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ORIGINAL ARTICLE

A Systematic Review of Randomized Controlled Trialson the Effectiveness of Computer-Tailored Physical Activityand Dietary Behavior Promotion Programs: an Update

Karen Broekhuizen, M.Sc. & Willemieke Kroeze, Ph.D. &Mireille NM van Poppel, Ph.D. & Anke Oenema, Ph.D. &Johannes Brug, Ph.D.

Published online: 6 July 2012

physical activity and nutrition education, and to comparethe results to the 2006 review.Methods Databases were searched for randomized controlledtrials evaluating computer-tailored physical activity andnutrition education aimed at primary prevention in adults,published from September 2004 through June 2011.Results Compared to the findings in 2006, a larger proportionof studies found positive effects for computer-tailoredprograms compared to generic or no information, includingthose for physical activity promotion. Effect sizes were smalland generally at short- or medium-term follow-up.Conclusions The results of the 2006 review were confirmedand reinforced. Future interventions should focus on

Introduction

The potential impact of physical activity and healthy dietaryhabits on the prevention of a range of chronic conditions issubstantial [1, 2]. Effective physical activity and dietarypromotion interventions are needed. Successful interventionstrategies and techniques to motivate and guide people toadopt healthy choices need to be identified. Over the lastdecades, computer tailoring has proven to be an innovative andpromising health education technique [3–12]. A computer-tailored intervention mimics interpersonal counseling using acomputerized process, but, unlike interpersonal counseling, itcan be widely distributed through interactive mediachannels at a relatively low cost. Computer tailoring allowsfor individualized feedback and advice on personal behavior,personal motivation, outcome expectations, self-efficacy,social and physical environmental opportunities, andother behavioral determinants.

In recent years, a number of systematic reviews andmeta-analyses have been published on the effectiveness ofcomputer-tailored health education covering a range ofbehaviors [4, 5, 9, 10, 13, 14]. The effects of tailoringmay, however, be behavior specific. It has been argued thatcomputer tailoring may be especially promising for complexhealth behaviors, such as physical activity and dietarybehaviors [15]. Examples of complex health behaviors are

Electronic supplementary material The online version of this article(doi:10.1007/s12160-012-9384-3) contains supplementary material,which is available to authorized users.

K. Broekhuizen (*) :M. NM. van Poppel : J. BrugEMGO+ Institute for Health and Care Research,Amsterdam, Netherlandse-mail: [email protected]

W. KroezeFaculty of Earth and Life Sciences, Vrije Universiteit Amsterdam,Amsterdam, The Netherlands

A. OenemaDepartment of Health Promotion,School for Public Health and Primary Care (CAPHRI),Maastricht University,Maastricht, The Netherlands

ann. behav. med. (2012) 44:259–286DOI 10.1007/s12160-012-9384-3

# The Author(s) 2012. This article is published with open access at Springerlink.com

AbstractBackground A review update is necessary to documentevidence regarding the effectiveness of computer-tailoredphysical activity and nutrition education.Purpose The purpose of this study was to summarize thelatest evidence on the effectiveness of computer-tailored

establishing larger effect sizes and sustained effectsand include more generic health education controlgroups and objective measurements of dietary behavior.

Keywords Computer tailoring . Physical activity .

Dietary behavior . Primary prevention

http://dx.doi.org/10.1007/s12160-012-9384-3

gaining increased awareness of personal behavioral patterns,comparing one's own behaviors with recommendations, andsetting and monitoring progress toward behavior changegoals. The first systematic review that explicitly focusedon the effectiveness of computer-tailored health educationon physical activity and dietary behaviors was published in2006 and included intervention studies published up toSeptember 2004 [3]. In concordance with other more narra-tive reviews on computer-tailored health education [15,16], the authors concluded that computer tailoring was prom-ising, especially for dietary behaviors, although the effectsizes were small. The authors made key recommendations forimproving research on computer tailoring, i.e., using objectiveoutcome measures instead of self-report or using generic healtheducation comparison groups instead of or in addition to no-intervention control groups. The latter would allowmore preciseevaluation of the effects of tailoring health education interven-tions. Finally, it was concluded that longer follow-up was need-ed to assess the sustained effects in all studies.

Since many original studies have been published since2004, a review update is needed to document evidenceregarding the effectiveness of computer-tailored physicalactivity and nutrition education programs. Furthermore,responding to recommendations made in 2006, comparingeffects and specific study and intervention characteristicsover time, is additive to other systematic reviews andmeta-analyses. This review update aims to: (1) review theevidence on computer-tailored physical activity and nutri-tion education from studies published since September 2004,(2) compare the evidence from this review update to thatderived from the original review regarding intervention char-acteristics, study characteristics, and effects, and (3) provideupdated recommendations for further research and practice.

Methods

This paper reports on a second systematic review conductedusing the study protocol of the original 2006 review. Thisprotocol was based on guidelines extracted from theCochrane Reviewers' Handbook [17].

Search Strategy and Data Sources

For the original review, intervention studies published from1965 to September 2004 were identified through astructured computerized search of PubMed, PsychInfo,and Web of Science. For this update, a nearly identicalsearch was conducted from September 2004 to June2011. The review differed from 2006 as we added thesearch engines' most recent thesaurus terms, resulting inthe following search terms for nutrition: ((nutrition OR feedingOR food OR diet OR dietary OR intake OR nutritional status

OR feeding behavi* OR food consumption) AND (educationOR behavior OR behavio* OR education)) AND (tailoredOR tailoring OR tailor* OR expert system) and for physicalactivity: (exercise OR motor activity OR sports OR leisureactivities) OR (physical* AND active) OR (physical* ANDactivity) OR (physical* AND activities) OR exercis* ORwalking OR cycling OR sport* OR leisure activit* AND(education OR behavior OR behavio* OR education) AND(tailored OR tailoring OR tailor* OR expert system). Nolimitations for age or study design were added.

Selection of Studies

Just as in the original 2006 review, new studies had toexamine a computer-tailored intervention aimed at promotinghealthy physical activity or dietary behaviors for primaryprevention of chronic diseases in apparently healthy adults.Evaluation studies that used a randomized controlled trialwere included. Tailoring was defined by Kreuter as “theintention to reach one specific person, based on characteristicsthat are unique to that person, are related to the outcomeof interest, and have been derived from an individualassessment” [18]. Interventions were considered to becomputer tailored if the tailored advice was generatedthrough a computerized process. Randomized controlled trialswere included if: (1) published in a peer-reviewed scientificjournal, 2) published in English, and 3) conducted in an adultsample (18+ years). Studies were excluded if the tailoredintervention was part of a larger intervention program thatmade it impossible to isolate the effect of tailoringcomponents from the other intervention components.

Data Extraction

Detailed information was extracted only from new studiesthat met the aforementioned inclusion criteria. Two reviewersindependently summarized the new studies for content andmethods. The following intervention characteristics wereextracted: theories used for intervention development,variables used to tailor the computer-tailored information,the “tool” that was used to provide individual feedback,frequency of tailored feedback, and additional healtheducation activities. Extracted study characteristics were:the country where the study was conducted, size and source ofthe study population, eligibility criteria, intervention modes,and primary outcome measures. Results from single and mul-tiple post-test measurements were extracted. The outcomesincluded all physical activity and dietary behavior measures.To interpret and compare results from the studies that useddiffering measures to assess physical activity and dietary out-comes, effect sizes (ESs) were calculated if significant effectswere found (provided the data were available). The effect size,Cohen’s ES, was calculated by dividing the difference

260 ann. behav. med. (2012) 44:259–286

between two means at follow-up by their pooled standarddeviation [19, 20]. Cutoff points for ESs were 0.2–0.5 forsmall ES, 0.5–0.8 for moderate ES, and >0.8 for large ES [21].The findings were summarized per behavioral outcome (phys-ical activity, fat intake, fruit and vegetable consumption, andother dietary behaviors) and separately for short- (<3 months),medium- (3–6months), and long-term (>6months) follow-up.

Apart from reporting the results found in the currentreview, we compared these with the results of the original2006 review. In order to check whether recommendationsfrom the original review were met, we compared interventionand study characteristics of the present review with the orig-inal one. Frequencies on the number of studies that foundsignificant effects, as well as the number of studies that usedobjective outcome measures, various types of comparisongroups (generic health education versus no-intervention con-trol groups), and long-term follow-up, as well as deliverymode (printed versus electronically) are provided, linked tothe original or current review.

Results

Study Selection

The initial cross-database search resulted in 2,590 publications.After eliminating duplicates, 1,562 remained. Titles andabstracts were reviewed for eligibility criteria, resulting in141 publications that were fully considered. Fifty publicationswere finally included: 29 studies on physical activity and 34 ondietary behaviors, 21 on fat consumption, 18 on fruit andvegetable consumption, and 14 on other dietary topics. Otherdietary topics included: energy/carbohydrate intake, theconsumption of sugar, dairy, fiber, whole grain, and body fat,as well as weight and waist circumference. Thirteen studies inthe current review evaluated interventions that targeted bothphysical activity and diet. Some publications reported on thecharacteristics and effects of one intervention using variousfollow-up measurements (e.g., short- and long-term effects)[22–26, 39], effects in a variety of study samples [27–30],effects on other types of outcomes (e.g., fruit intake andvariety of fruit intake) [31], or the effects of various doses ofthe intervention (e.g., delivered at once or at multiple timepoints) [32, 33]. As a consequence, this review update reportson the characteristics and effects of 25 interventions targetedat physical activity, 27 interventions targeted at dietary behav-ior, and 10 interventions for both behaviors. Of the 27 inter-ventions on dietary behavior, 17 were directed at fat reduction,14 at increasing fruit and vegetable intake, and 12 at otherdietary behaviors. The main reasons for exclusion were: theage of the study population was not in the required range, lackof randomized controlled trial design, no focus on primaryprevention, absence of behavioral outcomes, or the computer

tailoring was part of a multicomponent intervention that madeit impossible to isolate the effect of tailoring.

