This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (2939) Innovations and Technology for Physical Activity Education (742) mHealth for Wellness, Behavior Change and Prevention (2871)

Published on in Vol 22, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15642, first published .
Social Comparison Features in Physical Activity Promotion Apps: Scoping Meta-Review

Social Comparison Features in Physical Activity Promotion Apps: Scoping Meta-Review

Social Comparison Features in Physical Activity Promotion Apps: Scoping Meta-Review

Authors of this article:

Danielle Arigo1 Author Orcid Image ;   Megan M Brown1 Author Orcid Image ;   Kristen Pasko1 Author Orcid Image ;   Jerry Suls2 Author Orcid Image
Article Authors Cited by (33) Tweetations (24) Metrics

    Review

    1Department of Psychology, Rowan University, Glassboro, NJ, United States

    2Center for Personalized Health, Feinstein Institute for Medical Research, New York, NY, United States

    Corresponding Author:

    Danielle Arigo, PhD

    Department of Psychology

    Rowan University

    201 Mullica Hill Road

    Robinson 116G

    Glassboro, NJ, 08028

    United States

    Phone: 1 8562564500 ext 53775

    Email: Arigo@Rowan.edu


    Background: Smartphone apps promoting physical activity (PA) are abundant, but few produce substantial and sustained behavior change. Although many PA apps purport to induce users to compare themselves with others (by invoking social comparison processes), improvements in PA and other health behaviors are inconsistent. Existing literature suggests that social comparison may motivate PA for some people under some circumstances. However, 2 aspects of work that apply social comparison theory to PA apps remain unclear: (1) how comparison processes have been operationalized or harnessed in existing PA apps and (2) whether incorporating sources of variability in response to comparison have been used to tailor comparison features of apps, which could improve their effectiveness for promoting PA.

    Objective: The aim of this meta-review was to summarize existing systematic, quantitative, and narrative reviews of behavior change techniques in PA apps, with an emphasis on social comparison features, to examine how social comparison is operationalized and implemented.

    Methods: We searched PubMed, Web of Science, and PsycINFO for reviews of PA smartphone apps. Of the 3743 initial articles returned, 26 reviews met the inclusion criteria. Two independent raters extracted the data from these reviews, including the definition of social comparison used to categorize app features, the percentage of apps categorized as inducing comparison, specific features intended to induce comparison, and any mention of tailoring comparison features. For reference, these data were also extracted for related processes (such as behavioral modeling, norm referencing, and social networking).

    Results: Of the included review articles, 31% (8/26) categorized app features as prompting social comparison. The majority of these employed Abraham and Michie’s earliest definition of comparison, which differs from versions in later iterations of the same taxonomy. Very few reviews specified what dimension users were expected to compare (eg, steps, physical fitness) or which features of the apps were used to induce comparison (eg, leaderboards, message boards). No review referenced tailoring of comparison features. In contrast, 54% (14/26) reviews categorized features for prompting behavioral modeling and 31% (8/26) referenced tailoring app features for users’ personal goals or preferences.

    Conclusions: The heterogeneity across reviews of PA apps and the absence of relevant information (eg, about dimensions or features relevant for comparison) create confusion about how to best harness social comparison to increase PA and its effectiveness in future research. No evidence was found that important findings from the broader social comparison literature (eg, that people have differing preferences for and responses to social comparison information) have been incorporated in the design of existing PA apps. Greater integration of the mobile health (mHealth) and social comparison literatures may improve the effectiveness of PA apps, thereby increasing the public health impact of these mHealth tools.

    International Registered Report Identifier (IRRID): RR2-https://osf.io/nh4td/

    J Med Internet Res 2020;22(3):e15642

    doi:10.2196/15642

    Keywords

    smartphone app; physical activity; mHealth; social comparison; behavior change technique 


    Despite decades of intervention efforts by several health care disciplines, physical inactivity remains a leading cause of morbidity and mortality in the United States [1]. Many emerging digital health interventions focus on promoting physical activity (PA) [2], delivered via mobile health (mHealth) applications or smartphone apps. For example, more than 5000 apps available from the iTunes and Google Play app stores are designed to promote PA (alone or in the context of weight loss) [3]. Although many of these apps are user-friendly and elicit high user engagement [4], most are designed without input from behavioral scientists or other health professionals and reach the market without rigorous scientific evaluation [5,6]. Conversely, evidence-based PA apps have been developed by researchers, but these apps rarely reach the commercialization stage (due to a lack of resources) and research participants show modest engagement with them [7]. These limitations may contribute to the low efficacy of existing PA apps; those that have been tested in randomized controlled trials produce only short-term increases in activity [8].

    Thus, few existing PA apps are simultaneously grounded in behavior change science, engaging for potential users, and effective over the long term. Efforts are needed to improve PA app design to optimize both user engagement and intervention effectiveness. Currently, both commercial and researcher-developed PA apps vary in the extent to which they employ specific behavior change techniques (BCTs) [9]. In fact, considerable research effort has been devoted to determining the number and type of BCTs in existing apps. Social comparison (ie, evaluating one’s standing relative to others) [10] is a BCT used in several commercial and researcher-developed apps [6]. Comparison has also been identified as one of the most effective techniques for promoting PA in face-to-face behavioral interventions [11,12]. In PA apps, social comparison is activated when a user’s information is listed alongside that of other users, for example, via activity engagement rankings (leaderboards). Comparison may also be activated by any feature that exposes app users to information about other users (eg, message boards or other social networking features). However, PA app developers have not always recognized that social comparison is a complex process; it can be activated by various factors and has several possible outcomes. A comprehensive assessment of how social comparison is being currently used in PA apps and whether current methods capitalize fully on the theoretical and empirical social comparison literature has not been available. Such a review could begin to suggest how to optimize an app’s social comparison features and, potentially, improve its efficacy.

    To illustrate the complexities of social comparison processes, consider that PA is a multifaceted concept; there are various dimensions of PA (eg, steps per day, minutes of intense aerobic activity per week, appearance of muscularity, overall physical fitness), and app users may focus on any or all of these as the subject of social comparison. In addition, BCTs such as behavioral modeling (ie, providing examples of behavior engagement to encourage others to engage) and norm referencing (ie, providing information about group norms or averages) often are differentiated from social comparison as mechanisms of behavior change [9]. However, these mechanisms can explicitly or implicitly prompt a comparison of an aspect of the self to another person (or persons). Furthermore, modeling and norm referencing are assumed to prompt social comparisons in some classification systems [13]. An additional complication is that although research has found that social comparisons (via leaderboards or through these other processes) may promote PA [14,15], some experiments find that social comparisons can have negative consequences, such as worsened mood and decreased motivation for or engagement in healthy behavior [16-19]. Exposing users to others who have engaged in more PA than they have might be either inspiring (by learning what they might achieve [20]) or discouraging (by seeing themselves as inferior or incapable of achieving activity goals [16,21,22]). Conversely, exposing users to others who have engaged in less PA than they have may be satisfying (because they are outperforming their peers) or stressful (because they might also become more sedentary) [23,24].

