Computers in Human Behavior 29 (2013) 2674–2681

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Computers in Human Behavior

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Harnessing social dynamics through persuasive technology to promotehealthier lifestyle

0747-5632/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.chb.2013.07.008

⇑ Corresponding author. Tel.: +971 03 70 90 832; fax: +971 03 70 90 990.E-mail addresses: ashraf.khalil@adu.ac.ae (A. Khalil), Salam.abdallah@adu.ac.ae

(S. Abdallah).

Ashraf Khalil ⇑, Salam AbdallahAbu Dhabi University, College of Engineering and Computer Science, 1790 Al Ain, United Arab Emirates

a r t i c l e i n f o a b s t r a c t

Article history:Available online 27 July 2013

Keywords:Persuasive technologyPhysical activityTheory of reasoned actionBehavior changeMobileHealth

In light of current calls by medical professionals to confront the global issue of obesity and relatedillnesses, we developed a mobile application called STEP UP that monitors physical activity and providesdata that can be easily shared within a social network. We then conducted an exploratory, theoreticalstudy based on the theory of reasoned action (TRA) followed by an experimental trial and user study.The purpose of the studies was to explore the effect of persuasive technology on physical activity behav-ior and to investigate its effectiveness in motivating users to use the technology to be more physicallyactive. The application was found to have a positive effect on the participants and their level of physicalactivity. They enjoyed using the application and were motivated to walk more, especially when enabledto share their step counts with their friends. The social component of the application clearly enhancedusers’ walking experience, as the atmosphere of friendly competition motivated them to walk more.Based on user responses, we conclude that a further enhanced application that includes chat functionalitymay be even more successful in supporting increased physical activity and thus healthier lifestyle.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Modern lifestyles are increasingly sedentary (Foster, Linehan, &Lawson, 2010). The World Health Organization states that 60% ofthe worldwide population is not active enough (Maitland et al.,2006). On average, children and teenagers spend about 6 h everyday on sedentary activity such as playing video games, watchingTV, reading books and using computers (Arteaga, Kudeki, Wood-worth, & Kurniawan, 2010). The nature of sedentary activity is usu-ally self-reinforcing and addictive, thus adjusting the balance byincreasing physical activities and reducing sedentary activity ischallenging (Berkovsky, Coombe, Freyne, Bhandari, & Baghaei,2010). There is no doubt that overweight and obesity are the out-come of our modern sedentary lifestyle. The World Health Organi-zation reports that over 1.6 billion individuals are overweight orobese (Berkovsky et al., 2010). The main problem with obesity isthat it increases the risk of developing heart diseases, diabetes,high blood pressure, and some cancers. Not only that, but it alsoputs pressure on healthcare systems and local economies (Arteagaet al., 2010).

Physical activity combined with a healthy diet boasts directadvantages in combating obesity (Arteaga et al., 2010; Fialho

et al., 2009). Also, it helps in managing chronic diseases such asdiabetes, heart disease and depression (Bennett & Winters-Stone,2011). Among the many indirect effects is that they allow peopleto function better physically and mentally and live longer indepen-dently (Bennett & Winters-Stone, 2011). To take advantage of allthese benefits, the Center for Disease Control and Prevention(CDC), the American government entity to regulate issues relatedto disease control and prevention, recommends that adults engagein moderate-intensity physical activities for at least 30 min perday, five times a week (Bennett & Winters-Stone, 2011; Maitlandet al., 2006).

Since overweight and obesity are worldwide epidemics, they re-quire multifaceted actions. According to medical experts physicalactivity is important in maintaining fitness, reducing weight, andimproving health. Risks of sedentary lifestyle require a lifestylechanges. Glanz, Rimer, and Lewis (2002) argue that lifestylechanges can be achieved by awareness, change behavior and createenvironments to promote good health practices. Previous studieshave confirmed that by increasing number of steps one takes eachday and with social support can motivate people to be continuallyactive (Centers for Disease Control, 2003; Chan, Spangler, Valcour,& Tudor-Locke, 2003; Treiber, Baranowski, Braden, Strong, Levy, &Knox, 1991). To this end we have developed a health promotionmobile application to encourage healthy lifestyles with a groupof participants.

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2. Research motives and objectives

Having acknowledged the risks of sedentary lifestyle and theadvantages of physical activity, the question remaining is: Howcan people be motivated to become more physically active? Moti-vating not only a behavior change but a lifestyle change is a consid-erable challenge (Chris & Hudson, 2011). But inspiring people tobecome more physically active is a challenge worth taking. Thus,many approaches have been tested to this end.

