step count goals each week, without

consideration of their baseline performance. Results showed that only half of the participants

who completed the intervention achieved the goals (Schneider et al., 2006). The authors

suggested that the reasons why so few participants achieved step goals may include that goals

were increased independent of behavior, there was a lack of analysis of the variables controlling

physically active behavior, and reinforcement was not provided for step count goal attainment.

Having individually tailored interventions for increasing physical activity is important to address

the specific variables related to engagement in physical activity (Kowal & Fortier, 2007; Martin

& Dubbert, 1987). In addition to having individually tailored goals, consideration of the

individual needs and preferences of individuals who are beginning an exercise program is

essential, as this may lead to continued participation in the exercise program (Martin & Dubbert,

1987).

A form of exercise that is widely becoming popular in the United States is high-intensity

interval training (HIIT), which is comprised of short intervals of vigorous and high-impact

activity performed at high intensity, followed by short recovery periods in which individuals

engage in low- or moderate-intensity activities (Kessler et al., 2012). Activities completed during

these intervals can include aerobic exercises and strength-training exercises (Kessler et al.,

2012). CrossFit is a branded HIIT exercise program with gym locations across the United States

(Powers & Greenwell, 2017). CrossFit activities and workouts include a mix of high-intensity

sprinting, Olympic weightlifting, as well as strength training exercises completed in alternating

and rapid succession (Powers & Greenwell, 2017). A large component of CrossFit is the social

support created by having people complete exercise routines in teams in the CrossFit locations

(Powers & Greenwell, 2017). While research does support the effectiveness of HIIT for


improving cardiovascular fitness, prolonged moderate-intensity aerobic activities yield similar

improvements in blood pressure reduction and body fat reductions (Kessler et al., 2012). Despite

some health benefits of HIIT training, the risk of injury due to overexertion and improper

technique is high (Aune & Powers, 2017). Injuries incurred during HIIT training have been

attributed to high volume training with little built-in rest periods, and varied amounts of

supervision and training related to technique (Aune & Powers, 2017). Case reports show a link

between CrossFit and rhabdomyolysis (Meyer et al., 2017), a condition that causes muscles to

break down, potentially leading to kidney failure (Dawson, 2017), which highlights the need for

proper technique, training, and supervision when beginning a new exercise program (Meyer et

al., 2017).

Social Media

Social media sites aimed at increasing physical activity are becoming popular (Ba &

Wang, 2013). Social media is a term used to describe a variety of online websites including

blogs, social networks (i.e., Facebook), social media sites and applications (i.e., Instagram,

Snapchat), and virtual worlds (i.e., Second Life; Ba & Wang, 2013). DailyBurn is an online

fitness program that has an interactive online community that promotes physical activity through

the use of motivator networks in which members praise, challenge, and compete with one

another (Ba & Wing, 2013). There are free and paid versions of the program; with the paid

program, individuals have access to training plans, nutrition tracking, and meal planning (Ba &

Wing, 2013). DailyBurn also features a social component in which members who have similar

goals are matched into motivational groups, and group members can challenge one another to

reach goals, such as losing 20 pounds (Ba & Wing, 2013). Using this system, individuals self-

report their activity, and are awarded points for the self-reported data (Ba & Wing, 2013).


DailyBurn provides programs that are individually tailored for paid members; however, for

individuals with free memberships, the programs are generic and not individually tailored (Ba &

Wing, 2013).

While there appears to be benefits of using social media programs such as DailyBurn for

increasing activity, there are limited data from which to draw definitive conclusions. Doshi and

colleagues (2003) conducted a review of 24 existing websites focused on increasing physical

activity and found that overall, websites did not include assessment of physical activity or

physical fitness, did not provide feedback to users, and did not offer individually tailored

programs.

Behavioral Interventions for Increasing Physical Activity

A range of behavioral techniques can be used to establish exercise behaviors for

individuals, however maintenance of those exercise behaviors is often low once structured

interventions end (Petry et al., 2013). Behavioral interventions aimed at increasing physical

activity typically contain between 1-14 components, with the average number of components

being eight per intervention (Abraham & Michie, 2008). Most behavioral interventions for

increasing physical activity include provision of general information, review of behavioral goals,

prompted engagement in physical activity, follow-up on prompted behavior, and planning for

social support of physical activity (Abraham & Michie, 2008).

Previous research has identified that contingency management combined with goal

setting increased daily activity levels for underactive adults (Kurti & Dallery, 2013; Washington

et al., 2014). Similarly, research on increasing daily activity of adults has identified goal setting,

self-monitoring, and feedback as efficacious components of interventions (Normand, 2008;

Valbuena et al., 2015; Van Wormer, 2004). Other researchers have identified that individuals are


most likely to adopt and adhere to exercise interventions when the programs are convenient,

include goal-setting and tailored feedback, teach self-management skills, begin with low

intensity activities, and include social support (Martin & Dubbert, 1982).

Stimulus Control

Skinner (1953) described stimulus control as when responses are emitted in the presence

of a stimulus and not in its absence. That is, behavior occurs in the presence of specific

antecedent stimuli, in the presence of which responding has previously been reinforced

(Miltenberger, 2012). A person is more likely to go running when they arrive home if running

clothes and shoes are placed in a conspicuous location (i.e., antecedent stimuli), and going for

runs after work has led to reinforcement in the past (i.e., feelings of accomplishment). Similarly,

the person may be less likely to go running if when she arrives home from work, a loved one is

sitting on the couch enjoying a relaxing glass of wine and a snack and invites the person in to

relax along with them (i.e., competing contingencies, including discriminative stimuli that evoke

non-exercise behavior). In physical activity interventions, success requires ensuring that the

stimuli or cues that evoke desired physical activity are present in the person’s environment, and

that engaging in physical activities in response to those environmental cues is followed by

consequences that make the response more likely in the future (i.e., reinforcement).

Epstein and colleagues (2004) conducted a randomized control study in which they

investigated the effects of positive reinforcement and stimulus control on sedentary behaviors of

63 families with obese children. Participants were divided into one of two groups: reinforcing

reductions in sedentary behavior, and stimulus control of sedentary behaviors. Participants in

both groups were asked to decrease sedentary behaviors to fewer than 15 hours per week. In the

reinforcement for reducing sedentary behavior group, participants earned points contingent upon


gradual reductions in sedentary hours per week, with a decrease in criterion of five hours per

week. Participants in the stimulus control group received reinforcement for recording sedentary

behavior and were asked to alter the home environment to reduce the likelihood of sedentary

behaviors occurring and to set rules around sedentary behaviors. Some examples of

environmental changes included unplugging televisions at times when active behavior was

desired and posting signs about reducing sedentary behaviors. Results indicated that participants

in both groups showed statistically significant increases in physical activity and reductions in

sedentary behavior, which suggested that stimulus control techniques were effective for

increasing physical activity (Epstein et al., 2004).

Reinforcement

When a behavior is followed by the addition of a stimulus and the future frequency of

that and similar behaviors increases, that behavior has been positively reinforced (Skinner,

1953). Similarly, when a behavior is followed by the removal of a stimulus and the future

frequency of that and similar behaviors increases, the behavior has been negatively reinforced

(Skinner, 1953). In physical activity interventions, reinforcement can take the form of attention,

automatic reinforcement from engaging in stimulating activity (Martin & Dubbert, 1987), and

financial or other tangible items (Finkelstein et al. 2008).

Reinforcement can be delivered on different schedules, each with distinct effects on

behavior (Skinner, 1953). When every occurrence of a behavior is reinforced, the schedule of

reinforcement is referred to as a continuous schedule of reinforcement (Skinner, 1953). When

some but not all instances of behavior are reinforced, this is called intermittent reinforcement

(Skinner, 1953). Behaviors that are intermittently reinforced typically occur with stability and

will continue to occur long after reinforcement is no longer provided (Skinner, 1953). Behaviors


are reinforced on different schedules including interval and ratio schedules. When behavior is

reinforced on an interval schedule, the first response after a set amount of time passes is

reinforced (Skinner, 1953). There are two types of interval schedules: fixed and variable. With a

fixed interval schedule, the interval duration remains the same, and the first response after that

amount of time elapses is reinforced (Skinner, 1953). With variable interval schedules, the first

response after an average amount of time has passed is reinforced (Skinner, 1953). With ratio

schedules of reinforcement, responses are reinforced after a certain number of occurrences

(Skinner, 1953). As with interval schedules, reinforcement on ratio schedules can be fixed or

variable. With fixed ratio schedules, reinforcement is provided after a fixed number of responses;

for example, in a fixed ratio 4 (FR 4) schedule of reinforcement, every fourth instance of a

response is reinforced (Skinner, 1953). With variable ratio schedules, the response requirement

varies around a mean number of responses so that the number of responses required for

reinforcement changes (Skinner, 1953).

There have been studies published that investigated the effects of different reinforcement

schedules on physical activity and exercise behavior (Epstein et al., 1980; Finkelstein et al.,

2008; Petry et al., 2013). Strohacker and colleagues (2014) conducted a review of 10 randomized

controlled trials that utilized reinforcement to increase exercise, and found that four studies

utilized positive reinforcement on a fixed ratio schedule, two studies delivered positive

reinforcement on a variable ratio schedule, and three studies delivered positive reinforcement on

a fixed interval schedule. Strohacker and colleagues (2014) reviewed one study by Epstein and

colleagues (1980) that utilized negative reinforcement to increase exercise behavior. The

negative reinforcement contingency involved participants increasing attendance at exercise

sessions to avoid losing the deposited money or losing the opportunity to have their name


entered into a drawing (Epstein et al., 1980). In all of the studies reviewed by Strohacker and

colleagues (2014), half of the studies used cash as the reinforcer (i.e., Charness & Gneezy, 2009;

Epstein et al., 1980; Jeffrey et al., 1998; Pope & Harvey-Berino, 2013), one used tangible items

such as prizes (i.e., Hardman et al., 2009), two studies used lotteries with a variety of items of

differing value (i.e., Martin et al., 1984; Wing et al., 1996), and two used access to television

viewing as the reinforcer (i.e., Goldfield et al., 2006; Roemmich et al., 2012).

Finkelstein and colleagues (2008) conducted a randomized controlled trial in which

participants were either assigned to a reinforcement group or the control group. Participants in

the control group were provided $75.00 for attending an initial meeting, wearing a pedometer

daily, and then returning all materials at the conclusion of the study. Participants in the

reinforcement group were given $50.00 for attending the initial meeting, wearing the pedometer,

and returning all materials at the conclusion of the study. In addition, they were given financial

payments contingent upon walking. The higher the average number of minutes spent walking

each week, the higher the financial amount. Participants who walked an average of 40 or more

minutes a day were given $25.00 for that week, those who walked an average of 25-40 minutes a

day were given $15.00, and those who walked an average between 15-25 minutes per day were

given $10.00. Any participants who walked an average of 15 or fewer minutes per day did not

receive any money for that week. Total possible payout for the participants in the reinforcement

group over the course of the study equaled $150.00, and for participants in the control group the

total payout was $75.00. Results showed statistically significant differences between the walking

behavior of participants in the reinforcement group compared to the control group (Finkelstein et

al., 2008). These results suggested that reinforcing walking behavior with money led to

increases in walking behavior while the contingency was in effect.


Epstein and colleagues (1980) compared the effects of behavioral contracting and a

lottery-based reinforcement system on attendance to fitness sessions and running different

distances. Individuals in the behavioral contract groups deposited $5.00 at the start of the study

and received $1.00 each week for attending 80% of the exercise sessions and running different

distances, depending on which distance group they were assigned. In the lottery group, all

participants deposited $3.00 at the start of the study and were asked to run one mile per day for

five days in the week. Participant names were entered into a lottery box during each week that

they attended 80% of the running sessions. At the end of the study, one name was pulled from

the box and that participant earned all of the money that had been deposited by all participants at

the beginning of the study. In the control group, participants were asked to run one mile per day

without depositing any money. Results suggested that individuals in the behavioral contract and

the lottery group attended more running sessions than the no treatment control group.

Additionally, participants in all of the contracting groups increased their aerobic fitness and were

able to run significantly farther at the end of the study compared to the no treatment control

group and the lottery group (Epstein et al., 1980). It is possible that the immediate contingency to

earn a set amount of money each week for running 80% of the sessions in a week was more

effective at shaping running behavior than the delayed contingency of potentially winning the

lottery money at the end of the study.

Petry and colleagues (2013) conducted a randomized controlled trial in which they

compared step counts for individuals assigned to a reinforcement group to individuals who were

assigned to the attention control. In the attention control group, participants were instructed to

wear a pedometer daily and increase step counts gradually over a few weeks, and were provided

with praise during weekly meetings for all days with step counts that met criterion. In the


reinforcement group, participants were also instructed to wear a pedometer and increase step

counts gradually over the course of the study. However, participants in the reinforcement group

were also able to access prize drawings for every day in which participants’ step counts met

criterion, and there were bonus drawings for having consecutive days with target step counts.

Drawing prizes consisted of a range of items which included slips of paper that contained praise

statements, toiletries, water bottles, cameras, gift cards, watches, and iPods. The maximum prize

value that one participant could earn in the reinforcement group equaled $468.00. Participants

had a 41% chance of pulling out a small prize, an 8% chance of pulling out a large prize, a 2%

chance of pulling out a jumbo prize, and a 50% chance of pulling out a paper containing praise

statements. Results showed that participants in the reinforcement group walked significantly

more steps over the course of the study than the participants in the feedback only group.

Additionally, follow-up assessments showed that participants in the reinforcement group

continued to walk approximately 2,000 steps more than participants in the feedback only group

12 weeks after the intervention ended (Petry et al., 2013).

Andrade and colleagues (2014) examined the effects of gradually thinning the schedule

of positive reinforcement on walking behavior of adult participants. Conditions of the study

included: baseline, fixed interval monitoring plus reinforcement, monitoring plus reinforcement

thinning, and a follow-up. Following baseline, participants entered the 3-week fixed interval

reinforcement and self-monitoring condition in which they were instructed to wear a pedometer

daily and walk more than 10,000 steps per day. In the reinforcement phase, participants met with

the researcher three times a week to review data and access drawing prizes for all days with step

counts higher than 10,000, and participants earned bonus prizes for meeting the 10,000 step


criterion between two and four consecutive days between meetings. During the fixed interval

reinforcement condition, participants could earn up to $150.00 (Andrade et al., 2014).

Following the fixed interval reinforcement condition, participants who met criterion of

walking more than 10,000 steps on the majority of the 21 days in the 3-week period moved into

the second phase that included being assigned to one of two different conditions for 12 weeks:

monitoring-only, or a monitoring-plus-reinforcement thinning condition. In the second phase,

meetings for all participants were held on randomly assigned days with less frequency than in the

fixed interval condition, and session frequency was gradually decreased over the 12 weeks.

Participants in both the monitoring-only and monitoring-plus-reinforcement conditions were not

provided advanced notice of the meeting schedule. Participants in the monitoring-only condition

were instructed to wear the pedometer and walk more than 10,000 steps each day, and the

participants were required to meet with researchers on randomly selected meeting days to review

data. Participants in the monitoring-only group earned $5.00 gift cards for attending meetings

that were held on randomly selected days. In the monitoring-plus-reinforcement thinning group,

participants were also instructed to walk more than 10,000 steps each day. Participants in the

monitoring-plus-reinforcement group also earned $5.00 gift cards for attending randomly

selected meetings with the researcher to review data. In addition, during the randomly selected

meetings, participants in the monitoring-plus-reinforcement thinning group earned access to

prize drawings for each day that they walked more than 10,000 steps in the previous four days.

Not being informed of the meeting schedule created a variable schedule of reinforcement for the

monitoring-plus-reinforcement thinning condition (Andrade et al., 2014). In this variable

reinforcement condition, participants in the monitoring-plus-reinforcement thinning group

earned up to $77.00.


Results from Andrade and colleagues (2014) showed that participants in the monitoring-

plus-reinforcement thinning group continued to engage in higher than baseline levels of walking

when the reinforcement schedule was thinned, as compared to the monitoring-only group.

Individuals in the monitoring-only condition engaged in gradually lower levels of walking in the

weeks after the fixed reinforcement schedule for walking more than 10,000 steps a day ended.

Results from the variable reinforcement schedule condition showed that even when provided

with less monetary reinforcement, participants maintained or increased levels of walking

behavior during the variable interval conditions compared to the fixed interval condition

(Andrade et al., 2014). Data showed that during the follow-up, weeks after the intervention

ended, participants in both groups walked similar levels (Andrade et al., 2014). Implications of

these results were that intermittent reinforcement may adequately maintain physical activity

behavior once it is established, though once the reinforcement is faded out completely, physical

activity may decrease to near baseline levels (Andrade et al., 2014).

Self-Monitoring

Self-monitoring is defined as an individual recording his/her own target behavior

(Miltenberger, 2012). For example, if an individual sets a goal of running 30 consecutive

minutes by the end of 30 days, each day the individual runs, she would record cumulative

minutes run. Donaldson and Normand (2009) conducted a study in which they examined the

effects self-monitoring combined with goal setting and feedback to increase caloric expenditure

by obese adults. For the self-monitoring component during the intervention, participants wore a

heart rate tracking device throughout the day and emailed daily updated graphs showing caloric

expenditure to the researcher. In response, participants were provided with daily written

feedback on their performance via email. Additionally, participants received verbal feedback on


caloric expenditure during weekly meetings with the experimenter. Results showed an increase

in caloric expenditure for all participants, including one participant who did not receive feedback

on his performance as a result of not submitting data to the coach or attending the behavioral

coaching sessions (Donaldson & Normand, 2009). These findings suggested that self-

monitoring and goal setting were important components of the treatment package for all

participants, and that the addition of feedback was an important component to increase caloric

expenditure for some participants (Donaldson & Normand, 2009).

Iwaza and colleagues (2005) conducted a randomized controlled trial in which they

examined self-monitoring of physical activity levels of adults who had recently experienced a

myocardial infarction. Participants in the control group received supervised instruction through a

cardiac rehabilitation program. Participants in the self-monitoring group participated in the

cardiac rehabilitation program, but were also asked to self-monitor physical activity, weight,

blood pressure, and heart rate. Results showed that individuals in the self-monitoring group had

higher maintenance of physical activity than individuals in the control group (Iwaza et al., 2005),

supporting the use of self-monitoring in treatment packages for increasing and maintaining

physical activity.

Goal Setting

Goal setting is defined as recording a time frame and criterion for a desired target

behavior (Miltenberger, 2012). For instance, recording that by the end of 30 days, one would be

able to run for 30 consecutive minutes per day for 3 days per week. One method for gradually

increasing goals involves the use of shaping. Shaping is a behavioral process first described by

Skinner (1953) that increases behavior. Using shaping, a terminal behavior is gradually sculpted

and established using reinforcement and extinction (Skinner, 1953). Using shaping, the initial


criterion for reinforcement is set to reinforce some version or prerequisite of the terminal

behavior that is within the persons’ current repertoire to ensure that some variation of the

behavior is reinforced (Skinner, 1953). After the initial criterion for reinforcement is met, the

criterion is systematically increased and all previously set criteria are no longer reinforced

(Skinner, 1953).

Percentile schedules are a formalized method for applying shaping to increase complex

behaviors (Hanley & Tiger, 2011), and have been demonstrated to effectively shape levels of

physical activity (Galbicka, 1994; Hustyi et al., 2011; Valbuena et al., 2015). When using

percentile schedules, rules about when reinforcement is delivered are established, and then the

criterion for reinforcement is adjusted based on some dimension of the behavior, using rank

ordering of instances of the specified behavior (Hanley & Tiger, 2011). Galbicka (1994) first

described using percentile schedules to shape physical activity. Percentile schedules involve a

mathematical equation for reinforcement, k= (m+1)(1-w), where w is the density of

reinforcement, m equals the observation size, and k is the criterion for reinforcement (Galbicka,

1994). The majority of studies using percentile schedules to shape behavior have targeted

smoking behavior (i.e., Lamb et al., 2005; Lamb et al., 2007; Lamb et al., 2010), with fewer

studies incorporating percentile schedules to shape physical activity of young children (i.e.,

Hustyi et al., 2011), and physical activity of adults (i.e., Valbuena et al., 2015).

There have been a few studies have utilized percentile schedules to increase physical

activity (i.e., Hustyi et al., 2011; Valbuena et al., 2015; Washington et al., 2014). Hustyi and

colleagues (2011) used percentile schedules to increase steps taken during outdoor recess by two

preschool-aged participants. Researchers determined the third highest step count in the five last

sessions and used that as the goal for step counts for the day. At the start of each 20-minute


session, the participants were verbally notified of their step count goals, and the step counts were

written down on a sticker that was placed on the pedometer as a reminder for the participants.

Verbal feedback was provided to the participants halfway through the session. If participants had

reached or exceeded their step count during the session, they were allowed to access tangible

reinforcers at the end of the session. If participants had not reached their step counts, they were

not permitted to access the tangible reinforcers, and were instructed to increase their physical

activity during the following session. For both preschool-aged participants, step counts were

higher during the phase with goal setting and feedback when compared to baseline, suggesting

that using percentile schedules for goal setting and shaping physical activity, combined with

feedback was effective for increasing physical activity of children.

Washington and colleagues (2014) used percentile schedules for increasing daily step

counts of college students as part of a treatment package that included intermittent reinforcement

and goal setting. In this study, the percentile schedule used to increase step counts varied by

participant to allow each participant to contact reinforcement. For one participant, the goal was

set based on the third highest step count in the previous seven days. For another participant, new

step count goals were set based on the fourth highest step count in the previous seven days. For

all other participants, a more stringent criterion of the fifth highest step count in the previous

seven days was used to set goals for the following week. Results showed gradual increases in

daily step counts when the treatment package was in place.

