Brief Report
Behavior Change Techniques in Top-Ranked Mobile Apps for Physical Activity

https://doi.org/10.1016/j.amepre.2014.01.010Get rights and content

Background

Mobile applications (apps) have potential for helping people increase their physical activity, but little is known about the behavior change techniques marketed in these apps.

Purpose

The aim of this study was to characterize the behavior change techniques represented in online descriptions of top-ranked apps for physical activity.

Methods

Top-ranked apps (n=167) were identified on August 28, 2013, and coded using the Coventry, Aberdeen and London–Revised (CALO-RE) taxonomy of behavior change techniques during the following month. Analyses were conducted during 2013.

Results

Most descriptions of apps incorporated fewer than four behavior change techniques. The most common techniques involved providing instruction on how to perform exercises, modeling how to perform exercises, providing feedback on performance, goal-setting for physical activity, and planning social support/change. A latent class analysis revealed the existence of two types of apps, educational and motivational, based on their configurations of behavior change techniques.

Conclusions

Behavior change techniques are not widely marketed in contemporary physical activity apps. Based on the available descriptions and functions of the observed techniques in contemporary health behavior theories, people may need multiple apps to initiate and maintain behavior change. This audit provides a starting point for scientists, developers, clinicians, and consumers to evaluate and enhance apps in this market.

Introduction

Mobile health (mHealth) leverages technology, such as smartphones, to monitor and improve public health. Approximately one in five smartphone users utilize at least one software application (app) to support their health-related goals, and 38% of health app users have downloaded an app for physical activity.1 These apps tend not to be grounded explicitly in theories of health behavior, and the vast majority of commercial apps have not been evaluated using scientific methods.2, 3 Deconstructing this market may be useful for understanding why mHealth approaches have yet to realize their potential, particularly in the physical activity domain.

General parameters of physical activity apps, such as cost, acceptability, and theoretical representation, have been examined.2, 4 Others have reported on formative data, the process used to develop apps for research, or the acceptability and feasibility of using apps for behavior change.5, 6, 7, 8 The extent to which the techniques incorporated in physical activity apps have been examined has been to evaluate their fidelity with recommendations for weight loss and obesity prevention.9, 10 Characterizing the behavior change techniques in these apps would illuminate the landscape at the border of technology and behavior change, and could be valuable for both scientists and developers working in the mHealth domain, as well as physicians and other practitioners who currently have little information on which to base any app recommendations for patients who seek low-cost interventions to increase their physical activity. The present study examined how behavior change techniques are used to market top-ranked physical activity apps for the most common mobile operating systems.

Section snippets

Methods

The top-ranked “health and fitness” apps as of August 28, 2013, were identified on the two major online marketplaces: Apple iTunes (iPhone operating system [iOS]) and Google Play (Android). Apps were drawn from the top 50 paid and top 50 free lists in the “health and fitness” category for each operating system (resulting in four lists totaling 200 apps). Descriptions of each app were located online, reviewed, and coded independently by two trained coders using the Coventry, Aberdeen, and

Results

Of the 200 screened health and fitness apps, 167 (84%) involved physical activity (Android, 38 free and 37 paid; iOS, 43 free and 49 paid). The mean cost for paid apps was $1.97 (SD=1.96) and did not differ across operating systems (p>0.05).

Table 1 summarizes the frequencies of behavior change techniques marketed in apps. App descriptions had between one and 13 behavior change techniques (mean=4.2, SD=2.4, median=4). The most commonly observed techniques were as follows: providing instruction

Discussion

A review of documentation for top-ranked physical activity apps established that apps (1) emphasized a limited number of behavior change techniques and (2) could be separated into educational and motivational types. Others have reviewed apps for fidelity with evidence-based recommendations for obesity prevention or weight loss, but this study was the first to audit an array of behavior change techniques marketed in physical activity apps.9, 10

The most common behavior change techniques in

Acknowledgments

Funding for this work was provided in part by the Penn State Social Science Research Institute. The authors thank Rachel Angstadt, Leah Blatt, Kristen Elliot, Jazmine Gordon, Ashley Jones, Ashley Lutter, and Elisabet Polanco for their contributions as coders.

No financial disclosures were reported by the authors of this paper.

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