Many approaches have been employed to motivate people to bemore active. Fialho et al. (2009) used goal setting and proved it tobe a powerful strategy to boost motivation and realize behaviorchange. Bennett and Winters-Stone (2011) and Maitland et al.(2006) used pedometers as a self-monitoring tool and have shownthat this can be very effective in motivating people to be morephysically active. This can be attributed to two factors: (1) Relieffor the user from having to retrospectively log data from com-pleted activity and (2) the accurate and objective measurementof the pedometers. In a different appoach, Chris and Hudson(2011) applied social motivational strategies. The desire to projecta positive image about ourselves to others is at the heart of behav-ioral change. Technology can be used to simulate competition orfacilitate support groups, both of which motivate behavioralchange. The use of social motivation via persuasive technologyhas yielded a promising result (Fialho et al., 2009). In one study,it was shown that a single session with a person trained in motiva-tional interviewing followed up by motivational telephone calls in-creased exercise in older adult cancer survivors (Bennett &Winters-Stone, 2011). In addition, many studies have shown thatgames of various types can be utilized to encourage behaviorchange (Arteaga et al., 2010). A study has shown that virtual re-wards in a game can motivate players to do more physical activity(Berkovsky et al., 2010). According to that study, rewards found tobe effective even if only virtual such as receiving virtual ribbons ortrophies for achieving milestones or completing goals.

Thus, motivating a change from sedentary lifestyle can be ap-proached by multiple routes, but the question remains of howthe implementation of these various approaches can be facilitated.Augmentation of traditional exercise technologies with the perva-sive and ubiquitous computing of mobile devices is becoming anestablished area in both research and computing (Maitland et al.,2006). This is due to three reasons. First, mobile phones are themost uniformly adopted technology (Maitland et al., 2006). Second,mobile phones are personal and pervasive, which means users takethem everywhere they go (Arteaga et al., 2010). Third, nowadaysmobiles have built in sensors to detect users’ movement.

With the aim of developing a novel and effective solution to theproblem at hand of motivating a change from sedentary lifestyle,we set out to combine mobile technology with two of the above-described approaches: self-monitoring and social motivationalstrategies. We designed a mobile application called STEP UP, whichcounts the number of steps walked and shares the data with agroup of friends. Our approach capitalizes on the power inherentin peer pressure and aims to harness it. We used two methods toevaluate the efficacy of our STEP UP solution. The first study aimedto test whether the application positively affects users’ behavioralintention, which is to be more physically active. This study wasbased on the theory of reasoned action (TRA), which explains therelationship between behavioral intention and behavioral adop-tion. The study involved questionnaires based on the TRA andinvestigated the theoretical influence of STEP UP on user behavior.We used TRA to test two hypotheses: The first predicted that atti-tudes toward STEP UP would be positively associated with theintention of using it to be more physically active. The secondhypothesis predicted that the subjective norm of being physicallyactive would be positively associated with the intention to use

the application to be more physically active. After testing thehypotheses and confirming the associations predicted by the the-ory, we conducted an experiment with two groups of participantswhom we asked to use the STEP UP application for two weeks. Thisstudy investigated whether STEP UP motivated participants inpractice to be more physically active and investigated the effectof sharing steps with friends and the dynamics of peer pressure.We collected quantitative data during the testing period, afterwhich we collected qualitative data through surveys and inter-views at the end of the testing period.

In the remainder of the paper, we first discuss related work, andthen we discuss the two studies we conducted and also introducethe STEP UP application. After that we discuss the results as well asthe limitations of the study before we conclude.

3. Related work

Physical activity monitoring tools have attracted enormous re-search interest in recent years, specifically in the area of activitymonitoring tools and techniques, motivational methods and psy-chological theories. In this section we aim at listing the major re-search contributions in this field while focusing on methods usedto measure physical activity and techniques considered to moti-vate behavioral change.

3.1. Activity monitoring tools and techniques

In physical activity research, various methods have been used tomeasure physical activity. One of the most common tools is thepedometer, which is a small, wearable device that counts the num-ber of steps the user walks (Arsand, Tatara, Ostengen, & Hartvigsen,2010; Foster et al., 2010). Some researchers have developed mobileapplications that employ the mobile phone’s accelerometer tocount steps (Khalil & Glal, 2009). Another very common approachis the use of a tri-axial accelerometer (Berkovsky et al., 2010; Fia-lho et al., 2009) because it gives the researcher the freedom to de-tect various types of activity. For example, Berkovsky et al. (2010)used a tri-axial accelerometer to detect jump events. Some studieshave asked participants to manually log the time they spent per-forming physical activity and sometimes the type of physical activ-ity they performed (Arteaga et al., 2010; Munson & Consolvo,2012). However, manual logging is rarely used. Researchers gener-ally resort to it only when automatic logging is technically unfea-sible such as in the case of Arteaga et al. (2010) who found thatthe iPhone platform does not allow multiple applications to run. Fi-nally, one novel way to monitor physical activity was presented byMaitland et al. (2006) who used fluctuations in GSM signalstrength to infer whether the mobile holder is walking, sitting stillor traveling by car.