Valbuena and colleagues (2015) compared daily step counts measured by the Fitbit One

accelerometer under two conditions. The first condition included a generic goal of 10,000 steps

per day set by the Fitbit website, and in the second condition daily step count goals were

determined based on percentile schedules. In the second condition, weekly step count goals were


set at the 80th percentile, which was determined by calculating the third highest step count in the

previous ten days. Results showed that participants increased step counts when the percentile

schedule was used to determine step count goals, as compared to when a generic goal was set

(Valbuena et al., 2015).

Behavioral Coaching

Behavioral coaching involves instructions and feedback provided by another person, with

the goal of increasing some aspect of physical activity (Sullivan & Lachman, 2017). Martin and

Hrycaiko (1983) defined effective behavioral coaching as the incorporation of behavioral

principles to coaching practices to improve athletic behavior. Effective behavioral coaching

involves frequent measurement of the defined athletic behavior to develop and promote

maintenance of the athletic behavior (Martin & Hrycaiko, 1983). Using behavioral coaching,

new behaviors are developed using behavioral skills training, which includes instruction,

modeling, feedback, reinforcement and correction for errors (Martin & Hrycaiko, 1983).

Established behaviors are maintained using intermittent social positive reinforcement, self-

monitoring, goal setting, and behavioral contracting (Martin & Hrycaiko, 1983). Behavioral

coaching relies on frequent analysis of performance and procedural fidelity evaluations of the

behavioral coach to ensure that effective practices are implemented as prescribed (Martin &

Hrycaiko, 1983).

Behavioral coaching has shown to be an effective component of treatment packages for

increasing physical activity (Normand, 2008; Van Wormer, 2004). Van Wormer (2004) studied

the combined effects of tracking daily steps with a pedometer and behavioral coaching on daily

step counts for three healthy, overweight individuals over the span of 10 weeks. Van Wormer

(2004) included four phases in the study, which all participants experienced: baseline, self-


monitoring, self-monitoring plus coaching, and follow-up. During the self-monitoring phase,

participants wore a pedometer and recorded daily step counts at the end of each day. During the

coaching component, participants emailed the researcher updated graphic displays of their step

counts and conversed weekly via email with the coach, in addition to self-monitoring their data.

Results from the study showed increased daily step counts from baseline during the self-

monitoring and electronically-based behavioral coaching conditions. Participants with greater

increases in physical activity during the study saw greatest cumulative losses in weight.

In 2008, Normand conducted an extension of the study conducted by Van Wormer

(2004), investigating the effects of a treatment package including goal setting, self-monitoring,

and feedback on daily step counts of four healthy, non-obese individuals. During baseline,

participants wore covered activity trackers, and the experimenter recorded daily step counts from

the pedometer one time per week. In baseline, no feedback either from the device or the

experimenter was provided. When participants entered the goal setting, self-monitoring, and

feedback phase, they wore pedometers to measure daily step counts, and emailed daily step

counts to researchers, who then replied either with descriptive praise for reaching step count

goals, or with statements of encouragement when step count goals were not met. In addition,

participants attended weekly in-person meetings and received praise and graphic feedback.

Results showed that step counts were consistently higher when the treatment package was in

place, when compared to baseline. In addition, social validity results indicated that all

participants rated the intervention favorably.

Wack and colleagues (2014) conducted a study in which they increased running distance

for healthy female college students using goal setting and feedback. During the study, a

researcher met briefly with the participant to provide feedback on running behavior. During these


meetings, each participant was shown visual feedback in the form of graphic displays of her

running behavior from the previous week, and was provided with descriptive verbal feedback

about running performance. Results showed that both feedback and goal setting provided in the

behavioral coaching sessions were effective for increasing total weekly running distance for all

five participants.

Motl and Dlugonski (2011) used an interrupted time series design to evaluate the effects

of a website and web-based behavioral coaching to increase participant interaction with a

website designed to increase physical activity for individuals with multiple sclerosis. During

behavioral coaching sessions, the behavioral coach encouraged participants to access and use the

website to set goals for physical activity. Results showed that participants were more active

during the period with behavioral coaching than without, providing further support for the use of

behavioral coaching.

Technology

The use of technological devices to increase physical activity has been investigated in

recent years. Forms of technology incorporated into interventions include pedometers,

accelerometers (Muntaner et al., 2016), smart phones (Kinnafick et al., 2016), and computer and

phone applications (Cavallo et al., 2012; Middelweerd et al., 2014; Muntaner et al., 2016).

Recently, there has been an increase in the development of mobile applications designed to

increase physical activity among overweight and obese individuals (Tudor-McGrievy & Tate,

2011; Wong et al., 2014). The inclusion of social media sites in physical activity interventions

has also been investigated in recent years (Middelweerd et al., 2014).

In one physical activity study involving technology, Bond and colleagues (2014) utilized

a smartphone-based intervention to increase physical activity by prompting physical activity


after different durations of sedentary behavior. The smartphone-based intervention included

providing participants a smartphone that contained both an accelerometer and an application that

was used to provide the prompts to engage in physical activity. Participants were instructed to

carry the smartphone on their person for waking hours for the duration of the study. Sedentary

and active behaviors were measured by both the accelerometer in the smartphone and by an

accelerometer in an armband worn by the participants. After baseline, participants entered into

the prompting phase, in which prompts for activity were provided after 30, 60, or 120 minutes of

sedentary behavior, depending on the condition in effect (Bond et al., 2014). During the 30-

minute condition, a prompt appeared and remained on the screen of the smartphone until the

participant engaged in 3 minutes of physical activity, dismissed the prompt, or reset the prompt.

If participants engaged in 3 minutes of physical activity, a praise statement appeared on the

screen and the prompt disappeared. During the 60-minute condition, a prompt appeared and

remained on the screen as in the 30-minute condition, except that participants were expected to

engage in a 6-minute physical activity break following 60 minutes of sedentary behavior.

Finally, when the 120-minute condition was in effect, the same prompts appeared as in the other

conditions, but the expectation for a physical activity break increased to 12 minutes after 120

minutes of sedentary behavior. Results showed that participants increased physical activity in

the 30-minute, 60-minute, and 120-minute conditions when compared to baseline, and that the

greatest changes in light-and moderate-to-vigorous physical activity occurred during the 30-

minute condition. The prompts to engage in physical activity required little planning and

response effort for the participants, aside from the physical activity, which may have increased

the effectiveness of the intervention.


In an attempt to evaluate the effectiveness of social media on physical activity, Cavallo

and colleagues (2012) conducted one of the first studies to examine the effects of social support

from the social media site Facebook on physical activity of female college students. In this

randomized controlled trial, participants were randomly assigned to one of two groups including

the online social networking plus self-monitoring group and the education-only control group.

All participants had access to a website that provided educational information related to exercise.

Participants in the education-only group had access to the health website. Participants in the

social support group had access to a Facebook group in which participants sent feedback to one

another, as well as having access to the educational website with the added features of self-

monitoring and goal setting. Assessments of physical activity were based on self-report and

completed during baseline and at week 12. All participants received $30.00 once all study

measures were completed. Additionally, participants in the Facebook group were entered into

biweekly drawing for gift cards for contributing to the Facebook group website. Results did not

show a significant difference in self-reported physical activity between groups over the course of

the study. Cavallo and colleagues (2012) suggested adding in objective measurement of physical

activity may provide more accurate information about levels of physical activity and show

different results.

Middelweerd and colleagues (2014) conducted a review of smartphone applications

intended to promote physical activity via tailored feedback and guidance. The goal was to

determine how many applications included behavior change techniques as part of the application.

Abraham and Michie (2008) identified 26 behavior change tactics that are commonly included in

behavior change interventions. Middelweerd and colleagues (2014) included 23 of these

behavioral change techniques in their review of applications for increasing physical activity.


Some of the behavior change tactics examined by Middelweerd and colleagues included

individually tailored feedback on performance, prompting self-monitoring of behavior,

prompting specific goal setting, providing instructions, and providing contingent rewards. Of the

57 applications that were reviewed, all contained between at least two and eight behavior change

strategies, and there was no difference in the paid and free applications in terms of number of

behavioral strategies included. Results suggested that smartphone applications aimed at

increasing physical activity do include behavior change strategies; however, the results did not

account for whether the inclusion of behavior change tactics in smartphone applications altered

physical activity (Middelweerd et al, 2014).

Incorporating technology into treatment packages has been demonstrated to be

efficacious for increasing daily physical activity (Kinnafick et al., 2016; Kurti & Dallery, 2013;

Normand, 2008; Valbuena et al., 2015; Van Wormer, 2004; Washington et al., 2014). Kinnafick

and colleagues (2016) assessed the effects of supportive versus neutral feedback sent via text

messages to participants beginning an exercise program on self-reported exercise in a

randomized controlled trial. During the 10-week intervention phase, participants received two

text messages a week. Individuals in the supportive text message group received messages that

encouraged exercise behavior by connecting physical activity with achievement of weight loss

and other goals. Individuals in neutral feedback group received messages that were neutral in

nature. Results showed that participants in the supportive text message group self-reported

significantly more physical activity over the course of the study and at the follow-up session than

the participants in the neutral text message group. Exercise class attendance was objectively

measured for all participants in both groups and these data showed no significant difference

between groups. One limitation of this study was that researchers could not verify whether or


not participants actually read the text messages. Adding in a requirement to have participants

respond to the text message may address this limitation.

Zarate and colleagues (2019) also evaluated the effectiveness of textual feedback for

increasing moderate-intensity physical activity. During the goal setting and textual feedback

condition, the researcher sent weekly text messages to participants with feedback on goal

attainment for that week and a new goal for the following week. Results of the study showed that

three out of four participants continually increased physical activity during the intervention

condition.

Technology in the form of devices used to record and measure physical activity,

specifically the use of pedometers, has been shown to be effective for increasing step counts

(Normand, 2008; Valbuena et al., 2015; Van Wormer, 2004). Additionally, technology in the

form of text messages, video conferencing, and use of websites for behavioral coaching has been

correlated with high amounts of physical activity (Normand, 2008; Van Wormer, 2004;

Washington et al., 2014). With advances in technology, individuals can upload videos to share

data (Kurti & Dallery, 2013), text and email daily step counts (Normand, 2008; Washington et

al., 2014), and enter data into websites that can be accessed by a behavioral coach (Kurti &

Dallery, 2013; Valbuena et al., 2015).

Chan and colleagues (2004) examined the use of pedometers to increase daily steps taken

for healthy, sedentary adults in their workplace. Components of the intervention included having

participants wear a pedometer daily, record steps taken each day, and attend weekly meetings

with a facilitator. During the weekly meetings, participants were instructed on the benefits of

physical activity, taught strategies to initiate physical activity behaviors, and reviewed their

performance. After four weeks, the weekly meetings with the facilitator ended, and participants


continued to wear the pedometer and self-monitor their own physical activity. Overall, there was

an increase in average steps taken from baseline, which continued through the 4th week, and then

levels of activity for participants remained stable at around 10,000 steps per day through the end

of the study at 12-weeks. These data suggested that individuals continued to engage in similar

levels of walking even after the support from the facilitator ended. It may be interpreted that the

instructions for engaging in physical activity, combined with social contingencies operating in

the workplace, aided maintenance of exercise behaviors after the meetings with the facilitator

ended.

Valbuena and colleagues (2015) examined the effects of Fitbit One technology alone and

Fitbit One technology used in conjunction with behavioral coaching to increase daily step counts

for healthy, underactive adults. All participants received a Fitbit One and a Wi-Fi Aria scale,

which calculates BMI, weight, bone mass, and muscle mass. During baseline, participants were

asked to wear a covered Fitbit throughout the day, and to sync their Fitbit to their account daily.

During baseline, participants did not receive any feedback or information about their

performance from the Fitbit since it was covered, and did not have access to the Fitbit account.

After baseline, all participants entered into the Fitbit alone condition, during which the Fitbit was

uncovered, participants were provided with the login information for the Fitbit website, and were

asked to sync the Fitbit to the website daily. Participants were encouraged to use all components

of the Fitbit site during this phase. Participants were instructed to weigh in and sync the Fitbit

daily, and were informed that doing so 93% of the time in a 2-week period would result in a

$5.00 gift card. A weekly email was sent to each participant, thanking them for taking part in the

study and reminding them of the contingency for earning gift cards. After participants completed

the Fitbit alone condition, they were moved into the coaching component. During the behavioral


coaching component, the gift card contingency remained in effect and participants were

instructed to wear the Fitbit throughout the day, as in previous conditions. The difference in this

condition was a weekly video conference call between a behavioral coach and each individual

participant. During the weekly meetings, the behavioral coach set goals based on the 80th

percentile, or the third highest step count in the previous 10 days. The behavioral coach also

provided feedback, and offered recommendations for increasing activity. Results suggested that

participants increased daily activity with the use of the Fitbit alone; however, the effectiveness of

the Fitbit technology was enhanced by the addition of behavioral coaching. Additionally, on

social validity measures, all participants rated the intervention highly. One limitation identified

by the authors was having all participants experience the Fitbit alone as the first treatment

condition, and as a result, changes in steps taken noted in the behavioral coaching condition may

have been a result of sequence effects. Another identified limitation was the lack of verification

that participants actually contacted their data daily. Syncing the Fitbit did not require participants

to actually view the data; rather, syncing only connected the device with the website. Therefore,

it was not known if participants were reviewing their data on a regular basis.

In addition to being effective for increasing daily activity, interventions aimed at

increasing activity levels that involve technology have high acceptability ratings from

participants (Normand, 2008; Valbuena et al., 2015). Social validity is a subjective measurement

of participants’ opinions about the social importance of the goals of the intervention, the social

acceptability of the procedures used in the study, and the social relevance of the effects of the

intervention (Wolf, 1978). In a study by Normand (2008), 75% of participants rated the

intervention as being helpful for increasing physical activity levels, and that self-monitoring

daily activity with the pedometer, as well as receiving feedback through email were effective


components of the intervention. Similarly, Valbuena and colleagues (2015) surveyed participants

at the conclusion of their study, and discovered that participants indicated that they found using

the Fitbit activity tracker and website easy, and that they would be likely to continue using the

Fitbit after the study ended.

Additional Environmental Factors that Influence Physical Activity

The environment exerts control over all behavior (Skinner, 1953) including exercise

behavior (Martin, 2015). Understanding the environmental variables that influence the

probability of engaging in physical activity will help to alter physical activity behavior. Adults

are most likely to engage in physical activity when it is perceived as safe and convenient, and

built into daily routines (Owen et al., 2004). For example, walking may be incorporated into

breaks at work, while cleaning, or for transportation purposes (Owen et al., 2004). Research has

shown that support from family members and friends also affects adoption and continuation of

exercise behavior (Ba & Wang, 2013; Sherwood & Jeffrey, 2000). Social support can include

engaging in physical activity with another person, as well as praise and positive feedback from

others (Sherwood & Jeffrey, 2000). Another factor that has been shown to influence exercise

behavior is scheduling time for exercise. Individuals who schedule regular times for physical

activity are more likely to engage in physical activity than those who do not schedule the activity

(Sherwood & Jeffrey, 2000).

In addition to variables that increase the probability of engaging in exercise behavior,

there are also variables that decrease the probability of engaging in exercise behavior. Barriers

that reduce the likelihood of engaging in certain types of exercise behaviors include lack of

access to exercise facilities and equipment (Sherwood & Jeffrey, 2000), and the high costs

associated with different forms of exercise (Ba & Wing, 2013). Kowal and Fortier (2007)


conducted a longitudinal study examining the correlation between self-reported physical activity

and environmental characteristics that impacted physical activity. Results from Kowal and

Fortier showed that the barriers to engaging in physical activity included having daily activities

that required the majority of participants’ time, as well as being too busy and too tired, and not

having anyone with whom to engage in physical activity. The results also showed significant

correlations between the presence of safe and aesthetically pleasing locations to engage in

physical activity (Kowal & Fortier, 2007).

In addition to the environmental barriers noted above, individuals who are sedentary and

begin exercise programs are likely to initially contact aversive consequences in the form of pain,

discomfort, and loss of time engaging in highly reinforcing, lower effort sedentary behaviors that

may compete with continued engagement in physical activity (Coleman et al, 1997; Coleman et

al., 1999; Martin & Dubbert, 1987). Consequently, when beginning an intervention to increase

physical activity, there should be high rates of reinforcement for engaging in enjoyable, low-to-

moderate-intensity physical activity, with gradual shaping of behavior, and making sure that

individuals have access to safe and convenient locations in which to engage in physical activity

(Martin & Dubbert, 1987).

Functional Analysis of Physical Activity

In an attempt to experimentally evaluate effects of different reinforcers of physical

activity, Larson and colleagues (2013) conducted the first functional analysis of physical activity

of preschool children to determine what social and nonsocial reinforcers maintained physical

activity of two children. During baseline, no programmed contingencies were in place and the

children were observed during typical play activities to determine levels of physical activity

without environmental manipulations in place. During the interactive play condition, adult praise


and engagement were provided contingent upon physical activity. During the attention condition,

adult praise was provided contingent upon physical activity. During the escape condition of the

functional analysis, work tasks were presented on a table in the play area and participants were

informed that if they did not want to do their work they could engage in physical activity. The

work tasks were removed contingent upon engaging in physical activity. During the alone

condition, the participants were each alone in the play area with an adult supervising from a

hidden location. In the control condition, the participants were brought to a table in the play area

and provided with materials to color, and attention was delivered every 30 seconds, with no

programmed consequences for engaging in physical activity. Results of the functional analysis

for both participants showed increased physical activity during the interactive play condition and

the attention condition when compared to the control, escape, and alone conditions. These results

showed that for these participants, physical activity was maintained by social positive

reinforcement. In this analysis, Larson and colleagues were the first to experimentally analyze

consequences of physical activity.

Larson and colleagues (2014) conducted a functional analysis of outdoor play

environments and physical activity of preschoolers. In the study, the experimental conditions for

the functional analysis included outdoor toys, fixed equipment, open space, and a control

condition. After participants were exposed to environmental conditions alone, sessions of each

condition were conducted with the inclusion of one, two, and three peers present. Results of the

study showed that all of the children had higher levels of physical activity when peers were

present, as compared to the alone conditions. These findings suggest that physical activity, at

least for preschool-aged children, may be increased in the presence of peers.

Physical Activity Treatment Adherence


Despite having effective behavioral methods for increasing exercise behavior, adherence

to prescribed exercise regimes varies (Iwaza et al., 2005; Maki et al., 2008; Martin & Dubbert,

1982b; Martin et al., 1984). Treatment adherence has been defined as the degree to which a

participant attends exercise sessions (Epstein et al., 1980), completes prescribed amounts of

physical activity (Jansons et al., 2016), or completes assigned activities (e.g. self-monitoring;

Valbuena et al., 2015). Treatment adherence may be higher when the prescribed physical activity

is of moderate-or vigorous-intensity for shorter duration instead of vigorous-intensity for longer

duration (Slentz et al., 2004). Furthermore, treatment adherence may be higher when technology

is incorporated into the intervention (Valenzuela et al., 2016).

Research on treatment adherence for exercise programs has shown that adherence may be

higher when exercise bouts are shorter and more frequent in the initial stages and gradually

increase in duration (Jakicic et al., 1995; Linke et al., 2011). In one 20-week study examining the

effects of long, continuous bouts of exercise and short-bouts of exercise, Jakicic and colleagues

(1995) randomly assigned obese female participants to one of two groups: short-bout and long-

bout. Both groups were encouraged to follow a diet that was low in fat and calories, and to

engage in walking five days a week. Over the course of the intervention, the total daily duration

of walking increased from 20-40 minutes per day for all participants. Individuals in the short-

bout group completed walking in 10-minute intervals across the day, whereas participants in the

long-bout group completed their walking at one time in the day (i.e., 20, 30, or 40 minutes at one

time). Analysis of the results showed that individuals in the short-bout group demonstrated

higher levels of exercise adherence than the long-bout group over the course of the intervention

(Jakicic et al., 1995). These results suggested that individuals may exhibit higher rates of

exercise adherence when activity is prescribed in shorter, frequent episodes.


In another study that examined long-bouts versus short-bouts of exercise, Coleman and

colleagues (1999) compared physical activity levels of adult participants who were divided into

three groups: one 30-minute bout, three 10-minute bouts, and a choice of the length of bouts (at a

minimum of 5-minute increments) to reach 30 minutes per day. Results revealed that individuals

had the highest levels of physical activity in the choice condition and in the short-bouts

condition, adding to the extant literature that short bouts of exercise are more effective for

increasing adherence to exercise programs (Coleman et al., 1999). These results support that

choice, along with shorter bouts of exercise, may influence adherence (Coleman et al., 1999).

Another reason why adherence might be low for individuals with a limited history of

physical activity that begin an exercise program is that they are very likely to experience

physical discomfort when they become more active (Martin & Dubbert, 1987). The discomfort

experienced may function as a punisher, decreasing future frequency of physical activity (Martin

& Dubbert, 1987). Consequently, when beginning exercise programs, there should be high rates

of reinforcement for engaging in low-moderate intensity physical activity with gradual shaping

of behavior (Martin & Dubbert, 1987). Another variable that impacts treatment adherence is the

frequency of prescribed exercise. It appears that adherence is highest for exercise prescriptions

that have three to four days of exercise per week (Martin & Dubbert, 1987). Additionally,

allowing participants to set their own goals for exercise may increase adherence to exercise

programs (Martin et al., 1984).

Measurement of Adherence

Subjective measurement of adherence is typically conducted via self-report through

diaries or other written logs (Newman-Beinart et al., 2017). The Exercise Adherence Rating

Scale (EARS; Newman-Beinart et al., 2017) was recently developed in an effort to standardize


the assessment of exercise adherence via self-report in research studies. The EARS is a 6-item

questionnaire that can be used to assess adherence of prescribed physical activity that is based on

self-report (Newman-Beinart et al., 2017). While self-monitoring is an important behavior for

managing one’s own physical activity levels, self-reporting has limitations for evaluating

treatment effects; thus, having an additional objective measurement of treatment adherence is

recommended (Martin & Dubbert, 1987).