Intervention Characteristics

Characteristics of the interventions from studies in the cur-rent review are summarized in the Electronic SupplementaryMaterial. Both physical activity and nutrition educationinterventions were predominantly guided by the Trans-theoretical Model and Social Cognitive Theory. Mostinterventions (81 % of physical activity, 84 % of nutri-tion) provided tailored feedback on self-reported behav-ior. Two interventions (4 %) also provided feedbackbased on more objective data obtained from pedometers[34] or accelerometers [35]. Most interventions (92 %of physical activity, 68 % of nutrition) were tailored onpresumed behavioral determinants such as intention,motivation,and stage of change, as well as self-efficacy and skills. Regard-ing nutrition education interventions, equal numbers of inter-ventions provided print-delivered and electronically tailoredfeedback; however, the majority of physical activity interven-tions used electronic feedback formats (see also Table 1).Some interventions using electronic feedback had additionalonline discussion/message boards [36–38] (6 % of all inter-ventions) or an e-buddy system (2 % of all interventions) [22,38]. Electronic feedback was given on-screen (41 % of allinterventions), by email reports (10%), CD-ROM (4%), or bymobile phone (2 %). Approximately one third of the inter-ventions provided additional information such as booklets orinformation sheets. One intervention included weekly homevisits [26, 39]. Less than half of the interventions providedtailored feedbackmore than once for dietary behaviors (48%),and 65 % did so for physical activity.

Study Characteristics

The characteristics and effects for studies in the currentreview are shown in the Appendix. The majority of studieswere conducted in the USA, followed by the Netherlandsand Belgium, the UK, and several other countries.

Studies in the USA predominantly assessed physicalactivity with the validated 7-day Physical Activity Recall[40–43]; this was the most commonly used tool. The nextmost common tool was the validated Short QuestionnaireAssessing Health-Enhancing Physical Activity (SQUASH)[44] predominantly used by Dutch researchers. TheInternational Physical Activity Questionnaire (IPAQ)[45, 46] was the third most commonly used assessment tool.Six studies (21 %) included objective assessments of physicalactivity, i.e., pedometer, actigraph, or accelerometer. Fivestudies (17 %) measured aerobic fitness by either a (1 mile)walking test [47, 48], the Chester step test [49], or thesubmaximal exercise treadmill test [50].

ann. behav. med. (2012) 44:259–286 261

Fat reduction was most often assessed using food fre-quency questionnaires. In the USA, the Block questionnairewas used most frequently [51] and in the Netherlands, aquestionnaire developed by Van Assema et al. [52]. Twostudies obtained data from either an electronic scanner [53]or shopping receipts [34] in a supermarket setting. Data onfruit and vegetable consumption were obtained from ques-tionnaires (the Block questionnaire in the majority of stud-ies); one study also used shopping receipts [34]. Studies thatincluded measures of weight or BMI either used self-report[38, 54] or measured [24, 27, 28, 34, 55, 56]. Fiber, grain,energy, or added sugar intakes were assessed by food fre-quency questionnaires [57, 58].

Effects on Physical Activity (Section A, Appendix)

Of the 29 studies on physical activity, 20 (69 %) showedsignificant differences in favor of the computer-tailoredintervention. Five studies looked at short-term effects [36,37, 59–61], of which four found significant effects for thetailored intervention [36, 37, 59, 60] with small effect sizes,

compared to no intervention. In one study, this applied toparticipants who did not comply to the physical activityguidelines at baseline [60]. Of the 17 studies withmedium-term follow-up periods, 12 found significanteffects with small effect sizes: six compared to nointervention [22, 36, 62–65], five compared to generichealth education [24, 32, 33, 66, 67], and one comparedto a health risk assessment [67]. Studies that investigat-ed two computer-tailoring techniques [22, 54, 63, 67]found significant effects for both tailoring conditions.Six of the 13 studies with long-term follow-up foundsignificant effects of the tailored intervention [23, 25,32, 34, 65, 67]. Effect sizes were small except for onestudy that reported medium effect size for one of thetwo computer-tailored interventions investigated [67]. Ofthe eight studies that assessed effects at various follow-up periods, four studies reported no effects at eithershort, medium, or long term [35, 61, 68, 69]; sixstudies reported sustained effects over time[22, 23, 25,34, 36, 65, 67], and one study reported no effect atshort term but a significant effect at medium term [62].

Table 1 Study characteristics and effects of studies from the original (before 2004) and updated review (after 2004) compared

Dietary behavior Physical activity

Before 2004 (N=26) After 2004 (N=34) Before 2004 (N=10) After 2004 (N=29)Reference numbera Reference number Reference numbera Reference numberN (%) N (%) N (%) N (%)

Comparison of computer-tailoredintervention with a no interventioncontrol group

[33–35, 39, 42–44,46–48, 50–56, 60]

[29–31, 34, 36, 53, 60,65, 70, 71, 74, 78, 79, 82]

[33–35, 38] [22, 23, 34, 36, 37,60, 62–65, 74]

18 (69 %) 14 (41 %) 4 (40 %) 11 (38 %)

Comparison of computer-tailoredintervention with a generichealth education control groupb

[30–32, 40–42, 45,54–56]

[24–26, 32, 33, 38, 39, 55,56, 71–73, 75, 80, 81, 95]

[28–30, 32, 37, 38] [24, 25, 32, 33, 35, 56,59, 61, 66–69, 95–98]

10 (38 %) 16 (47 %) 6 (60 %) 16 (55 %)

Objective measurements ofeffect indicators

[39, 50–52] [24, 25, 34, 53, 56] [24, 27, 28, 34, 35, 37,66, 67, 69, 98]

4 (15 %) 5 (15 %) 0 (0 %) 10 (34 %)

Inclusion of long-term (≥6 months)follow-up

[32, 33, 36, 43, 46] [24–32, 34, 38, 39, 55, 56,65, 70, 71, 75, 78–81, 95]

[28, 32–34, 36, 37] [23, 27, 28, 32, 34, 35, 56,61, 65, 67–69, 71, 95]

7 (27 %) 23 (68 %) 6 (60 %) 14 (48 %)

Significant effects of computer-tailored interventions found

[30, 35, 39, 41, 43, 47,49, 53, 56]

[24–34, 36, 38, 39, 55, 56,60, 65, 70–75, 78–81]

[29, 35] [22–25, 32–34, 36, 37,54, 59, 60, 62–67, 74]

9 (35 %) 28 (82 %) 2 (20 %) 19 (66 %)

Printed intervention materials [30–34, 40–46, 48–50,53, 54, 56]

[24, 26, 28–30, 32, 33, 39,53, 73, 75, 77–79, 81, 95]

[28–34, 37, 38] [22, 23, 27, 28, 32, 33,64, 67, 68, 95]

18 (69 %) 15 (44 %) 9 (90 %) 10 (34 %)

Electronic intervention materials [35, 36, 39, 44, 47, 51,52, 55, 60]

[34, 36, 38, 55, 56, 60,70–72, 74, 80, 82]

[35, 36] [24, 25, 34–38, 54, 56,60–63, 66, 69, 96–98]

9 (35 %) 12 (35 %) 2 (20 %) 18 (62 %)

N number of studiesa Reference numbers of studies < 2004 are derived from the original review [3]. Reference numbers of studies after > refer to references used in thisreviewb In some studies, a no-intervention and generic health education control groups were both included

262 ann. behav. med. (2012) 44:259–286

Effects on Fat Consumption (Section B, Appendix)

Of the 21 studies on fat consumption, 17 (81 %) showedsignificant differences in favor of the computer-tailoredintervention. Six studies tested short-term effects andreported significant effects of tailoring compared to nointervention [36, 60, 70, 71], or generic health education[72, 73] with small effect sizes. Two of those studies (also)targeted an at-risk population [60, 72]. At medium term, alleight studies found significant effects compared to nointervention [36, 70, 74], or generic health education[33, 72–75]. One of those studies targeted a low-incomeethnically diverse population [76], and a second study alsofound a significant effect among risk consumers (i.e., peoplewith fat intake levels higher than recommended at baseline)[72]. Ten studies tested the long-term effects of an intervention,and five found significant effects for tailoring compared to nointervention [29, 30, 70] or generic health education [24, 32]with small effect sizes. Two of the ten studies (also) targetedhigh-risk populations [29, 30], and another study targetedwomen aged 50–69 years [24]. Multiple measurements in timewere reported for seven studies, of which five studies reportedsustained significant effects [25, 36, 70, 72, 73], one studyreported a significant effect at short term [26] that was notsustained in the long term [39], and one study reported noeffects at both medium- and long-term time periods [77].