    Moreover, existing literature on social comparison processes shows that people’s responses to comparison, as well as their preferences for the comparison information they receive, differ at 2 levels. At the between-person (or dispositional) level, different users may show different responses or preferences that are consistent over time [25]. At the within-person level, the same user may show variability in their responses and preferences over time [26,27]. Devising apps to modify social comparison features to match the general preferences of individual users or contextual preferences over time might be more effective for promoting PA, versus exposing everyone to the same comparison information. Such personalization or tailoring may prevent users from disengaging from social comparison or from PA apps altogether, especially if they repeatedly receive (potentially) discouraging comparison information [16,28].

    To what extent distinct dimensions and possible outcomes of social comparison are considered in existing PA apps remains an open question. A search of available literature reveals more than 100 published reviews about PA apps, surveying thousands of individual app-based programs. A number of these reviews intentionally categorize app features, including social comparison (using the BCT taxonomy [9] and other frameworks). These summaries are intended to inform future app design and evaluation [29,30]. However, to our knowledge, no review or synthesis of reviews has focused on social comparison or considered whether findings from the mainstream comparison literature have been incorporated.

    This scoping review had the following objectives: (1) to determine how social comparison is currently defined and categorized in existing systematic, meta-analytic, and narrative reviews of commercially available and researcher-developed PA apps, (2) to examine the methods for activating and facilitating social comparison in the PA apps identified in these reviews, and (3) to determine to what extent different elements of social comparison are included as design features in the PA apps. This review represents an initial step for the formulation of best practice recommendations for including social comparison features in PA apps.


    Guidelines

    This review followed the initial guidelines delineated by Arksey and O’Malley [31] and the recent Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews (PRISMA-ScR) [32]. A description of the protocol for this review is registered with the Open Science Framework.

    Research Questions

    This scoping review was guided by the following a priori research questions:

    1. How often does social comparison appear as a key behavior change mechanism in published reviews of PA smartphone apps?
    2. How is social comparison defined in published reviews of PA smartphone apps?
    3. How are app features categorized as social comparison (vs other behavior change processes) in published reviews of PA apps?
    4. What methods by which social comparison is activated or facilitated in PA apps are included in published reviews?
    5. To what extent (and how) have PA apps included in published reviews addressed between- and within-person variability in responses to social comparison (eg, via tailoring)?
    6. To what extent (and how) is social comparison differentiated from related processes, such as modeling and norm referencing, in published reviews of PA apps?

    How effective social comparison features of apps are in changing PA behavior is also an important question. It is not included in the preceding list because we did not find any randomized controlled trials, narrative reviews, meta-analyses, or dismantling studies focused on social comparison app features or directly comparing the effects of different app features. We elaborate on this point in the Discussion section.

    Identification and Selection of Relevant Reviews

    Inclusion and exclusion criteria were chosen by the first and last authors (DA and JS). Review articles were eligible for inclusion if they met the following criteria: (1) available in English; (2) published on or before May 31, 2019; (3) conducted a systematic or narrative review, or meta-analysis; (4) reviewed the features of commercially available smartphone apps or included formal intervention programs delivered via smartphone apps; and (5) used increasing PA or reducing sedentary time as a key behavioral outcome. An initial examination of the literature revealed that many reviews in the domain of mHealth combine PA with related weight control outcomes. Consequently, reviews that met the first 4 criteria and used weight loss or PA plus other behaviors (eg, diet and weight loss) as outcomes were included.

    Reviews were excluded if they considered interventions that combined an app modality with other modalities (eg, websites, text messages, etc) because they might obscure conclusions specific to apps. Apps geared toward particular medical populations also were excluded because these interventions tend to promote multiple behavior changes and set illness-specific PA targets, rather than focusing on broad-based PA increases. Finally, apps involving gamified interventions were excluded because they typically engage a variety of social processes, in addition to social comparison, to create competition with other users or teams of users. Dissecting comparison features from features intended to induce other processes in this context can be very challenging.

    We searched PubMed, PsycINFO, and Web of Science for publications related to the use of smartphone apps for increasing PA. Search terms were combinations of “physical activity” or “exercise” and “smartphone app(lication),” “mobile app(lication),” or “mHealth.” Resulting titles and abstracts were reviewed to determine relevance to our 6 research questions. Initial database and hand searches returned 3743 individual articles of which 2247 were duplicates, leaving 1496 unique articles. A PRISMA-ScR flowchart, shown in Figure 1, details the evaluation of each article for inclusion in this review. The majority of articles that were identified described empirical studies. Initial reviews were conducted by the first 3 authors (DA, MB, and KP) who were responsible for determining inclusion for an equal subset of identified articles. Final review and inclusion decisions were made by the first author (DA). The final set of 26 review articles were coded for the characteristics described in the following section.

    ‎
    Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews flowchart.
    View this figure

    Data Extraction and Article Coding

    The first and last authors (DA and JS) determined the types of data to be extracted from each article. The second and third authors (MB and KP, respectively) each independently read and extracted the following data from the 26 reviews: authors; year of publication; review of commercially available versus researcher-developed apps (or combination); number of apps reviewed; specific behavior change outcome targeted by the app (eg, overall PA, sedentary behavior, weight loss); percentage of apps that included social comparison features; the definition of social comparison; the specific features for inducing social comparison (eg, leaderboards); the social comparison dimension (eg, steps, physical fitness); and the presence (vs absence) and type or types of social comparison tailoring. Additional data extracted included the percentage of apps categorized as modeling/demonstrating a behavior, providing normative information about others' behavior, and social networking (eg, message boards). These features are associated with the opportunity to make comparisons, even if comparison is not considered the primary BCT induced.

    For reviews that explicitly categorized features based on social comparison or other types of social influence (eg, modeling), the percentages attributed to social comparison processes were taken directly from the original published review. For reviews that did not use these terms, the percentages were calculated manually by reviewing the details available in the original published review, where possible (eg, references to social networking features or exposure to information about other users). As for all other data extraction, the second and third authors (MB and KP, respectively) independently determined the percentages of apps that categorized features as inducing social comparison or other social processes. The first author (DA) then calculated the interrater agreement (91%) and independently rated a subset of included reviews to verify the accuracy; the remaining discrepancies were resolved by consensus.


    Types of Reviews

    Among the 26 articles reviewed, the number of apps identified as promoting PA or weight control ranged from 12 [33] to more than 28,000 [34]. Of these 26 articles, 10 (38%) focused exclusively on apps intended to increase PA and 10 (38%) focused on weight loss, weight management, or obesity intervention (the largest subsets; see Table 1). The remaining reviews (6/26, 23%) considered a combination of diet, PA, and/or weight control/obesity prevention. The majority of articles reviewed only commercially available apps (19/26, 73%), primarily those available through the iTunes App Store (Apple operating system). Only 27% (7/26) of reviews appeared to include apps developed or empirically tested by researchers. The popularity of these reviews appeared to increase through 2014 (peaking in 2014-2016) and then decrease through 2019.

