Supporting Engagement, Adherence, and Behavior Change in Online Dietary Interventions

Systematic Review Supporting Engagement, Adherence, and Behavior Change in Online Dietary Interventions Claire Young, MAIT, GradDip (Psych), BEng (Hons)1; Sara Campolonghi, MClinPsych1; Stephanie Ponsonby, BA (Hons)1; Samantha L. Dawson, MHumNutr1,2; Adrienne O’Neil, PhD3; Frances Kay-Lambkin, PhD4; Sarah A. McNaughton, PhD5; Michael Berk, PhD1; Felice N. Jacka, PhD1,6,7 ABSTRACT Introduction: Poor diet is a leading cause of death and disease globally. This epidemic requires effective and accessible interventions to stop the increasing number of diet-related deaths and the health and economic impacts of diet-related disease. Online interventions provide flexibility and accessibility. With the ubiquitous use of smartphones, they can be intertwined with daily activities such as shopping and eating. The aim of this review is to determine what features and behavior change techniques employed in online dietary interventions for adult populations promoting dietary behavior change. Methods: The researchers conducted a systematic search of Cumulative Index of Nursing and Allied Health, Cochrane Library, Global Health, MEDLINE, PsychINFO, and psychological and behavioral sciences electronic bibliography databases, and specialist electronic health (e-health) journals from database inception to January, 2018. Studies were included if they were randomized controlled trials of online dietary interventions with active comparator conditions in adult populations, and with reported dietary change measures. A quality score was applied to each study calculated by a developed scoring system. The review analyzed intervention dietary change measures, attrition (nonuse and dropout), engagement (metrics and intensity of use), adherence (defined as compliance to the treatment protocol), behavior change techniques employed to achieve dietary change, and techniques employed in successful (those who achieved significant results in the targeted dietary behavior) vs unsuccessful interventions as reported by the studies. Results: A total of 21 studies composed of a total of 7,455 adults and reporting on 19 different e-health interventions were included from 1,237 records. These studies targeted dietary change as measured by reduced energy intake (5) or changes in specific dietary components (15) and overall diet quality (4). Dietary change was a behavior target in general healthy populations (12) and for managing diseases such as obesity and cardiovascular disease (7), or for improving quality of life for those with chronic conditions (1). Improvements in dietary behavior were seen in 14 of the 19 interventions reported. Discussion: The results suggest that online interventions can be successful in achieving dietary behavior change across a range of defined populations. However, disparate reporting of engagement and limited reporting of nonuse attrition rates limited the analysis of which behavior change techniques were most effective in achieving this change. Implications for Research and Practice: The results of this review support the potential of online and smartphone dietary interventions as a method to achieve change in diet in defined populations. However, further work needs to be done in examining how users engage with interventions, and thus which behavior change techniques are most effective.

1

Food and Mood Centre, IMPACT Strategic Research Centre, School of Medicine, Deakin University, Barwon Health, Geelong, Australia Early Life Epigenetics Group, Murdoch Children’s Research Institute, Royal Children’s Hospital, Parkville, Victoria, Australia 3 Centre for Mental Health, Melbourne School of Population and Global Health, Melbourne, Australia 4 School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia 5 Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Victoria, Australia 6 Centre for Adolescent Health, Murdoch Children’s Research Institute, Victoria, Australia 7 Black Dog Institute, New South Wales, Australia Conflict of Interest Disclosure: The authors have not stated any conflicts of interest. Address for correspondence: Claire Young, MAIT, GradDip (Psych), BEng (Hons), Food and Mood Centre, IMPACT SRC, Deakin University, Health, Education, and Research Bldg (HERB) Level 3, PO Box 281, Geelong, VIC 3220, Australia; E-mail: [email protected] Ó 2019 Published by Elsevier Inc. on behalf of Society for Nutrition Education and Behavior. https://doi.org/10.1016/j.jneb.2019.03.006 2

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Key Words: adherence, behavior change techniques, behavior change, engagement, Internet (J Nutr Educ Behav. 2019; 51:719−739.) Accepted March 10, 2019.

INTRODUCTION Poor diet is the leading cause of preventable death, resulting in 1 in 5 deaths globally.1 It is a well-established risk factor for chronic conditions and noncommunicable diseases such as obesity and cardiovascular disease as well as common mental disorders (depression and anxiety). Conversely, specific positive dietary patterns such as the Mediterranean diet have been associated epidemiologically with protective health effects.2 Behavioral interventions aim to change health by targeting particular behaviors.3 They can be effective in changing eating behaviors, particularly when using multiple behavior change techniques (BCTs).4 These particular techniques are used in these interventions based on psychological theories of the determinants of behavior. In 2013, Michie et al5 published a hierarchal taxonomy of BCTs to standardize definitions of commonly applied BCTs. Achieving dietary improvement with effective online behavioral interventions at a population level could reduce the global burden of disease. The potential benefits of delivering behavioral interventions online include reach, accessibility, scalability, and cost-effectiveness.6,7 Searching for health information remains 1 of the most widely employed uses of the Internet, and searches for diet and nutrition are universally popular.8 Data from the US, Canada, Norway, and Australia show that the Internet is the most popular source of dietary and nutrition information among adult populations.9−12 Internet and smartphone (e-health and mobile health) technologies may be particularly useful as health interventions because they can be seamlessly integrated into daily activities.6,7,13,14 In relation to diet, this could mean users accessing intervention content or activities while shopping, cooking, or dining out. Communication technologies also increase opportunities to send reminders and notifications to participants to aid adherence to interventions. 7,15

However, online and smartphone-based interventions can be limited in their ability to engage and retain participants; high attrition is a well-recognized issue in the field. 16−18 High attrition rates often affect the ability to assess the efficacy and effectiveness of online interventions. Metrics of the success of online interventions need to include uptake, accessibility, and robust estimates of “effective engagement.”7 In e-health literature, engagement was traditionally measured by simple usage metrics such as module completion with little analysis of how users interacted with the intervention. Studies tended to analyze attrition, which was often counted as when a user had not completed a required measure, independent of the intervention content. Previous online interventions required further evaluation to determine their effectiveness among the target demographic, to understand which strategies worked best for enhancing user engagement and to determine requisite levels of engagement for optimal outcomes. In particular, understanding what works, and how, is essential to inform future interventions aiming to change dietary behaviors. Previous systematic reviews examined the effectiveness of online dietary Internet interventions as part of a broader review of Internet health behavior interventions19 and computer tailored dietary interventions,20,21 specific types of online dietary interventions such as adaptive electronic learning,22 or the effectiveness of computer-based dietary or lifestyle interventions in specific populations such as interventions targeted at obesity and diabetes.23 Those reviews examined literature up to 2011. To date, there have been no systematic reviews specifically investigating randomized control trials (RCTs) of Internet-based dietary interventions across content approaches (nontailored or tailored) and with active comparator conditions. The aim of this systematic review was to identify existing studies examining the efficacy or effectiveness of online dietary interventions

against active controls, user engagement, and adherence, and the behavior change techniques employed to achieve dietary behavior change. In particular, the review aimed to identify the features or designs that were successful in achieving dietary behavior change.