With the development of technology for tracking physical activity, an objective

measurement of adherence is possible through the use of pedometers, accelerometers, and mobile

technologies. Jakicic and colleagues (1995) measured exercise adherence by having participants

record physical activity, and measured physical activity levels of participants using an

accelerometer. Results showed correspondence on general increases in physical activity, but with

some discrepancies (Jakicic et al., 1995). Generally, the accelerometer data showed less activity

than the self-report, however the discrepancies might have been due to the inability of the

accelerometer to accurately capture different forms of exercise (Jakicic et al., 1995). One

consideration for measuring adherence with activity trackers is that it is not always possible to

ensure that data provided are actually from the individual for whom the exercise has been

prescribed (Washington et al., 2014).

Maintenance of Physical Activity

Despite having an arsenal of effective techniques to increase physical activity while

interventions are in place, it is still necessary to continue to study techniques for increasing

maintenance of exercise behaviors once the intervention ends (Jansons et al., 2016). Maintenance

of exercise behaviors can be defined as continuing to engage in regular and consistent levels of

physical activity for at least 6 months after the intervention has ended (Bock et al., 2001; Marcus


et al., 2000). Behavior analytic research has shown that interventions are effective for increasing

physical activity while the study contingencies are in effect; however, once removed,

continuance of exercise behaviors decreased (Andrade et al., 2014; Dishman, 1991; Martin et al.,

1984). Research on long-term maintenance of exercise behaviors has shown that roughly 50% of

individuals who begin exercise programs discontinue participation within 6 months (Bock et al.,

2001; Marcus et al., 2006). For individuals who start an exercise program on their own, without

the aid of a behavioral coach, trainer, or structured group, this percentage may be higher (Martin

& Dubbert, 1987). Health benefits of exercise last as long as physical activity is taking place, so

once an individual stops physical activity, those benefits slowly begin to be lost (Bock et al.,

2001). Due to the seriousness of the complications associated with decreased physical activity,

overweight, and obesity, it is imperative to identify methods for increasing both exercise

adherence and long-term maintenance of results.

Competing contingencies from job-related, familial, and other sources impact

continuation (or lack thereof) of exercise programs (Marcus et al., 2006). Maintenance of

exercise behaviors may also be low when there has been inadequate behavioral programming

during the initial stages of exercise interventions (Martin & Dubbert, 1987). When the

intervention ends, individuals are left to self-manage their own exercise behavior without having

established the appropriate skill set in the initial stages (Martin & Dubbert, 1987). Other factors

influencing maintenance of physical activity include lack of programming for transfer of

stimulus control to naturally occurring contingencies, and lack of naturally occurring reinforcers

to maintain exercise behavior. When behaviors are not reinforced, they eventually cease

(Skinner, 1953).


If participants are able to maintain some of the behaviors that are supportive of physical

activity (e.g., self-monitoring), maintenance may be higher (Iwaza et al., 2005). Choice is

another factor that may increase maintenance of exercise (Coleman et al., 1999; Marcus et al.,

2000). During physical activity interventions, choice about the duration (Coleman et al., 1999),

type of physical activity, and location where physical activity is performed, may lead to

increased adherence in the beginning of the intervention, and then later maintenance of physical

activity once the intervention ends. In addition to choice, having access to multiple forms of

physical activity may lead to increased maintenance of exercise behaviors (Marcus et al., 2000).

Iwaza and colleagues (2005) examined the effects of self-monitoring on exercise

maintenance for participants who had recently completed a supervised cardiac rehabilitation

program after suffering a myocardial infarction. During the supervised cardiac rehabilitation,

participants were provided information on healthy lifestyles, and engaged in low-intensity

walking and stretching. Once the supervised cardiac rehabilitation program ended, participants

entered supervised recovery-phase cardiac rehabilitation, and were divided in to one of two

groups: a self-monitoring group or a control group. In the self-monitoring group of the

supervised recovery-phase, participants were asked to wear a pedometer and record their

physical activity, weight, blood pressure, and heart rate. Participants in the control group did not

self-monitor these measures. Results showed that 100% of participants in the self-monitoring

group continued to exercise while self-monitoring only in the 6 months after the supervised

recovery-phase cardiac rehabilitation program ended, whereas 81% of participants in the control

group continued to exercise during that same time (Iwaza et al., 2005). These results suggested

that continuing to self-monitor physical activity after the intervention ends may lead to increased

maintenance of higher levels of physical activity.


In addition to continuing to engage in behaviors that support continued physical activity,

such as self-monitoring, maintenance rates may increase when participants engage in a variety of

forms of physical activity. Engaging in a variety of forms of exercise reduces risk of injury and

burnout (Sherwood & Jeffrey, 2000). During interventions, emphasizing generalization across

types of activities and locations of physical activity may help to address issues associated with

maintaining exercise behavior once the intervention ends (Martin & Dubbert, 1982). When

physical activity interventions include feedback and reinforcement from a behavioral coach, at

the end of the intervention when that source of reinforcement and feedback is no longer

available, physical activity levels may decrease. However, teaching individuals to access social

support and recruit social reinforcement from individuals in their lives after the intervention ends

may help to buffer the loss of the coach (Sherwood & Jeffrey, 2000). To address the issue of

declining physical activity once interventions have terminated, researchers have recently been

gradually fading support prior to the end of the study (Marcus et al., 2006). Despite this

procedural change, scant research findings on rates and duration of exercise behavior once

interventions have ended exist (Marcus et al., 2006).

There are gaps in the research on maintenance of physical activity. Most behavioral

interventions targeting increased physical activity do not include measurement of maintenance

(Kurti & Dallery, 2013; Normand, 2008; Valbuena et al., 2015; Zarate et al., 2019). Additionally,

very few studies have assessed maintenance in individuals who are sedentary at the start of the

intervention (Marcus et al., 2000). When maintenance is assessed, some components of the

intervention are still included during the maintenance period, which limits the conclusions that

can be drawn about maintenance in the absence of an intervention (Marcus et al., 2006). In

addition, the effects of different schedules of behavioral coaching have not yet been evaluated.


Purpose of Current Study

In light of research showing that physical activity tends to decrease when behavioral

coaching ends, the purpose of the current study was to investigate the degree to which systematic

reduction in the frequency of behavioral coaching affected the frequency and duration of exercise

behavior and maintenance. The first question was whether the behavioral coaching treatment

package was effective for increasing exercise behavior. The second question was how faded

contact with the behavioral coach prior to terminating the intervention affected treatment

outcomes and maintenance, when compared to consistent behavioral coaching that ended after a

predetermined period of time without gradual fading. The effects of faded versus continuous

behavioral coaching on physical activity were evaluated in conjunction with a treatment package

that consisted of self-monitoring and goal setting. Technology in the form of a wearable activity

tracker was used to measure levels of physical activity for the duration of the study.

Method

Participants

To be included in the study, participants were required to obtain medical clearance from

their physician, indicating no health complications that would preclude participation in the study

(See Figure 1). Inclusion criteria required participants have a BMI of 25 or above and be at least

18 years of age. To be included in the study, participants were required to not be actively

attempting to lose weight through medication, surgery, or any other methods. Participants were

required to have access to a computer or smartphone, for entering data into a shared Google

Sheet and syncing the Fitbit. Participants were included in the study if they reported exercising

three days or fewer during the initial screening process. The participants were recruited through

flyers advertising participation in a research study aimed at increasing fitness; these flyers were


posted in community locations, including stores, schools, and local colleges, and flyers were sent

via email to local schools and colleges.

In total, 69 individuals responded to the email and flyers (see Figure 1). From there, the

first 33 people who responded participated in a phone screening for participation in the study.

During the phone screening, a brief description of the purpose of the study was provided, and

individuals answered questions about daily exercise, any health conditions for which they were

receiving medical attention, weight, age, and height. Of the 33 people who participated in phone

screening, nine were excluded due to not meeting the inclusion criteria and 10 declined

participation. The study began with 14 participants. Three participants ended their participation

during the intervention period. One person withdrew during the 6th week of the intervention due

to a potential cancer diagnosis. Another person withdrew in the 9th week of the intervention after

experiencing a house fire. The third person withdrew during the 10th week of the intervention

due to needing emergency ankle surgery unrelated to the study. Two additional participants

completed the intervention but then were unable to attend the last repeated measures session due

to moving across the country during the maintenance period.

After the phone screening, 14 participants were assigned based on age and BMI to one of

two groups: faded coaching or continuous coaching (see Table 1). Participants 118, 119, 120,

121, 122, 123, and 124 were assigned to the faded coaching group. Participants 111, 112, 113,

114, 115, 116 and 117 were assigned to the continuous coaching group.

The participants in the continuous coaching group included seven women between 19

and 58 years old who reported engaging in exercise behavior between zero and two times per

month. The participants in the faded coaching group were seven women between the ages of

19 and 58 years. The participants in this group reported engaging on exercise on 0-3 days per


week prior to beginning the study.

Setting and Materials

The pre-baseline data collection sessions took place at a middle school and university

athletic center. During the intervention phases, the repeated measures assessment sessions were

conducted at the same locations. All other study activities took place in the participant’s natural

daily environments.

Pre-baseline assessment tools included one copy of the Decisional Balance Instrument

(see Appendix A) for each participant. Study materials included one Fitbit Charge 3 per person.

Email, Fitbit accounts, and a Google Sheet for self-monitoring data were created for each

participant. Each time participants traveled to the repeated measures assessment sessions, they

received $10.00 gift cards, except for the last follow-up session, for which they received a

$25.00 gift card to increase likelihood of attendance. Materials used to collect data during the

repeated measures assessments sessions included a FaceLake FL-100 Pulse Oximeter that was

used as the portable heart rate and oxygen sensor, a Generation Guard GM 500W wrist automatic

blood pressure monitor, a Seca 201 CM Girth Circumference inelastic tape measure, a Seca 213

portable stadiometer, a HealthOMeter 498KL Remote Display digital scale, and Life Fitness

treadmills. A coaching call script structured each behavioral coaching session and ensured

consistency across sessions. Each participant was provided a task analysis (see Appendix B) that

delineated the steps for setting goals based on her performance in the previous 10 days.

Dependent Variables and Measurement

Frequency and Duration of Exercise

The primary dependent variables were daily and weekly duration of exercise. Daily

duration was defined as the total number of minutes of moderate and/or vigorous physical


activity in a given 24-hour period. Weekly duration was defined as the total number of minutes

of moderate and/or vigorous intensity physical activity in a given week. Daily duration of

physical activity was collected by the Fitbit Charge 3 and accessed by extracting activity data

from each participants’ Fitbit account and recording the total number of active minutes for each

day. Weekly duration of exercise was also collected by the Fitbit Charge 3 and was accessed by

extracting activity data from each participants’ Fitbit account and recording the total number of

active minutes per week; weeks were defined as all activity that occurred between 12:00 am EST

on Sunday through 11:59 pm EST on Saturday. It is important to note that the Fitbit Charge 3

records duration of physical activity only after the wearer of the device has been engaged in 10

consecutive minutes of moderate-to-vigorous physical activity

Frequency of exercise was a secondary dependent variable, which included the frequency

of exercise per week. Weekly frequency of physical activity was defined as the number of

physical activity bouts in the week (i.e., Sunday through Saturday). Weekly frequency data were

collected by the Fitbit Charge 3, and accessed by the experimenter by extracting activity data

from each participants’ Fitbit account and counting the number of days with active minutes.

Daily Step Count

A third dependent variable included daily step count. Daily step counts were defined as

the number of steps walked in a 24-hour period (i.e., 12:00 am EST through 11:59 pm EST).

Daily step counts were recorded by the Fitbit Charge 3 and were accessed by the experimenter

by extracting activity data from each participants’ Fitbit account and recording the number of

steps recorded by the Fitbit Charge 3.

Physical Activity Adherence


Another dependent variable was physical activity adherence, defined as the percentage of

weeks in which participants met their physical activity goals, which was calculated by dividing

the total number of weeks in which participants met their duration of activity goals by the total

number of weeks, and multiplying by 100. For example, if a person met their goals on 11 out of

12 weeks, physical activity adherence for that week would be 140/150 x 100 = 92%.

Treatment Adherence

The final dependent variable was treatment adherence, which was defined as the extent to

which participants engaged in all of the following behaviors: participating in coaching sessions

(0-1 times per week), entering data into self-monitoring data sheet (7 times per week), syncing

the device (7 times per week), and setting goals (1 time per week). Treatment adherence data

were collected by the experimenter logging in to each Fitbit account every morning to verify that

the device had been synced the previous day. The experimenter then opened the shared

spreadsheet to record whether or not participants had entered all necessary data for the previous

day. Treatment adherence was calculated by dividing the total number of completed adherence

behaviors by the total number of possible treatment adherence behaviors and multiplying the

quotient by 100. For example, if a participant completed 14 treatment adherence behaviors out of

16 possible, treatment adherence would equal 87.5%.

Anthropometric Data

Weight measures were obtained by having participants remove their shoes and then stand

upright on the digital scale. Height measures were obtained by having participants remove heavy

outer clothing, shoes, and hair accessories, roll up their pants, and then stand straight with their

back against the stadiometer height rod. The head plate was then brought down and placed on the

crown of the head. Change in weight from pre-baseline to post-intervention was measured for


each participant. Percentage of weight change from pre-baseline to end of treatment was

calculated for each participant by subtracting their ending weight from their starting weight,

dividing the difference by the starting weight, and then multiplying by 100. In addition, BMI was

recorded and calculated for each participant using height and weight measurements collected

during the repeated measures assessment sessions.

Circumference measurements were collected during each of the repeated measures

sessions. Waist circumference measurements were obtained by applying an inelastic tape

measure horizontally at the midway point between the iliotibial crest and the lowest rib while the

participant stood with feet together and arms at his or her side. Next, with the participant in the

same position, the tape measure was placed around the widest portion of the buttocks and the

circumference was recorded to obtain the hips/buttocks circumference measurement. Once the

waist and hips/buttocks measurements were taken, they were immediately repeated following the

same procedures and recorded. If the first and second measurements were not within 5mm of one

another, an additional measurement was taken. The waist-to-hip ratio was determined by

dividing the circumference of the waist by the circumference of the hips. Waist-to-hip ratios

<0.85 are considered in the healthy range for women (World Health Organization, 2008).

Fitness Data

Rockport One-Mile Fitness Walking Test. Total duration of the Rockport One-Mile

Fitness Walking test was recorded for each participant during the repeated measures sessions.

Resting heart rate was taken prior to beginning the Rockport One-Mile Fitness Walking test, and

recovery heart rate was taken immediately after the walking test was completed. The Rockport

One-Mile Fitness Walking Test (ACSM, 2018) was completed, in which participants walked as

quickly as possible for one mile, and recovery heart rate was recorded for 10s following


completion of the mile. Heart rate data were collected using a pulse oximeter sensor worn on the

participant’s index finger.

Borg Rating of Perceived Exertion. Self-reported scores on the Borg Rating of

Perceived Exertion Scale (Borg, 1998) were collected during each repeated measures assessment

session to qualitatively assess changes in ratings of difficulty completing the Rockport One-Mile

Fitness Walking test. Scores on the Borg Rating of Perceived Exertion scale from pre-baseline to

post-treatment were compared to see if there was any difference in the rating of exertion.

Decisional Balance Scale. Ratings on the Decisional Balance Scale were obtained during

each of the repeated measures sessions by asking participants to rate 16 questions on a 5-point

Likert scale (see Appendix A). Lower scores on the questions related to the positive aspects of

exercise suggested that the participant found the positive aspects of exercise to be very important

factors influencing exercise behavior (i.e., increased confidence, improved sleep, reductions in

stress, etc.). In contrast, higher scores on the questions related to the negative aspects of exercise

suggested that the participant’s exercise behavior was not reduced due to the negative aspects of

exercise (i.e., loss of time with loved ones, being tired, etc.). Scores on the Decisional Balance

Scale were calculated by adding the scores for questions 1-10 and then dividing by 10 to yield an

average for the positive aspects of exercise, and then adding the scores for questions 11-16 and

dividing by six to obtain an average score for the negative aspects of exercise. Scores from pre-

baseline to post-treatment were compared to assess if the average scores for the positive aspects

of exercise decreased over the course of the study and the average scores for the negative aspects

of exercise increase over the course of the intervention. A total decisional balance score is

calculated by subtracting the cons score from the pros score and a higher decisional balance

score is indicates a greater motivation to engage in exercise behavior (Pinto et al., 1998). The


steps for each component of the repeated measures session were outlined to assess treatment

fidelity (see Appendix C), and for collection of interobserver agreement data (see Appendix D).

Experimental Design

The 14 participants were randomized into two groups of seven participants, matched

according to BMI and age (see Table 1). A multiple baseline across participants design (Baer,

Wolf, & Risley, 1968) was used to demonstrate the effectiveness of the interventions for all

participants in both groups. Individuals in the continuous coaching group participated in 12

behavioral coaching sessions and individuals in the faded coaching group participated in

seven behavioral coaching sessions over the course of the study (see Table 2).

Statistical Analysis

Statistical analyses were used to determine if there was a significant difference in the

duration and frequency of physical activity from baseline, end of treatment, and follow-up. Two

sample t-tests were used to answer the question of whether there was a difference in mean

duration and frequency of exercise from baseline to intervention and end of treatment to follow-

up for participants in the faded coaching group compared to the continuous coaching group. Two

sample t-tests were also used to determine if there were statistically significant group differences

in the percentage of change in duration and frequency of exercise from baseline to intervention

and end of treatment to follow-up. Paired samples t-tests were used to determine if there were

statistically significant differences in duration and frequency of exercise for each individual from

the end of treatment and follow-up. The significance threshold was set at p ≤ .05 for all of the t-

tests.

Procedures

Pre-Baseline


To determine eligibility, an individual session occurred in which the participants

completed the Decisional Balance Scale, and their height and weight were collected to determine

BMI. If the individual met eligibility criteria, they were provided with a wearable activity

tracker (i.e., Fitbit Charge 3) equipped with a tamper-evident tape that concealed all data on the

screen. Participants were instructed to charge the Fitbit Charge 3 every other day during the

baseline period. Participants were shown how to wear and charge the activity tracker and were

instructed to perform typical daily activities until contacted by the behavioral coach.

Repeated Measures

During the course of the study, participants took part in three individual repeated

measures assessment sessions: pre-baseline, at the end of the intervention, and a 3-month follow-

up. For each of the sessions, the participants met the researcher in a private room at a sports

facility. The participants sat in a chair and answered questions on the Decisional Balance Scale.

Then, resting heart rate and blood pressure measurements were taken. Following that, the

researcher took height, weight, and circumference measurements. Once those data were

collected, the Rockport One Mile Fitness Walking Test was completed on the treadmill. The

Rockport One Mile Fitness Walking Test can be completed on a track or other flat surface. To

control for variables such as weather, temperature, and surface coverage affecting mile times and

to increase consistency across participants, a treadmill was used to complete the walking test.

After the mile was complete, recovery heart rate was recorded and participants completed the

Borg Rating of Exertion Scale. Once all of these steps were complete, participants were given a

gift card and thanked for their participation. The researcher informed the participant of any

instructions related to next steps. After the first repeated measures assessment session, the

instructions included wearing the covered Fitbit Charge 3 daily and continuing to engage in their


typical daily behaviors and syncing the Fitbit Charge 3 every other day until being contacted by

the researcher. After the second repeated measures assessment session, the instructions included

continuing to engage in regular physical activity, setting goals, syncing daily, and engaging in

self-monitoring, until contacted by the researcher. After the third repeated measures assessment

session, the participants were thanked for their participation.

Baseline

Participants were instructed to go about their normal routine and not change activity

levels. During baseline, participants were instructed to wear the Fitbit Charge 3 that had the

display screen covered with tamper-evident tape from when they woke up until they went to

bed each night. No other contingencies were in place during baseline. Baseline levels of

activity were used to determine initial activity goals. Once the baseline period ended, each

participant met with the behavioral coach and tamper-evident tape was removed, and each

participant learned how to sync the device with a smartphone or computer. Participants were

instructed to charge the Fitbit Charge 3 every other day to prevent accidental loss of data.

During baseline, participants were not able to sync the Fitbit Charge 3, as they were not

provided with the login information for the Fitbit.com account. The device stored data for 30

days, and so for participants who had baseline periods that extended longer than 30 days, the

researcher met with these participants once during baseline to sync the devices on her

computer, to prevent the loss of data.

General Procedures

During the intervention phases, participants wore uncovered activity trackers daily and

set goals to engage in specific amounts of exercise, initially set based on their baseline

performance. Participants were limited to engaging in walking and other moderate-intensity


physical activity to reduce the probability of injury from overexertion. If a participant had not

previously engaged in any physical activity, the participant began with short bouts of walking

(e.g., 10 minutes). As participants met exercise goals that were gradually and systematically

increased based on her previous performance, other forms of exercise were permitted. Each

participant was advised not to exercise more than the goal set each week to prevent injury.

During the intervention phase, participants were asked to self-monitor their exercise behaviors

on a shared Google Sheet that tracked data on daily and weekly duration of exercise, as well

as frequency of exercise per day and week. Participants were able to view the Fitbit device

output and access the Fitbit website at any point during the intervention. During the

intervention phase, participants synced their Fitbit daily to the Fitbit account by either opening

the Fitbit application on their phone with Bluetooth enabled, or logging into their account on

Fitbit.com. Participants were instructed to wear the activity tracker, self-monitor their data,

and sync the device daily until notified by the behavioral coach, which was six months for each

participant.