    Table 1. Descriptive information for each included review of physical activity and related apps.
    Author and yearType(s) of appsApps reviewed, nOperating system(s)Behavioral target or targets
    Azar et al (2013) [35]Commercial23AppleWeight management
    Bardus et al (2016) [4]Commercial23Apple and AndroidWeight management
    Bondaronek et al (2018) [36]Commercial65Apple and AndroidIncrease physical activity
    Brannon and Cushing (2015) [37]Botha200 (number of PA apps only)AppleImprove diet or physical activity
    Breton et al (2011) [38]Commercial204AppleWeight management
    Conroy et al (2014) [29]Commercial200Apple and AndroidIncrease physical activity
    Direito et al (2014) [39]Commercial40AppleWeight management
    Dute et al (2016) [33]Researcher developed12AppleHealthy nutrition, or physical activity, or overweight prevention
    Jee (2017) [40]Both200Apple and AndroidIncrease physical activity
    Jeon et al (2014) [41]Commercial104AppleWeight management
    Knight et al (2015) [30]Commercial379Apple and AndroidIncrease physical activity
    Middelweerd et al (2014) [3]Commercial64Apple and AndroidIncrease physical activity
    Modave et al (2015) [42]Commercial30AppleIncrease physical activity
    Nikolaou and Lean (2017) [34]Commercial28,905Apple, Android, Amazon, Windows, BlackberryWeight management
    Payne et al (2015) [43]Commercial52AppleIncrease physical activity
    Quelly et al (2016) [44]Researcher developed9Not applicableImprove weight-related behaviors, psychosocial factors, and/or anthropometric outcomes
    Rivera et al (2016) [5]Commercial393Apple, Android, Blackberry, WindowsWeight management
    Schoeppe et al (2016) [45]Both27Not mentionedImprove diet, physical activity, and/or sedentary behavior
    Schoeppe et al (2017) [46]Commercial25Apple and AndroidImprove diet, physical activity, and/or sedentary behavior
    Schoffman et al (2013) [47]Commercial57ApplePediatric weight management
    Stuckey et al (2017) [48]Both18Not mentionedIncrease physical activity
    Vlahu-Gjorgievska et al (2018) [49]Researcher testedb6Not mentionedWeight management
    Wang et al (2015) [50]Commercial10AndroidIncrease physical activity
    Wearing et al (2014) [51]Commercial62ApplePediatric weight management
    West et al (2012) [52]Commercial3336AppleIncrease physical activity
    Yang et al (2015) [53]Commercial100Apple and AndroidIncrease physical activity

    aBoth: the article reviewed both commercially available and researcher-developed apps.

    bResearcher tested: commercially available apps evaluated in formal research studies.

    Social Comparison in Physical Activity Apps

    Reference to Social Comparison as a Behavior Change Mechanism

    Of the included review articles, 31% (8/26) categorized app features as inducing social comparison (see Table 2). The percentages of apps with social comparison features ranged from 8% (2/27) [45] to 66% (43/65) [36], with an average of 30% across reviews that used social comparison as a category (see Table 3).

    Table 2. Summary of social comparison data extracted from each review of physical activity apps.
    Author and yearSCa definitionApps with SC as a category, n (%)SC featuresApps specifying the SC dimension, n (%)Apps with SC tailoring, n (%)
    Azar et al (2013) [35]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Bardus et al (2016) [4]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Bondaronek et al (2018) [36]Michie et al (2013) [13]43 (66)bNot mentioned43 (66)b (comparison of behavior)Not mentioned
    Brannon and Cushing (2015) [37]Abraham and Michie (2008) [9]9 (14)c“Most commonly be seen in the case of group practice but could also be employed using detailed case studies in text or video or by pairing people as supports.”Not mentionedNot mentioned
    Breton et al (2011) [38]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Conroy et al (2014) [29]Michie et al (2011) [54]25 (15.0)dNot mentionedNot mentionedNot mentioned
    Direito et al (2014) [39]Abraham and Michie (2008) [9]22 (55)eNot mentionedNot mentionedNot mentioned
    Dute et al (2016) [33]Abraham and Michie (2008) [9]1 (8)fActivity sharing and connection to a partner whose activities are visible4 (33)f (sharing activities)Not mentioned
    Jee (2017) [40]Not mentionedNot mentionedCompetitionsNot specifiedNot mentioned
    Jeon et al (2014) [41]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Knight et al (2015) [30]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Middelweerd et al (2014) [3]Abraham and Michie (2008) [9]10 (16)gNot mentionedNot mentionedNot mentioned
    Modave et al (2015) [42]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Nikolaou and Lean (2017) [34]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Payne et al (2015) [43]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Quelly et al (2016) [44]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Rivera et al (2016) [5]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Schoeppe et al (2016) [45] Not mentionedNot mentionedSocial comparison with friends via leaderboardsNot specified (comparison of physical activity behavior)Not mentioned
    Schoeppe et al (2017) [46]Abraham and Michie (2008) [9]10 (40)hCompetitions, leaderboardsNot specifiedNot mentioned
    Schoffman et al (2013) [47]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Stuckey et al (2017) [48]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Vlahu-Gjorgievska et al (2018) [49]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Wang et al (2015) [50]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Wearing et al (2014) [51]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    West et al (2012) [52]Not mentionedNot mentionedNot mentionedNot mentionedNot mentioned
    Yang et al (2015) [53]Michie et al (2013) [13]25 (25.0)iNot specifiedNot specifiedNot mentioned

    aSC: social comparison.

    bN=65.

    cN=66.

    dN=167.

    eN=40.

    fN=12.

    gN=64.

    hN=25.

    iN=100.

    Table 3. Percentages of articles reviewed (26 articles) that included each behavior change technique (BCT) category, and average percentages of apps identified by these articles as including features that belong to each BCT category.
    Behavior change technique (BCT)Percentage of articles reviewed (N=26)Average percent of apps with designated features
    Social comparison3130
    Modeling5435
    Normative feedback1213
    Social networking3832
    Tailoring (general)3140
    Definitions of Social Comparison

    The majority of articles that referenced social comparison (5/8, 63%) employed Abraham and Michie’s [9] BCT definition of social comparison—“facilitate[ing] observation of nonexpert others’ performance for example, in a group class or using video or case study.” Other definitions included those proposed by Michie et al’s [54] revised Coventry, Aberdeen & London – Refined CALO-RE BCT or Michie et al’s [13] hierarchy of BCTs; see Table 4 for the full text and frequencies of these definitions. Of note, Abraham and Michie’s [9] definition specifies that comparison targets are nonexperts, and Michie et al’s [54] definition explicitly states that merely exposing users to others using group settings does not constitute social comparison, as several other processes could be engaged (eg, modeling, social support).