METHODS Databases Searched and Search Terms This systematic review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 reporting method and was registered with the International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42017062716), 24 before commencement. A predefined search strategy was applied to identify literature from Cumulative Index of Nursing and Allied Health, Cochrane Library, Global Health, MEDLINE, PsychINFO, and psychological and behavioral sciences electronic bibliography databases, and specialist ehealth journals (the Electronic Journal of Health Informatics, the Journal of the International Society for Telemedicine and eHealth, and the Journal of Technology in Human Services). Relevant keywords relating to diet in combination with Medical Subject Heading terms and text words (diet or diet therapy or diet education or diet counseling or diet intervention or diet treatment and their variants) were used in combination with words relating to the intervention delivery mode (online or Web-based or Internet or mobile phone or smartphone or app and their variants). Supplementary Data 1 contains a full list of the search terms and combinations used for the MEDLINE searches. Searches were limited to studies published in peer-reviewed journals with at least an abstract published in English (published up to January, 2018). Potentially eligible abstracts were retrieved and independently assessed against the inclusion criteria by 2 authors (CY and SC) using the online systematic review

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019 tool Rayyan.25 Conflicts in inclusion or exclusion were resolved by a third author (SD).

Criteria for Study Inclusion To be eligible for inclusion, articles were required to describe an online intervention that aimed to change participants’ diets and measured dietary change as a quantitative primary or secondary outcome. Eligible studies were RCTs with active control conditions and adult participants (aged ≥ 18 years), in which treatment relied on digital delivery of the intervention and was not used in conjunction with face-to-face treatment or facilitated extensive communication with a clinician or dietitian. The researchers chose RCTs because they are the most rigorous test of intervention and outcome cause-and-effect relations.26 Articles were excluded if no quantitative measure of dietary change was reported. Length of follow-up was not part of inclusion criteria but was considered in the quality scoring of articles in the full article review stage; longer follow-up periods and studies reporting followup assessments for >80% of intention-to-treat samples were awarded more points. Full article screening included an assessment of study reporting quality and study design using a quality rating checklist of methodological quality for each article (Supplementary Data 2). The checklist was developed

from the electronic Consolidated Standards of Reporting Trials (e-CONSORT) guidelines for reporting e-health interventions,27 as well as recommendations from Michie et al7 and Kiluk et al,28 and resulted in an overall quality score for each included article. The maximum achievable quality score was 40. In addition to study quality, the checklist assessed engagement and adherence measures, and these measures against dietary outcomes. The quality assessment also considered risk for bias, including allocation concealment, balanced groups, blinding, treatment of missing data, and lack of intention-to-treat analysis. Two authors (CY and SC) performed data extraction using standardised data extraction forms during full article screening. The first author completed a subsequent review of all data extracted from studies included in the review to confirm consistent and complete reporting. These results were further reviewed by a third author (SD). Intervention engagement was examined using attrition rates and measurement methods. The researchers defined engagement as the analysis of attrition and use of the intervention, and adherence as compliance with the requirements of the intervention. Attrition rates were categorized in accordance with Eysenbach’s17 definitions of dropout attrition or nonusage attrition measures. Non-usage attrition was the rate of maximum non-usage of the intervention as

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defined by the study authors. Adherence analysis was based on study definitions of intervention compliance and the metrics of use employed to measure compliance. Intensity of use and usage vs outcomes, as recommended by e-CONSORT,27 were also included in this analysis (Table 1). Behavior change technique coding of each article was performed by a third author (SP) who completed training in the behavior change taxonomy. Any ambiguities of BCT coding were discussed among 3 authors (SP, SD, and CY) to achieve concordance. Analysis of the BCTs across all included studies was performed and included examination of BCTs implemented in successful (those that achieved a significant result in at least 1 targeted dietary behavior) vs unsuccessful interventions. Intervention features that were used to implement the BCTs were also examined as part of this analysis.

RESULTS Study Selection and Characteristics The Figure shows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses diagram. Database searches identified 3,412 records; an additional 3 records were cross-referenced from the search results. After duplicates were removed, 1,237 abstracts were screened and 1,148 were excluded for not meeting criteria. Full

Table 1. Key Components of Quality Criteria

Quality Criteria Validated dietary change measurement instrument (including online use validation) Effect size for quantitative outcomes Electronic health literacy measured at baseline Engagement and attrition Effective engagement defined Metrics of use Attrition analysis Outcomes reported against engagement measures Intervention adherence measures ITT analysis Follow-up of > 80% of ITT sample ITT indicates intention to treat.

Electronic Consolidated Standards of Reporting Trials

Kiluk et al (2011)28

Michie et al (2017)7

x x x x x x x x

x

x x x

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Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019 quality problems were absence of usage vs outcomes data (15 studies), absence of quantitative usage metrics (10 studies), absence of concealment of allocation (7 studies), and absence of an intention-to-treat analysis (5 studies). Interrater agreement of quality scoring was high (kappa = 0.72, SE = 0.07; 95% confidence interval, 0.68−0.94).

Dietary Outcomes

Figure. Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart. RCT indicates randomized controlled trial. article screening of 89 records resulted in 21 records being included in the review after 68 were excluded. The most common reason for exclusion was no dietary change measure. The 21 included studies encompassed 7,389 participants (3,541 males and 3,848 females) and 19 different e-health interventions. A total of 813 participants were targeted owing to a chronic condition; the remaining 6576 were healthy. Table 2 lists the characteristics of each study, including the quality score calculated from the quality criteria. Four studies had been published in the past 2 years29−32 and all had been published within the past decade. All included studies were Internet-based interventions with an online component and of differing session lengths; the majority were composed of several modules. The interventions were conducted in Australia,33,34 Iran,31 South Korea,35 the Netherlands,36−38 Germany,39 the United Kingdom,39−41 and the United States;32,42−48 most employed national recruitment strategies. One large study covered 7 countries including Greece, Spain, the United Kingdom, Ireland, the Netherlands, Poland, and Germany.30