3.2. Motivational methods

Researchers usually employ one or more of the motivationalmethods mentioned above. The most used method is self-monitor-ing (Arsand et al., 2010; Arteaga et al., 2010; Chris & Hudson, 2011;Fialho et al., 2009; Foster et al., 2010; Khalil & Glal, 2009; Maitlandet al., 2006; Munson & Consolvo, 2012; Verwey, van der Weegen,Spreeuwenberg, Tange, van der Weijden, & de Witte, 2012). Self-monitoring is implemented by enabling users to track the amountand/or the duration of physical activities they perform during aspecific period of time. The only research cited that did not imple-ment self-monitoring was that by Berkovsky et al. (2010) becausetheir application is a game that targets young children between theage of 9 and 12. The game gives players, who are equipped byaccelerometer to recognize jump events, time-based rewards in ex-

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change for physical activity they perform in reality. The goal wasmotivating children to be more physically active while playing agame.

Another widely-used motivational technique is social motiva-tional methods (Arteaga et al., 2010; Fialho et al., 2009; Fosteret al., 2010; Halai, Farnham, Melander, Joffray, Roberton, & Jensen,2012; Maitland et al., 2006; Munson & Consolvo, 2012). This tech-nique is usually implemented by allowing users to share theirphysical activity achievements with others. The technique provedto be a strong motivator (Foster et al., 2010). Some researcherspointed out that few users had privacy concerns or fear of boringothers. Still, in the case of any privacy concerns, these issues canbe managed by careful system design.

Goal-setting has also proven to be a very successful techniqueto motivate user behavioral change. Many studies have used itand reported positive results (Arsand et al., 2010; Fialho et al.,2009; Munson & Consolvo, 2012). For example, participants whoused GoalLine (Munson & Consolvo, 2012), which is an applicationthat enables users to set weekly physical activity goals and allowsjournaling of physical activity and reviewing past progress, saidthat they used the application to set routine goals, backup goals,and farther-stretched goals. They said that the stretched goalsmotivated them to go above and beyond what they would other-wise have done. They said they were less committed to do thestretch goals but not meeting their routine or backup goal felt likea ‘‘failure’’ (Munson & Consolvo, 2012).

Games have additionally proven to be effective as a motiva-tional method (Arteaga et al., 2010; Berkovsky et al., 2010; Chittaro& Sioni, 2012). Research on games motivating physical activity tar-geting children and teens reported impressive results both in theamount of physical activity performed while playing the gameand in the perceived enjoyment of the game (Arteaga et al.,2010). Researchers hold the latter to be a very important factor be-cause if users do not enjoy the game they will stop playing it. Anarea that lacks investigation is the effect of such games on adults.

The final motivational technique is rewards, which nowadaystend to be virtual. For example in Munson and Consolvo (2012),the user is given a virtual ribbon and/or trophy as a reward foraccomplishing his/her physical activity goal. In Berkovsky et al.(2010), as aforementioned, users were given time-based rewards.Interestingly, Munson and Consolvo (2012) reported that the re-ward system failed to motivate the users while Berkovsky et al.(2010) said it did motivate the users to be more physically active.One explanation for the discrepancy might be that Berkovsky et al.(2010) used the reward system in a game context while Munsonand Consolvo (2012) did not. It might also be attributed to theage group used to test the application since Berkovsky et al.(2010) tested it on children between the ages of 9–12 years whileMunson and Consolvo (2012) tested it on adults between the agesof 21–49. The use of rewards, as a physical activity-motivatingmethod, needs further investigation.

3.3. Psychological theories

Various psychological theories have been utilized in physicalactivity research. These theories attempt to provide understandingor to predict behaviors that may influence physical health out-comes. These theories are commonly known as social cognitiontheories. Such theories have identified a number of cognitive andaffective factors such as attitudes and beliefs.