Effects on Fruit and Vegetable Consumption(Section C, Appendix)

Of the 18 studies on fruit and vegetable consumption, 15(83 %) showed significant differences in favor of thecomputer-tailored intervention. Two of these studies measuredthe short-term effects of a computer-tailored intervention, andboth found significant effects compared to no intervention [36,71] with small effect sizes in a general population. Six studiesmeasured medium-term effects, of which five found significanteffects compared to no intervention [36, 65, 78] or generichealth education [33, 75] with small effect sizes. One studyinvestigated the effects of two intervention conditions (eitherdelivered in one or four installments) compared to generichealth education and measured the effects of retailored feed-back [75]. The latter measured the effect of retailored feedbackprovided in four installments. Eight of the 12 studies that testedthe long-term effects of an intervention found significant effectsfor tailoring interventions compared to no intervention [31, 34,65, 79] or generic health education [24, 32, 80, 81]. The eightstudies found small effect sizes, except for one that had targetedchurch members, which found a large effect size over the longterm [31]. Two studies with effective long-term interventionstargeted populations who were over 50 years of age [24, 56].Heimendinger and colleagues found a significant effect of(re)tailored advice when spread across four booklets, as

opposed to no effect when the advice was delivered in a singlebooklet [81]. Nine studies reported multiple measurements intime, and seven of these reported sustained effects [25, 32, 34,36, 65, 75, 78]. One of the nine studies reported no medium-term effect but a significant long-term effect [79], and one studyreported no medium- or long-term effect [77].

Effects on Other Diet-Related Behaviors(Section D, Appendix)

Of the 14 studies on other dietary behaviors, 8 (57 %) showedsignificant differences in favor of the computer-tailored inter-vention. Four interventions for weight loss found significanteffects including: one short, medium, and long term [28]; onemedium and long term [38]; and two long term only [34, 55].Effect sizes were small [34, 55], medium [28], or large [38].Of the three interventions on energy intake, one reported asignificant short- and medium-term effect [72]. Thecorresponding effect size was small for the general studypopulation and medium among risk consumers in the shortterm. In addition, at medium term, only the effect of print-based advice (as opposed to delivery through CD-ROM) wasof significance in the general population with a small effectsize. Both studies considering fiber consumption found sig-nificant short-, medium- [70], and long-term effects [34] withsmall effect sizes. The intervention on grain intake showed nosignificant effect, nor did an intervention aimed at reducingadded sugar. No significant effect was observed for the inter-vention to change dairy consumption [82].

A Comparison Between the Present Update and the Original2006 Review

The present review included 50 publications over just under7 years, while the original review in 2006 included 30publications over 13 years, showing an apparent increasein studies on physical activity and tailored nutrition education.This increase was most obvious for physical activity (29studies in the present review, 11 in the original review).

Since 2004, the number of computer-tailored interventionselectronically delivered has increased, particularly in physicalactivity studies (see Table 1). New delivery modes, such asmobile phone and CD-ROM, were introduced since 2004.Similar to the original review, in the majority of studies includedin the present update, a no-intervention control group wasincluded without a generic health education comparison group.Most studies continue to lack objective assessments of effects ofnutrition interventions, but physical activity intervention studiesoften used objective assessments for behavior changes. Asrecommended in the original 2006 review, more nutrition in-tervention studies included long-term follow-up.

In this update, the majority of studies reported significanteffects of computer tailoring, both for dietary and physical

ann. behav. med. (2012) 44:259–286 263

activity behavior (the largest increase). However, effectssizes remained small in general for dietary as well as physicalactivity behavior.

Discussion

The present review update confirms and further strengthens theevidence that computer-tailored physical activity and nutritioneducation is likely to be effective [4, 5, 9, 10, 13, 14], althougheffect sizes related to tailored physical activity and nutritioneducation interventions are likely to be small. The evidence forlong-term effects of computer tailoring remains inconclusive.

The present review is an update of a 2006 review of theliterature published up to September 2004. A number ofdifferences in the results of the original and updated revieware noteworthy. First, both for physical activity and dietarybehavior, the number of published studies has increasedsubstantially. In addition, a larger proportion of publishedstudies reported favorable effects of tailored interventions inthe update period than in the original review. Evidence onthe efficacy of computer-tailored education is now alsoapparent for physical activity promotion. Second, the useof objective outcome measurement instruments increased instudies on physical activity education, but not for nutritioneducation studies. Third, overall, there was no increase incomparisons of interventions with generic health educationsince 2004. Fourth, remarkably more studies with long-termfollow-up were performed in the past years, particularly onnutrition education. Finally, the electronic delivery of feed-back increased, particularly in studies on physical activitypromotion; discussion boards/forums were frequently addedto interventions.

The observed differences over time for the use of objectiveoutcome measurements and various types of control groups,follow-up periods, and delivery modes require more attention.Since 2004, a larger number of objective measures have beenincluded in tailoring studies, especially regarding physical activ-ity education. In this field, accelerometers and pedometers havegrown in popularity, due to increased usability and feasibility[83]. In the field of nutrition, no such development was seen. Theobjective measurement of dietary intake can be achieved bymonitoring biologic dietary indicators, such as serum cholesteroland serum carotenoids [84]. However, the assessment of biologicindicators is relatively expensive, and these indicators are subjectto genetic differences. Alternatively, two studies used shoppingreceipts and electronic shop scanners as objective indicators offood purchases [34, 53]. In addition, anthropometrics and waistcircumference were the most frequent objective indicators.

The fact that the evidence in favor of computer-tailoredphysical activity and nutrition education is now stronger thanbased on the studies published up to 2004 is promising andimportant. However, the strongest evidence comes from

studies that compared tailored interventions to no-intervention control groups. Thus, these studies could notassess the effects of tailoring compared to non-tailored inter-ventions. Significant effects were most often found in studieswith a no-intervention control group. These findings do notdiffer from the results of the original review or other compa-rable reviews [3, 6–8, 13]. Therefore, the evidence is strongerfor a comparison between tailored interventions and with nointervention than with generic health education. However, thisis probably because of the larger number of studies thatincluded a no-intervention control group. If generic healtheducation control groups were included in a study, the evi-dence was quite consistently in favor of tailoring. If thisreview had been restricted only to comparisons between tai-lored interventions with generic health education comparisongroups, it would have focused specifically on the additionaleffects of tailoring in health education. Nevertheless, we be-lieve that the comparison with no-intervention control con-ditions is also important, because it shows that tailoredinterventions are likely to be effective—because of the tailor-ing or other factors—and that is important information forhealth education practice. In addition, further exploration ofthe effectiveness of computer-tailored interventions com-pared to other control conditions, such as theory-basedor personalized interventions, would be valuable to ver-ify whether individually tailored education is better thantheory-based and/or personalized education.

For physical activity and nutrition interventions to have aneffect on health, the effects should be sustained over longperiods of time [76]. The present review update shows thatsince 2004,more studieswith long-term follow-up (>6months)have been published. However, the positive effects of thesestudies were generally observed at short- and medium-termfollow-up. Lack of long-term effects of health education inter-ventions has been reported before. In a meta-analysis ofcomputer-tailored interventions, Krebs and colleagues alsofound a significant trend of decreasing effect size whenfollow-up time increased [4]. Some evidence suggests that“dynamic tailoring” with more tailored feedback momentsthroughout a long intervention period may improve effectsbeyond the short term. The present updated review furthershows that iterative feedback and tools supporting self-regulatory skills (e.g., goal setting activities, self-monitoringtools, skills building activities, email reminders, booster ses-sions, and interactive activities) are ways to realize such re-peated tailoring [4, 5, 15, 85].

Not only has the number of electronically delivered inter-ventions grown since 2004, but evidence for effectiveness hastoo. Before 2004, only a third of these “second-generation”dietary interventions were effective, compared to 60 % after2004. For effective promotion of physical activity, the likeli-hood of effect appears not to be dependent on delivery mode.Furthermore, mobile phones were a delivery mode that was

264 ann. behav. med. (2012) 44:259–286

not yet available in the studies in the original 2006 review. Astudy by Haapala et al. indicates that mobile phone deliverycan be an effective method for supporting weight loss. Byallowing for two-way communication and showing a log-onfrequency that is twice the rate of other web-based programs[86, 87], mobile phones have potential for the future. Becauseof these advantages and given the massive increase of the useof smartphones worldwide, mobile technologies will andprobably should be used more often to promote lifestylechanges [88].

Overall, studies published since 2004 appear to have par-tially taken into account the recommendations for further re-search in the original review. Althoughmore objective outcomemeasurement instruments were used in studies published after2004, this was restricted to interventions on physical activity.Further, despite the increased number of studies, the proportionof comparisons with generic health education has not increasedsince 2004. Long follow-ups have been included more fre-quently in more recent studies, but only in nutrition interven-tions. Comparisons with generic health education, instead ofno-intervention control groups, are most important becausethey provide information on the effects of tailoring. Therefore,we repeat and strongly advocate the recommendation to studytailoring as compared to other intervention methods, such asgeneric health education. Long-term follow-up should remain apriority, as well as the inclusion of objective outcome measuresincluding their use in nutrition intervention research.

This review update has limitations. We used the samereview protocol as was applied in the original 2006 review.Therefore, potential limitations such as the non-blinding ofreviewers to authorship or the journal of the reviewed publi-cations also applied to the present review. A lack of unequiv-ocal scientific evidence that blinding is essential to obtainvalid review results was already discussed in the original2006 review [3, 89, 90]. In addition, a new independentreviewer assessed eligibility of the studies for the presentupdate, which could have led to some differences in decisionsand interpretations. Previous research has shown that updatinga review can affect both the direction and the precision of theoutcome [91, 92]. Yet, two reviewers who were involved inthe reviewing process of the original 2006 review were alsopart of the present update team. No risk of bias and/or qualityassessment evaluations were performed for either the originaland updated review, although the use of such tools has beenrecommended for systematic reviews [17]. Fortunately, be-cause only randomized controlled trials were included, thevariety in methodological quality was small. Nevertheless,the methodological quality of the studies included in thisreview could have had an impact on estimates of effects,which might have affected the validity of the conclusions.Finally, as with any review of published literature, the presentupdate may have been affected by publication bias that mayhave caused an overestimation of the positive findings.