Dietary change targets included overall diet quality,30,32,34,40 energy intake,31,33,34,42,49 and specific aspects of dietary intake including increasing fruit and vegetable intake,29,35,38,39, 41,43,45,47 increasing dairy intake,46 increasing v-3 fatty acid consumption,48 and decreasing saturated fat intake.36−38,41 The interventions covered a range of defined target populations including those with chronic conditions such as obesity,33,34,42, 44,47,49 metabolic syndrome,31 cardiovascular disease,40 and breast cancer,35 and populations based on age or a particular stage of life, such as young adults at college,43,46 young African American women,32 middle-aged women,42,48 and older adults.40,45

Study Quality Most studies in the review rated as moderate against the quality criteria, achieving scores of 20−30 out of the maximum of 40. Studies with higher quality scores were those in larger samples, ones that used detailed dietary measures, and those that addressed more of the e-CONSORT requirements for reporting on digital interventions. The most common

Table 3 reports dietary behavior change outcomes achieved after the intervention. Of the 19 interventions, 12 (63%) reported a significant change in at least 1 of the targeted behaviors against the control group after the intervention and 10 of the 12 had targeted dietary behavior change as the primary outcome. Staffileno et al32 also reported a significant change in Dietary Approaches to Stop Hypertension scores of participants in the intervention group; however, this was not compared with the control group. Of these 12 studies, 4 (33%) reported follow-up measures to examine maintenance of effects after the intervention, but none of significance were reported. Lawrence et al49 reported a significant reduction in energy intake in their intervention group, as shown by 24hour food diaries recorded during the intervention; however, these measures were not repeated at 1- or 6-month follow-up. There were no obvious patterns in achieving outcomes in particular populations. Four interventions targeted overall diet quality30,32,35,40 and all 4 reported significant outcomes. There were no obvious patterns in the other dietary change targets.

Intervention Engagement and Attrition Engagement and adherence analysis varied among the studies; many used the concepts interchangeably (Table 4). Interventions that achieved a significant result in their targeted behavior change had low to moderate dropout attrition rates. Seven interventions reported non-usage attrition rates. In 5 of these, dropout rates were lower than non-usage attrition rates. Of the 21 studies, 15 had an engagement strategy and 70% of the strategies employed were to remind users to complete

Randomized Controlled Trial

Sample

Healthy populations Franko et al n = 476 (2008)42 I: 1 n = 165 2 n = 164 C: n = 147

Target Population

Gender and Age, y

College/university

Both 18−24

n = 423 I: n = 135 C: n = 138

General

Both >40

Kroeze et al (2008)35

n = 442 I: n = 151 C: n = 150 n = 1,154 I: n = 577 C: n = 577 n = 701 I: not reported C: not reported n = 1,607 I: n = 414, n = 404, n = 402 C: n = 387 n = 211 I: n = 107 C: n = 104 n = 1,349 I: 1: n = 456 2: n = 459 C: n = 434

General

Both 18−65

General

Both 14−79b

General

Both 15−773

General

Both >18

Lange et al (2013)38 Lippke et al (2016)28

Livingstone et al (2016)29

Poddar et al (2012)45 Springvloet et al (2015)36 Springvloet et al (2015)37 Staffileno et al (2018)31

n = 26 I: n = 14, C: n = 12

College/university

General

African American

18 to ≥ 22

Both adult

Female 18−45

MyStudentBody.comNutrition Internetbased nutrition and physical activity education program RealAge interventiona Web-based risk assessment and behavior-specific modules Online intervention using tailored feedback

Comparator

Quality Score

F&V intake

3 mo 6 mo

28

Printed health-promotion materials

F&V intake Percent energy from fat

None

24

Generic information

Saturated fat intake

6 mo

30

Fruit consumption

None

27

F&V intake

1 mo

29

Diet quality (Mediterranean diet score)

None

31

Dairy intake

None

22

High-energy snack intake F&V intake Saturated fat intake Diet quality (DASH score) DASH components

9 mo

31 20

None

24

Conventional dietary advice

Increasing dairy intake Stress Management based on Social Cognitive Theory Online intervention Generic nutrition information arranged for preaction, action, and evaluation of behavior change DASH electronic health online education modules

Follow-Up

Attention Placebo Control

Online intervention Knowledge-based prompting dietary planquiz on nutrition ning and action control Comparable length Online intervention online intervention prompting action plantargeting standard ning and coping care constructs planning Food4Me Web site Personalized advice delivered via e-mail

Dietary Change Target

Physical activity electronic health online education modules

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Hughes et al (2011)44

Intervention

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Table 2. Study Characteristics

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Table 2. (Continued)

Sample

Target Population

Tapper et al (2014)40

n = 100 I: n = 50 C: n = 50

General

Yen et al (2013)47

n = 88 I: n = 41 C: n = 44

University staff

Gold et al (2007)43

Jahangiry et al (2017)30

Lawrence et al (2015)48 Lee et al (2014)34

Lindsay et al (2008)39 Morgan et al (2009)33

Intervention

Quality Score

None

22

None

21

Assessment-only control

Female >45

Online intervention modules based on Health Belief Model and MyPyramid

Information on MyPyramid

SHED-IT using Calorie King Web site

Information-only control group

Energy intake

6 mo

21

Internet-based weight maintenance: diet and physical activity logs and online peer groups VTrim structured behavioral weight loss Web site

Self-directed weight maintenance and inperson peer group meetings eDiets.com commercial weight loss program

Energy intake

None

29

Dietary intake (kcal/d)

None

29

Online general information about cardiovascular disease

Energy intake Nutrient intake

None

26

Online response inhibition training (nonfood images) 50-page educational booklet on diet and exercise

Energy intake Snacking frequency F&V intake Diet quality (diet quality Index)

6 mo

28

None

30

Internet access to generic health information Information-only control group

Diet quality

None

15

Energy intake

6 mo

29

Male 18−60 Female 40−55

Metabolic syndrome

Both >20

n = 84 I: n = 82 C: n = 82 n = 59 I: n = 30 C: n = 29

Obese/overweight

Both 23−65

n = 108 I: n = 54 C: n = 54 n = 65 I: n = 34 C: n = 31

Coronary heart disease

Both 50−74

My Healthy Heart Profile interactive web-based program based on feedback and monitoring Online response inhibition training (food images) Web-based self-management exercise and diet intervention program (WSEDI) Hearts of Salford Health Portal

Obese/overweight

Male 18−60

SHED-IT using Calorie King Web site

Female adult

F&V intake Saturated fat intake Added sugar intake Omega-3 fatty acid consumption

Follow-Up

Online modules in 3 phases: motivational, volitional, maintenance

Both >18

Breast cancer

Dietary Change Target

Both >18

Obese/overweight

n = (185) 124c I: n = 62 C: n = 62 n = 160 I: n = 80 C: n = 80

Comparator

(continued)

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Specific health condition populations Collins et al Obese/overweight n = 65 (2011)32 I: n = 34 C: n = 31 Obese/overweight Cussler et al n = 135 (2008)41 I: n = 66 C: n = 69

Gender and Age, y

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Randomized Controlled Trial

DASH indicates Dietary Approaches to Stop Hypertension; F&V, fruits and vegetables; I, intervention; C, control; SHED-IT, Self-Help, Exercise, and Diet Using Internet Technology. a This intervention was 1 of 2 interventions in this study. The intervention met the review criteria whereas the first had a significant coaching element and was not part of this analysis; bAdult population with adolescents included as participants were not excluded by age; cn total includes a third arm that was unrelated to the reported study.