    Table 4. Definitions of social comparison used in existing reviews of physical activity apps.
    Author and yearDefinitionReviews using this definition, n (%)a
    Abraham and Michie (2008) [9]“Facilitate observation of nonexpert others’ performance for example, in a group class or using video or case study.”6 (67)
    Michie et al (2011) [54] “Facilitate social comparison Involves explicitly drawing attention to others’ performance to elicit comparisons. NB: The fact the intervention takes place in a group setting, or have been placed in groups on the basis of shared characteristics, does not necessarily mean social comparison is actually taking place. Social support may also be encouraged in such settings. Group classes may also involve instruction, demonstration, and practice.”1 (11)
    Michie et al (2013) [13]“Draw attention to others’ performance to allow comparison with the person’s own performance. Note: being in a group setting does not necessarily mean that social comparison is actually taking place.”2 (22)

    aPercentages above use a denominator of N=8, the number of reviews that categorized app features as social comparison.

    Social Comparison App Features

    Across definitions, only some of the articles that categorized social comparison (5/8, 63%) specified or implied which features they considered to induce comparison. These reviews referenced leaderboards [46], competitions [40], sharing information with other users [33], and connections between users [30]. One article described social comparison as features such as “group practice… [and] detailed case studies in text or video or by pairing people as supports” [37]. Another review indicated that friendly competitions were available in some apps but did not include them as features that prompt social comparison [45].

    Dimension of Comparison

    Of the 8 articles that categorized features inducing social comparison, 3 (38%) referenced the specific dimension. One review indicated that users could share/compare their activities (33% of apps reviewed) [33]; the other distinguished between apps that allowed for comparison of behavior (66% of apps reviewed) and comparison of outcomes (13% of apps reviewed) [36]. Comparison of behavior was most often described as a demonstration of particular exercises (ie, modeling), whereas comparison of outcomes referred to potential consequences of a behavior, rather than to social comparison [13]. The third review described apps that allowed sharing/comparing PA information [46], although without specifying the percentages of apps with such features.

    Acknowledgment of Between- and Within-Person Variability or Tailoring of Comparison Features

    None of the articles reviewed referred to individual (between-person) differences in social comparison responses or preferences, a change in these responses or preferences (within-person) over time, or tailoring social comparison features to address either level of variability. In contrast, 8 of the 26 included articles (31%) described tailoring or personalization with respect to feedback on user progress toward behavioral goals (92% of apps reviewed; see Table 5) [36]. For example, users who did not meet the PA guidelines for a given period were given a visual comparison of their PA to the recommended level of PA (vs reinforcement for those who met the guidelines), with PA information matched to users’ demographic characteristics (eg, PA and aging for those over 45, PA and weight loss for those with BMIs greater than 25) [55]. Reviews also referenced tailoring with respect to matching motivational cueing (28% of apps reviewed) [48], exercise prescriptions (11% of apps reviewed) [48], and encouraging messages (33% of apps reviewed) [49] to users’ progress and/or preferences.

    Table 5. Summary of tailoring, modeling, norm referencing, and social networking data extracted from each review of physical activity apps.
    Author and yearApps with any tailoring, n/N (%)Apps with modeling as a category, n/N (%)Apps with normative feedback, n/N (%)Apps with social networking capabilities, n/N (%)
    Azar et al (2013) [35]Not mentionedNot specifiedNot mentionedNot mentioned
    Bardus et al (2016) [4]Not mentionedNot mentionedNot mentioned3.45/23 (15)
    Bondaronek et al (2018) [36]60/65 (92)31/65 (47)Not mentionedNot mentioned
    Brannon and Cushing (2015) [37]Not mentioned124/200 (62.0)Not mentionedNot mentioned
    Breton et al (2011) [38]Not mentionedNot mentionedNot mentioned7/204 (3.4)
    Conroy et al (2014) [29]Not mentioned106/200 (53.0)2/200 (1.0)Not mentioned
    Direito et al (2014) [39]Not mentioned21/40 (53)Not mentionedNot mentioned
    Dute et al (2016) [33]4/12 (33)4/12 (33)Not mentioned3/12 (25)
    Jee (2017) [40]Not mentionedNot mentionedNot mentionedNot mentioned
    Jeon et al (2014) [41]Not mentionedNot mentionedNot mentionedNot mentioned
    Knight et al (2015) [30]Not mentionedNot mentionedNot mentioned209/379 (55.1)
    Middelweerd et al (2014) [3]64/64 (100)7/64 (11)Not mentionedNot mentioned
    Modave et al (2015) [42]Not mentionedNot mentionedNot mentioned1/30 (3)
    Nikolaou and Lean (2017) [34]Not mentionedNot mentionedNot mentionedNot mentioned
    Payne et al (2015) [43]Not mentioned16/52 (31)3/52 (6)Not mentioned
    Quelly et al (2016) [44]3/9 (33)1/9 (11)3/9 (33)Not mentioned
    Rivera et al (2016) [5]7/393 (1.8)Not mentionedNot mentionedNot mentioned
    Schoeppe et al (2016) [45]7/27 (26)2/27 (7)Not mentioned4/27 (15)
    Schoeppe et al (2017) [46]Not mentioned6.75/25 (27)Not mentionedNot mentioned
    Schoffman et al (2013) [47]5/57 (9)Not mentionedNot mentioned9/57 (16)
    Stuckey et al (2017) [48]5/18 (28)Not mentionedNot mentionedNot mentioned
    Vlahu-Gjorgievska et al (2018) [49]Not mentionedNot mentionedNot mentionedNot mentioned
    Wang et al (2015) [50]Not mentioned3.7/10 (37)Not mentioned7.8/10 (78)
    Wearing et al (2014) [51]Not mentioned18/62 (29)Not mentionedNot mentioned
    West et al (2012) [52]Not mentioned1235/3336 (37.0)Not mentioned1535/3336 (46.0)
    Yang et al (2015) [53]Not mentioned47/100 (47.0)Not mentioned32/100 (32.0)

    Other App Processes Relevant to Social Comparison

    Modeling/Demonstrating Behavior

    Of the 26 articles, 14 (54%) classified app features as modeling or demonstrating particular behaviors (eg, proper exercise form; see Table 5). The percentage of app features categorized as modeling in each review ranged in size from 7% [45] to 53% [29,38], with an average of 35%. One review indicated that modeling was a popular BCT but did not specify the percentage of apps with this feature [35]. Behavioral models were either fitness professionals (coaches) or app users who appeared via a photo or video. Although these features were not counted as inducing comparison, modeling represents an attempt to increase similarity (or decrease the perceived difference) between the app user's behavior and a comparison target's behavior. Consequently, modeling features may facilitate social comparison.