Notwithstanding these potential limitations, this reviewimportantly updates the systematic overview of developmentsand evidence regarding computer-tailored physical activity andnutrition education over the past years. Furthermore, this re-view update provides the most recent overview of the contentand effects of computer-tailored interventions in the field ofphysical activity and nutrition. Reviews of the literature need tobe updated regularly in order to provide up-to-date overviewsof the evidence base to inform health promotion practice and toprovide new recommendations for research to further strength-en the evidence base. This comparison is strengthened by ouruse of comparable reviewing methods at two time points, 2006and 2011, giving us the opportunity to compare effects, inter-vention, and study characteristics over time. Such updating ofreviews using a similar methodology is advocated and commonpractice in review consortia such as the Cochrane collaboration.

On the whole, from this updated review, it can be conclud-ed that the evidence on computer-tailored interventions for thepromotion of physical activity and dietary change has becomestronger and now is also convincing for physical activitypromotion. However, this effect particularly accounted forstudies with no-intervention control groups, effect sizes weregenerally small, and the evidence is generally restricted torather short-term effects, i.e., up to 3 months follow-up. Fur-ther, it remains unclear whether the effect of tailored inter-ventions is caused by tailoring as such or by the fact thattailored interventions are more likely to be carefully designedand based on behavioral theory. Previously formulated rec-ommendations regarding the use of objective outcome meas-urements, generic HE control groups, and long-term follow-up periods for the development of computer-tailored interven-tions were only partially met. Based on the present review, theuse of computer-tailored interventions in physical activity andhealthy nutrition promotion can be advocated, but futureinterventions should especially focus on: (1) establishing larg-er effect sizes and sustained effects, (2) using more objectivemeasurements in studies on dietary behavior, (3) using moregeneric HE control groups and especially control groups inwhich the generic health education is also carefully designedand theory-based in order to distinguish the effect of tailoringfrom the effects of theory-based intervention development,and (4) including more long-term follow-up measurements.Future research should also focus on why and how computer-tailored physical activity and nutrition interventions are effec-tive, by conducting mediation analyses [23, 93], and support-ing large-scale dissemination of such interventions [94].

Acknowledgments We gratefully acknowledge René Otten of theVU University Medical Library for his assistance in searching thedatabases.

Conflict of Interest The authors have no conflict of interest todisclose.

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Appendix

Tab

le2

Study

characteristicsandeffectsfoun

din

thestud

iesinclud

edin

thereview

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

A.Physicalactiv

ity

Adachi,2007

[28]

Japan

Overw

eightJapanese

wom

en[205]

recruitedfrom

thegeneral

populatio

n(A

dachi,2007)

CSelf-help

booklet

?15-item

Self-ratedphysical

activ

ities

(points

1(bad)–3(good)

LTNosignificanteffects

Tanaka,2010

[27]

Overw

eightJapanese

men

[51]

recruitedfrom

thegeneral

populatio

n(Tanaka,2010)

EXP1C+self-

monito

ring

ofweight

andwalking

Pedom

eter

Daily

walking

steps

EXP2CTadvice

EXP3f

CTadvice+

self-m

onito

ring

ofweightandwalking

Carroll,

2010

[96]

USA

Inactiv

eparticipants[394]

recruitedthroughprim

ary

care

providers

CGeneric

HE

Yes

7-Day

PALeisure-tim

ePA

(min/week)

MTNosignificanteffects

EXP1CTadvice

Recall

Non-leisure-tim

ePA

(min/week)

Dunton,

2008

[62]

USA

Wom

en[156](21–

65)

recruitedfrom

the

generalpopulatio

n

CNointerventio

nYes

Standardizedactiv

ityinventory

MVPA

(min/week)

STNosignificanteffects

EXP1CTadvice

Walking

(min/

week)

MTSignificant

effect

onMVPA

ES:0.24

MTSignificant

effect

onwalking

ES:0.21

Hagem

an,2005

[66]

USA

Wom

en[31]

(50–69

years)

recruitedthroughnewspaper

advertisem

ent

CGeneric

HE

Yes

Modified7-dayphysical

activ

ityrecall

MVPA

(min/week)

calories

expended

daily

MTSignificant

effect

onVO2max

EXP1CTadvice

Fitn

esswalking

test

Aerobic

fitness

(VO2max

inml/

kg/m

in),

flexibility

(cm)

ES:0.42

Sit-and-reachtest

Hurlin

g,2007

[37]

UK

Participants[77]

(30–55

years)

recruitedthroughmarket

research

recruitm

entagency

CNointerventio

nYes

IPAQ

OverallPA

(MET

min/week)

STSignificant

effecton

leisure-tim

ePA

EXP1CTadvice

Accelerom

eter

Leisure-tim

ePA

(METmin/week)

Accelerom

eter

data

Overallsitting

time

(h/week)

Significant

effect

onMPA

(3–6

MET

range)

Weekday

sitting

time(h/week)

ES:N/A

Weekend

sitting

time(h/week)

Jacobs,2004

[95]

USA

Wom

en[511](50–

64)recruited

from

nutrition

andPA

program

(WISEWOMAN)

CGeneric

HE

?31-item

PAA

questio

nnaire

Score

from

31-item

scale:

notvery

activ

e(0)–very

activ

e(42)

LTNosignificanteffect

onPA

score

266 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

EXP1CTadvice

Marcus,2007

[67]

USA

Sedentary

participants[239]

(18–

65)recruitedfrom

the

generalpopulatio

n

CGeneric

HE

Yes

7-Day

physical

activ

ityrecall

MPA

/VPA

(min/

week)

MTSignificant

effect

onPA

inEXP2

comparedto

C

EXP1CTadvice

(print-based)

Actigraph

Aerobic

fitness

(VO2m

axin

ml/

kg/m

in)

ES:0.46

EXP2CTadvice

(telephone-based)

Submaxim

alexercise

threadmill

test

MTSignificant

effect

onPA

inEXP1

comparedto

C

ES:0.39

MTNosignificantdifference

between

EXP1andEXP2

LTSignificant

effect

onPA

inEXP2

comparedto

C

ES:N/A


onPA

inEXP1comparedto

C

LTNosignificantdifference

between

EXP1andEXP2

Marcus,2007

[69]

USA

Sedentary

participants[249](18+

)from

thegeneralpopulatio

nC

Generic

HE

Yes

7-Day

physical

activ

ityrecall

MPA

/VPA

(min/

week)

MT/LTNosignificanteffect

onMVPA

EXP1CTadvice

(internet)

Submaxim

alexercise

treadm

illtest

Aerobic

fitness

(VO2m

axin

ml/

kg/m

in)

EXP2CTadvice

(print-based)

Napolitano,

2006

[68]

USA

Sedentary

wom

en[280]recruited

from

thegeneralpopulatio

nC1Generic

HE

Yes

7-Day

physical

activ

ityrecall

MPA

/VPA

(min/

week)

MT/LTNosignificanteffect

onMVPA

C2Self-help

booklet

EXP2CTadvice

Oenem

a,2008

[60]

The

Netherlands

Participants[2,159](>30)recruited

from

onlin

eresearch

panel

CNointerventio

nYes

Shortversionof

IPAQ

Self-ratedPA

level

(scale

from

−2to

+2)

STSignificant

effect

on%

compliant

toPA

guidelinein

at-riskgroup(those

who

didnotcomplywith

thePA

guidelines

atbaselin

e)

EXP1CTadvice

%compliant

toPA

guideline

(moderate

intensity

PAfor

atleast30

min/

dayin

atleast5days/

week)

ES:0.16

Pekmezi,2009

[97]

USA

Sedentary

Latinas

[93]

(18–

65)

recruitedfrom

thegeneral

populatio

n

CGeneric

HE

Yes

7-Day

physical

activ

ityrecall

MPA

/VPA

(min/

week)

MTNosignificanteffect

onMVPA

EXP1CTadvice

ann. behav. med. (2012) 44:259–286 267

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

Prochaska,

2008

[54]

USA

Participants[1400]

atrisk

forat

leastonerisk

behavior

(exercise,

stress,BMI>25

kg/m

2andsm

oking)

recruitedfrom

amajor

medical

university

CHealth

risk

assessment

Yes

Self-reported

levelof

exercise

%exercising

moderately

30min/day

forat

least

5days/week

MTSignificant

effect

on%

exercising

moderately30

min/day

forat

least

5days/weekin

EXP1andEXP2

comparedto

C

EXP1C+coaching

ES:N/A

EXP2C+

transtheoreticmodel-

basedfeedback

Quintiliani,

2010

[59]

USA

Fem

alecollege

students[408]

recruitedfrom

universities/colleges

CGeneric

HE

Yes

USBehavioralRiskFactorSurveillance

Survey

MVPA

(min/week)

STSignificant

effect

onVPA

inEXP2

comparedto

C

EXP1CTadvice

(topic

bychoice)

VPA

(min/week)

ES:0.41

EXP2CTadvice

(topic

byexpert)

Slootmaker,

2009

[35]

The

Netherlands

Participants[102](20–40

years)

recruitedfrom

worksites

CGeneric

HE

?AQuA

A[100

]LPA

/MPA

/VPA

(METmin/week)

MT/LTNosignificanteffects

EXP1CTadvice

Chester

StepTest

Aerobic

fitness

(VO2m

axin

ml/

kg/m

in)

Smeets,2007

[33]

The

Netherlands

Participants[2,827](18–

65)

recruitedfrom

companies

andthegeneralpopulatio

n

CGeneric

HE

Yes

SQUASH

Actionmom

ents/

week

MTSignificant

effect

onPA

ofEXP1

comparedto

C

DeVries,2008

[32]

EXP1CTadvice

(once

deliv

ered

in3months(Smeetset

al.))