25 None F&V intake Fatty foods intake Weight-loss podcast Weight-loss podcast based on Social Cognitive Theory Both >18 n = 78 I: n = 41 C: n = 36 TurnerMcGrievy et al (2009)46

Obese/overweight

Sample Randomized Controlled Trial

Table 2. (Continued)

Target Population

Gender and Age, y

Intervention

Comparator

Dietary Change Target

Follow-Up

Quality Score

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019 outcome or evaluation measures. Few of these strategies reminded participants to complete components of the interventions. Analysis of usage and non-usage attrition rate reporting was limited. No associations were found between dropout attrition rates and the target population (specific or general), dietary change target, or number of BCTs employed in the intervention. Metrics of use were reported in 12 of the 21 studies (57%) and included logins,31,44 completion of modules,37,38,41,44,49 logs33,34,46 and diaries, and specific activities.31−34 Only 1 study, that of Gold et al,44 reported intensity of use measures, calculating their users’ average logins per week. Four studies included usage in their outcome analysis, with 2 examining engagement metrics29,31 and 1 reporting metrics of use against dietary change outcomes.44 Table 4 shows the engagement and attrition analysis sorted by the dropout attrition rate.

Behavior Change Technique Usage Table 5 summarises the reported behavior change theory upon which the interventions were based and the resultant count and grouping of BCTs from BCT coding of each study. For studies reporting significant results, 9 of 13 interventions (69%) were based on a behavior change theory, compared with 1 of 7 reporting a nonsignificant result. There were no associations between the number of BCTs employed and achievement of a significant outcome in the dietary change target. The majority of studies reporting significant outcomes had the dietary change target as a primary outcome. Table 6 reports the specific BCTs employed across the interventions when a technique was used in >1 study. The most commonly used BCT was goal setting [1.1] (16 of 21 studies), followed by instructions on how to perform a behavior [4.1] (12 of 21 studies), feedback on behavior [2.2] (10 of 21 studies), and self-monitoring of behavior [2.3] (11 of 21 studies). Of the commonly used BCTs, feedback on behavior and self-monitoring of behavior featured in more interventions reporting significant outcomes than did those that did not

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report a significant outcome. All of the studies that implemented BCTs from the repetition and substitution, reward and threat, and antecedents groupings reported significant outcomes; however, these techniques were not used in isolation.

DISCUSSION This review’s results suggest that online interventions can be successful in achieving dietary behavior change across a range of defined populations. Although the 21 studies varied in dietary outcome measures, intervention implementation and design, duration, sample sizes, and study quality, 12 of the 19 interventions (63%) reported a statistically significant change in at least 1 of the targeted dietary behaviors against a comparator group. However, these results need to be interpreted in the context of the use of the interventions, and with the recognition that the analysis of non-usage attrition and reporting of quantitative engagement metrics across the studies were limited. Lack of engagement data, particularly usage vs outcome data, means that the researchers’ ability to identify the most effective BCTs or intervention features in achieving dietary change remained an unmet need. The 4 interventions that targeted overall dietary quality (as opposed to calorific intake or specific nutrients) all reported a significant change in diet outcome.30,32,35,40 Diet quality refers to a measure of the total diet or dietary pattern and is usually assessed as compliance with existing dietary guidelines or recommendations.50 In the 4 diet quality studies analyzed, diet quality scores were calculated using data from specific screening surveys,32,34,40 a food frequency questionnaire,30 and a 3-day dietary recall.35 The most methodologically robust study of the 4, that of Livingstone et al,30 examined diet quality using a Mediterranean diet score in a large sample with moderate attrition. The other studies with larger sample sizes (>1,000 participants) both reported significant results in their targeted dietary intakes (reducing high energy and fat intake37,38 and increasing fruit intake39), which is

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Table 3. Outcome Summary

Poddar et al (2012)45 Lindsay et al (2008)39

Livingstone et al (2016)29

Staffileno et al (2018)31

Gold et al (2007)43

Jahangiry et al (2017)30 Kroeze et al (2008)35

Lawrence et al (2015)48

n = 211 I: n = 107 C: n = 104 n = 108 I: n = 54 C: n = 54 n = 1,607 I: 1: n = 414 2: n = 404 3: n = 402 C: n = 387 n = 26 I: n = 14 C: n = 12 n = 65 I: n = 34 C: n = 31 n = 135 I: n = 66 C: n = 69 n = (185) 124 I: n = 62 C: n = 62 n = 160 I: n = 80 C: n = 80 n = 442 I: n = 151 C: n = 150

n = 84 I: n = 82 C: n = 82

Dietary Change Target

Dietary Assessment

Dairy intake

7-d food record for dairy intake

Diet quality (score from Health Survey for England variables) Diet quality (Mediterranean diet score)

Variables from Health Survey for England

Postintervention

Effect Size

Follow-Up

Significant increase in Marginal mean change total dairy intake for reported intervention vs control Significant improvement in diet quality (eating bad foods less)

N/A

Food4Me FFQ

Significant improvement in Mediterranean diet scores in experimental group vs control

N/A

Diet quality (DASH score) DASH components

6-item DASH screener

N/A

Energy intake

Dietary Questionnaire for Epidemiological Studies FFQ

Significant improvement in DASH score in DASH intervention group, not compared against control No significant differences between groups

Energy intake

Dietary intake log Food Pyramid

No significant differences between groups

N/A

Dietary intake (kcal/d)

Block FFQ

No significant differences between groups

N/A

Energy intake Nutrient intake

FFQ Iranian version

N/A

Saturated fat Total fat intake Energy intake

FFQ

Significant changes in total calories (−) P = .03 and cholesterol (+) P = .007 Mean total fat, saturated fat, and energy intake significantly lower in intervention condition compared with generic information control