    Normative Feedback

    Providing normative information about others’ behavior is intended to give an individual user a sense of how they compare to the average for a relevant group. Although social comparison often refers to comparisons against individual targets, comparison to a group average is a related process [56]. Of the 26 articles, 3 (12%) evaluated whether apps provided normative information to users. These articles reported that normative information appeared in 1% [29] to 33% [44] of the apps reviewed, with an average of 13%.

    Social Networking

    Of the 26 articles, 10 (38%) referenced social networking features via app-specific communities or connections to existing social media platforms. Percentages of apps designated as offering these features ranged from 3% [38,42] to 78% [50], with an average of 32%. Although social networking platforms can facilitate several social influence processes (eg, social reinforcement or support), social comparisons between users of these platforms are common (based on shared text, objective data, or images) and are associated with a range of affective and behavioral responses [57,58].


    Reviewing Evidence of Social Comparison in Physical Activity Apps

    Social comparison is known to influence motivation and health behavior and is frequently manipulated in health behavior change interventions [9]. Comparison processes may be particularly useful for promoting PA with technology such as smartphone apps; objective measures of PA can be visualized and shared between app users, and users can see evidence of change in their relative standing by increasing their PA behavior over short time frames. Despite the interest in social comparison as a motivator of PA change and the exponential increase in publications about digital health interventions [59], no review to date has attempted to summarize existing literature on the social comparison features of PA apps. We undertook the present scoping review to address this gap and provide recommendations for future research in this area.

    Defining and Classifying Social Comparison

    A modest proportion of the 26 available and eligible reviews of PA promotion apps categorized app features as eliciting social comparison (31%). Comparison fell behind modeling as a popular intervention process (54%) but was as common as social networking (38%; which also may facilitate comparison) and was more common than related processes such as norm referencing (12%). All the articles that included social comparison as a category used versions of the BCT taxonomy [9,13,54]. However, the versions differ in their definitions of social comparison. The original BCT taxonomy specifies that the potential target of comparison must be a nonexpert [9]; exposure to an expert is classified as modeling. Although modeling appeared more frequently in apps than did social comparison, the percentages of apps with features in each category differed modestly (ie, 35% vs 30%; see Table 3). Later iterations of the BCT taxonomy removed the requirement that only social comparisons with nonexperts would qualify [13,54]. Visual inspection of the percentage of apps classified as having social comparison features suggests that using the broader definition, ie, including experts, slightly increases the average proportions of apps that receive a social comparison designation (ie, 27% to 35%). The broader definition also is consistent with definitions of social comparison used in the mainstream comparison literature, where targets often include media figures or fashion models, in addition to peers [60].

    Abraham and Michie’s [9] initial taxonomy also defined comparison as simply observation of another’s performance, which may occur in a variety of contexts (eg, group classes). Using this definition, PA app features such as social networking or message boards (where users can report on their performance) may count as social comparison [30,33]. In contrast, later versions explicitly state that attention must be drawn to the other’s performance and that contexts such as group classes do not necessarily induce comparison (vs other social processes) [13,54]. This definition implies that social networking and message boards would not count as social comparison, whereas leaderboards or competitions would [40,46].

    The majority of reviews did not include any mention of specific dimensions of social comparison, and those that did made only vague references to dimensions (eg, comparison of behavior without specifying which behavior, eg, steps, etc). A recent meta-analysis suggests that comparison dimension provides information about the target’s relevance to the self; if relevance to the self is not clear, the individual might reflect on their target’s performance but not engage in comparative self-evaluation [61]. Owing to the many dimensions potentially relevant to PA promotion (eg, steps, calories burned, minutes of activity, and overall fitness) and the likelihood that these dimensions are not relevant for all app users [62], this review highlights the need for increased specificity in future work that describes social comparison features of apps.

    As very few articles included descriptions of the specific features eliciting comparison, the exact degree of heterogeneity is unclear. What can be concluded is that existing reviews of PA apps show considerable variability in their approaches to defining and classifying social comparison. Specifically, comparison, modeling, and information sharing are not consistently differentiated. The heterogeneity associated with which features activate social comparison represents a challenge for future research to evaluate the unique effect of comparison as a mechanism of app-based behavior change, or its efficacy relative to other mechanisms [15]. Inconsistency in the definition of comparison also creates challenges for optimizing app-based interventions to address comparison preferences and needs between users, which may be either stable or dynamic. In this vein, PA app development has not yet integrated theoretical and empirical advances that the mainstream social comparison literature has made.

    Social Comparison Theory and Evidence Relevant to Physical Activity App Design

    Interest in and responsiveness to social comparison information vary across individuals. This construct, called social comparison orientation (SCO) [63], has been positively associated with engagement in PA [64]. PA app users with strong SCOs may engage in comparison in response to a wide variety of social features in PA apps, including social networking and message boards, and they may find this information motivating. Here, comparison information is available, but the comparison process itself is not intentionally activated. In contrast, users with weaker SCOs may engage in comparison only when the comparison process is deliberately induced, such as by competitive challenges or leaderboards that display PA data ranked from most to least [65]. Social comparison features also may be ineffective for users with weaker SCOs. These hypotheses imply that PA app effectiveness might be improved by guiding users toward the types of social features that match their level of SCO or away from social comparison features at particularly low levels of SCO.

    Additional variability may exist with respect to users’ social comparison preferences and their affective and behavioral responses to comparisons. As noted, users may find comparisons to targets who are doing better with respect to PA (ie, upward comparisons) either inspiring or disheartening and may find comparisons to targets who are doing worse (ie, downward comparisons) either comforting or anxiety-provoking [18,23]. Which combinations lead to the greatest increases in PA (or lead to increases vs decreases) and for whom are significant empirical questions [25,66,67]. Basic research indicates that the opportunity to select a comparison target does not always lead to optimal affective or health-relevant outcomes, nor does it always fulfill comparers’ goals (eg, to feel better) [18,68,69]. Thus, providing information about only the targets that a PA app user wants may not lead to benefits. Providing only the targets that they do not want may create an aversive experience, however, and may lead users to discontinue engagement with the app [28].

    The optimal combination of comparison target and affective response for increasing PA may differ between people. The best combination may also vary within the same person over time, as a function context (eg, precomparison mood), shift over the course of behavior change (eg, as users experience progress and setbacks) [56,70], and differ from users’ stated preferences, depending on whether users are just starting with the app or have been engaged for some time. The degree of within-person variability in social comparison preference and response (either affective or behavioral) remains unclear. The quantification of within-person variability and its responsiveness to social comparison interventions (eg, using N-of-1 designs) represent important next steps for PA app development and a broader understanding of social comparison processes [71].

