The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review

Abstract

Background Mobile health (mHealth) apps targeting health behaviors using behavior change techniques (BCTs) have been successful in promoting healthy behaviors; however, their efficacy with sleep is unclear. Some work has shown success in promoting sleep through mHealth, whereas there have been reports that sleep apps can be adverse and lead to unhealthy obsessions with achieving perfect sleep. Objective This study aims to report and describe the use of BCTs in mHealth apps for sleep with the following research questions: How many BCTs are used on average in sleep apps, and does this relate to their effectiveness on sleep outcomes? Are there specific BCTs used more or less often in sleep apps, and does this relate to their effectiveness on sleep outcomes? Does the effect of mHealth app interventions on sleep change when distinguishing between dimension and measurement of sleep? Methods We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to review articles on mHealth app interventions for sleep published between 2010 and 2020. Results A total of 12 studies met the eligibility criteria. Most studies reported positive sleep outcomes, and there were no negative effects reported. Sleep quality was the most common dimension of sleep targeted. Subjective measures of sleep were used across all apps, whereas objective measures were often assessed but rarely reported as part of results. The average number of BCTs used was 7.67 (SD 2.32; range 3-11) of 16. Of the 12 studies, the most commonly used BCTs were feedback and monitoring (n=11, 92%), shaping knowledge (n=11, 92%), goals and planning (n=10, 83%), and antecedents (n=10, 83%), whereas the least common were scheduled consequences (n=0, 0%), self-belief (n=0, 0%), and covert learning (n=0, 0%). Most apps used a similar set of BCTs that unfortunately did not allow us to distinguish which BCTs were present when studies reported more positive outcomes. Conclusions Our study describes the peer-reviewed literature on sleep apps and provides a foundation for further examination and optimization of BCTs used in mHealth apps for sleep. We found strong evidence that mHealth apps are effective in improving sleep, and the potential reasons for the lack of adverse sleep outcome reporting are discussed. We found evidence that the type of BCTs used in mHealth apps for sleep differed from other health outcomes, although more research is needed to understand how BCTs can be implemented effectively to improve sleep using mHealth and the mechanisms of action through which they are effective (eg, self-efficacy, social norms, and attitudes).

The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System

“…In addition, mHealth apps for treating sleep disturbance have been developed rapidly [30,31], and more than 2000 mHealth apps targeting sleep are presently available in commercial app stores [32]. Pulantara et al [33,34] developed the interactive Resilience Enhancing Sleep Tactics app and examined its clinical feasibility as a treatment for sleep behavior.…”

“…This research investigating the usefulness of mHealth apps in enhancing sleep has several limitations. First, most mHealth trials have not assessed sleep behavior objectively [31], and the feedback provided depended on self-report assessments by the participants. While some studies incorporated wearable devices into the trials [38,39], they used the measurements only to assess the efficacy of the trial but not to objectivize the feedback.…”

“…Given the importance of self-management for habitual sleep behavior to prevent future health problems, it is beneficial to implement objective sleep feedback into the apps. Second, previous mHealth studies focused on improvement in limited aspects of habitual sleep behavior, such as sleep quality and sleep duration [31], and the dynamic aspects of sleep, including intraindividual variability in sleep measurements over the trial period, have tended to be ignored. To the best of our knowledge, only 1 mHealth study by Murawski et al [40] reported the dynamic aspects of sleep behavior and found that variability in sleep timing, as assessed by a self-report questionnaire, was improved after their intervention.…”

“…Several mHealth apps have been developed for treating sleep disturbances such as insomnia [30,31], but the objective sleep feedback function has not been implemented in these apps. While users of consumer-grade wearable devices can take advantage of objective sleep measurements, these are regarded as pull-type interventions, as these devices require the user to actively access an app to receive feedback, which can sometimes be burdensome.…”

Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review

“…Interestingly, most of the systems reviewed in this article has not formally self-identified as RS despite of offering tailored actionable recommendations for improving sleep. There have been promising reviews on sleep apps but they are notably limited to the traditional scope of mHealth [40][41][42][43]94,97,141]. To our knowledge, the present study is the first to examine digital sleep health systems through the lens of CARS and to formally introduce the concept of RS to ubiquitous sleep computing research.…”