One of these common theories is the self-efficacy theoryadapted from Bandura’s (1986) social cognitive theory. Self-effi-cacy refers to peoples’ sense of confidence in their ability to per-form a particular behavior (Bandura, 1986, 1997). The notionbehind this theory is that the higher the confidence of an individ-ual to perform an act, the more likely he or she will have sustained

engagement in a particular activity. On the other hand, those witha lower level of self-efficacy may not sustain or engage in carryingout a particular activity (Bandura & Cervone, 1983). Several studieshave been conducted to examine exercise self-efficacy, which wasa strong predictor for physical activity engagement and sustain-ability over a long period of time (Dishman, Sallis, & Orenstein,1985; Garcia & King, 1991; King et al., 1992; McAuley, 1992; Salliset al., 1990). Self-efficacy is also enhanced if one had a positiveexperience; likewise, negative experience will weaken it (van deLaar & van der Bijl, 2001). At the early stages, support is recom-mended to enhance confidence and avoid discouragement thatmay affect self-confidence (Bandura, 1995). It has also been re-vealed that writing a personal diary to record exercise may boostself-confidence thus increasing physical activity (Lee, Arthur, &Avis, 2007a). Self-efficacy may also be promoted by observingthe achievements of others, watching videos and exchanging ver-bal encouragement (social persuasion) (Bandura, 1997). A studyby Allison and Keller (2004) tested the effect of self-efficacy bydelivering statements of encouragement through telephone con-tacts, which had a positive effect on the distance walked.

Another theory related to attitudes and the attitude–behaviorrelationship is the theory of reasoned action (TRA) (Ajzen & Fish-bein, 1980). This theory posits that a person’s intention to performa behavior is determined by his or her attitude towards the behav-ior and how others that are important to the individual perceivethe behavior being examined (subjective norm). Another well-established theory within this context is the theory of plannedbehavior (TPB), which has close resemblance to TRA. TPB is basedon a person’s attitude towards a behavior, which may be positiveor negative; the second variable is the perceived social pressureon the individual to carry out the behavior. The third variable isthe perceived behavioral control, which refers to a person’s percep-tion of the amount of self-confidence one has in performing thebehavior. This third variable is the main difference between TRAand TPB.

Many studies have applied the TRA and TPB to the prediction ofexercise intentions and behavior (Conner & Sparks, 1996). Arteagaet al. (2010) used the TPB to guide their physical activity-motivat-ing mobile application. Blue (1995) found that TPB and TRA can beused to predict individuals’ exercise behavior. Through reviewingsixteen TRA and seven TPB studies, he found that exercise pro-grams that offer a positive experience would support an intentionto exercise, which in turn positively influences exercise behavior.

For the purpose of this study we have adopted the TRA overother models to examine the potential of persuasive technologiesin promoting healthier lifestyle. TRA was a good candidate to meetthe objectives of this research and has been extensively used formeasuring user behavior (Lee, Tsai, & Jih, 2006; Song & Kim,2006; Woolley & Eining, 2006; Wu & Liu, 2007). Furthermore, ithas been used many times to examine behavior change throughthe use of information systems and technologies (Hansen, Jensen,& Solgaard, 2004; Kwok & Gao, 2006; Njite & Parsa, 2005; Kwon& Zmud, 1987). The theory of reasoned action is further explainedin the next section.

4. Step up studies

We have performed two studies to test STEP UP. The first was anexploratory empirical study based on TRA, and the second was anexperimental study.

4.1. Exploratory study

The first study was conducted to explore the influence of theSTEP UP application on user behavior. Our main goal in this

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exploratory study is to examine theories about the motivational ef-fects of the STEP UP application. STEP UP tries to change users’ atti-tude towards exercise. It also tries to create a social peer-pressureon users to make them more motivated to exercise. Those two as-pects of STEP UP exemplify a particular approach that is groundedin theory, which is TRA. Specifically, we employed the TRA to testwhether the application positively affects users’ behavioral inten-tion. The behavioral intention in our case is to be more physicallyactive. The TRA is a theory that explains behavioral intention andbehavior adoption. This theory states that behavior is impactedand shaped by individual’s behavioral beliefs, behavioral intentionand normative beliefs (see Fig. 1).

The components of TRA are three general constructs: behavioralintention, attitude, and subjective norm. TRA suggests that a per-son’s behavioral intention is influenced by the person’s attitude to-ward that behavior and how others will perceive that behavior. Aperson’s attitude together with subjective norms make up his/herbehavioral intention. Definitions of the model’s variables are asfollows:

� Behavioral beliefs involve the expected outcome of thebehavior.� Attitude towards the behavior is how the individual feel about

the behavior, and it can be favorable or unfavorable.� Normative beliefs refer to the individual’s perception of what

people will think about the behavior in question.� Subjective norm is defined as how the behavior is viewed by

one’s social circle or others who influence one’s decisions.� Intention is defined as the propensity to engage in the behavior.