Behavioral Coaching

For the behavioral coaching component, participants met individually with the behavioral

coach for 15- to 30-min sessions via an online videoconferencing platform. Behavioral coaching

sessions were video-recorded for the duration of the meeting, for the purpose of collecting

treatment integrity data. The behavioral coaching sessions occurred weekly for the first five

weeks of the study for both groups. During the behavioral coaching sessions, feedback was

provided on the participant’s performance and goal attainment since the previous session, and

new physical activity goals were set. Goals were tailored for each participant, and set using

percentile schedules (Galbicka, 1994). Duration of physical activity goals were set at the 80th


percentile, which was determined by the third longest duration in the previous 10 days.

Subsequent goals were never set lower than the lowest preceding goal. During the behavioral

coaching sessions, strategies for increasing activity were discussed, and recommendations for

increasing levels of physical activity throughout the day (e.g., parking farthest from door at

stores) were provided. The behavioral coach recorded all strategies for increasing physical

activity that were discussed during each of the behavioral coaching sessions, and followed up on

previously recommended strategies in subsequent behavioral coaching sessions. Participants

were also encouraged to ask questions and to generate ideas for problem-solving and increasing

activity. A behavioral coaching session checklist was used to ensure that all essential information

was covered during each behavioral coaching session (see Appendix E).

During the behavioral coaching session in the fourth week of the study, participants in

both groups were provided with a task analysis (see Appendix B) that delineated the steps for

determining daily duration goals, and the participants practiced setting goals with the behavioral

coach using hypothetical data during that session until the participant determined a goal with

100% accuracy. During the behavioral coaching session in the fifth week, participants in both

groups began setting their own goals with feedback from the behavioral coach. The purpose of

fading out support for goal setting was to establish that skillset for participants, with the goal of

increasing maintenance of exercise behaviors once the study ended. In the last behavioral

coaching session, participants in both groups were encouraged to continue self-monitoring and

goal setting on their own.

Continuous Coaching Group. Participants in the continuous coaching group

participated in 12 total consecutive weekly coaching calls over the course of the study (see Table

2).


Faded Coaching Group. Participants in the faded coaching group experienced faded

coaching calls with weekly calls during Weeks 1 through 5, one call during Week 7, and one call

during Week 11, for a total of seven coaching calls over the course of the study (see Table 2).

Maintenance

Following the 12 weeks of intervention for both groups, participants entered into the

maintenance phase. Participants were asked to continue to sync the Fitbit daily and enter the data

into the shared Google Sheet during this period. During maintenance, the behavioral coach asked

participants to continue to review data and set goals, following the format of the behavioral

coaching sessions. Specifically, participants were asked to sit down on their own and review

their data once a week, identify what went well in the previous week, determine what their goal

would be based on the 80th percentile, as well as identify which strategies they would use to

increase activity in the coming week. At the beginning of the maintenance period, participants

were asked to email the behavioral coach if they experienced any issues with the Fitbit Charge 3

devices. During this maintenance period, if a participant did not sync the Fitbit for seven days,

the behavioral coach emailed the participant and asked her to sync.

Follow-Up Sessions

An individual follow-up session (i.e. the final repeated measures assessment session) was

completed with participants approximately three months post-intervention, to assess short-term

maintenance of exercise behaviors. Follow-up data included analysis of two weeks of data on

physical activity at three months post-intervention. Data from the last two weeks of the

maintenance period were used for analysis.

Social Validity

At the conclusion of the study, participants were asked to evaluate the value of the


behavioral coaching component, the goals of the study, and to assess overall satisfaction with

participation in the study. An anonymous online survey with a 5-point Likert rating scale was

sent via email to each participant; a score of 0 indicated strong disagreement and a score of 5

indicated strong agreement (see Appendix F). Participants completed the survey by clicking on

the number that corresponded with their rating for each question, and then clicked an icon to

submit their responses and send the information anonymously to the experimenter.

Interobserver Agreement

All data collection was automated and recorded by the Fitbit Charge 3. An independent

research assistant reviewed 40% of the data from all participants in the 6-month period, with a

focus on duration and frequency of exercise, as well as daily step counts.

Daily duration data were accessed by the research assistant extracting activity data from

each participants’ Fitbit account. Weekly duration of exercise data were accessed by the research

assistant extracting activity data from each participants’ Fitbit account and recording the number

of active minutes per day and week. The data from the research assistant were then compared to

the data collected by the primary researcher to determine the level of agreement. Interobserver

agreement for duration of exercise was calculated by dividing the number of agreements by total

number of data points assessed, and multiplying by 100 (e.g., 11 agreements out of 18 weeks =

61% agreement). Interobserver agreement for daily duration of exercise was 99%. Interobserver

agreement for weekly duration was 88%.

Interobserver agreement data were also collected on the frequency of exercise and these

data were accessed by the research assistant extracting activity data from each participants’ Fitbit

account and recording the number bouts of exercise per day and week. The data from the

research assistant were then compared to the data collected by the primary researcher to


determine the degree to which the data matched. Interobserver agreement for frequency of

exercise were calculated by dividing the number of agreements by total number of data points

assessed, and multiplying by 100 (e.g., 151 agreements out of 157 data points = 96% agreement).

For daily frequency of exercise, interobserver agreement was 96%. Interobserver agreement for

weekly frequency was 84%.

Interobserver agreement data were also collected on daily step counts, which were

accessed by the research assistant extracting activity data from each participants’ Fitbit account

and recording the number of steps recorded by the Fitbit Charge 3. The data from the research

assistant were then compared to the data collected by the primary researcher to determine to

what extent the data matched. Interobserver agreement for step counts were calculated by

dividing the number of agreements by total number of data points assessed, and multiplying by

100 (e.g., 151 agreements out of 157 data points = 96% agreement). Interobserver agreement on

daily step counts was 96%.

Treatment Fidelity

To measure treatment integrity, components of the behavioral coaching calls were

outlined in a behavioral coaching session checklist (see Appendix E). A research assistant

independently reviewed 20% of the recorded coaching calls to determine the degree to which the

behavioral coach addressed all components outlined on the checklist. Treatment fidelity scores

were calculated by totaling the number of steps implemented correctly as written divided by the

total number of steps outlined, and then multiplying that quotient by 100. The mean fidelity

score for coaching calls was 91.85% (range 11%-100%). During the session with 11% fidelity, it

was the first behavioral coaching session and the participant experienced technical issues with


the Fitbit Charge 3, and as a result the session ended early and was rescheduled for once the

issue was resolved.

Results

There was an increase in frequency of exercise for all participants and the duration for

most participants in both the continuous coaching and the faded coaching groups from baseline

to intervention. Examining data from intervention to maintenance, Participants 121, 123, 113,

114, and 117 increased mean frequency of exercise, and Participants 122, 123, and 114 increased

mean duration of exercise.

Frequency of Exercise

Across all participants, the weekly frequency of physical activity was variable over the

course of the intervention period, as well as after the intervention ended. While intervention was

in place, both groups engaged in more frequent physical activity compared to baseline (see

Figures 2 & 3 and Appendix G).

Faded Coaching Group

The mean frequency of exercise for all participants in the faded coaching group was 0.22

times per week during baseline, 2.75 times per week during the intervention period, and 2.29

times per week during maintenance (see Figure 2 and Appendix G).

Continuous Coaching Group

The mean frequency of exercise for all participants in the continuous coaching group was

0.18 times per week during baseline, 4.38 times per week during the intervention period, and

5.15 times per week during maintenance (see Figure 3 and Appendix G).

Duration of Exercise


Across all participants, duration of exercise behavior was variable over the course of the

intervention period, as well as after the intervention ended (see Figures 4 & 5 and Appendices H

& I). Both groups showed gradual increases in duration of physical activity over the course of

the study. The mean weekly duration of physical activity was higher during the intervention

period for both groups, and during baseline (see Appendix J). As a group, participants in the

faded coaching group showed an increase in the mean weekly duration of exercise from the

intervention period to maintenance. As a whole, the mean weekly duration of exercise for the

continuous coaching group remained the same from intervention to maintenance.

Faded Coaching Group

In the faded coaching group, Participants 121 and 123 had higher baseline mean duration

of physical activity compared to the intervention. Participants 119 and 121 showed a decrease in

mean weekly duration during the maintenance period, and Participants 122 and 123 showed a

mean weekly duration increase during the follow-up period compared to the intervention period.

Participant 124 had mean weekly duration that remained the same from intervention to

maintenance.

Continuous Coaching Group

In the continuous coaching group, one participant (112) had the highest mean duration

during the baseline period, compared to intervention and maintenance. Four participants (111,

113, 116, 117) had the highest weekly mean duration during the intervention period, and

Participant 114 had the highest weekly mean duration during the follow-up period.

Step Count

Both groups increased daily step counts from baseline to intervention (see Table 3). For

all participants in the faded coaching group, the mean increase in step count was 12% from


baseline to the intervention period, and there was a 10% decrease from intervention to the

maintenance phase. For all participants in the continuous coaching group, the mean change in

step count from baseline to intervention was a 12% increase, and the mean change from

intervention to maintenance was an additional 10% increase in step counts. Overall, the faded

coaching group had the highest mean step counts during the intervention period, with an average

of 7,391 steps (range 0–21,312 steps; see Appendix K) and the continuous coaching group had

the highest mean step counts during the maintenance period, with an average of 8,718 steps

(range 0–24,717 steps; see Appendix L).

Statistical Analysis

The two sample t-tests were used to answer the question of whether or not there were

statistically significant group differences in frequency and duration of physical activity from

baseline to intervention and intervention to maintenance. Two sample t-tests compared the mean

difference in duration and frequency of exercise, as well as the percentage difference in duration

and frequency between groups.

There was no statistically significant difference found from baseline to intervention for

mean difference in duration between the faded coaching group (M= 60.1 min; SD= 90.3 min)

and the continuous coaching group (M= -131.5 min; SD=139.8 min); t(1.02), p = 0.33 , 95% CI

[18.3, 179.7] (see Table 4). Those results suggest that the 12-week faded coaching intervention

was equally effective as continuous coaching for increasing duration of physical activity. We can

be 95% confident that the mean difference in duration from baseline to intervention is between

18.3 and 179.7 minutes. There was also no statistically significant difference found from baseline

to intervention for mean frequency of physical activity between the faded coaching group

(M=2.52; SD=2.3) and the continuous coaching group (M=4.18; SD=2.3); t(0.59), p = .06, 95%


CI [1.9, 5.0] (see Table 4). Those results suggest that the 12-week faded coaching intervention

was equally effective as continuous coaching for increasing frequency of physical activity. We

can be 95% confident that the mean difference in frequency from baseline to intervention is

between 1.9 and 5.0 times a week.

Results of the two sample t-tests comparing mean changes from end of intervention and

follow-up showed that there was a statistically significant group difference in duration of

physical activity between the faded coaching group (M= 57.9 min; SD= 185.4 min) and the

continuous coaching group (M= -186.9 min; SD= 172.3 min); t(-2.25), p = 0.05 , 95% CI [-218,

66] (see Table 5). Those results suggest that the continuous coaching group decreased the

duration of physical activity more than the faded coaching group in the follow-up period. We can

be 95% confident that the mean difference in duration from end of intervention and follow-up is

between -218 and 66 minutes. However, results from the two sample t-test comparing group

differences in mean frequency of physical activity from intervention to maintenance showed

there was not a significant difference between the faded coaching group (M= -2.3; SD= 2.6) and

the continuous coaching group (M= -0.6; SD= 2.9); t(2.3), p = 0.3, 95% CI [-3.3, 0.3]. Those

results suggest that the continuous coaching group did not decrease frequency of physical

activity more than the faded coaching group in the follow-up period. We can be 95% confident

that the mean difference in frequency from end of intervention and follow-up is between -3.3 and

0.3 times a week.

Two sample t-tests were also used to determine if there were statistically significant

group differences in percentage of change from baseline to intervention and end of intervention

to follow-up. There was a statistically significant difference found from baseline to intervention

for percentage change in duration between the faded coaching group (M= .63; SD= 0.11) and the


continuous coaching group (M= .42; SD= 0.19); t(-2.31), p = 0.05 , 95% CI [.38, .64] (see Table

6). Those results suggest that the faded coaching group showed greater percentage of change in

duration of physical activity from baseline to intervention, compared to the continuous coaching

group. We can be 95% confident that the percentage change increase in duration from baseline to

intervention is between 38% and 64%. There was not a statistically significant difference found

from baseline to intervention for percentage change in frequency between the faded coaching

group (M= 14%; SD= 0.23) and the continuous coaching group (M= 6%; SD= 0.09); t(-0.06), p

= 0.05 , 95% CI [-2.05, .19] (see Table 7). Those results suggest that the 12-week faded coaching

intervention was equally effective as continuous coaching for increasing frequency of physical

activity. We can be 95% confident that the percentage change in frequency of physical activity

from baseline to intervention is between -2% and 19%.

Results of the two sample t-tests comparing percentage of change from end of

intervention and follow-up showed that there was not a statistically significant group difference

in duration of physical activity between the faded coaching group (M= 1.18; SD= 0.29) and the

continuous coaching group (M= .47; SD= 0.34); t(-2.52), p = 0.06, 95% CI [.40, .87] (see Table

7). Those results suggest that both groups decreased the duration of physical activity in the

follow-up period. We can be 95% confident that the percentage change in duration from end of

intervention and follow-up is between 40% and 87%. Results from the two sample t-test

comparing group differences in percentage of change in frequency of physical activity from

intervention to maintenance showed there was not a significant difference between the faded

coaching group (M= 1.6; SD= 2.3) and the continuous coaching group (M= .47; SD= 0.31); t(-

0.89), p = 0.4, 95% CI [-.21, 190] (see Table 7). Those results suggest that the continuous

coaching group did not decrease the frequency of physical activity more than the faded coaching


group in the follow-up period. We can be 95% confident that the percentage of change from end

of intervention and follow-up is between -21% 190%.

Paired samples t-tests were used to answer the question about the effects of the

intervention on maintenance for individuals irrespective of group. Results from the paired

samples t-test showed that there was not a statistically significant difference in duration of

physical activity for participants at the end of intervention (M= 275.5 min; SD= 190.4 min) and

the follow-up period (M= 200.0 min; SD= 216.9 min); t(1.18), p. 2.23, 95% CI [-139.6, -11.7]

(see Table 8). Those results suggest all participants decreased duration of physical activity at

similar rates from end of intervention to follow-up. We can be 95% confident that the mean

difference in duration from end of intervention and follow-up is between 139 and 11 minutes a

week less. Results from the paired samples t-test examining individual differences in frequency

of physical activity from end of intervention to follow-up also showed that there was not a

statistically significant difference for participants at the end of intervention (M= 3.6; SD= 3.4)

and the follow-up period (M= 2.1; SD= 2.1); t(1.81), p. 0.09, 95% CI [-2.3, -0.7]. Those results

suggest that all participants decreased frequency of physical activity at similar rates from end of

intervention to follow-up. We can be 95% confident that the mean difference in frequency from

end of intervention and follow-up is between -2.3 and -0.7 times a week.

Physical Activity Adherence

Mean physical activity adherence for the faded coaching group was 29% (range 0%–

50%), meaning that participants in this group reached their physical activity goals on an average

of 29% of weeks (see Table 9).


Mean physical activity adherence for the continuous coaching group was 40% (range

18%–82%), meaning that participants in this group reached their physical activity goals on an

average of 40% of weeks.

Treatment Adherence

Mean treatment adherence for the faded coaching group was 47% (range 33%–81%)

including syncing the Fitbit daily, updating the shared Google Sheet, participating in the

behavioral coaching sessions, and attending the repeated measures assessment sessions.

Mean treatment adherence for the continuous coaching group was 58% (range 44%–

76%) including syncing the Fitbit daily, updating the shared Google Sheet, participating in the

weekly behavioral coaching sessions, and attending the in-person repeated measures assessment

sessions.

Further analysis of the individual components of treatment adherence revealed that

adherence for the daily syncing component was 71% (range 21%–96%) for the faded coaching

group and 82% (range 68%–98%) for the continuous coaching group (see Table 10). For the self-

monitoring component, adherence was 21% (range 4%–57%) for the faded coaching group and

34% (range 7%–81%) for the continuous coaching group. Adherence for participation in the

behavioral coaching sessions was 100% for both the faded coaching group and the continuous

coaching group. Attendance for the repeated measures assessment session was 100% for the

faded coaching group and 89% (range 66%–100%) for the continuous coaching group.

Weight Change

Percentage of weight change from pre-baseline to end of treatment was low and similar

across participants in both groups (see Table 1). In the continuous coaching group, Participant

111 was the only participant to lose weight, and she lost 3.4% of her body weight from baseline


to end of maintenance. The three other participants from the continuous coaching group who

participated in the last repeated measures session gained weight. Participant 112 gained .08%,

Participant 116 gained 3.46% of her body weight, and Participant 117 gained 3.75% of her body

weight from baseline to end of maintenance. In the faded coaching group, three of the five

participants who attended the final repeated measures assessment session lost weight. Participant

119 lost 6.18% of her body weight, Participants 121 and 122 each lost .35% of their body weight

from baseline to end of treatment. BMI measures did not change substantially for participants in

either group, and all participants continued to have BMI in the overweight or obese range after

the maintenance period.

Repeated Measures

There were participants in both groups who demonstrated reductions in resting heart rate,

blood pressure, and mile times on the Rockport One Mile Walking Fitness Test (see Table 11)

over the course of the study. There were also participants in both groups whose blood pressure,

resting heart rate, and mile times increased from pre-baseline to post-intervention.

Resting Heart Rate

Comparing health measures from pre-baseline and post-intervention for the continuous

coaching group, Participants 111, 112, and 114 each had lower resting heart rate measurements,

whereas Participants 113, 114, 116, and 117 each had higher resting heart rate measurements.

Despite the increases noted in resting heart rate, all participants in the continuous coaching group

had resting heart rates that fell within the normal range at pre-baseline and post-intervention.

Comparing health measures from pre-baseline and post-intervention for the faded coaching

group, Participant 119 had a lower resting heart rate measurement, whereas Participants 121,

122, 123, and 124 each had higher resting heart rate measurements.


Blood Pressure

Blood pressure measurements from pre-baseline to post-intervention showed variable

results for participants in the continuous coaching group. Three participants had lower blood

pressure measurements post-intervention, one participant’s blood pressure measurements

remained the same, and two participants had blood pressure measurements that increased from

pre-baseline to post-intervention. Blood pressure measurements from pre-baseline to post-

intervention showed variable results for participants in the faded coaching group as well. There

were two participants, Participants 122 and 124, who had lower blood pressure measurements

post-intervention, and three participants had higher blood pressure measurements post-

intervention including Participants 119, 121, and 123.

Rockport One-Mile Fitness Walking Test

Examining the results of the Rockport One-Mile Fitness Walking Test times for the

continuous coaching group from pre-baseline to post-intervention, decreased times were

observed for five of the six participants who completed both repeated measures sessions.

The results of the Rockport One-Mile Fitness Walking Test duration from pre-baseline to

post-intervention, three out of the six participants in the faded coaching group evidenced

decreased mile times.

Borg Rating of Perceived Exertion

Borg Rating of Perceived Exertion scores were recorded following the completion of the

Rockport One Mile Fitness Walking Test, wherein participants assigned a rating for the overall

level of exertion they experienced while completing the walking test (see Table 12). Scores

range from 6–20, with higher scores suggesting greater perceived exertion. As fitness improves,

scores should decrease. For participants in the continuous coaching group who completed the last


repeated measures assessment, Participants 111, 112, and 116 reported decreased ratings of

exertion during the Rockport One Mile Fitness Walking Test, while Participant 117 had a rating

that increased.

In the faded coaching group, Participants 122 and 124 had scores that decreased from

baseline to follow-up and Participants 119 and 121 and assigned exertion ratings that remained

the same from pre-baseline to follow-up.

Decisional Balance Scale

Decisional Balance Scale Scores from pre-baseline to post-treatment were compared to

assess if the scores for the positive aspects of exercise exceeded the scores for the negative

aspects of exercise. A total decisional balance score is calculated by subtracting the cons score

from the pros score. A higher decisional balance score indicates a greater motivational readiness

to exercise. Due to discontinued participation and two participants who completed the

intervention but did not attend the last repeated measures assessment, comparison data for

baseline and follow-up for the Decisional Balance Scale are only available for eight participants

(see Table 13). Of those participants, five reported higher scores in follow-up than in baseline,

two participants had scores that remained the same, and two had scores that reflected greater

readiness to exercise in baseline.

Body Composition

Direct body composition scores collected during the repeated measures assessment

sessions shows that for the nine participants in both groups who had comparison data to report,

three participants had lower circumference measurements in follow-up compared to baseline,

four evidenced larger circumference measurements in follow-up compared to baseline, and two

participants remained the same (see Table 14). The waist-to-hip ratios show that in the faded


coaching group, Participants 119 and 121 had ratios in the healthy range at follow-up, but not at

the other sessions. Participant 122 had waist-to-hip ratios in the healthy range during all of the

repeated measures assessment sessions, with the lowest ratio observed in the follow-up session.

Participants 123 and 124 had waist-to-hip ratios that exceeded the healthy range during all of the

repeated measures assessment sessions. There were no comparison data available for Participants

118 and 120. In the continuous coaching group, three participants showed a decrease in

circumference measurements. Participant 112 had a lower waist-to-hip ratio in the follow-up

session. Participants 116 and 117 had waist-to-hip ratios in the healthy range across all repeated

measures assessment sessions, however both had higher ratios at follow-up compared to

baseline. Participant 111 had waist-to-hip ratios that were similar across all sessions and

exceeded healthy range scores. There were no comparison data available for Participants 113,

114, and 115.