    Future Directions for Social Comparison Features of Physical Activity Apps: Social Comparison Tailoring

    Despite gaps in the social comparison literature, evidence suggests that the effects of social comparison and preferences for a comparison type differ between people and within people over time. This review, however, detected no reference to between- or within-person variability in comparison response/preference or to tailoring social comparison features of PA apps. In contrast, this review indicates that tailoring in PA apps is common with respect to goals and feedback, which suggests that technology for such tailoring is currently in use. Tailoring the PA app experience to match user characteristics such as SCO or user-relevant PA comparison dimensions might improve the app’s acceptability and engagement and, in turn, enhance PA outcomes [28]. Indeed, tailoring has been shown to outperform generic messaging in PA interventions across a range of modalities, including apps [48,72]. Tailoring also might discourage negative consequences of comparison (eg, giving up in response to a failure to match another user's achievements) by matching a user’s comparison preferences with the types of comparisons that optimize engagement in PA. Such tailoring will require nuanced assessment of the effect of factors such as SCO, dimensions of relevance, comparison preferences, affective response to comparison, and PA engagement. The adaptive capabilities of many existing apps and those under development may lend themselves to such tailoring [73].

    Strengths, Limitations, and Additional Future Directions

    Strengths of this scoping review include its use of preregistered methods, adherence to PRISMA-ScR guidelines, and a comprehensive search for relevant reviews to provide insights into how social comparison is currently applied in existing PA apps. A subset of pertinent articles may have been overlooked, but the extensive and systematic search increases confidence in the overall conclusions. Additional app comparison features (eg, specific dimensions and tailoring) may have not been described in the reviews or missed by our coders. As a check, we examined several primary sources of empirical data and failed to find these additional details. One exception, an empirical study by Mollee and Klein [28], demonstrated PA benefits of matching (tailoring) versus not matching comparison targets to user preferences. There is need for additional work of this kind to inform best practices for tailoring social comparison features of PA apps.

    Although social comparison has been shown as effective for increasing PA in other types of interventions (eg, team-based competitions) [26], there are very few studies of the effectiveness of social comparison as a mechanism of change in PA apps (eg, randomized controlled trials, meta-analyses, and dismantling studies) to answer the question of whether, for whom, or under what circumstances social comparison features of apps produce positive changes in PA. Such research is critical to advance our basic understanding of comparison processes and their utility as BCTs, as is further information about within-person variability in comparison preferences and responses. This information would inform the necessary or sufficient social comparison features of PA apps needed for a successful intervention. To what extent our findings and conclusions apply beyond PA promotion (alone or in the context of weight control) to such health behaviors as smoking cessation or skin cancer prevention [74,75] remains to be addressed in future research.

    Conclusions

    This review documents that social comparison is frequently identified as a potential mechanism of behavior change in smartphone apps designed to promote PA, on par with mechanisms such as social networking (broadly defined). Behavioral modeling, which is considered in some reviews as a means of inducing social comparison, was the only comparison-related mechanism to appear in more reviews of PA apps than social comparison (as explicitly differentiated from other processes). Our findings highlight the need for careful consideration of social processes as behavior change mechanisms in app design and evaluation. Considerable gaps currently exist between theory and evidence relevant to social comparison and its implementation in PA apps. Greater attention to individual differences, dynamic responses, relevant PA dimensions, and comparison preferences and the potential to tailor apps on the basis of these characteristics may meaningfully improve the effectiveness of existing PA promotion apps.

    Acknowledgments

    DA was supported by NIH (grant no. K23HL136657; PI: Arigo).

    Conflicts of Interest

    None declared.