4.1.1. HypothesesTo explore the relationship between the TRA and the STEP UP

application two hypotheses were developed. In our case, thebehavior is defined as the use of STEP UP to be more physically ac-tive and keep a healthy weight.

Hypothesis 1. Attitude toward STEP UP is positively associatedwith intention to use the application to be more physically activeand keep a healthy weight.

Hypothesis 2. Subjective norm of (being physically active andkeeping a healthy weight) is positively associated with intentionto use the application to be more physically active and keep ahealthy weight.

4.1.2. MethodologyA survey was conducted based on the TRA and was collected

from 50 participants. 35% of the participants were under 24 yearsold, 45% were between 24 and 34, 8% were between 35–44 and12% were above 45 years old. The gender mix was slightly skewedwith 61% females. 57% of the participants reported never exercis-ing and 24% exercised 1 to 2 days per week. 10% exercised 3–4times per week and only 2 participants exercised daily. We ex-plained to the 50 participants how the application works and whatit does exactly and presented them with the questionnaire.

Fig. 1. Theory of reasoned action (Fishbein & Ajzen, 1975).

The questions were based on a seven-point Likert scale and in-cluded 10 items: three items to measure intention, four items tomeasure attitude and three to measure subjective norm. SPSS20.0 was used to analyze the data and test the proposed hypothe-ses. Scale reliability, factor analysis and structural equation model-ing were conducted. The variables needed to test TRA include:

� Attitudes – The survey contained questions that addressedrespondents’ attitudes toward the use of STEP UP to monitortheir physical activity. The four questions asked if they felt thatthe application is useful, pleasant, and beneficial.� Subjective Norm – The subjective norm we considered is the

user’s perception that most people who are important to himor her think he should or should not be physically active andkeep a healthy weight. Three questions measured subjectivenorm.� Behavioral Intentions – The behavioral intentions are the prob-

ability that the subject will use the application to monitor his/her physical activity. The survey questions included 3 questionsto measure respondents’ intention to use STEP UP.

4.1.3. ResultsConfirmatory factor analysis (CFA) and scale reliability testing

were used to determine the factors used in the model. The overallscale reliability provided a Cronbach’s alpha of 0.837, well abovethe acceptable minimum of 0.7 (Nunnally, 1978). The CFA ex-tracted three components corresponding to the three constructsin the TRA structural model. The rotated component matrixshowed three distinct factors with their respective items, whichconfirmed convergent validity (see Table 1). The factors were con-firmed with eigenvalue over 1.0, which is generally seen as the le-vel of acceptability (Moore, 2000). The result of rotation revealedthat the percentages of the total cumulative variances of the 3 ex-tracted components account for 73.59% of the total variance of theobserved variables.

The first factor was named Intention and had three items withCronbach’s alpha of 0.924. The rotated component matrix elementsall were above 0.85 (minimum acceptable, (Moore, 2000). The sec-ond factor was named Attitude, which had four items and provideda Cronbach’s alpha of 0.763. All components loading was higherthan 0.5. Subjective Norm, the third factor had three items result-ing in a Cronbach’s alpha of 0.665. All components were over 0.63.

Once the factors were determined, the results were analyzed inAMOS 20.0 to test the hypotheses and test the model using struc-tural equation modeling (see Fig. 2).

Hypothesis 1 proposed a positive association between attitudetoward the STEP UP application and intention to use the applica-tion to be more physically active. As shown in Fig. 2, attitude to-ward STEP UP was positively associated with intention to use theapplication. The coefficient was 0.32. Attitude toward STEP UPdid have an impact on intention to use the application. Hypothesis1 was supported.

Hypothesis 2 proposed a positive association between subjec-tive norm and intention to use the application to be more physi-cally active. As shown in Fig. 2, subjective norm was positivelyassociated with intention to use the application. The coefficientwas 0.41. Subjective norm did have an impact on intention touse STEP UP application. Hypothesis 2 was supported.

The model provided a marginally acceptable overall fit, whichresulted in CMIN/DF = 1.344, GFI = .862, NFI = .845, CFI = .952, theRMSEA is 0.08, which is within the recommended 0.06–0.08 (Sty-lianou & Jackson, 2007). Findings indicate that the Reasoned Actionmodel can predict the effect of the physical activity monitoringapplication on future exercise intention and behavior. This studyshowed that STEP UP may change users’ behavior and motivatethem to be more physically active.

Table 1Rotated component matrix (Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization).

