Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial (Preprint)

Kramer, Jan-Niklas; Künzler, Florian; Mishra, Varun; Presset, Bastien; Kotz, David; Smith, Shawna N.; Scholz, Urte; Kowatsch, Tobias

doi:10.2196/preprints.11540

Cited by 2 publications

References 56 publications

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From sensing to intervention for mental and behavioral health

Mishra

2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Advances in mobile, wearable and embedded sensing technology have created new opportunities for research into a variety of health conditions. This has led to the field of mobile health (mHealth), which covers a full spectrum of works, including but not limited to disease surveillance, treatment support, epidemic outbreak tracking, and chronic disease management. An important sub-field which has been rising in the past years is the application of mHealth technology in the field of mental and behavioral health, enabling researchers to study stress, depression, mood, personality change, schizophrenia, physical activity and addictive behavior, among other things. The ubiquitous presence of sensor-rich mobile and wearable devices has helped researchers to passively track and

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From sensing to intervention for mental and behavioral health

Mishra

2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Exploring the State-of-Receptivity for mHealth Interventions

Künzler

Mishra

Kramer

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users' responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors afecting users' receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms.We deine several metrics to gauge receptivity towards the interventions, and found that (1) several participant-speciic characteristics (age, personality, and device type) show signiicant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show signiicant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the efectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classiier.

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Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial (Preprint)

Cited by 2 publications

References 56 publications

From sensing to intervention for mental and behavioral health

From sensing to intervention for mental and behavioral health

Exploring the State-of-Receptivity for mHealth Interventions

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