    1. Borrell LN. The effects of smoking and physical inactivity on advancing mortality in US adults. Ann Epidemiol 2014 Jun;24(6):484-487. [CrossRef] [Medline]
    2. Lewis BA, Napolitano MA, Buman MP, Williams DM, Nigg CR. Future directions in physical activity intervention research: expanding our focus to sedentary behaviors, technology, and dissemination. J Behav Med 2017 Feb;40(1):112-126 [FREE Full text] [CrossRef] [Medline]
    3. Middelweerd A, Mollee JS, van der Wal CN, Brug J, Te Velde SJ. Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nutr Phys Act 2014 Jul 25;11:97 [FREE Full text] [CrossRef] [Medline]
    4. Bardus M, van Beurden SB, Smith JR, Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int J Behav Nutr Phys Act 2016 Mar 10;13:35 [FREE Full text] [CrossRef] [Medline]
    5. Rivera J, McPherson A, Hamilton J, Birken C, Coons M, Iyer S, et al. Mobile apps for weight management: A scoping review. JMIR Mhealth Uhealth 2016 Jul 26;4(3):e87 [FREE Full text] [CrossRef] [Medline]
    6. Simões P, Silva AG, Amaral J, Queirós A, Rocha NP, Rodrigues M. Features, behavioral change techniques, and quality of the most popular mobile apps to measure physical activity: systematic search in app stores. JMIR Mhealth Uhealth 2018 Oct 26;6(10):e11281 [FREE Full text] [CrossRef] [Medline]
    7. Arigo D, Jake-Schoffman DE, Wolin K, Beckjord E, Hekler EB, Pagoto SL. The history and future of digital health in the field of behavioral medicine. J Behav Med 2019 Feb;42(1):67-83 [FREE Full text] [CrossRef] [Medline]
    8. Romeo A, Edney S, Plotnikoff R, Curtis R, Ryan J, Sanders I, et al. Can smartphone apps increase physical activity? Systematic review and meta-analysis. J Med Internet Res 2019 Mar 19;21(3):e12053 [FREE Full text] [CrossRef] [Medline]
    9. Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol 2008 May;27(3):379-387. [CrossRef] [Medline]
    10. Festinger L. A theory of social comparison processes. Hum Relat 1954;7(2):117-140. [CrossRef]
    11. Olander EK, Fletcher H, Williams S, Atkinson L, Turner A, French DP. What are the most effective techniques in changing obese individuals' physical activity self-efficacy and behaviour: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2013 Mar 3;10:29 [FREE Full text] [CrossRef] [Medline]
    12. Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour--and are they the same? Health Educ Res 2011 Apr;26(2):308-322. [CrossRef] [Medline]
    13. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013 Aug;46(1):81-95. [CrossRef] [Medline]
    14. Leahey TM, Crane MM, Pinto AM, Weinberg B, Kumar R, Wing RR. Effect of teammates on changes in physical activity in a statewide campaign. Prev Med 2010 Jul;51(1):45-49 [FREE Full text] [CrossRef] [Medline]
    15. Zhang J, Brackbill D, Yang S, Becker J, Herbert N, Centola D. Support or competition? How online social networks increase physical activity: A randomized controlled trial. Prev Med Rep 2016 Dec;4:453-458 [FREE Full text] [CrossRef] [Medline]
    16. Arigo D, Smyth JM, Suls JM. Perceptions of similarity and response to selected comparison targets in type 2 diabetes. Psychol Health 2015;30(10):1206-1220. [CrossRef] [Medline]
    17. Patel MS, Volpp KG, Rosin R, Bellamy SL, Small DS, Fletcher MA, et al. A randomized trial of social comparison feedback and financial incentives to increase physical activity. Am J Health Promot 2016 Jul;30(6):416-424 [FREE Full text] [CrossRef] [Medline]
    18. Schokker MC, Keers JC, Bouma J, Links TP, Sanderman R, Wolffenbuttel BHR, et al. The impact of social comparison information on motivation in patients with diabetes as a function of regulatory focus and self-efficacy. Health Psychol 2010 Jul;29(4):438-445. [CrossRef] [Medline]
    19. Tiggemann M, Polivy J. Upward and downward: Social comparison processing of thin idealized media images. Psychol Women Q 2010;34(3):356-364. [CrossRef]
    20. Sabiston CM, Chandler K. Effects of fitness advertising on weight and body shape dissatisfaction, social physique anxiety, and exercise motives in a sample of healthy-weight females. J Appl Biobehav Res 2009;14(4):165-180. [CrossRef]
    21. Pila E, Barlow MA, Wrosch C, Sabiston CM. Comparing the body to superior others: Associations with daily exercise and body evaluation in men and women. Psychol Sport Exerc 2016;27:120-127. [CrossRef]
    22. Zuckerman O, Gal-Oz A. Deconstructing gamification: evaluating the effectiveness of continuous measurement, virtual rewards, and social comparison for promoting physical activity. Pers Ubiquit Comput 2014;18(7):1705-1719. [CrossRef]
    23. Buunk BP, Ybema JF. Social comparisons and occupational stress: The identification-contrast model. In: Buunk BP, Gibbons FX, editors. Health, Coping, and Well-being: Perspectives From Social Comparison Theory. New Jersey: Lawrence Erlbaum Associates Inc; 1997:359-388.
    24. Wills TA. Downward comparison principles in social psychology. Psychol Bull 1981;90(2):245-271. [CrossRef]
    25. van der Zee K, Buunk B, Sanderman R, Botke G, van den Bergh F. Social comparison and coping with cancer treatment. Pers Indiv Differ 2000;28(1):17-34. [CrossRef]
    26. Leahey TM, Crowther JH, Ciesla JA. An ecological momentary assessment of the effects of weight and shape social comparisons on women with eating pathology, high body dissatisfaction, and low body dissatisfaction. Behav Ther 2011 Jun;42(2):197-210 [FREE Full text] [CrossRef] [Medline]
    27. Wheeler L, Miyake K. Social comparison in everyday life. J Pers Soc Psychol 1992;62(5):760-773. [CrossRef]
    28. Mollee JS, Klein MC. The Effectiveness of Upward and Downward Social Comparison of Physical Activity in an Online Intervention. In: Proceedings of the 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security.: IEEE; 2016 Presented at: IUCC-CSS'16; December 14-16, 2016; Granada, Spain p. 109-115. [CrossRef]
    29. Conroy DE, Yang C, Maher JP. Behavior change techniques in top-ranked mobile apps for physical activity. Am J Prev Med 2014 Jun;46(6):649-652. [CrossRef] [Medline]
    30. Knight E, Stuckey MI, Prapavessis H, Petrella RJ. Public health guidelines for physical activity: is there an app for that? A review of android and apple app stores. JMIR Mhealth Uhealth 2015 May 21;3(2):e43 [FREE Full text] [CrossRef] [Medline]
    31. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Method 2005;8(1):19-32. [CrossRef]
    32. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Ann Intern Med 2018 Oct 2;169(7):467-473. [CrossRef] [Medline]
    33. Dute DJ, Bemelmans WJ, Breda J. Using mobile apps to promote a healthy lifestyle among adolescents and students: A review of the theoretical basis and lessons learned. JMIR Mhealth Uhealth 2016 May 5;4(2):e39 [FREE Full text] [CrossRef] [Medline]
    34. Nikolaou CK, Lean ME. Mobile applications for obesity and weight management: current market characteristics. Int J Obes (Lond) 2017 Jan;41(1):200-202. [CrossRef] [Medline]
    35. Azar KM, Lesser LI, Laing BY, Stephens J, Aurora MS, Burke LE, et al. Mobile applications for weight management: theory-based content analysis. Am J Prev Med 2013 Nov;45(5):583-589. [CrossRef] [Medline]
    36. Bondaronek P, Alkhaldi G, Slee A, Hamilton FL, Murray E. Quality of publicly available physical activity apps: Review and content analysis. JMIR Mhealth Uhealth 2018 Mar 21;6(3):e53 [FREE Full text] [CrossRef] [Medline]
    37. Brannon EE, Cushing CC. A systematic review: is there an app for that? Translational science of pediatric behavior change for physical activity and dietary interventions. J Pediatr Psychol 2015 May;40(4):373-384. [CrossRef] [Medline]
    38. Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! But does it adhere to evidence-informed practices? Transl Behav Med 2011 Dec;1(4):523-529 [FREE Full text] [CrossRef] [Medline]
    39. Direito A, Dale LP, Shields E, Dobson R, Whittaker R, Maddison R. Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health 2014 Jun 25;14:646 [FREE Full text] [CrossRef] [Medline]
    40. Jee H. Review of researches on smartphone applications for physical activity promotion in healthy adults. J Exerc Rehabil 2017 Feb;13(1):3-11 [FREE Full text] [CrossRef] [Medline]