%compliant

toPA

guideline

(moderate

intensity

PAfor

atleast30

min/

dayin

atleast5days/

week)

ES:0.12

EXP2CTadvice

(3tim

esdeliv

ered

in9months(D

eVries

etal.))

LTSignificant

effect

onPA

and%

complianceto

PAguidelineof

EXP2

comparedto

C

ES:0.15

ES:0.14

Smeets,2008

[64]

The

Netherlands


year)

recruitedfrom

thegeneral

populatio

n

CNointerventio

nYes

SQUASH

Total

PA(M

ET

min/week)

MTSignificant

effect

ontransport

relatedPA

andtotalPA

among

motivated

participants

EXP1CTadvice

Transportrelated

PA(M

ETmin/

week)

ES:0.48

Leisure-tim

erelatedPA

(MET

min/week)

ES:0.49

SportsrelatedPA

(METmin/week)

Spittaels,2007

[63]

Belgium


year)

recruitedthroughparentsand

CNointerventio

nYes

IPAQ

Total

MVPA

(min/

week)

MTSignificant

effect

ontransportatio

nPA

,leisure-tim

ePA

andweekday

268 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

staffof

prim

ary/secondary

schools

sitting

timein

EXP1andEXP2

comparedto

C

EXP1CTadvice

TransportationPA

(min/week)

EXP2comparedto

C

EXP2CTadvice+

repeated

feedback

Household

PA(m

in/week)

ES(transportationPA

):0.21

Leisure-tim

ePA

(min/week)

ES(leisure-tim

ePA

):0.52

Job-relatedPA

(min/week)

weekday

sitting

time(m

in/day)

ES(w

eekday

sitting

time):1.58

Weekend

sitting

time(m

in/day)

EXP1comparedto

C

ES(transportationPA

):0.18

ES(leisure-tim

ePA

):0.40

ES(w

eekday

sitting

time):1.62

Spittaels,2007

[98]

Belgium


year)

recruitedfrom

worksites

CGeneric

HE

Yes

IPAQ

Total

PA(m

in/

week)

MTNosignificanteffectsin

EXP1

orEXP2comparedto

C

EXP1CTadvice

Accelerom

eter

MVPA

(min/week)

EXP2CTadvice+

stage-of-change

basedem

ails

30min

ofPA

onmostdays

(%)

Sternfeld,2009

[36]

USA

Participants[787]recruitedfrom

administrationofficesof

alarge

healthcare

organizatio

n

CNointerventio

nYes

PhysicalActivity

Questionnaire

adapted

from

Cross-Cultural

Activity

Patterns

Questionnaire

Total

PA(M

ET

min/week)

STSignificant

effect

onMPA

,VPA

,walking,andsedentary

behavior

EXP1CTadvice

MTSignificant

effect

onMPA

,walking,andsedentarybehavior

MPA

(min/week)

STSignificant

effect

onMPA

,VPA

,walking

andsedentarybehavior

among

thosewho

chosethePA

path

ofthe

interventio

n

VPA

(min/week)

ES:N/A

Walking

(min/week

Sedentary

behavior

(min/week)

Van

Keulen,

2011

[65]

The

Netherlands


70)

recruitedfrom

general

practices

C1Nointerventio

nYes

28-item

modifiedCom

munity

Health

Activities

Model

Program

forSeniors

PA(hours/week)

MTSignificant

effect

ofEXP1

comparedto

C1

C2Coaching

ES:0.20

C3C2+EXP1

LT(~11

months)

Significant

effect

ofEXP1comparedto

C1andC3

EXP1TCadvice

ES(EXP1-C1):0.32

ES(EXP1-C3):0.15

LT(~18

months)

nosignificanteffects

ann. behav. med. (2012) 44:259–286 269

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

Van

Stralen,

2009

[22]

The

Netherlands

Participants[1971]

(>50

years)

recruitedfrom

Regional

Municipal

Health

Councils

CNointerventio

nYes

1-item

from

SQUASH

Self-ratedPA

(total

weekly

days

ofMPA

)

MT(3

months)

Significant

effect

onself-rated

PAin

EXP1andEXP2comparedto

C

Van

Stralen,

2011

[23]

EXP1CTadvice

(psychosocial)

Self-rated

compliancewith

PAguidelines

(%of

participants

that

show

compliancewith

guidelines)

ES:0.20

EXP2CTadvice

(psychosocial+

environm

ental)

ES:0.20

MT(3

months)Significant

effecton

PAinitiationam

onginsufficiently

activ

eparticipantsin

EXP1andEXP2

comparedto

C

ES:0.26

ES:0.21

MT(6

months)

Significant

effect

onself-rated

PAin


C

ES:0.30

ES:0.35

MT(6

months)Significant

effecton

PAinitiationam

onginsufficiently

activ

eparticipantsin

EXP1andEXP2

comparedto

C

ES:0.32

ES:0.27

MT(6

months)Significant

effecton

PAmaintenance

amongsufficiently

activ

eparticipantsin

EXP1andEXP2

comparedto

C

ES:0.33

ES:0.34

LT(12months)

Significant

effect

onself-rated

PAin

EXP1andEXP2

comparedto

C

ES:0.18

(for

both

EXP1andEXP2)

Walker,2009

[24]

USA

Wom

en[225](50–69)recruited

from

thegeneralpopulatio

nC

Generic

HEEXP1

CTadvice

Yes

Modified7-dayPhysicalActivity

Recall

MVPA

(min/day)

MTSignificant

effect

onlower

body

muscularstrength

Walker,2010

[25]

ES:−0

.36

1mile

walktestModifiedsit-and-reach

test

Kilo

calories

expended

per

kilogram

/day

LT(12months)

Significant

effect

onlower

body

muscularstrength

270 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

Repeatedtim

edchairstands

Tim

eengagedin

strengthening

andstretching

exercise

(min/week)

ES:−0

.41

Aerobic

fitness

(VO2m

axin

ml/k

g/min)

LT(18months)

Significant

effect

onlower

body

muscularstrength

Low

erbody

muscularstrength

(tim

edchair

stands

ins)

ES:−0

.51

Wanner,2009

[61]

Switzerland

Participants[1,531]recruitedfrom

thegeneralpopulatio

nC

Generic

HEEXP1

CTadvice

?4-item

derivedfrom

officialPA

monitoring

inSwisspopulation

Accelerom

eter

MPA

/VPA

(min/

week)

ST/LTNosignificanteffect

onMPA

andVPA

Werkm

an,2010

[56]

The

Netherlands

Recentretirees[415](55–

65)

recruitedfrom

pre-retirem

ent

workshops

CGeneric

HEEXP1

CTadvice

Yes

Dutch

versionof

thePA

Scale

forthe

Elderly

(PASE)[96]

Daily

routinePA

(min/week)


(12and

24months)

ondaily

routinePA

,recreatio

n/sports

PA,

Σhouseholdactiv

ities

(0–6

)andPA

SE-score

Recreation/sports

PA(m

in/week)

Σhousehold

activ

ities

(0–6

)PA

SE-score

(0–

400)

Winett,2007

[34]

USA

Participants[1071]

recruited

from

churches

CNointerventio

n?

Pedom

eter

Daily

step

counts

LT(7

and16

mon

ths)

Significant

effect

onPA

inEXP2comparedto

C

EXP1CTadvice

ES(7

months):0.23

EXP2CTadvice+

church

support

ES(16months):0.27

B.Fat

consum

ption

BlairIrvine,

2004

[71]

USA

Participants[517]recruited

from

alargehospital

CNointerventio

nYes

21-item

DietHabits

Questionnaire

Fat

eatin

ghabits/

behavior

score

STSignificant

effectson

fateatin

ghabits/behavior

EXP1CTadvice

ES(1-m

onth):−0

.49

ES(2-m

onths):−0

.18

Dutton,

2008

[77]

USA

Sedentary

wom

en[280]

recruitedfrom

thegeneral

populatio

n

CGeneric

HE

Yes

NationalCancerInstitu

teScreeners

Fat

intake

(en%

)MT/LTNosignificanteffects

onfatintake

EXP1Self-help

book-

let

EXP2CTadvice

Elder,2

005[26]

USA

Latinas

[357]recruited

from

thegeneralpopulatio

nC

Generic

HE

Yes

Nutritio

ndata

system

:24

hdietary

recallinterview

%calories

from

fat

STSignificant

effectson

totaland

saturatedfatintake

inEXP2

comparedto

EXP1

Elder,2

006[39]

EXP1CTadvice

Total

andsaturated

fatintake

(g)

LTNosustainedsignificanteffects

ann. behav. med. (2012) 44:259–286 271

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

EXP2CTadvice+

Promotoras

Fries,2005

[70]

USA

Participants[754](18–72)recruitedfrom

physicianpractices

CNointerventio

n?