Items Component

1 2 3

Q1 I expect to use this mobile application to monitor my physical activity (1) Strongly disagree (7) Strongly Agree .851Q2 I want to use this mobile application to monitor my physical activity (1) Strongly disagree (7) Strongly Agree .909Q3 I intend to use this mobile application to monitor my physical activity (1) Strongly disagree (7) Strongly Agree .873Q4 Using this mobile application is (1) Harmful (7) Beneficial .638Q5 Using this mobile application is (1) Good (7) Bad .794Q6 Using this mobile application is (1) Pleasant for me (7) Unpleasant for me .852Q7 Using this mobile application is (1) Worthless (7) Useful .540Q8 Most people who are important to me think that be more physically active and keep a healthy weight. (1) I should (7) I should not .807Q9 It is expected of me to be more physically active and keep a healthy weight. (1) Strongly disagree and (7) Strongly Agree .743Q10 I feel under social pressure to be more active and keep a healthy weight (1) Strongly disagree (7) Strongly Agree .638

Fig. 2. Theory of reasoned action model with standardized estimates.

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4.2. Experimental study

This first study showed us that, theoretically, STEP UP can moti-vate people to change their behavior and be more physically active.But does STEP UP motive people to be more physically active inpractice? To test whether the system indeed motivates people inpractice, we performed an experiment using two versions of theapplication: (1) a base application with step count functionalityalong with daily and weekly progress reports and (2) the baseapplication with an added social component that allows data shar-ing with friends. Based on the results from TRA, we hypothesizedthat STEP UP would motivate users to achieve higher step counts,especially when participating as a group. In the next section, wedescribe the design of the STEP UP.

Fig. 3. STEP UP client application architecture.

4.2.1. System design and implementationThe STEP UP system consists of two parts, the client application

and the server application, and the two communicate via the inter-net using a socket connection. The architecture of each part is de-tailed below.

As Fig. 3 shows, the STEP UP client application comprises thefollowing software components: Symbian Servers and J2ME mid-let. The Symbian C++ modules are daemons that produce data forrelated J2ME client methods through socket connections. The mo-tion classifier daemon classifies motion and sends the number ofsteps walked, while the watcher daemon starts the motion classi-fier daemon when the user runs the STEP UP midlet and stops itwhen the user closes the STEP UP midlet.

Once the user runs the STEP UP midlet, the daemon client noti-fies the watcher to run the motion classifier daemon. This processis very useful as it helps to reduce the energy consumption result-ing from reading the accelerometer data. Then, the motion classi-fier starts sending the number of steps walked to the motionclassifier client. The controller records the number of steps walkedto the local database. Every 30 min it pulls the number of stepswalked from the local database and saves them in the remote data-base using the connection manager. The GUI component is respon-sible for displaying various types of information to the user,including number of steps walked, distance traveled and numberof calories burned. Also, the user can display his/her history andhis/her team’s history.

The client application provides users the following functionality

1. View number of steps walked, distance traveled and caloriesburned.

2. View walking history.3. View progress during the current week.4. View one’s team’s progress during the week.

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4.2.2. MethodologyWe conducted a 2-week trial, installing the version with the

added social component only in the second week of the experi-ment. Participants therefore tried the base application withoutthe data sharing functionality for one week and then used the up-graded version with which they could share step counts with theirfriends for one week. We followed up the experiment with a usersurvey. Eight participants were recruited for the trial. All partici-pants were females with average age of 23 (standard deviation of2.6) and average weight of 70 kilograms (standard deviation of17.5). Four of them were close friends and university studentswhile the other 4 were co-workers. We organized the close friendsinto group A and the co-workers into group B. As part of the study,participants were required to keep their phones with them at alltimes for the duration of the 2-week trial. Due to technical andpractical problems with the activity monitors (erroneous measure-ment and loss of monitor), data of one participant (from group B)had to be discarded.

4.2.3. ResultsOur results pointed out that the base application with the added

social component was judged as more enjoyable, useful and moti-vating (Refer to Table 2). These findings appear to explain the in-crease in step count for most of the participants while using theenhanced version of the application.

The application was found to be easy and fun to use, as the re-sults of the user survey show. When we asked the participants ifthe ability to view their daily step count was a motivating factorto walk more, 80% said yes, and 20% said no. We also asked themif using the application as a group motivated them to walk morethan when they used it individually; all participants said yes ex-cept for one who was unsure.