Energy intake Snacking frequency

24-h food diary FFQ

Significant reduction in energy intake

Outcome

N/A

6 mo

TotF: b (unstandardized) = −10.93 SatF: b (unstandardized) = −3.15 Energy: b (unstandardized) = −1.07 d = 0.5

6 mo

6 mo

Measure not repeated at follow-up

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Collins et al (2011)32 Morgan et al (2009)33 Cussler et al (2008)41

Sample

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Randomized Controlled Trial

Randomized Controlled Trial Springvloet et al (2015)36 Springvloet et al (2015)37

Sample n = 1,349 I: 1: n = 456 2: n = 459 C: n = 434

Franko et al (2008)42

n = 476 I: n = 165; 164 C: n = 147

Hughes et al (2011)44,a

n = 423 I: n = 135 C: n = 138

Lange et al (2013)38

n = 1,154 I: n = 577 C: n = 577 n = 59 I: n = 30 C: n = 29

Lee et al (2014)34

Lippke et al (2016)28 Tapper et al (2014)40 Turner-McGrievy et al (2009)46 Yen et al (2013)47

n = 701 I: not reported C: not reported n = 100 I: n = 50 C: n = 50 n = 78 I: n = 41 C: n = 36 n = 88 I: n = 41 C: n = 44

Dietary Change Target

Dietary Assessment

High-energy snack intake F&V intake Saturated fat intake

FFQ

F&V intake

Single-item measure of F&V intake

F&V intake NCI All-Day Fruit and Percent energy from Vegetable Screener fat NCI percent energy from Fat Screener Fruit consumption Single-item average fruit servings/d F&V intake Diet quality (Diet Quality Index)

3-d dietary recall

F&V intake

Single item(s) average servings of F&V/d

F&V intake Saturated fat intake Added sugar intake F&V intake Fatty foods intake

Block fat/sugar/F&V screener (FFQ)

Omega-3 fatty acid consumption

Postintervention Significant decrease in highenergy snack intake both interventions vs control Significant decrease in saturated fat intake basic intervention vs control Significant increase in F&V intake servings/d

Effect Size

Follow-Up

I1 vs C: T1 ES −0.30; T2 ES −0.18; I2 vs C: T1 ES −0.19; T2 ES −0.17 I1 vs C: T1 ES −0.30; T2 ES −0.18

9 mo

No long-term intervention effects

Report general effect sizes of significant findings ranging from 0.11 to 0.19

3 mo 6 mo

No significant differences

No significant differences between intervention (RealAge) and control

N/A

Significant improvement in fruit F(1,784) = 9.69, P < .01, intake in intervention vs h2 = .02 control Significant increase in F&V intake (eating 5 servings/d) Significant increase in Diet Quality Index No significant difference between groups

N/A

Significant increase in F&V intake for intervention vs control Automated Self-Adminis- Significant change between tered 24-hour Dietary groups for fruit consumption and vegetable consumption Recall FFQ No significant differences between groups

F1, 98 = 3.1, P = .08, partial h2 = .03

N/A

1 mo

N/A

Outcome

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Table 3. (Continued)

N/A

N/A

DASH indicates Dietary Approaches to Stop Hypertension; F&V, fruits and vegetables; N/A, not available; NCI, National Cancer Institute. a Two interventions were reported in this study against control. For the purposes of this analysis, the Web-only RealAge intervention was examined.

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Table 4. Engagement and Attrition

Significant Outcome

Sample Size

Intervention Length

Dropout Attrition Rate

NonUsage Attrition Rate

Lee et al (2014)34

F&V intake, diet quality

59

12 weekly sessions

3%

11%

Lawrence et al (2015)48 Tapper et al (2014)40

Energy intake

84

3%

18%

F&V intake

100

4 10-min sessions 24 weekly sessions

5%

20%

Lindsay et al (2008)39

Diet quality

108

6 mo, self-paced unlimited access Single session

6%

−

7%

23%

Mean total fat, saturated fat, and energy intake Turner-McGrievy F&V intake et al (2009)46

442

78

24 podcasts 2/wk over 12 wk

9%

Poddar et al (2012)45

211

8 weekly modules

9%

Franko et al F&V intake (2008)42 Yen et al (2013)47

476

3 timed lab sessions 6 weekly modules

12%

Collins et al (2010)32 Morgan et al (2009)33

65

Gold et al (2007)43

Dairy intake

88

(185) 124

Daily logs over 3 mo

40%a

13%a 18%

Daily logs and 18%/19% weekly sessions 35%/ 23% (6 mo) Daily logs and fortnightly sessions (6 mo)

−

Short message service reminders − E-mail reminders Material Incentives Weekly drop-in sessions Phone support

Qualitative Usage Measures

Intensity of Use Measures

Outcomes Reported Against Usage

−

−

−

Completion of Debriefing training sessions interview Number of sessions completed

−

−

−

−

−

−

−

−

−

−

Quantitative Usage Measures Completion of daily diary activities

− −

−

Self-reported use of materials

E-mail and phone Self-reported comfollow-ups if jourpletion of nal not episodes completed E-mail reminders Report percentage − Incentives for of completed completion of checklists activities Booked access − − sessions Weekly e-mail with link to module Notifications Number of daily diet and exercise entries, and weekly weigh-ins −

Counts of logins, page access, and feature use

Per-protocol analysis (weight and waist circumference) Average Logins vs weight logins/wk loss

(continued)

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Kroeze et al (2008)35

Usage Prompts and Incentives

Young et al

Randomized Controlled Trial

Randomized Controlled Trial Jahangiry et al (2017)30

Significant Outcome Total calories, cholesterol

Cussler et al (2008)41

Livingstone et al Diet quality (2016)29

Sample Size

NonUsage Attrition Rate −

Usage Prompts and Incentives

160

Unrestricted access over 6 mo

20%

135

Self-paced modules, weekly logs, and support groups over 12 mo 6 mo, monthly measures Varied levels of frequency of feedback 12 weekly modules

21%

Incentive programs

1: 25% 2: 20% 3: 20%

E-mail reminders

1,607

Staffileno et al (2018)31

Diet quality

26

Lange et al (2013)38 Springvloet et al (2015)36 Springvloet et al (2015)37 Hughes et al (2011)44

Fruit intake

1,154

High-energy snack intake, fat intake

1,349

Lippke et al (2016)28

Intervention Length

Dropout Attrition Rate

423

701

26%

29%

1 1-h session

32%

−

4 modules, 3 sessions over 6 wk

Basic: 34% Plus: 37% 43%

Basic: 26%b Plus: 30%b

Unlimited access after initial assessment over 12 mo Single session

70%

E-mail reminders

Reimbursement for expenses related to participation −

Quantitative Usage Measures

Qualitative Usage Measures

Intensity of Use Measures

Outcomes Reported Against Usage

Login frequency, number of components accessed

−

−

Greater number of logins and use of components related to improvement in metabolic equivalent of tasks indicators Outcomes vs dropouts

Number of completed program activities

−

−

−

−

−

−

−

E-mail reminders Module completion Material incentives

E-mail notifications Incentive programs

Self-reported engagement (Likert scales)

Engagement against outcomes

Young et al

F&V indicates fruits and vegetables. a Average participation rate across checklists; bPercentage of users who did not use the intervention at all and those who did not complete 1 session.