Fat

andfiberbehavior-related

questio

n-naire

Score

from

0–3

STSignificant

effect

ondietaryfat

behavior

EXP1CTadvice

ES:−0

.41

MTSignificant

effect

ondietary

fatbehavior

ES:−0

.29

LTSignificant

effect

ondietary

fatbehavior

ES:−0

.23

Gans,2009

[75]

USA

Participants[1841]

with

low

income,

recruitedfrom

waitin

groom

sof

public

health

clinics

CGeneric

HE

Yes

Adapted

FoodHabits

Questionnaire

Fat

intake

(Food

Habits

Questionnaire

score:

low

score0

high

prevalence

fat-

lowering

behavior,thus

lower

fatintake)

MTSignificant

effect

onfat

intake

inEXP2andEXP3

comparedto

C

EXP1CTadvice

(at

once)

ES(EXP2-C):−0

.31

EXP2CTadvice

(in4

installm

ents)

ES(EXP3-C):−0

.31

EXP3EXP2with

retailo

ring

Jacobs,2004

[95]

USA

Wom


from

nutrition

andPA

program

(WISEWOMAN)

CGeneric

HE

Yes

54-item

Dietary

risk

assessment

Score

from

54-item

scale:

0–108not

very

atherogenic

(0)to

very

ath-

erogenic

diet

(108)


onsaturatedfatand

cholesterolintake

EXP1CTadvice

Kroeze,

2008

[72]

The

Netherlands

Participants[442](18–65)

recruitedfrom

companies

andgeneralpopulatio

n

CGeneric

HE

Yes

104-item

FFQ

Total

fatintake

(g/

day,en%)

STSignificant

effectson

total

fatandsaturatedfatintake

inEXP1comparedto

C

EXP1CTadvice

(interactiv

eCD-ROM)

Saturated

fatintake

(g/day,%en)

ES(total

fat):−0

.31

EXP2CTadvice

(print)

ES(saturated

fat):−0

.22

STSignificant

effectson

totalfatintake

amongrisk

consum

ersin

EXP1

comparedto

C

ES:−0

.41

STSignificant

effectson

totalfatin

EXP2comparedto

C

ES:−0

.23

272 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

STSignificant

effectson

totalfatand

saturatedfatintake

amongrisk

consum

ersin

EXP2comparedto

C

ES(total

fat):−0

.49

ES(saturated

fat):−0

.42

MTSignificant

effect

ontotalfatand

saturatedfatintake

amongrisk

consum

ersin

EXP2comparedto

C

ES(total

fat):−0

.53

ES(saturated

fat):−0

.54

Kroeze,

2008

[73]

The

Netherlands

Participants[574](18–65)recruited

from

largecompanies

andthe

generalpopulatio

n

CGeneric

HE

Yes

104-item

FFQ

Total

fatintake

(g/

day)

STSignificant

effect

onaw

areness

offatintake

inEXP1andEXP3

comparedto

C

EXP1CTadvice

(personal)

1-item

Saturated

fatintake

(g/day)

ES(EXP1):0.30

EXP2CTadvice

(personal–

norm

ative)

Self-ratedfatintake

(awareness)

(−2

to+2)

ES(EXP3):0.41

EXP3CTadvice

(personal–

norm

ative–actio

n)

STSignificant

effect

onfatintake

andsaturatedfatintake

inEXP3

comparedto

C

ES(fat

intake):−0

.52

ES(saturated

fatintake):−0

.46

MTSignificant

effect

onfatintake

inEXP1,


C

ES(EXP1):0.34

ES(EXP2):0.55

ES(EXP3):0.53

MTSignificant

effect

onsaturated

fatintake

inEXP3comparedto

C

ES:−0

.51

MTSignificant

effect

onfatand

saturatedfatintake

among

underestim

atorsin

EXP3

comparedto

C

ES(fat

intake):−0

.64

ES(saturated

fatintake):-0.63

NiMhurchu,

2010

[53]

New

Zealand

Participants[1,104]recruited

from

aselectionof

custom

ers

registered

tousetheShop‘N

Go

System

andin-store

andcommunity

-based

recruitm

ent

CNointerventio

n?

Electronicscanner(Shop‘N

Gosystem

)%

ofenergy

from

saturatedfatsin

purchases


onsaturatedfatpurchases

EXP1CTadvice

EXP2CTadvice+

discount

ann. behav. med. (2012) 44:259–286 273

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

EXP3Discount

Oenem

a,2008

[60]

The

Netherlands

Participants[2,159](>30)recruited

from

onlin

eresearch

panel

CNointerventio

nYes

35-item

FFQ

Saturated

fatintake

(fat

points/day

from

0to

80)

STSignificant

effect

onsaturatedfatintake

EXP1CTadvice

1-item

Self-ratedintake

(scale

from

−2to

+2)

ES:−0

.16

STSignificant

effect

onsaturatedfat

intake

inat-riskgroup(those

who

didnotcom

plywith

therecommended

levelof

saturatedfatintake

atbase-

line)

ES:−0

.23

Prochaska,

2005

[30]

USA

Sedentary

prim

arycare

patients

[5,407]at

risk

forat

leastone

ofthetarget

behaviorsrecruited

from

prim

arycare

practices

(Prochaska,2005-458).

CNointerventio

nYes

22-item

Dietary

BehaviorQuestionnaire

Score

onsubscales:

avoidance

substitution

modification

Amongsedentaryprimarycare

patients

Prochaska,

2004

[29]

Parentsof

teenagers[2,460]at

risk

forat

leastoneof

thetarget

behaviorsrecruitedfrom

schools

(Prochaska,2005-486)

EXP1CTadvice

LT(12mon

ths)

Significant

effects

onavoidance,modificationand

substitution

ES(avoidance):0.24

ES(m

odification):0.18

ES(substitu

tion):0.22

LT(24mon

ths)

Significant

effectson

avoidance

ES(avoidance):0.27

ES(substitu

tion):0.20

Amongparentsof

teenagers

LT(12mon

ths)

Significant

effects

onavoidanceandsubstitution

ES(avoidance):0.16

ES(substitu

tion):0.19

LT(24mon

ths)

Significant

effects

onavoidanceandsubstitution

ES(avoidance):0.18

ES(substitu

tion):0.23

Smeets,2007

[33]

The

Netherlands


65)

recruitedfrom

companies

andthegeneralpopulatio

n

CGeneric

HE

Yes

FFQ

Fat

intake

(g)

MTSignificant

effect

onfatintake

inEXP1comparedto

C

DeVries,2008

[32]

EXP1CTadvice

(once

deliv

ered

in3months(Smeets,

2007)

Saturated

fatintake

(g)

ES:−0

.12

274 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

EXP2CTadvice

(3tim

esdeliv

ered

in9months(D

eVries,

2008)

%compliant

toguidelines

forsaturatedfat

intake

LTSignificant

effect

on%

compliant

toguidelineon

saturatedfatintake

inEXP2comparedto

C

ES:−0

.18

Sternfeld,2009

[36]

USA


from

administrationoffices

ofalargehealthcare

organizatio

n

CNointerventio

nYes

Dietquestio

nnaire

based

onBlock

FoodQuestionnaire

Saturated

fats(g/

day)

STSignificant

effect

onsaturated

andtransfatintake

EXP1CTadvice

Trans

fats(g/day)

STSignificant

effect

onsaturated

andtransfatintake

amongthose

who

chosethefats/sugar

path

oftheinterventio

n

MTSignificant

effect

onsaturated

andtransfatintake

ES:N/A

De Bourdeaud-

huij,

2007

[74]

Belgium


from

companies

CNointerventio

nYes

48-item

FFQ

Total

fatintake

(g/

day)

MTSignificant

effect

onenergy

from

fatandtotalfatintake

inEXP1

comparedto

C1andC2

EXP1CTadvice

onPA

andfatintake

sequentially

deliv

ery

Energyfrom

fat(%

)

EXP2CTadvice

onPA

andfatintake

simultaneously

deliv

ered

Fat

intake

(seperatefood

groups)

(g/day)

EXP3CTadvice

only

onfatintake

EXP1comparedto

C1

ES(energyfrom

fat):−0

.37

ES(total

fatintake):−0

.32

EXP1comparedto

C2

ES(energyfrom

fat):−0

.13

ES(total

fatintake):0.09

MTSignificant

difference

inenergy

from

fatbetweenC1andC2

ES:−0

.24

MTSignificant

effect

onenergy

from

fatandtotalfatintake

among

participants

who

meet/d

onotmeetfatintake

recommendatio

nsin

EXP1compared

toC1andC2

ES:N/A

Walker,2009

[24]

USA

Wom


from

thegeneralpopulatio

nC

Generic

HE

Yes

Web-based

Block98

FFQ

%calories

from

fat

LT(6

mon

ths)

Significant

effect

on%

calories

from

saturatedfat

Walker,2010

[25]

EXP1CTadvice

%calories

from

saturatedfat

ES:−0

.30

ann. behav. med. (2012) 44:259–286 275

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

LT(12mon

ths)Significant

effecton

%calories

from

saturatedfat

ES:−0

.49

LT(18mon

ths)Significant

effecton

%calories

from

saturatedfat

ES:−0

.56

Werkm

an,2010

[56]

The

Netherlands


65)

recruitedfrom

pre-retirem

ent

workshops

CGeneric

HE

EXP1CTadvice

Yes

Sem

iquantitative

Fat

intake

(en%

)LTNosignificanteffectson

fatintake

FFQ

Winett,2007

[34]

USA


from

churches

CNointerventio

nYes

Block98

FFQ

%kcal

from

fat

LTNosignificanteffectson

fatintake

EXP1CTadvice

Foodshopping

receipts

EXP2CTadvice+

church

support

C.Fruitandvegetableconsum

ption

Alexander,

2010

[80]