Moreover, when we asked the participants which views theyliked the most, 100% of participants mentioned friends’ steps astheir favorite feature. Twenty percent of participants also listedstep history, and another 20% also mentioned weekly steps as theirfavorite. One participant said, ‘‘I liked the friends’ steps view verymuch it motivated me to walk more, I kept opening it to see if I’mthe best or not.’’ Another said, ‘‘Seeing my friends’ no. of steps moti-vated me to walk as much as they walk I did not want to be the worst.’’Another participant said ‘‘before you add the social component when-ever the mobile battery run off I leave it until the next morning butafter adding the social component I recharged it immediately to notgive my friends a chance to beat me.’’ All participants said that ifthe application is available for download they will definitely useit. In fact one participant asked us to install the application inher iPhone, but we explained that the application cannot run oniPhone. She said that taking part in this study made her realizethe importance of keeping a record of her daily activities. She alsotold us that she had wanted to buy the Nikki activity monitoringtool, but it is too expensive and it requires her to buy a set ofthings, unlike STEP UP.

Table 2Comparison between number of steps walked in week 1 (without sharing) and week 2(with sharing).

Group Participants No of steps for week 1 No of steps for week 2

A 1 2797 30,7762 20,177 30,7763 2509 10,2184 27,191 21,659

B 1 877 18912 19,372 219,893 16,178 12,531

When we asked the participants if they have any problem orconcern about their friends’ abilities to view their steps none ofthem expressed any concern. We also asked them if it was fun toview their friends’ number of steps, and they all said it was funand motivating. One participant said, ‘‘It was like a race betweenus and it was fun,’’ and another said, ‘‘I tried to be the highest betweenthem all, it was really fun.’’ We have also asked the participants ifthey communicated with each other if they noticed if one of theirgroup members was not walking as much as she should or if shewas walking too much. 90% of the participants said they communi-cated with each other and discussed such issues. In fact one partic-ipant asked us to add chat functionality to the application, andanother one said, ‘‘When one of our team members walk more thanusual we ask her, ‘Where did you go yesterday? You walked manysteps.’ ’’ Another participant said ‘‘one of our friends had a low stepscount. I called her and asked her, ‘‘Why are not you walking as muchas you should?’’ She told me she was busy but will start walking more,so the application not only helped them to compete but also it allowedthem to monitor their friends and motivate them to walk more’’. Shealso said, ‘‘We used to call each other to discuss our results, so if youcan add within the application it would be great.’’

A one-way between subjects ANOVA was conducted to comparethe effect of walking as a group and sharing physical activity infor-mation opponent to walking as an individual and keeping track ofone’s own physical activity. Given that our sample is small and theperiod of use is relatively short, it was expected that the result willshow no significant effect which was the case at the p < 0.05 levelfor the two setups [F (1, 12) = 1.040024, p = 0.327937]. Althoughresults were not significant, we did observe a trend for bothgroups, which is walking more (and enjoying walking more) whileusing the base application with the added social component.

4.2.4. DiscussionWhile the basic version of STEP UP was found to be effective in

motivating participants to walk more, our results pointed out thatthe full version that includes friends’ step count was judged asmore enjoyable, useful and motivating. These findings appear toexplain the increase in steps when participants used the full ver-sion as groups. The social component of the full version signifi-cantly increased participants’ interest and motivation. The userstudy showed that sharing data within a social group was moremotivating to the participants than monitoring data individually.Increasing physical activity in the context of a social network, evenif participants are connected only remotely via a mobile applica-tion, clearly promotes a healthy competitive atmosphere as wellas motivation, support and fun. Sharing data may have resultedin extrinsic motivation due to its social influence. This is similarto the environment created by counselors in building relationshipswith clients aiming for behavior change (Arkin, 1981; Corrigan,Dell, Lewis, & Schmidt, 1980; McNeill & Stoltenberg, 1989).

Studies have revealed that the telephone can provide a mediumfor social support to promote behavior change (Soet & Basch,1997). In support of that finding, our user study revealed that di-rect communication between group members plays a vital role inmotivating participants to be more physically active. Most partici-pants mentioned that they called each other either to clarify thehigh step counts their friends achieved or to discuss their physicalactivity during the day and motivate each other. Two participantsurged us to add chat functionality into the application to facilitatedirect communication between users. Chatting within the applica-tion would facilitate and complement data sharing within socialgroups by allowing for instant feedback. A chat archive functionby which participants could review their chat history may hold po-tential to further motivate participants, as an increase in stepsovertime would be reflected in a record of direct feedback. Evenwithout chat capability, however, direct communication by phone

2680 A. Khalil, S. Abdallah / Computers in Human Behavior 29 (2013) 2674–2681

provided the participants with a mechanism to offer feedback ontheir performance. Interestingly, some studies comparing auto-mated versus human-based information on feedback performancehave reported that individuals prefer feedback from an automatedsource rather than from a human (Karabenick & Knapp, 1988; Klu-ger & Adler, 1993). Computer mediated feedback is more accept-able for those who have low self-esteem (Kluger & Adler, 1993;Resnik & Lammers, 1985).Thus, we recommend, for designers ofpersuasive applications, to include features of sharing data andthe options of direct or indirect communication (i.e. computermediated) since they can both be motivational forces to encouragesustained physical activities and behavior change.

