Abstract

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

Highlights

  • Mobile applications 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

  • One in five smartphone users utilize at least one software application 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 Mobile health (mHealth) approaches have yet to realize their potential, in the physical activity domain

  • The present study examined how behavior change techniques are used to market top-ranked physical activity apps for the most common mobile operating systems

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Summary

Objectives

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

Methods
Results
Discussion
Conclusion
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