USA


65)

recruitedfrom

health

plans

CGeneric

HE

Yes

16-item

FFQ

byNationalCancer

Institu

teFruitand

vegetables

intake

(servings

inpastmonth)

LTSignificant

effect

onfruitand

vegetables

intake

inthepast

month

inEXP2comparedto

C

EXP1CTadvice

2-item

Fruitand

vegetables

intake

(servings

onatypicalday)

ES:0.10

EXP2CTadvice+

personal

counselin

gLTSignificant

effect

onfruitand

vegetables

intake

onatypicalday

inEXP1andEXP2comparedto

C

ES(EXP1):0.08

ES(EXP2):0.13

BlairIrvine,

2004

[71]

USA


from

alargehospital

CNointerventio

nYes

5-A-D

ayScreener

Fruitand

vegetables

consum

ption

score

STSignificant

effectson

fruit

andvegetables

consum

ption

EXP1CTadvice

ES(1

month):0.21

ES(2

months):0.04

Dutton,

2008

[77]

USA

Sedentary

wom

en[280]

recruitedfrom

thegeneral

populatio

n

CGeneric

HE

Yes


teScreeners

Fruitand

vegetables

intake

(daily

servings)

MT/LTNosignificanteffectson

fruitandvegetables

intake

EXP1Self-help

book-

let

EXP2CTadvice

Gans,2009

[75]

USA

Participants[1,841]with

low

income,recruitedfrom

waitin

groom

sof

public

health

clinics

CGeneric

HE

?7-item


tefruit

andvegetables

screener

assessment

tool

Fruitand

vegetables

intake

(servings/day)

MTSignificant

effect

onfruitand

vegetables

intake

inEXP1and

EXP2comparedto

CandEXP3

ES(EXP1-C):0.18

276 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

EXP1CTadvice

(at

once)

EXP2CTadvice

(in4

installm

ents)

ES(EXP1-EXP3):0.20

EXP3EXP2with

retailo

ring

ES(EXP2-C):0.12

ES(EXP2-EXP3):0.14

LTSignificant

effect

onfruit

andvegetables

intake

inEXP2

comparedto

C

ES:0.17

Heimendinger,

2005

[81]

USA

Participants[3.402](18+

)recruited

throughCancerInform

ation

Service

offices(callers)

CGeneric

HE(1

booklet)

Yes

1-item

Fruitand

vegetables

intake

(daily

servings)

LTSignificant

effect

onfruitand

vegetables

intake

inEXP2and

EXP3comparedto

C

EXP1CTadvice

(1booklet)

7-item

FFQ

ES:N/A

EXP2CTadvice

(4booklets)

EXP3CTadvice

(4booklets+retailo

ring)

Kreuter,2005

[79]

USA

Low

er-incom

eAfrican–A

merican

wom

en[1,227](18–65)from

10urbanpublic

health

centers

CNointerventio

nYes

13-item

FFQ

Fruitand

vegetables

intake

(servings/day)

MTNosignificanteffectson

fruitandvegetables

intake

EXP1CTadvice

tailo

redon

behavioralconstructs

LTSignificant

effect

onfruitand

vegetables

intake

inEXP3

comparedto

othergroups

EXP2CTadvice

tailo

redon

cultu

ral

factors

LTSignificant

effect

amonglower

motivated

wom

enon

fruitand

vegetables

intake

inEXP3

comparedto

othergroups

EXP3EXP1+EXP2

ES:N/A

Nitzke,2007

[78]

USA


24)

recruitedfrom

non-college

venues

CNointerventio

nYes

5A

Day

Screener

Fruitand

vegetables

intake

(servings)

MTSignificant

effectson

fruitand

fruitandvegetables

intake

and

perceivedvegetables

intake

ES

(fruitintake):0.12

Do,

2008

[31]

EXP1CTadvice

2-item

Perceived

daily

intake

ES(fruitandvegetables

intake):0.14

26-item

FFQ

Variety

infruitand

vegetables

intake

(num

berof

differentitems

consum

edatleast

once

amonth,

regardless

ofam

ount)

ES(perceived

vegetables

intake):0.08

LTSignificant

effectson

fruitandfruit

andvegetables

intake

andperceived

intake

ofvegetables

andfruitand

vegetables

ann. behav. med. (2012) 44:259–286 277

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

ES(fruitintake):0.15

ES(fruitandvegetables

intake):0.13

ES(perceived

vegetables

intake):0.11

ES(perceived

intake

fruitand

vegetables):0.12

LTSignificant

effectson

varietyin

fruitandvegetables

consum

ption,

consum

ptionof

seasonal

fruits,

juices

andhigh

beta-carotenevegeta-

bles

ES(variety

fruit)>1.00

ES(variety

vegetables)>1.00

ES(seasonalfruitsconsum

ption)

>1.00

ES(juicesconsum

ption)

>1.00

ES(highbeta-carotenevegetables

consum

ption)>1.00

Prochaska,

2005

[30]

USA

Sedentary

prim

arycare

patients

[5,407]at

risk

forat

leastone

ofthetarget

behaviorsrecruited

from

prim

arycare

practices

CNointerventio

nYes

22-item

Dietary

BehaviorQuestionnaire

Score

onsubscale

fruitand

vegetables


onfruitand

vegetables

inboth

studysamples

Prochaska,

2004

[29]

Parentsof

teenagers[2,460]at

risk

forat

leastoneof

thetarget

behaviorsrecruitedfrom

schools

EXP1CTadvice

Smeets,2007

[33]

The

Netherlands


65)

recruitedfrom

companies

and

thegeneralpopulatio

n

CGeneric

HE

Yes

FFQ

Fruitintake

(pieces/

day)

MTSignificant

effect

onfruitintake

amongparticipantswho

didnotmeet

recommendatio

nsforanybehavior

inEXP1comparedto

C

DeVries,2008

[32]

EXP1CTadvice

(once

delivered

in3months

(Smeetsetal.))

Vegetablesintake

(g/day)

ES:0.30

EXP2CTadvice

(3tim

esdeliv

ered

in9months(D

eVries

etal.))

%compliant

toguidelines

for

fruitintake

(at

least2pieces

offruitfor7days/

week)

MTSignificant

effect

onvegetables

intake

inEXP1comparedto

C

Vegetablesintake

ES:0.10

%compliant

toguidelines

for

vegetables

intake

(atleast200gof

vegetables/day

for7days/week)

LTSignificant

effect

onfruitintake

and%

compliant

tofruitguidelines

inEXP2comparedto

C

ES:0.35

ES:0.24

278 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

LTSignificant

effect

onvegetable

intake

and%

compliant

tovegetables

guidelines

inEXP2comparedto

C

ES:0.32

ES:0.08

Sternfeld,2009

[36]

USA


from

administrationoffices

ofalargehealthcare

organizatio

n

CNointerventio

nYes

Dietquestio

nnaire

basedon

Block

Food

Questionnaire

Fruitand

vegetables

intake

(cup-

equivalents/day)

STSignificant

effect

onfruitand

vegetables

intake

EXP1CTadvice

STSignificant

effect

onfruitand

vegetables

intake

amongthosewho

chosethefruitandvegetables

path

oftheinterventio

n

MTSignificant

effect

onfruitand

vegetables

intake

ES:N/A

Van

Keulen,

2011

[65]

The

Netherlands


70)

recruitedfrom

generalpractices

C1Nointerventio

nYes

16-item

shortquestio

nnaire

Fruitintake

(servings/day)

MTSignificant

effect

onfruit

intake

ofEXP1compared

toC1andC3

C2Coaching

Vegetables(g/day)

ES(EXP1-C1):0.19

C3C2+EXP1

ES(EXP1-C3):0.18

EXP1TCadvice

MTSignificant

effect

onvegetables

intake

ofEXP1comparedto

C1and

C3

ES(EXP1-C1):0.10

ES(EXP1-C3):0.12

LT(~11

months)

Significant

effect

onfruitintake

ofEXP1

comparedto

C1

ES:0.32

LT(~11

months)

Significant

effect

onvegetables

intake

ofEXP1comparedto

C1,

C2andC3

ES(EXP1-C1):0.33

ES(EXP1-C2):0.24

ES(EXP1-C3):0.19

LT(~18

months)

Significant

effect

onfruitintake

ofEXP1compared

toC1,

C2andC3

ES(EXP1-C1):0.35

ES(EXP1-C2):0.22

ES(EXP1-C3):0.24

LT(~18

months)

Significant

effect

onvegetables

intake

ofEXP1

comparedto

C1

ann. behav. med. (2012) 44:259–286 279

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

ES:0.27

Walker,2009

[24]

USA

Wom

en[225](50–69)

recruitedfrom

the

generalpopulatio

n

CGeneric

HE

Yes

Web-based

Block98

FFQ

Fruitand

vegetables

intake

(daily

servings)

LT(6

months)

Significant

effect

onfruitandvegetables

intake

Walker,2010

[25]

EXP1CTadvice

ES:0.22

LT(12months)

Significant

effect


intake

ES:0.41

LT(18months)

Significant

effect


intake

ES:0.40

Werkm

an,2010

[56]

The

Netherlands


65)

recruitedfrom

pre-retirem

ent

workshops

CGeneric

HE

Yes

Sem

iquantitative

Fruitand

vegetables

intake

(g/M

J)


onfruit

andvegetables

intake

EXP1CTadvice

FFQ

Winett,2007

[34]

USA


from

churches

CNointerventio

nYes

Block98

FFQ

Fruitand

vegetables

intake

(g/

1000

kcal)

LT(7

mon

ths)