Social Validity

The results of the social validity assessment indicated that the five participants who

completed the survey viewed the intervention favorably (see Table 15; Valbuena et al., 2015).

Participants reported feeling prepared to set their own goals once the behavioral coaching

component of the study ended. Additionally, participants reported that participating in the

behavioral coaching sessions helped them increase their physical activity while the coaching

sessions were in place, and that they were more active during the period with behavioral

coaching than during maintenance. Participants reported low scores on weight loss during the

study, indicating that weight loss was limited.

Discussion


The first purpose of the study was to evaluate the effects of behavioral coaching on

increasing physical activity in women and the results of the study showed that all participants

increased frequency of physical activity during the behavioral coaching condition. However, due

to high duration of physical activity in baseline for some participants, four participants in the

faded coaching group and six participants in the continuous coaching group increased duration of

physical activity during the intervention period compared to baseline. The second purpose of the

study was to compare the efficacy of faded versus continuous coaching on increasing physical

activity during the intervention and in maintenance. Three participants in the faded coaching

group increased weekly duration of exercise from baseline to intervention and six participants in

the continuous coaching group increased weekly duration of physical activity from baseline to

intervention. The results from the current study support earlier research on the effectiveness of

goal setting, feedback, and behavioral coaching for increasing physical activity (Valbuena et al.,

2015). Results of the current study offer mixed support for the use of faded coaching as a buffer

against the tendency for individuals to decrease exercise once the intervention ends. Fewer

participants in the faded coaching group evidenced decreases in the duration (n=3) and frequency

(n=2) of exercise during the maintenance period, compared to individuals who participated in the

continuous coaching group (see Figures 2, 3, 4 and 5), which provides evidence in support of

faded behavioral coaching.

However, results from the current study also support findings from past research that

show once the contingencies of the treatment package end, physical activity reduces (Marcus et

al., 2000). Reductions in physical activity were observed for participants in both groups (see

Figures 2, 3, 4 and 5). National recommendations for physical activity state that adults should

participate in moderate intensity aerobic activity for 30 minutes, five times per week for a total


of 150 minutes per week (AHA, 2016). Despite the observed reductions in physical activity post-

intervention, two participants from the faded coaching group and four participants from the

continuous coaching group continued to meet national recommendations of 150 minutes of

physical activity per week during the maintenance period. More research on faded behavioral

coaching is needed to determine its effectiveness for reducing decreased physical activity after

the intervention ends.

Coleman and colleagues (1999) found that exercise adherence was higher during the

conditions in which participants could choose how to divide the bouts of exercise and found that

participants had highest activity levels during short bouts of exercise in the choice condition.

Similar results were found in the current study. Participants were permitted to choose the number

of bouts in which to achieve their daily and weekly goals, and all participants showed an increase

in physical activity from baseline to the intervention.

Similar to findings from earlier research (i.e., Hustyi et al. 2011; Normand, 2008;

Valbuena et al., 2015; Van Wormer, 2004; Washington et al., 2014), in the current study,

percentile schedules led to gradual increases in physical activity over the course of the

intervention. The use of percentile schedules can allow for goals that decrease if physical activity

levels consistently decrease. So, if previous physical activity levels were reduced, the next goal

would also be reduced (Washington et al., 2014), which is problematic due to potentially

stagnant goals and limited increases in physical activity. In the current study, as the intervention

progressed goals could not be set lower than the lowest established goal, to reduce the possibility

of decreasing goals (Washington et al., 2014). Using percentile schedules in addition to other

treatment elements was effective for increasing physical activity in the present study. However,

it is interesting to note that physical activity adherence was low across groups. One reason goal


attainment may have been low is that there was no ceiling for how high the goals could be set.

Setting a ceiling for how high goals can be set may increase goal attainment in future research.

Evaluating Treatment Effectiveness

Due to the high level of variability inherent in physical activity levels across days

(Donaldson & Normand, 2009; Valbuena et al., 2015), visual analysis alone may not be as useful

for determining treatment effects (Scruggs & Mastropieri, 2001; Valbuena et al., 2017). When

visually inspecting these data, there is often overlap between conditions due to the natural

variability of physical activity, which makes it difficult to determine treatment effects, and this is

consistent with previous research (Valbuena et al., 2017). The mean level line used in the current

study helps to show the differences in average frequency and duration of activity across

conditions, and to assist with visual analysis, which is especially useful for daily data (see

Appendices I and J).

Calculating percentage of nonoverlapping data is one method used in single subject

research to determine if results from a treatment condition vary compared to a baseline condition

(Scruggs & Mastropieri, 2001; Purswell & Ray, 2014; Tarlow & Penland, 2016). Higher

percentages indicate greater amounts of nonoverlapping data, or a larger treatment effect

(Scruggs & Mastropieri, 2001; Tarlow & Penland, 2016). When the percentage of

nonoverlapping data is greater than 70%, this is suggestive of an effective treatment (Scruggs &

Mastropieri, 2001).

One of the questions in the present study was whether participants who experienced a

thinning schedule of coaching calls showed higher levels of exercise behavior in maintenance,

compared to participants who had weekly coaching calls that were continuous throughout the

intervention. Results of the percentage of nonoverlapping data points from intervention to


maintenance for the faded coaching group revealed that all scores were below 50% (range 0%–

31%; see Table 16). Results of the percentage of nonoverlapping data points from intervention to

maintenance for the continuous coaching group revealed that all scores were below 60% (range

0%–57%; see Table 16). Based on the percentage of nonoverlapping data point scores between

intervention and maintenance, participants in the faded coaching group did not have higher

percentages than participants in the continuous coaching group.

The second question of the current study was whether the faded coaching group

displayed higher levels of physical activity in the intervention condition than the continuous

coaching group. Results of the percentage of nonoverlapping data points from baseline to

intervention for the faded coaching group revealed that all scores were below 60% (range 0%–

58%; see Table 16). Results from calculating the percentage of nonoverlapping data points from

baseline to intervention for the continuous coaching group revealed that all scores were more

variable (range 0%–100%; see Table 16). Based on the percentage of nonoverlapping data point

scores between baseline and intervention, participants in the faded coaching group did not have

higher percentages than participants in the continuous coaching group.

Limitations and Confounds

The present study is not without limitations. It is possible that the dose of the intervention

was a limitation, and that more time with the behavioral coach would have led to different results

and increased physical activity once the coaching was gradually faded (Marcus et al., 2006).

Many of the participants in the faded coaching group reported sedentary lifestyle for many years,

and five weeks with a behavioral coach may not have been sufficient to modify that behavior.

Both groups experienced a different number of behavioral coaching sessions. The continuous

coaching group participated in 12 total behavioral coaching sessions, whereas the faded coaching


group participated in seven total behavioral coaching sessions. This discrepancy was an

intentional result of fading out support over the course of the intervention; however, the

difference in the number of sessions may have influenced physical activity levels in unintended

ways. For instance, it is possible that some participants in the continuous coaching group showed

increases in physical activity during the follow-up period due to participating in more behavioral

coaching sessions over the course of the intervention. Additionally, earlier research has identified

that individuals who have a BMI in the obese range may have better outcomes following longer

assistance from a behavioral coach (King et al., 2006). Future research could study the effects of

longer doses of behavioral coaching prior to fading support.

Attrition was another limitation in the current study. Several outside factors affected

continued participation in the study for Participants 118, 120, and 115. Midway through the

intervention period, Participant 118 experienced a house fire and became homeless, which

competed with her ability to complete the study activities and caused her to withdraw.

Participant 120 had an unexpected ankle surgery that reduced her ability to perform many

exercise behaviors, which competed with her ability to complete the study activities and caused

her to withdraw from the study during Week 10. Finally, Participant 115 began experiencing

health issues during the intervention period and ended her participation during Week 6, as she

was undergoing testing for cancer. It would have been impossible for the experimenter to prevent

the loss of these participants; however, steps were taken at the beginning of the study to attempt

to reduce attrition. For example, the gift card contingencies were in place to increase attendance

at the repeated measures assessment sessions.

Treatment adherence scores were very low across both groups (see Table 7). When

looking at all of the behaviors combined in the definition of treatment adherence, the mean


scores were 47% and 58% for the faded and continuous coaching groups, respectively. However,

analyzing each variable separately showed that the self-monitoring component was the element

that caused the overall mean scores to be so low. It is possible that response effort related to

entering the device data into the spreadsheet was too high, reducing the likelihood of participants

performing that step. Entering the data into the spreadsheet was included as a way to ensure that

participants viewed and contacted their data on a daily basis, as syncing the Fitbit Charge 3 with

the application did not necessarily require participants to actively look at the data. Adding in a

reinforcement contingency for self-monitoring may help to increase observable self-monitoring

behaviors such as entering data into shared spreadsheets.

Physical activity levels may have been influenced by other lifestyle changes that were not

accounted for during the current study. Participants were asked not to start any other weight loss

programs while they were participating in the study; however, participants may have changed

eating behaviors over the course of the study as a result of increased physical activity (Ambler et

al., 1998). This was not a weight loss study; however, the limited changes in weight status are

interesting. Most participants did not experience weight loss over the course of the current study.

Due to many variables affecting weight, it cannot be determined whether limited reductions in

weight were due to physical activity levels, changes in body composition, or other lifestyle

changes, such as medications or caloric intake. Participant 116 reported the she started a new

medication for depression midway through the intervention period and reported that one of the

side effects of that medication was weight gain. Similarly, Participant 123 also began taking a

new medication during the maintenance period and reported that weight gain was a side effect of

the medication.


Depression is another example of an extraneous variable that may have influenced

physical activity levels. Participant 116 reported existing diagnosis of depression prior to

beginning the study. During the beginning of the intervention period she reported having many

days in which she did not get out of bed, except to use the bathroom. During the behavioral

coaching sessions, strategies for addressing private events associated with staying in bed all day

were developed and discussed. Strategies included walking up and down the stairs in her

apartment building in her pajamas, as changing into exercise clothes required a level of response

effort that competed with her ability to perform the physical activity. By Week 9, she reported,

“51 minutes a day, I can do that”. In addition, Participant 113 reported having a diagnosis of

depression, but after 3 weeks into the intervention phase, she was reporting the use of several

antecedent strategies discussed in the coaching sessions to address private events and behaviors

associated with what she identified as depression. Future researchers should identify specific

antecedent strategies that effectively target private events and observable behaviors associated

with depression.

Another limitation is the use of a multiple baseline design. One drawback of using a

multiple baseline design is the withholding of the intervention for all but the first participants.

This is an ethical concern, but it also may have led to participants increasing physical activity

during baseline. For last participants to receive the treatment package, the baseline period

extended for 8 weeks. It is possible that having baseline periods extend 8 weeks for the last

participants increased the likelihood of beginning exercise before the intervention started. All of

the participants were asked to wait to begin exercising until they were notified by the behavioral

coach to begin; however, it appeared that some participants may have started exercising toward

the end of the baseline period, as indicated by the increasing trend in physical activity during the


final weeks of baseline. Examining the data, it appears that Participant 120, Participant 123,

Participant 124, Participant 116, and Participant 117 may have started exercising before being

contacted by the behavioral coach (see Figures 4 and 5). The extended baseline limitation is one

that is difficult to mitigate when using the multiple baseline design, however, reducing the

number of staggered start dates may have lessened the effects.

When using the Fitbit, all data are stored on the device. In baseline, the participants did

not have access to their data to limit any feedback from the device during this condition.

However, that meant that the researcher also did not have access to updated baseline data until

the participants synced the devices at the end of baseline. Thus, the behavioral coaching

intervention began without stability in baseline, which weakens the demonstration of

experimental control.

Aside from the individualized content of the behavioral coaching sessions, the behavioral

coaching was not individually tailored. Individually tailoring interventions addresses specific

variables related to engagement in physical activity (Kowal & Fortier, 2007; Martin & Dubbert,

1987). It is possible that individually tailoring the intervention would have led to different

results, as consideration of individual needs and preferences leads to continued participation in

physical activity programs (Martin & Dubbert, 1987). In addition, no analysis of the variables

that maintain physical activity was included, which resulted in an intervention that was not

function-based.

A final limitation was the use of a treatment package to increase physical activity, with

all components of the intervention being added at the same time, making it difficult to discern

which components of the treatment package were effective for increasing activity. However, due

to health risks associated with inactivity (Pescatello et al., 2004; Warburton et al., 2006), it is


imperative to use all resources to address inactivity as quickly as possible. In addition, behavior

analytic interventions focusing on increasing physical activity most commonly include eight

intervention components (Abraham & Michie, 2008). By systematically fading behavioral

coaching for one group while keeping it constant in the other group, the effects of this one

component of the treatment package were isolated. Future research should systematically add

components of the treatment package to determine the relative contributions of each element of

the treatment package.

Recommendations for Future Research

As this was one of the first studies to examine the effects of the schedule of behavioral

coaching on physical activity, it is important to continue this line of research to further

understanding of how behavioral coaching can be utilized to promote long-term maintenance of

exercise behaviors. Social support has been identified as an effective component in behavioral

interventions (Abraham & Michie, 2008), consequently future research should investigate the

effects of behavioral coaching with a group component compared to behavioral coaching

conducted on an individual basis. In the current study, the behavioral coaching component was

gradually faded over the course of the intervention. In future studies, gradually fading individual

components of the behavioral coaching prior to terminating the intervention should be evaluated.

For instance, assessing the effects of continuing to provide text message prompts in the initial

stages, after the individual coaching sessions have ended (Fischer et al., 2019). Further

evaluation of the components of behavioral coaching, including elements such as goal setting,

and antecedent and consequent strategies may increase effectiveness of behavioral coaching. In

addition, comparing maintenance of physical activity using behavioral coaching as described in

the methods of this study with maintenance of physical activity after participating in


commercially available programs would be useful. If findings show a commercially available

program is equally effective, it may have a wider reaching impact.

Previous studies conducting functional analyses of physical activity with children have

found that attention is a reinforcer for engaging in physical activity (Larson et al., 2013; Larson

et al., 2014). Extending previous research to include functional analyses with adults would be

valuable. When conducting functional analyses of physical activity under naturalistic conditions,

it would be difficult to control for all variables. However, manipulating variables such as the

presence or absence of others, time of day, and location of physical activity would be possible.

Assessing consequences including access to money, attention, and removal of household

responsibilities contingent upon engaging in physical activity may add to research on function-

based physical activity interventions for adults.

While physical activity interventions typically last 4-12 weeks (Weber & Sharma, 2011),

based on the results of the present study, examining longer intervention durations may be

beneficial, especially for older individuals who have a longer history of inactivity. The

maintenance period in the current study assessed short-term maintenance, and so examining

longer maintenance periods is necessary. Physical activity interventions often include some

components of the intervention during the maintenance period (Marcus et al., 2006), and so it

would be worth examining more closely the parameters of continued contact with the behavioral

coach during the maintenance period (Marcus et al., 2006). Measuring variables such as the

mode of contact, schedule of contact, and the length of contact would be beneficial.

Contributions to Existing Literature

This study adds to the existing literature on behavioral coaching and physical activity as

it was successful for increasing physical activity in previously sedentary women. Evaluating the


schedule of behavioral coaching adds more information about the effectiveness for behavioral

coaching on increasing physical activity during the intervention and post-intervention. Results

of the current study add to the literature on remote behavioral coaching, as each of the sessions

was completed via a videoconferencing platform.

Previously, there have not been any objective data to support or corroborate responses on

the Decisional Balance Scale (Marcus et al., 1992). The Decisional Balance Scale is often used

in studies aimed at increasing physical activity to measure participants’ willingness and

motivation for changing physical activity levels. The current study was the first study found by

the author that incorporated the Decisional Balance Scale in each of the coaching sessions.

Having responses on the Decisional Balance Scale, along with objective measurement of

physical activity, allowed for the analysis of the correspondence between the subjective tool and

objectively recorded physical activity levels. Five of the participants in the study reported higher

scores on the Decisional Balance Scale in the follow-up condition compared to the other repeated

measures assessment sessions, suggesting that they had increased motivation to exercise. Two of

those five participants increased the frequency of exercise in the maintenance period as well,

suggesting that not only did they report feeling more motivated, but also showed increased

frequency of exercise during this period. Three of the five participants who reported greater

motivation to exercise engaged in similar frequencies of exercise during the intervention and

maintenance period. None of the participants who reported increased motivation to exercise

displayed decreased frequency of physical activity in the maintenance period.

Behavioral interventions targeting physical activity do not always include assessments of

maintenance (Kurti & Dallery, 2013; Normand, 2008; Valbuena et al., 2015; Zarate et al., 2019),

and so a strength of this study was that it included a short-term maintenance assessment 3-


months post-intervention. In physical activity research, individuals for whom maintenance data

are available are not necessarily sedentary or inactive at the start of the intervention (Marcus et

al., 2000). In the current study, most participants had low levels of physical activity at the start of

the intervention. This was the first study found to evaluate faded support from a behavioral coach

on maintenance of physical activity of the first studies to show that systematic fading of support

from a behavioral coach prior to ending treatment may lead to increased short-term maintenance

of physical activity post-intervention, once the participants no longer had the support and

guidance from the behavioral coach.


References

Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in

interventions. Health Psychology, 27(3), 379-387. https://doi.org/10.1037/0278-

6133.27.3.379

Ambler, C., Eliakim, A., Brasel, J. A., Lee, W. P., Burke, G., & Cooper, D. M. (1998). Fitness

and the effect of exercise training on the dietary intake of healthy adolescents.

International Journal of Obesity & Related Metabolic Disorders, 22, 354-362.

https://doi.org/10.1038/sj.ijo.0800595

American College of Sports Medicine (2018). ACSM’s guidelines for exercise testing and

prescription (10th ed.). Wolters Kluwer.

American Heart Association (2016). American Heart Association recommendations for physical

activity in adults.

http://www.heart.org/HEARTORG/HealthyLiving/PhysicalActivity/FitnessBasics/Ameri

can-Heart-Association-Recommendations-for-Physical-Activity-in-

Adults_UCM_307976_Article.jsp#.V20XBI5CUpJ.

American Heart Association (2015). All about heart rate (pulse).

http://www.heart.org/HEARTORG/Conditions/HighBloodPressure/GettheFactsAboutHig

hBloodPressure/All-About-Heart-Rate-

Pulse_UCM_438850_Article.jsp#.WhgeOUtryqA.

Anderson, D. A., Shapiro, J. R., & Lundgren, J. D. (2001). The behavioral treatment of obesity.

The Behavior Analyst Today, 2(2), 133-138. https://doi.org/10.1037/h0099921

https://doi.org/10.1037/0278-6133.27.3.379

https://doi.org/10.1037/0278-6133.27.3.379

https://doi.org/10.1038/sj.ijo.0800595

http://www.heart.org/HEARTORG/HealthyLiving/PhysicalActivity/FitnessBasics/American-Heart-Association-Recommendations-for-Physical-Activity-in-Adults_UCM_307976_Article.jsp#.V20XBI5CUpJ



http://www.heart.org/HEARTORG/Conditions/HighBloodPressure/GettheFactsAboutHighBloodPressure/All-About-Heart-Rate-Pulse_UCM_438850_Article.jsp#.WhgeOUtryqA



https://doi.org/10.1037/h0099921


Andrade, L. F., Barry, D., Litt, M. D., & Petry, N. M. (2014). Maintaining high activity levels in

sedentary adults with a reinforcement-thinning schedule. Journal of Applied Behavior

Analysis, 47(3), 523-526. https://doi.org/10.1002/jaba.147

Aune, K. T., & Powers, J. M. (2016). Injuries in an extreme conditioning program. Sports

Health: A Multidisciplinary Approach, 9(1), 52-58.

https://doi.org/10.1177/1941738116674895

Ba, S., & Wang, L. (2013). Digital health communities: The effects of their motivation

mechanisms. Decision Support Systems, 55, 941-947.

https://doi.org/10.1016.j.dss.2013.01.003

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior

analysis. Journal of Applied Behavior Analysis, 1, 91-97.

https://doi.org/10.1901/jaba.1968.1-91

Bock, B. C., Marcus, B. H., Pinto, B. M., & Forsyth, L. A. (2001). Maintenance of physical

activity following an individualized motivationally tailored intervention. Annals of

Behavioral Medicine, 23(2), 79-87. https://doi.org/10.1207/S15324796ABM2302_2

Bond, D. S., Thomas, G., Raynor, H. A., Moon, J., Sieling, J., Trautvetter, J., Leblond, T., &

Wing, R. (2014). B-MOBLE- a smartphone-based intervention to reduce sedentary time

in overweight/obese individuals: A within-subjects experimental trial. PLOS ONE, (6), 1-

8. http://10.1371/journal.pone.0100821

Borg, G. (1998). Borg’s perceived exertion and pain scales. Human Kinetics.

Butte, N. F., Ekelund, U., & Westerterp, K. R. (2012). Assessing physical activity using

wearable monitors: Measures of physical activity. Medicine & Science in Sports &

Exercise, 44, S5-S12. https://doi.org/10.1249/MSS.0b013e3182399c0e

https://doi.org/10.1002/jaba.147

https://doi.org/10.1177/1941738116674895

https://doi.org/10.1016.j.dss.2013.01.003

https://doi.org/10.1901/jaba.1968.1-91

https://doi.org/10.1207/S15324796ABM2302_2

http://10.0.5.91/journal.pone.0100821

https://doi.org/10.1249/MSS.0b013e3182399c0e


Caspersen, C. J., Powell, K. E., & Christenson, G. M. (1985). Physical activity, exercise, and

physical fitness: Definitions and distinctions. Public Health Reports, 100(2), 126-131.

Cavallo, D. N., Tate, D. F., Ries, A. V., Brown, J. D., DeVellis, R. F., Ammerman, A. S. (2012).

A social media-based physical activity intervention: A randomized controlled trial.