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Table 4. (Continued)

729

730

Table 5. Behavior Change Theory and Techniques

Population

Target Behavior(s)

Diet as Primary Outcome?

Significant Diet Outcomes

Behavior Change Theory

F&V intake Saturated fat intake Added sugar intake

Primary

−

Model of Action Phases Self-Determination Theory

Springvloet Adults et al (2015)36 Springvloet et al (2015)37

Improve compliance to dietary guidelines F&V intake High-energy snack intake Saturated fat intake

Primary

−

Self-Regulation Theory Theory of Planned Behavior Precaution Adoption Process Model

Poddar et al (2012)45

Increase dairy intake

Primary

−

Social Cognitive Theory

College students

Groupings

n

17 Implementation inten1. Goals and planning tions 2. Feedback and User rating adherence to monitoring implementation 3. Social support intentions 4. Shaping knowledge 5. Natural consequences 7. Associations 8. Repetition and substitution 9. Comparison of outcomes 10. Reward and threat 15. Self-belief 16. Covert learning 11 Environmental level 1. Goals and planning feedback 2. Feedback and Targets for risk groups monitoring 3. Social support 4. Shaping knowledge 9. Comparison of outcomes 12. Antecedents 9 Weekly checklists 1. Goals and planning Social events 2. Feedback and Family member monitoring participation 3. Social support 4. Shaping knowledge 5. Natural consequences 8. Repetition and substitution 9. Comparison of outcomes 10. Reward and threat (continued)

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Adults

Tapper et al (2014)40

Intervention Features Promoting Adherence/ Behavior Change

Young et al

Randomized Controlled Trial

Behavior Change Techniquesa

Randomized Controlled Trial

Population

Target Behavior(s)

Diet as Primary Outcome?

Significant Diet Outcomes

Behavior Change Techniquesa Behavior Change Theory

Lange et al (2013)38

Motivated adults

Increase fruit consumption

Primary

−

Dietary Planning and Action Planning

Lee et al (2014)34

Patients who had breast cancer

F&V intake Diet quality Exercise

Primary

−

Social Cognitive Theory User Control Theory Cognitive Load Theory Elaboration Likelihood Model

Kroeze et al (2008)35

Adults

Reduction in saturated fat intake

Primary

−

Livingstone Adults et al (2016)29

Consumption of Mediterranean diet

Primary

−

TurnerAdults McGrievy et al (2009)46

Weight loss Increased F&V intake Decreased fat intake

Secondary

−

Groupings

n

1. Goals and planning 7. Associations 8. Repetition and substitution 9. Comparison of outcomes 1. Goals and planning 2. Feedback and monitoring 4. Shaping knowledge 5. Natural consequences

8

Goal setting and action planning activities

7

Tailored information Automated feedback Action planning

1. Goals and planning 2. Feedback and monitoring 3. Social support 4. Shaping knowledge 5. Natural consequences 6. Comparison of behavior 13. Identity 1. Goals and planning 2. Feedback and monitoring 3. Social support 4. Shaping knowledge 1. Goals and planning 2. Feedback and monitoring 4. Shaping knowledge 5. Natural consequences

7

Tailored feedback

6

Personalized recommendations Feedback reports (Baseline and 3 and 6 mo) Weekly journal

5

(continued)

Young et al

Social Cognitive Theory User Control Theory Cognitive Load Theory Elaboration Likelihood Model

Intervention Features Promoting Adherence/ Behavior Change

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Table 5. (Continued)

731

732

Table 5. (Continued)

Population

Jahangiry Patients with et al (2017)30 metabolic syndrome

Target Behavior(s) Dietary intake and physical activity

Diet as Primary Outcome? Primary

Staffileno et al Young African Improved nutrition (2018)31 American behaviors and physiwomen cal activity

Cussler et al (2008)41

Weight management Overweight, perimenopausal, middle-aged women

Primary

Secondary

Behavior Change Theory

−

−

−

Theory of Planned Behavior Self-Regulation Theory

−

Response Inhibition Training

− Significant increase in F&V intake

Social Cognitive − Significant Theory improvement in DASH scores in intervention group

Groupings

n

Intervention Features Promoting Adherence/ Behavior Change

1. Goals and planning 2. Feedback and monitoring 4. Shaping knowledge 3. Social support 9. Comparison of outcomes

4

Online self-monitoring of metabolic equivalent of task indicators

2

Forum

7. Associations

1

Go/No-Go training

1. Goals and planning 2. Feedback and monitoring 4. Shaping knowledge 9. Comparison of outcomes 1. Goals and planning 2. Feedback and monitoring 3. Social support 6. Comparison of behavior 8. Repetition and substitution 12. Antecedents 13. Identity 1. Goals and planning 2. Feedback and monitoring 3. Social support 4. Shaping knowledge

5

14 Individualized Feedback Weekly modules DASH screener

7

Diet-log pyramid

(continued)

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Primary Change in health Low−sociobehaviors including economic status adults diet with coronary heart disease adults Lawrence et al Overweight Secondary Weight loss (2015)39 Decreased snacking and obese frequency adults Decreased energy intake Franko et al College-age Improved nutrition Primary (2008)42 students behaviors Lindsay et al (2008)39

Behavior Change Techniquesa

Significant Diet Outcomes

Young et al

Randomized Controlled Trial

Randomized Controlled Trial Lippke et al (2016)28

Collins et al (2010)32

Hughes et al (2011)44

Population

Target Behavior(s)

Adults

Action planning Coping planning Increased F&V consumption Improvement in dieAdult overtary intake for weight/ obese males weight loss

Morgan et al (2008)33

Older workers Change in health (aged ≥ 40 y) behaviors Increase in F&V consumption Decrease in fat intake Adult men Dietary intake for weight loss

Gold et al (2007)43

Overweight/ Weight loss obese adults

Yen et al (2013)47

n-3 fatty acid University middle-aged consumption women

Diet as Primary Outcome?