Significant

effect


intake

inEXP1comparedto

C

EXP1CTadvice

Foodshopping

receipts

ES:0.44

EXP2CTadvice+

church

support

Significant

effect

onfruitand

vegetables

intake

inEXP2

comparedto

C

ES:0.57

LT(16mon

ths)

Significant

effect


intake

inEXP1comparedto

C

ES:0.12

Significant

effect

onfruitand

vegetables

intake

inEXP2

comparedto

C

ES:0.32

D.Other

dietarytopics

Adachi,2007

[28]

Japan

OverweightJapanesewom

en[205]

recruitedfrom

thegeneral

population(Adachi,2007)

C1Self-help

booklet

?Weightparameters

BMI(kg/m

2)

STSignificant

effect

onBMI

inEXP1&

EXP2comparedto

C1&

C2am

ongoverweigh

Japanese

wom

en

Tanaka,2010

[27]

Overw

eightJapanese

men

[51]

recruitedfrom

thegeneral

populatio

n(Tanaka,2010)

C2C+self-monito

ring

ofweightand

walking

BMI

EXP1CTadvice

ESEXP1-C1:

−0.60

EXP2f

CTadvice+

self-monito

ring

ofweightandwalking

ESEXP1-C2:

−0.48

ESEXP2-C1:

−0.77

ESEXP2-C2:

−0.66

280 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

STSignificant

effect

onBMIin

EXP2comparedto

C1among

overweigh

Japanese

men

BMI

ESEXP2-C1:

−0.69

MTSignificant

effect

onBMI

inEXP2comparedto

C1&

C2am

ongoverweightJapanese

wom

en

BMI

ESEXP2-C1:

−0.70

ESEXP2-C2:

−0.58

LTSignificant

effect

onBMIin

EXP2comparedto

C1andC2

amongoverweightJapanese

wom

en

BMI

ESEXP2-C1:

−0.59

ESEXP2-C2:

−0.55


onBMIin

EXP2

comparedto

C1amongoverweigh

Japanese

men

Elder,2

005[26]

USA

Latinas

[357]recruited

from

thegeneralpopulatio

nC

Generic

HE

Yes

Nutritio

ndata

system

(NDS):24

hdietaryrecallinterview

Total

energy

intake

(kcal)

ST/LTNosignificanteffects

Elder,2

006[39]

EXP1CTadvice

Totalcarbohydrates

intake

(g)

EXP2CTadvice+

prom

otoras

Fries,2005

[70]

USA


recruitedfrom

physician

practices

CNointerventio

n?

Fat

andfiberbehavior-related

questio

n-naire

Score

from

0–3

STSignificant

effect

onfiberbehavior

EXP1CTadvice

ES:−0

.35

MTSignificant

effect

onfiberbehavior

ES:−0

.24

Haapala

2009

[55]

Finland

Overw

eightparticipants

[125](25-44)from

the

generalpopulatio

n

CGeneric

HE

Weight

parameters

Bodyweight(kg)

LTSignificant

effect

onweightloss

andwaistcircum

ference

EXP1CTadvice

%Weightloss

ES(w

eightloss):−0

.14

Waistcircum

ference

ES(w

aistcircum

ference):−0

.18

Kroeze,

2008

[72]

The

Netherlands


recruitedfrom

companies

andgeneralpopulatio

n

CGeneric

HE

Yes

104-item

FFQ

Energyintake

(MJ/

day)

STSignificant

effectson

energy

intake

inEXP1andEXP2comparedto

C

EXP1CTadvice

(CD-

ROM)

ES:−0

.28

EXP2CTadvice

(print)

ES:−0

.38

ann. behav. med. (2012) 44:259–286 281

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

STSignificant

effectson

energy

intake

amongrisk

consum

ersin

EXP1

andEXP2comparedto

C

ES:−0

.50

ES:−0

.66

MTSignificant

effectson

energy

intake

amongrisk

consum

ersin

EXP1

andEXP2comparedto

C

ES:−0

.68

ES:−0

.44

MTSignificant

effectson

energy

intake

inEXP2comparedto

C

ES:−0

.26

Poddar,2010

[82]

USA

College

students[294]recruited

from

aland

grant,research-intensive

university

CNointerventio

n?

7dayfood

records

Average

daily

dairy

servings


EXP1CTadvice

Prochaska,

2008

[54]

USA

Participants[1400]

atrisk

forat

leastonerisk

behavior

(exercise,

stress,BMI>25

kg/m

2andsm

oking)

recruitedfrom

amajor

medical

university

CHealth

Risk

Assesment

Yes

Self-report

%above/below

BMI0

25kg/m

2MTNosignificanteffect

onBMI

EXP1C+coaching

EXP2C+TTM-based

feedback

Rothert,2006

[38]

USA

Overw

eightandobese(BMI0

27–4

0kg/m

2)

participants[2862]

recruitedfrom

health

care

deliv

erysystem

CGeneric

HE

?Self-report

%of

baselin

eweightlost

MT/LTSignificant

effect

on%

ofbaselin

eweightlost

EXP1CTadvice

ES>1.00

Sternfeld,2009

[36]

USA

Participants[787]recruitedfrom

administrationofficesof

alarge

healthcare

organizatio

n

CNointerventio

nYes

Dietquestio

nnaire

basedon

Block

Food

Questionnaire

Added

sugars

(g/

day)

ST/M

TNosignificanteffects

onaddedsugars

EXP1CTadvice

Walker,2009

[24]

USA

Wom


from

thegeneralpopulatio

nC

Generic

HE

Yes

Web-based

Block98

FFQ

Whole-grain

intake

(daily

servings)

LTNosignificanteffects

EXP1CTadvice

Bioelectrical

impedanceanalysis

%Bodyfat

Weightparameters

BMI(kg/m

2)

Werkm

an,2010

[56]

The

Netherlands

Recentretirees[415](55–65)

recruitedfrom

pre-retirem

ent

workshops

CGeneric

HE

Yes

Weightparameters

Waist

circum

ference

(cm),BMI(kg/

m2)

LTSignificant

effect

onwaist

circum

ferenceam

ongmen

with

low

education

EXP1CTadvice

Sem

iquantitative

Energyintake

(MJ/

day)

FFQ

282 ann. behav. med. (2012) 44:259–286

Tab

le2(con

tinued)

Firstauthor(s)a

[reference

number]

Country

Study

populatio

n[N]

Interventio

nmodes

bValidated

questio

nnaire

Outcomemeasurement

instruments

Outcome

measurementunits

Resultscandeffect

size

dat

short(ST),

medium

(MT),or

long

term

(LT)e

Winett,2007

[34]

USA


from

churches

CNointerventio

nYes

Block98

FFQ

Fiber

intake

(g/

1,000kcal)

LT(7

mon

ths)

EXP1CTadvice

Weightparameters

Weight(lb)

Significant

effect

onfruitand

vegetables

intake

inEXP1

comparedto

C

EXP2CTadvice+

church

support

Foodshopping

receipts

ES:

0.35

Significant

effect

onfruitand

vegetables

intake

inEXP2

comparedto

C

ES:

0.44

Significant

effect

onweight

InEXP2comparedto

C

ES:

0.21

LT(16mon

ths)

Significant

effect

onfruitand

vegetables

intake

inEXP1

comparedto

C

ES:

0.20

Significant

effect

onfruitand

vegetables

intake

inEXP2

comparedto

C

ES:

0.28

Ccontrolcond

ition

,EXP1experimentalcond

ition

1,EXP2experimentalcond

ition

2,EXP3experimentalcond

ition

3,ESeffectsize,[12

5]12

5participants,(50

–69)

50to

69yearsold,

HEhealth

education,

(L/M

/V/M

V)PA

(low

-/mod

erate-/vigorou

s-/m

oderateto

vigo

rous-intensity)ph

ysical

activ

ity,CTcompu

ter-tailo

red,

VO2m

axmaxim

alox

ygen

uptake,METmetabolic

equivalent,FFQ

food

frequencyqu

estio

nnaire,IPA

QInternationalPhy

sicalActivity

Questionn

aire,SQ

UASH

Sho

rtQuestionn

aire

Assessing

Health

-enh

ancing

physicalactiv

ity,A

QuA

AActivity

Questionn

aire

for

Ado

lescentsandAdu

lts,BMIbo

dymassindex,

N/A

notavailable

aSom

epu

blications

repo

rted

onthecharacteristicsandeffectsof

thesameinterventio

nandarethereforeclusteredin

onecell

bNointerventio

nequalsno

info

inthe20

06review

;genericHEequalsgenericinfo

inthe20

06review

cSignificant

effect

0effect

that

reachedstatistical

sign

ificance

(p<0.05

)dEffectsizes

werecalculated

whenmeanandSDwereavailableatpo

st-testand

asign

ificanteffectinfavo

rof

tailo

ring

hadbeen

foun

d.ESisinterpretedaccordingto

Coh

en’sgu

idelines

[67]

based

onan

applicationin

Dolan

etal.[69];cutoffvalues

of0.2–0.50sm

all,0.5–

0.80mod

erate,and>0.80largeeffects

eSho

rtterm

(ST),<3mon

ths;medium

term

(MT),3–

6mon

ths;long

term

(LT),>6mon

ths

fIn

thestud

yof

Tanakaet

al.[27],on

lyEXP2versus

theself-helpbo

okletwas

tested

ann. behav. med. (2012) 44:259–286 283

Open Access This article is distributed under the terms of the Crea-tive Commons Attribution License which permits any use, distribution,and reproduction in any medium, provided the original author(s) andthe source are credited.

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