American Journal of Preventive Medicine, 43(5), 527-532.

http://doi.org/10.1016/jamepre/2012.07.019

Centers for Disease Control and Prevention. (2016). Obesity and Overweight. National Center

for Health Statistics. http://www.cdc.gov/nchs/fastats/obesity-overweight.htm

Centers for Disease Control and Prevention. (2015). About Adult BMI. Division of Nutrition,

Physical Activity, and Obesity, National Center for Chronic Disease Prevention and

Health Promotion.

https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html

Chan, C. B., Ryan, D. A. J., & Tudor-Locke, C. (2004). Health benefits of a pedometer-based

physical activity intervention in sedentary workers. Preventive Medicine, 39, 1215-1222.

http://doi.org/10.1016/j.ypmed.2004.04.053

Charness, G., & Gneezy, U. (2009). Incentives to exercise. Econometrica, 77, 909-931.

http://doi.org/10.3982/ECTA7416

Coleman, K. J., Paluch, R. A., Epstein, L. H. (1997). A method for the delivery of reinforcement

during exercise. Behavior Research Methods, Instruments, & Computers, 29(2), 286-290.

http://doi.org/10.3758/BF03204828

Coleman, K. J., Raynor, H. R., Mueller, D. M., Cerny, F. J., Dorn, J. M., & Epstein, L. H.

(1999). Providing sedentary adults with choices for meeting their walking goals.

Preventive Medicine, 28, 510-519. http://doi.org/10.1006/pmed.1998.0471

http://doi.org/10.1016/jamepre/2012.07.019


http://doi.org/10.3982/ECTA7416

http://doi.org/10.3758/BF03204828

http://doi.org/10.1006/pmed.1998.0471


Dankel, S. J., Loenneke, J. P., & Loprinzi, P. D. (2016). Mild depressive symptoms among

Americans in relation to physical activity, current overweight/obesity, and self-reported

history of overweight/obesity. Journal of Behavioral Medicine, 23, 553-560.

http://doi.org/10.1007/s12529-016-9541-3

Dawson, M. C. (2017). CrossFit: Fitness cult or reinventive institution? International Review for

the Sociology of Sport, 52, 361-379. http://doi.org/10.1177/1012690215591793

Dishman, R. K. (1991). Increasing and maintaining exercise and physical activity. Behavior

Therapy, 22, 345-378. http://doi.org/10.1016/S0005-7894(05)80371-5

Donaldson, J. M. & Normand, M. P. (2009). Using goal setting, self-monitoring, and feedback to

increase calorie expenditure in obese adults. Behavioral Interventions, 24, 73-83.

http://doi.org/10.1002/bin.277

Donnelly, J. E., Blair, S. N., Jakicic, J. M., Manore, M. M., Rankin, J. W., & Smith, B. K.

(2009). Medicine & Science in Sports & Exercise, 41, 459-471.

http://doi.org/10.1249/MSS.0b013e3181949333

Doshi, A., Patrick, K., Sallis, J. F., & Calfas, K. (2003). Evaluation of physical activity web sites

for use of behavior change theories. Annals of Behavioral Medicine, 25(2), 105-111.

http://doi.org/10.1207/S15324796ABM2502_06

Drenowatz, C., Prasad, V. K., Hand, G. A., Shook, R. P., & Blair, S. N. (2016). Effects of

moderate and vigorous physical activity on fitness and body composition. Journal of

Behavioral Medicine, 39, 624-632. http://doi.org/10.1007/s10865-016-9740-z

Epstein, L. H., Paluch, R. A., Kilanowski, C. K., & Raynor, H. A. (2004). The effect of

reinforcement or stimulus control to reduce sedentary behavior in the treatment of

http://doi.org/10.1007/s12529-016-9541-3

http://doi.org/10.1177/1012690215591793

http://doi.org/10.1016/S0005-7894(05)80371-5


http://doi.org/10.1249/MSS.0b013e3181949333

http://doi.org/10.1207/S15324796ABM2502_06

http://doi.org/10.1007/s10865-016-9740-z


pediatric obesity. Health Psychology, 23(4), 371-380. http://doi.org/10.1037/0278-

6133.23.4.371

Epstein, L. H., Wing, R. R., Thompson, J. K., & Griffin, W. (1980). Attendance and fitness in

aerobic exercise: The effects of contract and lottery procedures. Behavior Modification,

4(4), 465-479. http://doi.org/10.1177/014544558044003

Fagard, R. H. (2006). Exercise is good for your blood pressure: Effects of endurance training and

resistance training. Clinical and Experimental Pharmacology and Physiology, 33, 853-

856. http://doi.org/10.1111/j.1440-1681.2006.04453.x

Finkelstein, E. A., Brown, D. S., Brown, D. R., & Buchner, D. M. (2008). A randomized study

of financial incentives to increase physical activity among sedentary older adults.

Preventive Medicine, 47, 182-187. http://doi.org/10.1016/j.ypmed.2008.05.002

Fischer, X., Donath, L., Zwygart, K., Gerber, M., Faude, O., & Zahner, L. (2019). Coaching and

prompting for remote physical activity promotion: Study protocol of a three-arm

randomized controlled trial (Movingcall). International Journal of Environmental

Research and Public Health, 16, 331-349. https://doi.org/10.3390/ijerph16030331

Flegal, K. M., Graubard, B. I., Williamson, D. F., & Gail, M. H. (2005). Excess deaths

associated with underweight, overweight, and obesity. Journal of the American Medical

Association, 293(15), 1861-1867. http://doi.org./10.1001/jama.293.15.1861

Fontaine, K. R., Redden, D. T., Wang, C., Westfall, A. O., & Allison, D. B. (2003). Years of life

lost to obesity. Journal of the American Medical Association, 289(2), 187-193.

http://do.org/10.1001/jama.289.2.187

Fox, F., Borer, J. S., Camm, A. J., Danchin, N., Ferrari, R., Lopez Sendon, J. L., Steg, P. G.,

Tardif, J. C., Tavazzi, L., & Tendera, M. (2007). Resting heart rate in cardiovascular

http://doi.org/10.1037/0278-6133.23.4.371

http://doi.org/10.1037/0278-6133.23.4.371

http://doi.org/10.1177/014544558044003

http://doi.org/10.1111/j.1440-1681.2006.04453.x


http://doi.org./10.1001/jama.293.15.1861

http://do.org/10.1001/jama.289.2.187


disease. Journal of the American College of Cardiology, 50(9), 823-830.

http://doi.org/10.1016/j.jacc.2007.04.079

Galbicka, G. (1994). Shaping in the 21st century: Moving percentile schedules into applied

settings. Journal of Applied Behavior Analysis, 27(4), 739-760.

http://doi.org/10.1901/jaba.1994.27-739

Goldfield, G. S., Mallory, R., Parker, T., Cunningham, T., Legg, C., Lumb, A., Parker, K.,

Prud’homme, D., Gaboury, I., & Adamo, K. B. (2006). Effects of open-loop feedback on

physical activity and television viewing in overweight and obese children: A randomized,

controlled trial. Pediatrics, 118, e157-e166. http://doi.org/10.1542/peds.2005-3052

Hanley, G. P., & Tiger, J. H. (2011). Differential reinforcement procedures. In Fisher, W. W.,

Piazza, C. C., & Roane, H. S. (Eds.). Handbook of Applied Behavior Analysis, (pp. 229-

249). The Guilford Press.

Hardman, C. A., Home, P. J., & Fregus Lowe, C. (2011). Effects of rewards, peer-modeling and

pedometer targets on children’s physical activity: A school-based intervention study.

Psychology & Health, 26, 3-21. http://doi.org/10.1080/08870440903318119

Hedley, A. A., Ogden, C. L., Johnson, C. L., Carroll, M. D., Curtin, L. R., Flegal, K. M. (2004).

Prevalence of overweight and obesity among us children, adolescents, and adults, 1999-

2002. The Journal of the American Medical Association. 291, 2847-2850.

http://doi.org/10.1001/jama.291.23.2847

Hustyi, K. M., Normand, M. P., & Larson, T. A. (2011). Behavioral assessment of physical

activity in obese preschool children. Journal of Applied Behavior Analysis, 44(3), 635-

639. http://doi.org/10.1901/jaba/2011.44-635

http://doi.org/10.1016/j.jacc.2007.04.079

http://doi.org/10.1901/jaba.1994.27-739

http://doi.org/10.1542/peds.2005-3052

http://doi.org/10.1080/08870440903318119

http://doi.org/10.1001/jama.291.23.2847

http://doi.org/10.1901/jaba/2011.44-635


Izawa, K., Watanabe, S., Omiya, K., Hirano, Y., Oka, K., Osada, N., & Iijima, S. (2005). Effect

of the self-monitoring approach on exercise maintenance during cardiac rehabilitation: A

randomized, controlled trial. American Journal of Physical Medicine & Rehabilitation,

84(5), 313-321. http://doi.org/10.1097/01.PHM.0000156901.95289.09

Jakicic, J. M., Wing, R. R., Butler, B. A., & Robsertson, R. J. (1995). Prescribing exercise in

short bouts versus one continuous bout: Effects on adherence, cardiorespiratory fitness,

and weight loss in overweight women. International Journal of Obesity, 19, 893-901.

Jansons, P. S., Haines, T. P., & O’Brien, L. (2016). Interventions to achieve ongoing exercise

adherence for adults with chronic health conditions who have completed a supervised

exercise program: Systematic review and meta-analysis. Clinical Rehabilitation, 1-13.

http://doi.org/10.1177/0229215516653995

Jeffery, R. W., Wing, R. R., Thomspon, C., & Burton, L. R. (1998). Use of personal trainers and

financial incentives to increase exercise in a behavioral weight-loss program. Journal of

Consulting and Clinical Psychology, 66, 77-83. http://doi.org/10.1037/0022-

006X.66.5.777

Kessler, H. S., Sisson, S. B., & Short, K. R. (2012). The potential for high-intensity interval

training to reduce cardiometabolic disease risk. Sports Medicine, 42(6), 389-509.

http://doi.org./10.2165/11630910-000000000-00000

Kinnafick, F. E., Thogersen-Ntoumani, C., & Duda, J. (2016). The effect of need supportive text

messages on motivation and physical activity behavior. Journal of Behavioral Medicine,

39, 574-586. http://doi.org/10.1007/s10865-016-9722-1

http://doi.org/10.1097/01.PHM.0000156901.95289.09

http://doi.org/10.1177/0229215516653995

http://doi.org/10.1037/0022-006X.66.5.777

http://doi.org/10.1037/0022-006X.66.5.777

http://doi.org./10.2165/11630910-000000000-00000

http://doi.org/10.1007/s10865-016-9722-1


Kohrt, W. M., Bloomfield, S. A., Little, K. D., Nelson, M. E., & Yingling, V. R. (2004). Physical

activity and bone health. Medicine & Science in Sports & Exercise, 36, 1985-1996.

http://doi.org/10.1249/01.MSS.0000142662.21767.58

Kooiman, T. J. M., Dontje, M L., Sprenger, S. R., Krijnen, W. P., van der Schans, C. P., & de

Groot, M. (2015). Reliability and validity of ten consumer activity trackers. BMC Sports

Science, Medicine and Rehabilitation, 7, 24-34. http://doi.org/10.1186/s13102-015-0018-

5

Kopelman, P. (2007). Health risks associated with overweight and obesity. Obesity Reviews,

8(1), 13-17. http://doi.org/10.1111/j.1467-789X.2007.00311.x

Kowal, J., & Fortier, M. S. (2007). Physical activity behavior change in middle-aged and older

women: The role of barriers and of environmental characteristics. Journal of Behavioral

Medicine, 30, 2330242. http://doi.org/10.1007/s10865-007-9102-y

Kredlow, M. A., Capozzoli, M. C., Hearon, B. A., Calkins, A. W., Otto, M. W. (2015). The

effects of physical activity on sleep: A meta-analytic review. Journal of Behavioral

Medicine, 38, 427-449. http://doi.org/10.1007/s10865-015-9617-6

Kurti, A. N., & Dallery, J. (2013). Internet-based contingency management increases walking in

sedentary adults. Journal of Applied Behavior Analysis, 46(3), 568-581.

http://doi.org/10.1002/jaba.58

Lamb, R. J., Kirby, K. C., Morral, A. R., & Galbicka, G. (2010). Shaping smoking cessation in

hard-to-treat smokers. Journal of Consulting and Clinical Psychology, 78, 62-71.

http://doi.org/10.1037/a0018323

http://doi.org/10.1249/01.MSS.0000142662.21767.58

http://doi.org/10.1186/s13102-015-0018-5

http://doi.org/10.1186/s13102-015-0018-5

http://doi.org/10.1111/j.1467-789X.2007.00311.x

http://doi.org/10.1007/s10865-007-9102-y

http://doi.org/10.1007/s10865-015-9617-6


http://doi.org/10.1037/a0018323


Lamb, R. J., Morral, A. R., Galbicka, G., & Kirby, K. C. (2005). Shaping reduced smoking in

smokers without cessation plans. Experimental and Clinical Psychopharmacology, 13,

83-92. http://doi.org/10.1037/1064-1297.13.2.83

Lamb, R. J., Morral, A. R., Kirby, K. C., Javors, M. A., Galbicka, G., & Iguchi, M. (2007).

Contingencies for change in complacent smokers. Experimental and Clinical

Psychopharmacology, 15(3), 245-255. http://doi.org/10.1037/1064-1297.15.3.245

Larson, T. A., Normand, M. P., & Hustyi, K. M. (2011). Preliminary evaluation of an

observation system for recording physical activity in children. Behavioral Interventions,

26, 193-203. http://doi.org/10.1002/bin.332

Larson, T. A., Normand, M. P., Morley, A. J., & Hustyi, K. M. (2014). The role of the physical

environment in promoting physical activity in children across different group

compositions. Behavior Modification, 36(6), 837-851.

https://doi.org/10.1177/0145445514543466

Larson, T. A., Normand, M. P., Morley, A. J., & Miller, B. G. (2013). A functional analysis of

moderate-to-vigorous physical activity in young children. Journal of Applied Behavior

Analysis, 46(1), 199-207. http://doi.org/10.1002.jaba.8

Leung, R. W., Kamla, J., Lee, M-C, & Mak, J. (2007). Preventing and treating Type 2 diabetes

through a physically active lifestyle. Journal of Physical Education, Recreation & Dance,

78(4), 38-54. http://doi.org/10.1080/07303084.2007.10598006

LeWine, H. (2018). Increase in resting heart rate is a signal worth watching. Retrieved from:

https://www.health.harvard.edu/blog/increase-in-resting-heart-rate-is-a-signal-worth-

watching-201112214013

http://doi.org/10.1037/1064-1297.13.2.83

http://doi.org/10.1037/1064-1297.15.3.245


http://doi.org/10.1002.jaba.8

http://doi.org/10.1080/07303084.2007.10598006


Linke, S. E., Gallo, L. C., & Norman, G. J. (2011). Attrition and adherence rates of sustained vs.

intermittent exercise interventions. Annals of Behavioral Medicine, 42, 197-209.

http://doi.org/10.1007/s12160-011-9279-8

Luiselli, J. K., (2016). Increasing and maintaining exercise- physical activity. In Luiselli, J. K.

(Ed.), Behavioral Health Promotion and Intervention in Intellectual and Developmental

Disabilities (pp. 73-93). Evidence-Based Practices in Behavioral Health.

https://doi.org/10.10007/978-3-319-27297-9_4

Marcus, B. H., Dubbert, P. H., Forsyth, L. H., McKenzie, T. L., Stone, E. J., Dunn, A. L., &

Blair, S. N. (2000). Physical activity behavior change: Issues in adoption and

maintenance. Health Psychology, 19(1), 32-41. http://doi.org/10.1037/0278-

6133.19.Suppl1.32

Marcus, B. H., Rakowski, W., & Rossi, J. S. (1992). Assessing motivational readiness and

decision making for exercise. Health Psychology, 11(4), 257-261.

http://doi.org/10.1037/0278-6133.11.4.257

Marcus, B. H., Williams, D. M., Dubbert, P. M., Sallis, J. F., King, A. C., Yancey, A. K.,

Franklin, B. A., Buchner, D., Daniels, S. R., & Claytor, R. P. (2006). Physical activity

intervention studies: What we know and what we need to know. Circulation, 114, 2739-

2752. http://doi.org/10.1161/CIRCULATIONAHA106.179683

Marshall, S. J., Levy, S., Tudor-Locke, C. E., Kolkhorst, F. W., Wooten, K. M., Ji, M., Macera,

C. A., & Ainsworth, B. E. (2009). Translating physical activity recommendations into a

pedometer-based step goal. American Journal of Preventive Medicine, 36(5), 410-415.

http://doi.org/10.1016/j.amepre.2009.01.021

http://doi.org/10.1007/s12160-011-9279-8

http://doi.org/10.1037/0278-6133.19.Suppl1.32

http://doi.org/10.1037/0278-6133.19.Suppl1.32

http://doi.org/10.1037/0278-6133.11.4.257

http://doi.org/10.1161/CIRCULATIONAHA106.179683



Martin, G., & Hrycaiko, D. (1983). Effective behavioral coaching: What’s it all about? Journal

of Sports Psychology,5, 8-20. http://doi.org/10.1123/jsp.5.1.8

Martin, G. L., Thompson, K., & Regehr, K. (2004). Studies using single-subject designs in sports

psychology: 30 years of research. The Behavior Analyst, 27(2), 263-280.

http://doi.org/10.1007/BF03393185

Martin, J. (2015). Behavior analysis in sport and exercise psychology. Behavior Analysis:

Research and Practice, 15(2), 148-151. http://doi.org/10.1037/bar0000018

Martin, J. E., & Dubbert, P. M. (1982a). Exercise and health: The adherence problem.

Behavioral Medicine Update, 4(1), 16-23.

Martin, J. E., & Dubbert, P. M. (1982b). Exercise applications and promotion in behavioral

medicine: Current status and future directions. Journal of Consulting and Clinical

Psychology, 50(6), 1004-1017. http://doi.org/10.1037/0022-006X.50.6.1004

Martin, J. E., & Dubbert, P. M. (1987). Exercise promotion. In Blumenthal, J. A., & McKee, D.

C. (Eds.), Applications in Behavioral Medicine and Health Psychology (pp. 361-398).

Professional Resource Exchange.

Martin, J. E., Dubbert, P. M., Katell, A. D., Thompson, J. K., Raczynski, J. R., Lake, M., Smith,

P. O., Webster, J. S., Sikora, T., & Cohen, R. E. (1984). Behavioral control of exercise in

sedentary adults: Studies 1 through 6. Journal of Consulting and Clinical Psychology,

52(5), 795-811. http://doi.org/10.1037/0022-006X.52.5.795

Meyer, M., Sundaram, S., & Schafhalter-Zoppoth, I. (2017). Exertional and CrossFit-induced

rhabdomyolysis. Clinical Journal of Sports Medicine: Official Journal of the Canadian

Academy of Sport Medicine, 0, 1-3. http://doi.org10/1097/JSM.0000000000000480

http://doi.org/10.1123/jsp.5.1.8

http://doi.org/10.1007/BF03393185

http://doi.org/10.1037/bar0000018

http://doi.org/10.1037/0022-006X.50.6.1004

http://doi.org/10.1037/0022-006X.52.5.795

http://doi.org10/1097/JSM.0000000000000480


Middelweerd, A., Mollee, J. S., van der Val, C. N., Brug, J., & te Velde, S. J. (2014). Apps to

promote physical activity among adults: A review and content analysis. International

Journal of Behavioral Nutrition and Physical Activity, 11. http://doi.org/10.1186/s12966-

014-0097-9

Miltenberger, R. G. (2012). Behavior modification (5th ed.). Wadsworth.

Mokdad, A. H., Ford, E. S., Bowman, B.A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J.

S. (2003). Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001.

Journal of the American Medical Association, 289, 76-79.

http://doi.org/10.1001/jama.289.1.76

Motl, R. W., & Dlugonski, D. (2011). Increasing physical activity in multiple sclerosis using a

behavioral intervention. Behavioral Medicine, 37, 125-131.

http://doi.org/10.1080/08964289.2011.636769

Muntaner A., Vidal-Conti, J., & Palou, P. (2016). Increasing physical activity through mobile

device interventions: A systematic review. Health Informatics Journal, 22(3), 451-469.

http://doi.org/10.1177/1460458214567004. e

Must, A., Spadano, J., Coakley, E. H., Field, A. E., Colditz, G., & Dietz, W. H. (1999). The

disease burden associated with overweight and obesity. Journal of the American Medical

Association, 282(16), 1523-1529. http://doi.org/10.1001/jama.282.16.1523

National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease

Control and Prevention (2017). BRFSS prevalence & trends data. National Center for

Chronic Disease Prevention and Health Promotion, Division of Population.

https://www.cdc.gov/brfss/brfssprevalence/index.html

http://doi.org/10.1186/s12966-014-0097-9

http://doi.org/10.1186/s12966-014-0097-9

http://doi.org/10.1001/jama.289.1.76

http://doi.org/10.1080/08964289.2011.636769

http://doi.org/10.1177/1460458214567004

http://doi.org/10.1001/jama.282.16.1523


Newman-Beinart, N. A., Norton, S., Dowling, D., Gavriloff, D., Vari, C., Weinman, J. A., &

Godfrey, E. L. (2017). The development and initial psychometric evaluation of a measure

assessing adherence to prescribed exercise: The Exercise Adherence Rating Scale

(EARS). Physiotherapy, 103, 180-185. http://doi.org/10.1016/j.physio.2016.11.001

Normand, M. P. (2008). Increasing physical activity through self-monitoring, goal setting, and

feedback. Behavioral Interventions, 23, 227-236. http://doi.org/10.1002/bin.267

Normand, M. P., & Bober, J. (2020). Health coaching by behavior analysts in practice: How and

why. Behavior Analysis: Research and Practice. Advance online publication.

https://dx.doi.org/10.1037/bar0000171

Ogden, C. L., Carroll, M. D., Fryer, C. D., & Flegal, K. M. (2015, November). Prevalence of

obesity among adults and youth: United States, 2011-2014. [Data Brief No. 219].

