JMIR Publications

ABSTRACT

Background:

Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user and time-varying context characteristics. Ideally, JITAIs include effective intervention components and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring.

Objective:

The primary objective is to quantify main effects, interactions and moderators of three intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, i.e. situations in which participants are more likely to react to intervention notifications, through collection of smartphone sensor data.

Methods:

In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mHealth intervention for increasing physical activity that utilizes incentives, planning and self-monitoring prompts to help participants meet personalized step goals. We used a micro-randomized trial design to meet the study objectives. Insurees of large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a six-week intervention period. Upon enrolment, participants were randomly allocated to either a financial incentive, a charity incentive or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not, and on a weekly basis to receive one of two planning interventions or no planning. Participants completed a web-based questionnaire at baseline and post-intervention follow-up.

Results:

Data collection finished in January 2018. In total, 274 insurees enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (20%) and collection of sensor data (19%). Step data is available for 227/274 participants (82.85%) and smartphone sensor data is available for 247/274 participants (90.15%).

Conclusions:

This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. Clinical Trial: ClinicalTrials.gov NCT03384550