    41. Jeon E, Park H, Min YH, Kim H. Analysis of the information quality of Korean obesity-management smartphone applications. Healthc Inform Res 2014 Jan;20(1):23-29 [FREE Full text] [CrossRef] [Medline]
    42. Modave F, Bian J, Leavitt T, Bromwell J, Iii CH, Vincent H. Low quality of free coaching apps with respect to The American College of Sports Medicine Guidelines: a review of current mobile apps. JMIR Mhealth Uhealth 2015 Jul 24;3(3):e77 [FREE Full text] [CrossRef] [Medline]
    43. Payne HE, Moxley VB, MacDonald E. Health behavior theory in physical activity game apps: a content analysis. JMIR Serious Games 2015 Jul 13;3(2):e4 [FREE Full text] [CrossRef] [Medline]
    44. Quelly SB, Norris AE, DiPietro JL. Impact of mobile apps to combat obesity in children and adolescents: A systematic literature review. J Spec Pediatr Nurs 2016 Jan;21(1):5-17. [CrossRef] [Medline]
    45. Schoeppe S, Alley S, van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act 2016 Dec 7;13(1):127 [FREE Full text] [CrossRef] [Medline]
    46. Schoeppe S, Alley S, Rebar AL, Hayman M, Bray NA, van Lippevelde W, et al. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: a review of quality, features and behaviour change techniques. Int J Behav Nutr Phys Act 2017 Jun 24;14(1):83 [FREE Full text] [CrossRef] [Medline]
    47. Schoffman DE, Turner-McGrievy G, Jones SJ, Wilcox S. Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: just fun and games? Transl Behav Med 2013 Sep;3(3):320-325 [FREE Full text] [CrossRef] [Medline]
    48. Stuckey MI, Carter SW, Knight E. The role of smartphones in encouraging physical activity in adults. Int J Gen Med 2017;10:293-303 [FREE Full text] [CrossRef] [Medline]
    49. Vlahu-Gjorgievska E, Mulakaparambil US, Win K. mHealth applications: A tool for behaviour change in weight management. In: Studies in Health Technology and Informatics. Amsterdam, Netherlands: IOS Press; 2018:158-163.
    50. Wang Y, Sun L, Xu Y, Xiao Q, Chang P, Wu Y. Comparision and analysis of top 10 exercise android apps in mainland China. Stud Health Technol Inform 2015;216:1024. [Medline]
    51. Wearing JR, Nollen N, Befort C, Davis AM, Agemy CK. iPhone app adherence to expert-recommended guidelines for pediatric obesity prevention. Child Obes 2014 Apr;10(2):132-144 [FREE Full text] [CrossRef] [Medline]
    52. West JH, Hall PC, Hanson CL, Barnes MD, Giraud-Carrier C, Barrett J. There's an app for that: content analysis of paid health and fitness apps. J Med Internet Res 2012 May 14;14(3):e72 [FREE Full text] [CrossRef] [Medline]
    53. Yang C, Maher JP, Conroy DE. Implementation of behavior change techniques in mobile applications for physical activity. Am J Prev Med 2015 Apr;48(4):452-455. [CrossRef] [Medline]
    54. Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health 2011 Nov;26(11):1479-1498. [CrossRef] [Medline]
    55. Soetens KCM, Vandelanotte C, de Vries H, Mummery KW. Using online computer tailoring to promote physical activity: a randomized trial of text, video, and combined intervention delivery modes. J Health Commun 2014 Dec;19(12):1377-1392. [CrossRef] [Medline]
    56. Zell E, Alicke MD. The local dominance effect in self-evaluation: evidence and explanations. Pers Soc Psychol Rev 2010 Nov;14(4):368-384. [CrossRef] [Medline]
    57. Fardouly J, Diedrichs PC, Vartanian LR, Halliwell E. Social comparisons on social media: the impact of Facebook on young women's body image concerns and mood. Body Image 2015 Mar;13:38-45. [CrossRef] [Medline]
    58. Vogel EA, Rose JP, Okdie BM, Eckles K, Franz B. Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Pers Indiv Differ 2015;86:249-256. [CrossRef]
    59. Müller AM, Maher CA, Vandelanotte C, Hingle M, Middelweerd A, Lopez ML, et al. Physical activity, sedentary behavior, and diet-related eHealth and mHealth research: Bibliometric analysis. J Med Internet Res 2018 Apr 18;20(4):e122 [FREE Full text] [CrossRef] [Medline]
    60. Leahey TM, Crowther JH. An ecological momentary assessment of comparison target as a moderator of the effects of appearance-focused social comparisons. Body Image 2008 Sep;5(3):307-311. [CrossRef] [Medline]
    61. Gerber JP, Wheeler L, Suls J. A social comparison theory meta-analysis 60+ years on. Psychol Bull 2018 Feb;144(2):177-197. [CrossRef] [Medline]
    62. Harrison D, Marshall P, Bianchi-Berthouze N, Bird J. Activity Tracking: Barriers, Workarounds and Customisation. In: Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing.: Association for Computing Machinery; 2015 Presented at: UbiComp'15; September 7 - 11, 2015; Osaka, Japan p. 617-621. [CrossRef]
    63. Gibbons FX, Buunk BP. Individual differences in social comparison: Development of a scale of social comparison orientation. J Pers Soc Psychol 1999;76(1):129-142. [CrossRef]
    64. Luszczynska A, Gibbons FX, Piko BF, Tekozel M. Self-regulatory cognitions, social comparison, and perceived peers' behaviors as predictors of nutrition and physical activity: a comparison among adolescents in Hungary, Poland, Turkey, and USA. Psychol Health 2004;19(5):577-593. [CrossRef]
    65. Arigo D, Suls JM. Smartphone apps providing social comparison for health behavior change: a need for better tailoring to person and context. Mhealth 2018;4:46 [FREE Full text] [CrossRef] [Medline]
    66. Bennenbroek FT, Buunk BP, van der Zee KI, Grol B. Social comparison and patient information: what do cancer patients want? Patient Educ Couns 2002 May;47(1):5-12. [CrossRef] [Medline]
    67. DeVellis RF, Holt K, Renner BR, Blabock SJ, Blanchard LW, Cook HL, et al. The relationship of social comparison to rheumatoid arthritis symptoms and affect. Basic Appl Soc Psych 1990;11(1):1-18. [CrossRef]
    68. Arigo D, Cornell M, Smyth JM. Social comparisons in adults with type 2 diabetes: Patients' reasons for target selection. Psychol Health 2018 Jul;33(7):906-924. [CrossRef] [Medline]
    69. French D, Sutton S, Marteau T, Kinmonth A. The impact of personal and social comparison information about health risk. Br J Health Psychol 2004 May;9(Pt 2):187-200. [CrossRef] [Medline]
    70. Johnson BK, Knobloch-Westerwick S. Glancing up or down: Mood management and selective social comparisons on social networking sites. Comput Hum Behav 2014;41:33-39. [CrossRef]
    71. Arigo D, Mogle JA, Brown MM, Pasko K, Travers L, Sweeder L, et al. Methods to assess social comparison processes within persons in daily life: A scoping review. Front Psychol 2019;10:2909 [FREE Full text] [CrossRef] [Medline]
    72. Direito A, Walsh D, Hinbarji M, Albatal R, Tooley M, Whittaker R, et al. Using the intervention mapping and behavioral intervention technology frameworks: Development of an mHealth intervention for physical activity and sedentary behavior change. Health Educ Behav 2018 Jun;45(3):331-348. [CrossRef] [Medline]
    73. Nahum-Shani I, Smith S, Spring B, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Ann Behav Med 2018 May 18;52(6):446-462 [FREE Full text] [CrossRef] [Medline]
    74. Gerrard M, Gibbons FX, Lane DJ, Stock ML. Smoking cessation: social comparison level predicts success for adult smokers. Health Psychol 2005 Nov;24(6):623-629. [CrossRef] [Medline]
    75. Mahler HI, Kulik JA, Gerrard M, Gibbons FX. Effects of upward and downward social comparison information on the efficacy of an appearance-based sun protection intervention: a randomized, controlled experiment. J Behav Med 2010 Dec;33(6):496-507 [FREE Full text] [CrossRef] [Medline]

    ‎
    BCT: behavior change technique
    mHealth: mobile health
    PA: physical activity
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    SC: social comparison

    Edited by G Eysenbach; submitted 30.07.19; peer-reviewed by FJ Yang, R Ciptaningtyas, E Kozasa; comments to author 07.12.19; revised version received 06.01.20; accepted 24.01.20; published 27.03.20

    Copyright

    ©Danielle Arigo, Megan M Brown, Kristen Pasko, Jerry Suls. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.03.2020.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.