Surprisingly, the study indicated that participants have no pri-vacy concerns and they do not mind sharing their number of stepswith their friends and female co-workers, but our study includedonly female participants. It would be useful to explore this findingin a similar study in which male participants are included as wellas strangers rather than close friends or coworkers. It remains un-clear to what extent gender-related or other cultural norms influ-enced our participants’ remarkable non-concern for personalprivacy. It is reasonable to conclude that the particular demo-graphic make-up of our sample offered participants a social envi-ronment in which they felt safe to share personal data. Ifaccurate, this conclusion could substantially inform the design ofsocial components of other persuasive applications.

Moreover, we have noticed that participants were not inter-ested in monitoring their daily step counts; they were either inter-ested in their weekly progress, cumulative progress over a numberof days or their friends’ progress. Thus, instead of keeping ‘‘DailySteps’’ as the application’s homepage, we will consider putting‘‘Friends’ Progress’’ or ‘‘Weekly Progress’’ as the homepage. There-fore, we recommend involving users early in the design process toavoid such issues. Involving them in the design process will givethem a better user experience when using the application and itwill give them a sense of ownership, which may make them moremotivated to use the application.

As discussed in the literature, various psychological theorieshave been utilized in physical activity research. These theories at-tempt to provide understanding or to predict behaviors that mayinfluence physical health outcomes (Ajzen & Fishbein, 1980; Bandu-ra, 1986; Dishman et al., 1985; Garcia & King, 1991; King et al.,1992; McAuley, 1992; Sallis et al., 1990). In particular the theoriesof reasoned action (TRA) by Ajzen and Fishbein (1980) and the the-ory of planned behavior (TPB) have been used to predict exerciseintentions and behavior (Conner & Sparks, 1996). In our study, thetheory of planned behavior as depicted in Fig. 2 has been provento be useful in predicting the behavior of users towards the STEPUP application. The TRA constructs are good predictors and corre-late well with intentions towards the application that may lead tochange of behavior towards becoming physically active. The TRAmodel and the results from the experiment are in agreement.

5. Limitations and future work

The main limitation of the study was the small number of par-ticipants and the short duration of the study. The main purpose ofthe study is to investigate the effect of social pressure on motivat-ing healthier lifestyle and more physical activities. Even thoughour experiment was mainly conducted in a university settingwhere students are expected to be more educated and comfortablewith new technologies and social media, we do not believe that theresults were only relevant to that setting. Our mobile applicationwas very simple with minimal interface components and withoutany integration of existing social networks. In the future we wouldlike to conduct the study with a larger pool of participants to en-

able us to investigate the effect of some factors of interest suchas gender and age. We would also like to investigate the effect ofvarious social relationships among group members. We will creategroups with family members, close friends, colleagues, and possi-bly strangers. The future study will also last for a significantly long-er period such as months instead of weeks in order to study thelong-term effect and to rule out any bias that might be caused bycuriosity to the application. We understand that for any behavioralchange to be effective it has to be consistent and long lasting.

The STEP UP application was found to have many bugs when itwas used for extensive periods of time, and that caused inconve-nience for participants. We will work on fixing these bugs as wellas on adding more features to the application such as the ability tointeract with other group members from within the application.

6. Conclusion

In this paper, we described the STEP UP application that wedeveloped to motivate individuals to be more physically active byfacilitating their self-monitoring and data sharing with friendsand group members. We performed two studies. The first studywas an empirical study to test the hypothesis that monitoring phys-ical activity and sharing it with friends motivates behavioralchange. We tested the assumption using the theory of reasoned ac-tion (TRA) and we found that the application indeed may motivatebehavioral change. We found that the TRA constructs are good pre-dictors and correlate well with intentions towards the applicationthat may lead to change of behavior towards becoming physicallyactive. In the second study, we ran a 2-week field experiment fol-lowed up with a user study. We found that the physical activity-sharing feature motivated the participants to walk more than theywould usually do. Also, the ability to view one’s own progress dur-ing the week was a popular feature. We confirm the importance ofsocial context in increasing activity level and found direct commu-nication in persuasive applications in sharing data and the optionsof direct or indirect communication can both be motivational forcesto encourage sustained physical activities and behavior change. TheTRA model and the results from the experiment are in agreement.

Acknowledgements

We would like to thank Emirates Foundation for their supportto that research project. Special thanks go to Suha Jlal and JamieSnyder for all their help and feedback.

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