National Center for Health Statistics.

https://www.cybermedlife.eu/attachments/article/2468/Prevalence%20of%20Obesity%20

Among%20Adults%20and%20Youth.pdf

Owen, N., Humpel, N., Leslie, E., Bauman, A., & Sallis, J. F. (2004). Understanding

environmental influences on walking: Review and research agenda. American Journal of

Preventative Medicine, 27(1), 67-76. http://doi.org/10.1016/j.amepre.2004.03.006

Pescatello, L. S., Franklin, B. A., Fagard, R., Farquhar, W. B., Kelley, G. A., & Ray, C. A.

(2004). Exercise and hypertension. Medicine & Science in Sports & Exercise, 36, 533-

553. http://doi.org/10.1249/01.MSS.0000115224.88514.3A

Petry, N. M., Andrade, L. F., Barry, D., & Byrne, S. (2013). A randomized study of reinforcing

ambulatory exercise in older adults. Psychology and Aging, 28(4), 1164-1173.

http://doi.org/10.1037/a0032563

http://doi.org/10.1016/j.physio.2016.11.001


https://dx.doi.org/10.1037/bar0000171

https://www.cybermedlife.eu/attachments/article/2468/Prevalence%20of%20Obesity%20Among%20Adults%20and%20Youth.pdf

https://www.cybermedlife.eu/attachments/article/2468/Prevalence%20of%20Obesity%20Among%20Adults%20and%20Youth.pdf


http://doi.org/10.1249/01.MSS.0000115224.88514.3A

http://doi.org/10.1037/a0032563


Pinto, B. M., Clark, M. M., Cruess, D. G., Szymanski, L., & Pera, V. (1998). Changes in self-

efficacy and decisional balance for exercise among obese women in a weight

management program. Obesity Research, 7, 288-292. http://doi/org.10.1002/j.1550-

8528.1999.tb00408.x

Pope, L., & Harvey-Berino, J. (2013). Burn and earn: A randomized controlled trial incentivizing

exercise during fall semester for college first-year students. Preventive Medicine, 56,

197-201. http://doi.org/10.1016/j.ypmed.2012.12.020

Powers, D., & Greenwall, D. M. (2017). Branded fitness: Exercise and promotional culture.

Journal of Consumer Culture, 17(3), 523-541. http://doi.org/10.1177/1469540515623606

Pratt, M., Macera, C. A., & Wang, G. (2000). Higher direct medical costs associated with

physical inactivity. The Physician and Sportsmedicine, 28(10), 63-70.

http://doi.org/10.3810/psm.2000.10.1237

Pratt, M., Norris, J., Lobelo, R., Roux, L., & Wang, G. (2014). The cost of physical inactivity:

Moving into the 21st century. British Journal of Sports Medicine, 48, 171-173.

http://doi.org/10.1136/bjsports-2012-091810

Puhl, R. M., & Heuer, C. A. (2010). Obesity stigma: Important considerations for public health.

American Journal of Public Health, 100(6), 1019-1028.

http://doi.org/10.2105/AJPH.2009.159491

Purswell, K. E. & Ray, D. C. (2014). Research with small samples: Considerations for single

case and randomized small group experimental designs. Counseling Outcome Research

and Evaluation, 1-11. http://doi.org/10.177/2150137814552474

Rahmouni, K., Correia, M. L. G., Haynes, W. G., & Mark, A. L. (2004). Obesity-associated

hypertension. Hypertension, 45, 9-14. http://doi.org/10.1161.HYP.0000151325.83008.b4

http://doi/org.10.1002/j.1550-8528.1999.tb00408.x

http://doi/org.10.1002/j.1550-8528.1999.tb00408.x


http://doi.org/10.1177/1469540515623606

http://doi.org/10.3810/psm.2000.10.1237

http://doi.org/10.1136/bjsports-2012-091810

http://doi.org/10.2105/AJPH.2009.159491

http://doi.org/10.177/2150137814552474

http://doi.org/10.1161.HYP.0000151325.83008.b4


Roemmich, J. N., Lobarinas, C. L., Barkley, J. E., White, T. M., Paluch, R., & Epstein, L. H.

(2012). Use of an open-loop system to increase physical activity. Pediatric Exercise

Science, 24, 384-398. http://doi.org/10.1123/pes.24.3.384

Sallis, J. F., & Owen, N. (1999). Physical activity & behavioral medicine. Sage Publications.

Schneider, P. L., Bassett, D. R., Thompson, D. L., Pronk, N. P., & Bielak, K. M. (2006). Effects

of a 10,000 steps per day goal in overweight adults. American Journal of Health

Promotion, 21, 85-89.

Scruggs, T. E. & Mastropieri, M. A. (2001). How to summarize single-participant research: Ideas

and applications. Exceptionality, 9(4), 227-244.

https://doi.org/10.1207/S15327035EX0904_5

Sherwood, N. E., & Jeffery, R. W. (2000). The behavioral determinants of exercise: Implications

for physical activity interventions. Annual Review of Nutrition, 20, 21-44.

Siegel, P. Z., Brackbill, R. M., & Heath, G. W. (1995). The epidemiology of walking for

exercise: Implications for promoting activity among sedentary groups. American Journal

of Public Health, 85(5), 706- 710. http://doi.org/10.2105/AJPH.85.5.706

Skinner, B. F. (1953). Science and human behavior. The Free Press.

Slentz, C. A., Duscha, B. D., Johnson, J. L., Ketchum, K., Aiken, L. B., Samsa, G. P., Houmard,

J. A., Bales, C. W., & Kraus, W. E. (2004). Effects of the amount of exercise on body

weight, body composition, and measures of central obesity. Archives of Internal

Medicine, 164, 31-39. https://jamanetwork-

com.ezproxy.simmons.edu/journals/jamainternalmedicine/issue/164/1

Strath, S. J., Kaminsky, L. A., Ainsworth, R. A., Ekelund, U., Freedson, P. S., Gary, R. A.,

Richardson, C. R., Smith, D. T., & Swartz, A. M. (2013). Guide to the assessment of

http://doi.org/10.1123/pes.24.3.384

https://doi.org/10.1207/S15327035EX0904_5

http://doi.org/10.2105/AJPH.85.5.706


physical activity: Clinical and research applications. Circulation, 128, 2259-2279.

http://doi.org/10.1161/01.cir.0000435708.67487.da

Strohacker, K., Galarraga, O., Williams, D. M. (2014). The impact of incentives on exercise

behavior: A systematic review of randomized controlled trials. Annals of Behavioral

Medicine, 48, 92-99. http://doi.org/10.1007/s12160-013-9577-4

Sullivan, A. N. & Lachman, M. E. (2017). Behavior change with fitness technology in sedentary

adults: A review of the evidence for increasing physical activity. Frontiers in Public

Health, 4, 1-16. http://doi.org/10.3389/fpubh.2016.00289

Sylvia, L. G., Bernstein, E. E., Hubbard, J. L., Keating, L., & Anderson, E. J. (2014). A practical

guide to measuring physical activity. Journal of the Academy of Nutrition and Dietetics,

114(2), 199-208. http://doi.org/10.1016/j.jand.2013.09.018

Tarlow, K. R., & Penland, A. (2016). Outcome assessment and inference with the percentage of

nonoverlapping data (PND) single-case statistic. Practice Innovations, 1(4), 221-233.

http://doi.org/10.1037/pri0000029

Troiano, R. P., Berrigan, D., Dodd, K. W., Masse, L. C., Tilert, T., & McDowell, M. (2008).

Physical activity in the United States measured by accelerometer. Medicine & Science in

Sports & Exercise, 40, 181-188. http://doi.org/10.1249/mss.0b013e31815a51b3

Tudor-Locke, C., Ainsworth, B. E., Thompson, R. W., & Matthews, C. E. (2002). Comparison of

pedometer and accelerometer measures of free-living physical activity. Medicine &

Science in Sports & Exercise, 34, 2045-2051. http://doi.org/10.1097/00005768-

200212000-00027

http://doi.org/10.1161/01.cir.0000435708.67487.da

http://doi.org/10.1007/s12160-013-9577-4

http://doi.org/10.3389/fpubh.2016.00289

http://doi.org/10.1016/j.jand.2013.09.018

http://doi.org/10.1037/pri0000029

http://doi.org/10.1249/mss.0b013e31815a51b3

http://doi.org/10.1097/00005768-200212000-00027

http://doi.org/10.1097/00005768-200212000-00027


Turner-McGrievy, G., & Tate, D. (2011). Tweets, apps, and pods: Results of the 6-month Mobile

Pounds Off Digitally (Mobile POD) randomized weight-loss intervention among adults.

Journal of Medical Internet Research, 13(4), e120. http://doi.org/10.2196/jmir1841

U.S. Department of Health and Human Services [USDHHS]. (2008). 2008 Physical activity

guidelines for Americans: Fact sheet for health professionals on physical activity

guidelines for adults.

http://www.cdc.gov/physicalactivity/downloads/pa_fact_sheet_adults.pdf.

U.S. Department of Health and Human Services [USDHHS]. (2015). Nutrition, physical activity

and obesity data, trends and maps web site.

http://www.cdc.gov/nccdphp/DNPAO/index.html.

Valbuena, D., Miller, B. G., Samaha, A. L., & Miltenberger, R. G. (2017). Data presentation

options to manage variability in physical activity research. Journal of Applied Behavior

Analysis, 50, 622-640. http://doi.org/10.1002/jaba.397

Valbuena, D., Miltenberger, R., & Solley, E. (2015). Evaluating an internet-based program and a

behavioral coach for increasing physical activity. Behavior Analysis: Research and

Practice, 15(2), 122-138. http://doi.org/10/1037/bar0000013

Valenzuela, T., Okubo, Y., Woodbury, A., Lord, S. R., & Delbaere, K. (2017). Adherence to

technology-based exercise programs in older adults: A systematic review. Journal of

Geriatric Physical Therapy, 00, 1-13. http://doi.org/10.1519/JPT.0000000000000095

Van Camp, C. M. & Hayes, L.B. (2012). Assessing and increasing physical activity. Journal of

Applied Behavior Analysis, 45(4), 871-875. http://doi.org/10.1901/jaba.2012.45-871

http://doi.org/10.2196/jmir1841

http://www.cdc.gov/physicalactivity/downloads/pa_fact_sheet_adults.pdf

http://www.cdc.gov/nccdphp/DNPAO/index.html


http://doi.org/10/1037/bar0000013

http://doi.org/10.1519/JPT.0000000000000095

http://doi.org/10.1901/jaba.2012.45-871


Van Itallie, T. B. (1985). Health implications of overweight and obesity in the United States.

Annals of Internal Medicine, 103(6), 983-988. http://doi.org/10.7326/0003-4819-103-6-

983

Van Wormer, J. J. (2004). Pedometers and brief E-counseling: Increasing physical activity for

overweight adults. Journal of Applied Behavior Analysis, 37(3), 421-425.

http://doi.org/10.1901/jaba.2004.37-421

Wack, S. R., Crosland, K. A., & Miltenberger, R. G. (2014). Using goal setting and feedback to

increase weekly running distance. Journal of Applied Behavior Analysis, 47(1), 181-185.


Wing, R. R., Jeffery, R. W., Pronk, N., & Hellerstedt, W. L. (1996). Effects of a personal trainer

and financial incentives on exercise adherence in overweight women in a behavioral

weight loss program. Obesity Research, 4, 457-462. http://doi.org/10.1002/j.1550-

8528.1996.tb00254.x

Washington, W. D., Banna, K. M., & Gibson, A. L. (2014). Preliminary efficacy of prize-based

contingency management to increase activity levels in healthy adults. Journal of Applied

Behavior Analysis, 47(2), 231-245. http://doi.org/10.1002/jaba.119

Warburton, D. E. R., Nicol, C. W., & Bredin, S. S. D. (2006). Health benefits of physical

activity: The evidence. Canadian Medical Association Journal, 174(6), 801-809.

http://doi.org/10.1503/cmaj.051351

Wee, C. C., Phillips, R. S., Legedza, A. T. R., Davis, R. B., Soukup, J. R., Colditz, G. A., &

Hamel, M. (2005). Health care expenditures associated with overweight and obesity

among US adults: Importance of age and race. American Journal of Public Health, 95(1),

159-165. http://doi.org/10.2105/AJPH.2003.027946

http://doi.org/10.7326/0003-4819-103-6-983

http://doi.org/10.7326/0003-4819-103-6-983

http://doi.org/10.1901/jaba.2004.37-421


http://doi.org/10.1002/j.1550-8528.1996.tb00254.x

http://doi.org/10.1002/j.1550-8528.1996.tb00254.x


http://doi.org/10.1503/cmaj.051351

http://doi.org/10.2105/AJPH.2003.027946


World Health Organization. (2008). Waist Circumference and Waist-Hip Ration: Report of a

WHO Expert Consultation. World Health Organization.

https://apps.who.int/iris/bitstream/handle/10665/44583/9789241501491_eng.pdf;jsessioni

d=955D8DFBD0CD15428BFB8EE11A798DB8?sequence=1.

Wolf, M. M. (1978). Social validity: The case for subjective measurement or how applied

behavior analysis is finding its heart. Journal of Applied Behavior Analysis, 11(2), 203–

214. http://doi.org/10.1901/jaba.1978.11-203

Wong, S. S., Meng, Y., Loprinzi, P. D., & Hongu, N. (2014). Smart applications to track and

record physical activity: Implications for obesity treatment. Smart Homecare Technology

and TeleHealth, 2, 77-91. http://doi.org/10.2147/SHTT.S41484

Wyatt, S. B., Winters, K. P., & Dubbert, P. M. (2006). Overweight and obesity: Prevalence,

consequences, and causes of a growing public health problem. The American Journal of

Medical Sciences, 331(4), 166-174. http://doi.org/10.1097/00000441-200604000-00002

Zarate, M., Miltenberger, R.,, & Valbuena, D. (2019). Evaluating the effectiveness of goal

setting and textual feedback for increasing moderate-intensity physical activity in adults.

Behavioral Interventions, 34, 553-563. https://doi.org/10.1002/bin1679

https://apps.who.int/iris/bitstream/handle/10665/44583/9789241501491_eng.pdf;jsessionid=955D8DFBD0CD15428BFB8EE11A798DB8?sequence=1

https://apps.who.int/iris/bitstream/handle/10665/44583/9789241501491_eng.pdf;jsessionid=955D8DFBD0CD15428BFB8EE11A798DB8?sequence=1

http://doi.org/10.1901/jaba.1978.11-203

http://doi.org/10.2147/SHTT.S41484

http://doi.org/10.1097/00000441-200604000-00002


Table 1

Participant Demographics

Participant Age Starting

Weight (kg)

Starting

BMI

Ending Weight

(% change) a

Ending

BMI

FC 118 40 81.5 33.3 --- ---

FC 119 39 98.3 34 92.3 (-6.18%) 31.9

FC 120 58 77.4 32 --- ---

FC 121 30 76.9 28 76.6 (-0.35%) 27.9

FC 122 27 130.2 48.5 129.7 (-0.35%) 48.3

FC 123 36 101.9 35.7 110.0 (+7.83%) 38.5

FC 124 19 69.1 26.6 69.2 (0.00%) 26.6

CC 111 54 115.2 38 111.0 (-3.39%) 36.7

CC 112 47 105.0 40.3 105.1 (+0.08%) 40.4

CC 113 21 78.2 32.6 --- ---

CC 114 19 85.2 30.8 --- ---

CC 115 39 66.4 25.9 --- ---

CC 116 50 89.3 35.4 92.3 (+3.46%) 36.6

CC 117 58 79.1 32 82.7 (+3.75%) 36.6

Note. BMI = body mass index, and is calculated by 𝑘𝑔

ℎ𝑒𝑖𝑔ℎ𝑡2 ; BMI between 18.5 and 24.9 is

categorized as healthy, BMI between 25.0 and 29.9 is overweight, BMI of ≥ 30.0 is obese

(CDC, 2015). FC= Faded Coaching group. CC= Continuous Coaching group. --- indicates no

data were collected.

a The numerals in parentheses after ending weight show the percentage of weight change from

pre-baseline to the end of the final treatment.


Table 2

Schedule of Behavioral Coaching Sessions

Week Continuous Coaching Faded Coaching

Week 1

X

X

Week 2 X X

Week 3 X X

Week 4 X X

Week 5 X X

Week 6 X

Week 7 X X

Week 8 X

Week 9 X

Week 10 X

Week 11 X X

Week 12 X

Note. Schedule of weekly behavioral coaching sessions for each group. X indicates a week that

contained a behavioral coaching session.


Table 3

Average Daily Step Counts a

Participant Baseline Behavioral Coaching Maintenance

FC 118 10,771 8,204 ---

FC 119 9,181 7,166 5,158

FC 120 7,607 8,733 ---

FC 121 3,665 5,221 5,953

FC 122 2,156 6,986 5,890

FC 123 5,492 5,324 4,948

FC 124 6,995 10,102 11,111

M 6,552 7,391 6,612

CC 111 8,569 10,625 10,098

CC 112 10,191 10,116 10,040

CC 113 5,565 9,571 11,298

CC 114 3,016 9,806 11,755

CC 115 11,587 12,742 ---

CC 116 4,056 4,000 5,043

CC 117 5,885 8,894 4,076

M 6,981 7,877 8,718

Note. Average daily step counts in each condition for each participant. FC= Faded Coaching

group. CC= Continuous Coaching group. --- indicates no data were collected for that condition.

a10,000 steps is the recommended goal set by the American Heart Association (AHA, 2016).

Bolded values fall within healthy range.


Table 4

Results of the Two-Sample t-Tests for Mean Difference Baseline to Intervention

Variable Faded

Coaching

Continuous

Coaching

t p

M SD M SD

Weekly duration (min) 60.1 90.27 131.47 139.83 1.02 0.33

Weekly frequency 2.52 2.29 4.18 2.29 0.59 0.58

Note. For each group, the change from baseline to intervention compared. Mean values are

shown for the faded coaching group (n= 5) and the continuous coaching group (n=6), as well as

the results of the two-sample t-test assuming unequal variances. The significance threshold was

set at p ≤ .05.


Table 5

Results of the Two-Sample t-Tests for Mean Difference Intervention to Maintenance

Variable Faded

Coaching

Continuous

Coaching

t p

M SD M SD

Weekly duration (min) 57.9 185.40 -186.92 172.38 -2.25 0.05

Weekly frequency -0.6 2.89 -2.25 2.62 -0.98 0.35

Note. For each group, the change from end of intervention to end of maintenance period was

compared. Mean values are shown for the faded coaching group (n= 5) and the continuous

coaching group (n=6), as well as the results of the two-sample t-test assuming unequal variances.

The significance threshold was set at p ≤ .05.


Table 6

Results of the Two-Sample t-Tests for Percentage Difference Baseline to Intervention

Variable Faded

Coaching

Continuous

Coaching

t p

M SD M SD

Weekly duration (min) 0.63 0.19 0.42 0.11 -2.31 0.05

Weekly frequency 0.14 0.09 0.06 0.23 -0.06 0.58

Note. For each group, the percentage change from baseline to intervention was compared.

Percentage change values are shown for the faded coaching group (n= 5) and the continuous

coaching group (n=6), as well as the results of the two-sample t-test assuming unequal variances.

The significance threshold was set at p ≤ .05.


Table 7

Results of the Two-Sample t-Tests for Percentage Difference Intervention to Maintenance

Variable Faded

Coaching

Continuous

Coaching

t p

M SD M SD

Weekly duration (min) 1.19 0.29 0.40 0.34 -2.52 0.06

Weekly frequency 1.63 0.32 0.47 2.37 -0.90 0.43

Note. For each group, the percentage change from end of intervention to follow-up was

compared. Percentage change values are shown for the faded coaching group (n= 5) and the

continuous coaching group (n=6), as well as the results of the two-sample t-test assuming

unequal variances. The significance threshold was set at p ≤ .05.


Table 8

Results of the Paired Sample t-Tests

Variable End of

Intervention

End of

Maintenance

t p

M SD M SD

Weekly duration (min) 275.73 190.39 200.09 216.85 1.18 0.26

Weekly frequency 3.63 3.46 2.14 2.13 1.81 0.09

Note. For each participant, the change from end of intervention to end of maintenance period was

compared. Mean values are shown for all participants (n=11),as well as the results of the paired

sample t-test assuming unequal variances. The significance threshold was set at ≤ .05.


Table 9

Physical Activity Adherence Results

Participant Weeks with Goal Attainment

FC 118 a 50%

FC 119 50%

FC 120 b 17%

FC 121 50%

FC 122 17%

FC 123 17%

FC 124 0%

M 29%

CC 111 82%

CC 112 45%

CC 113 27%

CC 114 36%

CC 115 c 25%

CC 116 45%

CC 117 18%

M 40%

Note. Percentage of weeks with behavioral coaching sessions when participants reached the

weekly duration goal. M= mean adherence for the experimental group. FC= Faded Coaching

group. CC= Continuous Coaching group.

a Participant 118 withdrew from the study following a house fire.

b Participant 120 withdrew from the study due to needing emergency surgery.

c Participant 115 withdrew from the study following a potential cancer diagnosis.