Behavior Change Techniquesa

Significant Diet Outcomes

Behavior Change Theory

Secondary

Secondary

Primary

Secondary

Secondary

Primary

Health Belief Model

Groupings

n

1. Goals and planning 5. Natural consequences 9. Comparison of outcomes 1. Goals and planning 2. Feedback and monitoring 4. Shaping knowledge 5. Natural consequences 6. Comparison of behavior 1. Goals and planning 2. Feedback and monitoring 3. Social support 5. Natural consequences

7

Goal setting Action planning Coping planning

6

Daily eating and exercise diaries Tailored feedback

1. Goals and planning 2. Feedback and monitoring 3. Social support 1. Goals and planning 2. Feedback and monitoring 3. Social support

6

Daily diet entries Daily exercise entries Weekly weigh-ins

5

Meal planning Tailored fitness program Support Central: Facilitated meetings, chat rooms, discussion boards, and frequently asked questions Blog-style education Goal setting

1. Goals and planning 4. Shaping knowledge 5. Natural consequences

6

3

Young et al

DASH indicates Dietary Approaches to Stop Hypertension; F&V, fruits and vegetables. a As defined by Michie et al.5

Intervention Features Promoting Adherence/ Behavior Change

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Table 5. (Continued)

733

734

Table 6. Summary of Behavior Change Techniques Used in >1 Intervention

Result

1.1

Secondary

X

Secondary

X

Primary

b

X

Primary

X a

Primary

a

Primary

a

Secondary

a

Primary

a

Primary

a

Secondary Primary Secondary

1.4

1.5

1.6

1.8

2.2

2.3

X

X

X

2.4

X X

X X

X

X

X

X

X

4.1

5.1

6.1

X

X

X

7.1

8.3

X

X

X

X

X

X

Primary

a

X

Primary

c

X

X

X

Primary

a

X

X

X

X

12.1

X

X

X

X

X

X X

X

X

X

X

X X

X X

10.1

X

X X

9.1

X

X

X

8.1

X

X X

3.2

X

X

X

3.2

X

X

X X

3.1

X X

X

2.7

X

X

X a

1.3

X

Secondary

Primary

1.2

X X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X X X X

X

X X

X

X

X

X

X

X X

X

(continued)

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

Collins et al (2010)32 Cussler et al (2008)41 Franko et al (2008)42 Gold et al (2007)43 Hughes et al (2011)44 Jahangiry et al (2017)30 Kroeze et al (2008)35 Lange et al (2013)38 Lawrence et al (2015) Lee et al (2014)34 Lindsay et al (2008)39 Lippke et al (2016)28 Livingstone et al (2016)29 Morgan et al (2008)33 Poddar et al (2012)45 Staffileno et al (2018)31 Springvloet et al (2015)36

Diet Outcome

Young et al

Randomized Controlled Trial

2 4 2 2 9 12 16

X X X

X X

2 7

3.2

5

X

3.1

2

2.7

10

X

11

2

2.4 2.3 X

2.2

2

1.8

3

X

5 7 X

3

1.6 1.5

X X

1.4 1.3

6

1.2

Primary

Secondary

a

X

1.1 Result

a

Primary

Tapper et al (2014)40 TurnerMcGrievy et al (2009)46 Yen et al (2013)47 Total

Diet Outcome Randomized Controlled Trial

Table 6. (Continued)

a Significant outcome in dietary target; bSignificant outcome in 1 dietary outcome; cSignificant improvement in Dietary Approaches to Stop Hypertension (DASH) scores in the intervention group. Notes: Behavior change techniques: 1.1 Goal setting (behavior); 1.2 Problem solving; 1.3 Goal setting (outcome); 1.4 Action planning; 1.5 Review behavior goals; 1.6 Discrepancy between current behaviour and goal; 1.8 Behavioral contract; 2.2 Feedback on behavior; 2.3 Self-monitoring of behavior; 2.4 Self-monitoring the outcome of behavior; 2.7 Feedback of outcome of behavior; 3.1 Social support (unspecified); 3.2 Social support (practical); 3.3 Social support (emotional); 4.1 Instruction on how to perform the behavior; 5.1 Information about health consequences; 6.1 Demonstration of behavior; 7.1 Prompts/cues; 8.1 Behavioral rehearsal/practice; 8.3 Habit formation; 9.1 Credible source; 10.1. Material incentive (behavior); 12.1 Restructuring the physical environment.

2 5

X X

8.3 8.1

X X X

3.2

4.1

5.1

6.1

7.1

X

9.1

10.1

2

12.1

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019

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735

another encouraging result for future e-health dietary interventions. The varied methods employed to measure dietary intakes make interpretation of the results difficult. Most of the dietary measures were employed as baseline and postintervention measures and required selfreported dietary intake. None of the studies reviewed validated these selfreported data against other measures. Franko et al43 reported a significant difference in fruit and vegetable intake in the intervention groups compared with the control group, as measured by a single-item measure of fruit and vegetable intake. They also used a validated food frequency questionnaire to determine daily servings of fruit and vegetable intake; however, this measure was not taken at posttest owing to an inadvertent data error.43 Consistent measures across each time point would have enabled validation of the single-item measure and offered further support for the significant result reported. Effect size reporting was inconsistent, and of studies that reported effect size, the methods varied greatly, which made comparison of outcomes difficult. Most studies reported some digital divide demographics in their baseline analysis of participants. However, few required defined levels of computer or e-health literacy in their eligibility criteria. Lee et al35 included the ability to use computers in their eligibility criteria and reported both low dropout and low non-usage attrition rates. Gold et al44 required participants to complete a technology check before commencing the intervention and Lindsay et al40 provided one-on-one training for computer use for participants, but neither of those studies reported non-usage attrition. Ensuring that participants have the required skills to use an online intervention is an important consideration for future studies. Including computer literacy as an inclusion criterion or providing adequate training for participants before the commencement of the intervention may minimize attrition, and particularly regarding nonusage. Analyzing why people drop out of an intervention or fail to use it, although often difficult to assess, would greatly aid future development of

736

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engaging interventions and the understanding of effective engagement.

