Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol

Abstract

Background: Recent advances in mobile and wearable technologies have led to new forms of interventions, called “Just-in-Time Adaptive Interventions” (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology. Methods: The study involves a year-long deployment of HeartSteps, a JITAI for physical activity and sedentary behavior, with 96 sedentary, overweight, but otherwise healthy adults. The central purpose is twofold: (1) to support the development of modeling approaches for operationalizing dynamic, mathematically rigorous theories of health behavior; and (2) to serve as a testbed for the development of learning algorithms that JITAIs can use to individualize intervention provision in real time at multiple timescales. Discussion and Conclusions: We outline an innovative modeling paradigm to model and use ILD in real- or near-time to individually tailor JITIAs.

Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review

“…16 out of the 20 studies (80%) to measure engagement included at least one measure of this category. Generally, researchers measured usage in three domains: (1) usage of a mobile application [17,21,28,35,38], (2) participant retention [18,25,30,31],…”

“…and for studies involving wearable sensor technology the (3) usage of wearable sensors [19,22,23,26,28,29,36].…”

“…Valle et al [38] on the other hand, measured usage as the proportion of intervention messages viewed daily. One study planned to measure usage in terms of time since last interaction with the intervention application [28]. In four studies, usage (or rather the lack thereof) was operationalized as participant retention [18,25,30,31].…”

“…Interestingly, the measurement of engagement as participant retention was unique to studies that were designed to evaluate the effect of intervention components on health outcomes. For engagement as usage of wearable sensors, time spent wearing the sensors was the only indicator of engagement [19,22,23,26,28,29,36]. Wang et al [29] for example, measured the proportion of days in a week that participants wore the study's…”

“…11 out of the 20 studies (55%) to measure engagement included at least one measure of this category. 10 out of 11 studies measured responsiveness in terms of survey completion (completion of single items, logs, diaries, or ecological momentary assessments) [18,19,21,22,25,28,30,31,36,37]. Aguilera et al [18] for example, measured engagement as response rates to daily text messages tasking participants to rate their mood.…”