Table 10

Treatment Adherence Results

Participant Syncing Self-

Monitoring

Behavioral

Coaching Sessions

Repeated Measures

Assessments

FC 118 a 72% 11% --- ---

FC 119 67% 13% 100% 100%

FC 120 b 94% 38% --- ---

FC 121 88% 15% 100% 100%

FC 122 21% 4% 100% 100%

FC 123 58% 6% 100% 100%

FC 124 96% 57% 100% 100%

M 71% 21% 100% 100%

CC 111 68% 40% 100% 100%

CC 112 68% 38% 100% 100%

CC 113 84% 14% 100% 66%

CC 114 98% 81% 100% 66%

CC 115 c 91% 43% --- ---

CC 116 92% 7% 100% 100%

CC 117 71% 12% 100% 100%

M 82% 34% 100% 89%

Note. Percentage of adherence for the individual components of the treatment adherence

dependent variable. FC= Faded Coaching group. CC= Continuous Coaching group. --- indicates

no data were collected.

a Participant 118 withdrew from the study following a house fire.

b Participant 120 withdrew from the study due to needing emergency surgery.

c Participant 115 withdrew from the study following a potential cancer diagnosis.


Table 11

Repeated Assessment Measures Data

Participant Resting Heart Rate a Mile Time BP (mm Hg) b

BSL PI FU BSL PI FU BSL PI FU

FC 118 65 --- --- 21:24 --- --- 147/98 --- ---

FC 119 78 76 71 20:18 17:29 17:22 115/73 123/78 113/73

FC 120 65 --- --- 20:00 --- --- 123/73 --- ---

FC 121 69 75 81 14:59 17:13 16:11 139/82 159/99 124/32

FC 122 78 80 94 17:38 17:48 17:56 191/123 166/112 168/105

FC 123 88 94 83 19:06 18:49 --- 130/87 134/91 131/81

FC 124 79 94 100 15:48 15:43 15:35 140/94 133/90 140/90

CC 111 83 78 72 25:37 20:40 19:53 139/89 102/56 105/60

CC 112 78 75 80 23:25 16:46 16:29 135/89 146/92 151/99

CC 113 85 93 --- 15:39 15:17 --- 124/81 104/67 ---

CC 114 80 76 --- 19:46 16:58 --- 118/78 118/71 ---

CC 115 87 --- --- 14:30 --- --- 192/122 --- ---

CC 116 79 84 75 14:18 14:12 15:05 151/102 134/93 155/103

CC 117 73 90 76 20:25 20:37 19:26 114/71 123/75 123/86

Note. Anthropometric data for participants in both groups. Mile= Time required to complete the Rockport One Mile Walking Fitness

Test. BP= blood pressure, systolic/diastolic mm Hg. BSL= Baseline assessment data. PI= Post-intervention data. FU= Follow-up


assessment data. FC= Faded Coaching group. CC= Continuous Coaching group. --- indicates no data were collected for that

condition.

a Healthy range for resting heart rate is 60-100 beats per minute. Bolded values fall within healthy range.

b Healthy range for blood pressure is < 120/<80 mm Hg. Bolded values fall within healthy range.


Table 12

BORG Rating a of Perceived Exertion Scores

Note. BORG Rating of Perceived Exertion scores range from 6-20 (Borg, 1998). FC= Faded

Coaching group. CC= Continuous Coaching group. .--- indicates no data were collected.

a Higher ratings indicate greater amounts of exertion from person reporting.

Participant Pre-Baseline End of Intervention End of Maintenance

FC 118 13 --- ---

FC 119 12 11 12

FC 120 19 --- ---

FC 121 12 13 12

FC 122 14 15 13

FC 123 12 15 ---

FC 124 12 12 11

CC 111 19 15 10

CC 112 13 13 12

CC 113 14 13 ---

CC 114 12 14 ---

CC 115 12 15 ---

CC 116 17 13 14

CC 117 12 13 14


Table 13

Decisional Balance Scale a Scores

Participant Pre-Baseline End of Intervention End of Maintenance

FC 118 2 --- ---

FC 119 2 2 2

FC 120 2 --- ---

FC 121 1 2 2

FC 122 0 1 2

FC 123 3 4 4

FC 124 1 1 2

CC 111 5 2 2

CC 112 3 4 2

CC 113 1 1 ---

CC 114 1 3 ---

CC 115 3 0 ---

CC 116 1 1 2

CC 117 -2 -1 -2

Note. The difference between the positive and negative questions about exercise on the

Decisional Balance Scale. FC= Faded Coaching group. CC= Continuous Coaching group. ---

indicates no data were collected.

a A higher decisional balance score indicates a greater motivational readiness to exercise.


Table 14

Waist-to-Hip Ratio a Circumference Measurement Results

Participant Pre-Baseline End of Intervention Follow-Up

FC 118 .87 --- ---

FC 119 .87 .89 .83

FC 120 .83 --- ---

FC 121 .80 .80 .81

FC 122 .75 .77 .74

FC 123 .87 .96 .87

FC 124 .91 .94 .91

CC 111 .99 .95 .98

CC 112 .92 .92 .89

CC 113 .86 .89 ---

CC 114 .85 .87 ---

CC 115 .89 --- ---

CC 116 .80 .77 .84

CC 117 .78 .83 .80

Note. Waist-to-hip ratio is calculated by dividing waist measurement by hip measurement. FC=

Faded Coaching group. CC= Continuous Coaching group. --- indicates no data were recorded.

a Waist-to-hip ratio <0.85 is considered in the healthy range for women according to the World

Health Organization (2008). Bolded values fall within healthy range.


Table 15

Social Validity Results

Item Question Mean Range

1

I felt prepared to set my own goals once the coaching ended.

4.2

2-5

2 I found the coaching sessions to be helpful for reaching my

goals.

4.2 4-5

3 Overall, my activity levels increased while participating in this

study.

4.2 3-5

4 I liked using the Fitbit. 3.8 1-5

5 I found participating in this study to be convenient and easy. 4 3-5

6 I was able to set my own goals after a few weeks of coaching. 4.5 3-5

7 I will continue to use the same system on my own, now that the

study is complete.

3.8 3-5

8 I lost weight while participating in this study. 2 0-5

9

10

I liked seeing my progress when I entered my data on the shared

Google Sheet.

I was more active during the times when I had coaching

sessions compared to when I did not have coaching sessions.

3.8

4.2

1-5

3-5

Note. Statements were scored from 0 “strongly disagree” to 5 “strongly agree” by participants

once involvement in with the study had ended. This table contains responses from five of the

participants who had completed the study.


Table 16

Percentage of Nonoverlapping Data a for Weekly Duration

Participant BSL-Intervention BSL- Maintenance Intervention-

Maintenance

FC 118 0% --- ---

FC 119 50% 14% 0%

FC 120 58% 0% ---

FC 121 0% --- 0%

FC 122 0% 25% 25%

FC 123 0% 6% 31%

FC 124 0% 0% 0%

CC 111 100% 100% 0%

CC 112 0% 0% 6%

CC 113 58% 47% 0%

CC 114 42% 71% 57%

CC 115 40% ---- ---

CC 116 50% 31% 0%

CC 117 58% 7% 0%

Note. Percentage of weeks that reached or exceeded the highest activity duration data point in

baseline compared to intervention, baseline compared to maintenance, and intervention compared

to maintenance. BSL= Baseline. = Faded Coaching group. CC= Continuous Coaching group.

a The higher the percentage, the greater the treatment effect.


Figure 1

CONSORT Flowchart of Participants

Note. Consort diagram showing the enrollment, assignment, and analysis of participants in the

study.


Figure 2

Weekly Frequency of Exercise for Faded Coaching Group


Note. Weekly frequency of exercise bouts for participants in the faded coaching group. BSL=

baseline. Horizontal line shows mean weekly frequency of physical activity.

a Participant 118 withdrew after house fire.

b Participant 120 withdrew after surgery.


Figure 3

Weekly Frequency of Exercise for Continuous Coaching Group


Note. Weekly frequency of exercise bouts for participants in the continuous coaching group.

BSL= baseline. Horizontal line shows mean weekly frequency of physical activity.

a Participant 115 withdrew after receiving a potential cancer diagnosis.


Figure 4

Weekly Duration of Exercise for Faded Coaching Group


Note. Weekly duration of physical activity for the faded coaching group. BSL= baseline.

Horizontal line represents mean weekly duration for each condition.




Figure 5

Weekly Duration of Exercise for Continuous Coaching Group


Note. Mean weekly duration of physical activity for participants in the continuous coaching

group. BSL= baseline. Horizontal line represents mean weekly duration for each condition.

a Participant 115 withdrew after receiving a potential cancer diagnosis


Appendix A

Decisional Balance Instrument

This form asks about the positive and negative aspects of exercise. Please read the following

statements and indicate how important each statement is with respect to your decision to exercise

or not to exercise in your free time.

If you disagree with a statement and/or are unsure how to respond to an item, the statement is

probably not important to you.

How important are the following statements in your decision to exercise or not to exercise?

Statement Extremely

Important

1

Quite

Important

2

Somewhat

Important

3

A Little

Important

4

Not

Important

5

1) I would have more energy for

my family and friends if I

exercised regularly

2) Regular exercise would help

relieve tension

3) I would feel more confident if

I exercised regularly

4) I would sleep more soundly if


5) I would feel good about

myself if I kept my commitment

to exercise

6) I would like my body better if

I exercised

7) It would be easier for me to

perform routine physical tasks if


8) I would feel less stressed if I

exercised regularly

9) I would feel more comfortable

with my body if I exercised

regularly

10) Regular exercise would help

me have a more positive outlook

on life

11) I think I would be too tired to

do my daily work after

exercising


12) I would find it difficult to

find an exercise activity that I

enjoy that is not affected by bad

weather

13) I feel uncomfortable when I

exercise because I get out of

breath and my heart beats very

fast

14) Regular exercise would take

too much of my time

15) I would have less time for

my family and friends if I

exercised regularly

16) At the end of the day, I am

too exhausted to exercise

Modified from Marcus, Rakowski, & Rossi (1992)

Scoring Directions

Record and then add scores for questions 1-10 below.

_____ ______ ____ _____ ____ _____ _____ _____ _____ _____ = ________/ 10 = _______

Record and then add scores for questions 11-16 below

_____ _____ _____ _____ _____ _____ = _____ /6 = _________


Appendix B

Goal Setting Task Analysis

Participant #:___________________

Coaching Session:____________________

Directions: This form should be used to determine the daily duration goals for physical activity

Step Completed

1. Log in to fitibt.com account

2. Sync Fitbit Charge 3 and reload webpage (by

clicking rounded arrow at top of web browser) if

most recent data does not appear on screen

3. Click on dashboard button at top of screen

4. Look for “active minutes” tile

5. Click 28 day view on “active minutes” tile

6. Count back 10 days on the graph, starting with

yesterday (last full day of data)

7. Find the 3rd highest number of minutes in the

previous 10 days

8. That 3rd highest duration becomes the new daily

duration goal

NEW DAILY MINUTES GOAL:______________


Appendix C

Repeated Measures Assessment Data Sheet and Task Analysis

Participant Number:_______________ Session: Pre-baseline 3-months Follow-Up

Directions: The researcher will take all measurements and perform all steps outlined below.

The research assistant will stand in a position where he/she can see all steps being performed and

will refrain from talking to anyone during the repeated measures assessment. All measurements

recorded by the researcher and the research assistant will be taken independently without

conferring and/or observation of what is recorded.

Height

Step

Performed

Correctly?

Yes/ No

Notes

Have participant remove heavy outer clothing and

shoes

Have participant roll up pants to check the position of

the heels

Remove hair accessories

Have participant stand straight with back against the

stadiometer

Head should be straight and positioned in middle of

body and arms hanging loosely at sides

Bring head plate down onto the crown of the head

Record measurement:

Total Number of Steps Completed Correctly: __________ / Total Number of

Steps:____________ x 100 = ________% correct

Weight

Step

Performed

Correctly?

Yes/ No

Notes

Have participant remove shoes and socks

Have participant stand straight on scale


Record measurement:


Steps:____________ x 100 = ________% correct

Circumference Measurements

Step

Performed

Correctly?

Yes/ No

Notes

Have participant remove all clothing except

undergarments

Have participant stand straight, with arms at sides,

feet together, and abdomen relaxed

Locate the iliac crest (highest point on hips)

Mark iliac crest with pen

Locate the lowest rib

Mark lowest rib with pen

Measure distance between pen marks on iliac crest

and lowest rib to locate middle point and mark with

pen

Place inelastic cloth tape horizontally at the marked

middle point

Record Measurement:

Have the participant stand straight, with arms at side

and feet together

Place the inelastic tape measure horizontally around

the widest portion of the buttocks

Record Measurement:

Have participant stand straight, with arms at sides,

feet together, and abdomen relaxed

Locate the iliac crest (highest point on hips)

Mark iliac crest with pen

Locate the lowest rib

Mark lowest rib with pen


Measure distance between pen marks on iliac crest

and lowest rib to locate middle point and mark with

pen

Place inelastic cloth tape horizontally at the marked

middle point

Record Measurement:

Have the participant stand straight, with arms at side

and feet together

Place the inelastic tape measure horizontally around

the widest portion of the buttocks

Record Measurement:

Repeat the process if 2nd measurements are not within

5mm of one another


Steps:____________ x 100 = ________% correct

Resting HR (taken before any exercise)

Step

Performed

Correctly?

Yes/ No

Notes

Clean participant finger with alcohol swab

Clean inside rubber portion of oximeter with alcohol

swab

Turn the pulse oximeter on

Fully place index finger with nail facing upward into

the rubber hole and close clamp

Record measurement:


Steps:____________ x 100 = ________% correct

Blood Pressure

Step

Performed

Correctly?

Yes/ No

Notes

Have participant remain seated for 5 minutes


Have participant remove shirt from left forearm

Wrap the BP cuff around the wrist 1-2 cm above the

hand with the screen on the inside of the forearm

Fasten the cuff tightly using the Velcro strip

Have participant raise hand to heart level with elbow

on table until measurement is complete

Have participant remain still and quiet during the

measurement

Once the cuff loosens and the numbers on the display

screen have not changed for a few seconds record

measurement

Measurement: /


Steps:____________ x 100 = ________% correct

Rockport 1-Mile Fitness Walking Test

Step

Performed

Correctly?

Yes/ No

Notes

Walk to track or treadmill

Have participant walk 4 laps or 1 mile as quickly as

possible on the track or treadmill

Start stopwatch immediately when participant starts

moving.

Stop stopwatch immediately when participant

completes the mile

Record Total Time to Complete Mile:

Record recovery HR immediately within 10s of

completing the mile


Steps:____________ x 100 = ________% correct



Step

Performed

Correctly?

Yes/ No

Notes

Before the participant begins the Rockport One-Mile

Fitness Walking test, tell the participant “While doing

physical activity, please rate how hard and strenuous

you feel this activity is.

This feeling should reflect how heavy and strenuous

the exercise feels to you, combining all sensations and

feelings of physical stress, effort, and fatigue.

Do not only consider one factor, such as leg pain or

shortness of breath, but try to focus on your total

feeling of exertion.

Look at the rating scale below while you are engaging

in an activity; it ranges from 6 to 20, where 6 means

"no exertion at all" and 20 means "maximal exertion."

Choose the number from below that best describes

your level of exertion.”

Give participant the Borg Rating of Perceived

Exertion table with ratings and their associated

exertion descriptions

After the Rockport One-Mile Fitness Walking Test is

over, tell participants. “Try to appraise your feeling of

exertion as honestly as possible. Your own feeling of

effort and exertion is important, not how it compares

to other people's. Look at the scales and the

expressions and then give a number.”

Record Rating:

Directions taken from: https://www.cdc.gov/physicalactivity/basics/measuring/exertion.htm


Steps:____________ x 100 = ________% correct



Step

Performed

Correctly?

Yes/ No

Notes

Read the directions on the Decisional Balance

Instrument form

Give participant a copy of the rating system (1-5 and

what each represents)

Ask participants all 16 questions (can vary order that

questions are asked)

Record participant responses to each of the questions

Record Averages for

Positive: Negative:


Steps:____________ x 100 = ________% correct


Appendix D

IOA Data Sheet- Repeated Measures

Participant Number:_______________

Resting Heart Rate

Observer/ Session Pre-Baseline 3-Months Follow-Up

Researcher

Research Assistant

Total Number of Agreements: _____/ Total Number of Opportunities: 6 = ____ x 100= ____%

Blood Pressure


Researcher

Research Assistant


Height


Researcher

Research Assistant



Weight


Researcher

Research Assistant


Circumference Measurements


Researcher Waist: Waist: Waist:

Hips/Buttocks: Hips/Buttocks: Hips/Buttocks:

Waist: Waist: Waist:


Research Assistant Waist: Waist: Waist:


Waist: Waist: Waist:



Rockport One-Mile Fitness Walking Test (record duration)


Researcher

Research Assistant





Researcher

Research Assistant




Researcher Positive average:

Negative average:

Positive average:

Negative average:

Positive average:

Negative average:

Research Assistant Positive average:

Negative average:

Positive average:

Negative average:

Positive average:

Negative average:



Appendix E

Behavioral Coaching Session Checklist

Coaching Session Date:_______________ Coaching Call Number:_______________

Participant ID Number:__________________ Total Duration:__________________

New daily duration goal: ___________________________________

Items to Cover During Session

Completed

First session only: Thank participant for involvement in the study

Yes No

First session only: Explain coaching component of study Yes No

First session only: Discuss data from baseline phase Yes No

Review goals from past week Yes No

Provide feedback on goal performance- constructive feedback Yes No

Discuss types of exercise completed in the week Yes No

Discuss barriers encountered Yes No

Discuss antecedent manipulations to increase likelihood of

exercise completion

Yes No

Modify goals for following week Yes No

Discuss reinforcers for completing exercise and meeting goals Yes No

Week 4 Session Only: Teach how to determine next goal Yes No

Discuss treatment adherence (data entry, coaching sessions, etc.) Yes No

Remind participants to contact via email with any questions Yes No

Total Number Completed Correctly _____________

Total Number of Items to be Addressed _____________

% Correct _____________


Appendix F

Treatment Acceptability Survey

Directions: Please respond to the following questions by circling the answer that best describes

your opinions about the Fitbit study.

1) I felt prepared to set my own goals once the coaching ended.

0 1 2 3 4 5

Strongly Disagree Disagree Neutral Somewhat Agree Agree Strongly Agree

If

2) I found the coaching sessions to be helpful for reaching my goals.

0 1 2 3 4 5


3) I liked seeing my progress when I entered my data on the website.

0 1 2 3 4 5


4) Overall, my activity levels increased while participating in this study.

0 1 2 3 4 5


5) I liked using the Fitbit.

0 1 2 3 4 5



6) I was able to set my own goals after a few weeks of coaching.

0 1 2 3 4 5


7) I found participating in this study to be easy and convenient.

0 1 2 3 4 5


8) I was more active during the times when I had coaching sessions compared to when I did

not have coaching sessions.

0 1 2 3 4 5


9) I will continue to use the same system on my own, now that the study is complete.

0 1 2 3 4 5


10) I lost weight while participating in this study.

0 1 2 3 4 5



Appendix G

Weekly Mean Frequency of Physical Activity


FC 118 1.0 2.8 ---

FC 119 1.0 1.9 1.6

FC 120 0 2.6 ---

FC 121 0 2.3 2.6

FC 122 0 2.1 2.1

FC 123 0 0.8 1.16

FC 124 0.3 6.8 4.0

M 0.2 2.8 2.3

CC 111 0 5.7 4.3

CC 112 0.5 2.2 1.2

CC 113 0.6 5.3 6.7

CC 114 0 7.4 16.1

CC 115 0.5 7.4 ---

CC 116 0 1.5 1.2

CC 117 0 1.2 1.4

M 0.2 4.4 5.2

Note. Mean weekly frequency of physical activity in each condition for each participant. FC=

Faded Coaching group. CC= Continuous Coaching group. --- indicates no data were collected for

that condition.


Appendix H

Daily Duration for the Faded Coaching Group


Note. Daily duration of exercise for participants in the faded coaching group. BSL= baseline.

Horizontal line represents mean daily exercise duration for each condition.




Appendix I

Daily Duration for Continuous Coaching Group


Note. Daily duration of exercise for the continuous coaching group. BSL= baseline. Horizontal

line represents mean daily exercise duration for each condition.

a Participant 115 withdrew after a potential cancer diagnosis.


Appendix J

Weekly Mean Frequency of Physical Activity


FC 118 1.0 2.8 ---

FC 119 1.0 1.9 1.6

FC 120 0 2.6 ---

FC 121 0 2.3 2.6

FC 122 0 2.1 2.1

FC 123 0 0.8 1.16

FC 124 0.3 6.8 4.0

M 0.2 2.8 2.3

CC 111 0 5.7 4.3

CC 112 0.5 2.2 1.2

CC 113 0.6 5.3 6.7

CC 114 0 7.4 16.1

CC 115 0.5 7.4 ---

CC 116 0 1.5 1.2

CC 117 0 1.2 1.4

M 0.2 4.4 5.2

Note. Mean weekly frequency of physical activity in each condition for each participant. FC=

Faded Coaching group. CC= Continuous Coaching group. --- indicates no data were collected for

that condition.


Appendix K

Daily Step Counts for the Faded Coaching Group


Note. Daily step counts for participants in the faded coaching group. BSL= baseline. Horizontal

line represents mean daily exercise duration for each condition.




Appendix L

Daily Step Counts for the Continuous Coaching Group


Note. Daily step counts for the continuous coaching group. BSL= baseline. Horizontal line

represents mean daily exercise duration for each condition.

a Participant 115 withdrew after a potential cancer diagnosis.

Date post:	28-Sep-2020
Category:	Documents
Upload:	others
View:	2 times
Download:	0 times

Effects of Behavioral Coaching on Exercise Behavior and Adherence … · 2020. 4. 29. · EXERCISE...

Documents