Intervention Design Dropout attrition rates for the reviewed interventions were relatively low compared with e-health interventions generally, for which reported attrition rates as high as 60% to 80% are common.7,17−19,51 This is encouraging for future interventions. There were no standout features or designs of the interventions that were most commonly associated with low attrition rates. Of the interventions achieving <10% attrition, there was no apparent optimum intervention length; long, moderate, and short interventions all reported low attrition. Most studies employed a notification or reminder system to encourage participants to use the intervention or to complete required measures. Independent of intervention design length, consistent contact seems to be an important design consideration for keeping participants engaged in the intervention, or at least keeping them completing measures when required. Viewing dropout rates in isolation is problematic because these measures analyze the number of participants who had completed the required assessments or measures, independent of analysis of engaging with intervention content. Of greater interest when examining engagement is the non-usage attrition rate, which was not widely reported or analyzed in the studies reviewed. Of the studies that reported non-usage attrition levels, these tended to be significantly higher than the dropout attrition rate, except in the studies by Steffalino et al32 and Springvloet et al,37,38 which indicates that a number of participants completed the required measures but did not actively participate in the intervention or adhere to the intervention requirements. For example, Poddar et al46 reported an average 40% nonusage attrition rate (calculated by the completion of required checklists throughout their intervention), but only a 9% dropout attrition rate despite having an incentive for completing activities. Although participation in their study was not

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019 mandatory, it was open to students enrolled in a college course and a percentage of the course credit was offered for completing the study. Securing credit vs material incentives offered might have contributed to the disparate rates of dropout and non-usage attrition. These disparate rates highlight the need to better understand engagement in these kinds of interventions, which in turn will give a more robust understanding of the efficacy or effectiveness of the intervention. Metrics of engagement against the targeted outcome was reported in 5 of the studies reviewed, or in the case of Morgan et al,34 in a per-protocol analysis of outcomes for those who complied with the intervention requirements. This kind of analysis gives a better understanding of the efficacy or effectiveness of the intervention by looking at outcomes related to actual intervention use. Analysis of engagement metrics vs outcomes can give researchers insight into what effective engagement for their intervention really looks like, and not a skewed analysis including the results of participants who had not accessed the intervention but completed the required measures. Of the reviewed studies that considered these concepts, all reported positive correlations between intervention use and the targeted outcomes; however, only 1 of those studies analyzed use against a dietary change outcome. Lippke et al29 reported that engagement moderated the mediation effect of their intervention on fruit and vegetable consumption via changes in both coping planning and action planning. They showed an inverse U-shaped relation between intervention response and engagement, as reported by participants using Likert scales in a postintervention questionnaire. None of the other studies reported an analysis of what constituted effective engagement for their intervention. Springvelot et al37 reported the number of users in each intervention arm who had used no modules or had completed only 1 module; however, those data were not reported against outcomes. Staffileno et al32 reported that active engagement was required

by participants of both of their e-health interventions, but this was maximum engagement in their program as opposed to a defined optimum level and required the completion of modules including activities and completion of measures. Future e-health interventions might include the evaluation of effective engagement as a key part of the analysis of intervention efficacy or effectiveness, as Michie and colleagues7 recommended. Not only does this support the intervention analysis, it contributes to the field by guiding the development of future interventions with a real understanding of how engagement in an online dietary intervention can improve outcomes and how best to support participants to meet their needs.

Behavior Change Technique Use The studies that reported significant outcomes had the dietary change target as a primary outcome, reflecting the importance of intervention design. Of the studies that report significant results, more of those reported that the intervention was based on a behavior change theory, which was consistent with existing behavior change literature.52,53 Of course, it is possible that others might have been based on theory but did not report it. The most common BCTs were within the groupings for goals and planning, and feedback and monitoring. Within these groupings, goal setting (behavior) and selfmonitoring of behavior were the most frequently employed. This was consistent with previous findings for face-to-face diet and physical activity interventions.4 Instructions regarding how to perform behavior were also common across the interventions. There were no standout BCTs that were employed only in interventions that achieved significant results, but rather many common techniques that were used across the interventions. This perhaps reflects the ease of implementing these techniques online, or a lack of attention in the field given to dismantling the most effective components of BCTs for dietary interventions. In comparison, labor- and timeintensive techniques such as elaborate reward and punishment

Journal of Nutrition Education and Behavior  Volume 51, Number 6, 2019 schedules were not commonly used in these interventions. The interventions typically incorporated only a few groupings of BCTs, which suggests that there is an opportunity for future studies to apply less frequently used but potentially high-impact techniques. The approach of coding BCTs to the behavior change taxonomy may also limit the number the techniques attributable to the intervention, owing to discrepancies between the coding process and the study reporting. For example, some common BCTs such as verbal persuasion about capabilities were employed but not formally reported in the interventions’ methodologies. This is highlighted by the BCT coding results for Collins et al33 and Morgan et al,34 in which different BCTs were identified in both articles although different aspects of the same trial were reported upon. Although many successful interventions used similar BCTs, it is not clear whether their success was due to the techniques’ effectiveness or their ease of implementation in online interventions. This review indicates the need for a better understanding of participant engagement to assess fully which behavior change techniques and intervention features best support dietary change. This analysis provides insight into the design features and BCTs employed in online dietary interventions. It is an objective review of the current evidence of online dietary interventions that were tested against active comparators. Article screening for this review was comprehensive and conducted by 2 independent authors. The analysis of BCTs was conducted by a trained practitioner in accordance with Behavior Change Taxonomy (v1) of Michie et al.5 On the other hand, this analysis of engagement, adherence, and effective BCTs was limited by disparate reporting of engagement in the interventions and limited reporting of non-usage attrition rates. In addition, few studies complied with the reporting guidelines of Behavior Change Taxonomy v1.0.5 More robust analysis of intervention effects should include an examination of non-usage attrition vs outcomes to assess intervention effectiveness fully.

Young et al

IMPLICATIONS FOR RESEARCH AND PRACTICE This review supports the use of online dietary interventions for achieving behavior change and improvements in targeted dietary change. The BCTs from the groupings goals and planning, and feedback and monitoring were commonly employed in interventions reporting significant results in their dietary change targets. However, thorough reporting of engagement is needed to provide a basis for understanding which behavior change techniques are most effective in promoting dietary change and which design elements are critical to efficacious applications. Future studies will benefit from analyzing and defining effective engagement by developing more comprehensive measures of engagement and adherence and examining these against outcomes.

3.

4.

5.

6.

7.

ACKNOWLEDGMENTS F.J. was supported by a National Health and Medical Research Council (NHMRC) Career Development Fellowship Level 2 (No. 1108125). M.B. was supported by an NHMRC Senior Principal Research Fellowship (No. 1059660). F.K.L. was supported by an NHMRC Senior Research Fellowship (No. 1110371). S.A.M. was supported by an NHMRC Career Development Fellowship Level 2 (No. 1104636). A. O. was supported by a Future Leader Fellowship (No. 101160) from the Heart Foundation, Australia.

8.

9.

10.

SUPPLEMENTARY DATA Supplementary data related to this article can be found at https://doi. org/10.1016/j.jneb.2019.03.006.

11.

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