Document Type

Article

Publication Date

12-1-2019

Publication Title

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Department

Department of Computer Science

Abstract

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.

DOI

10.1145/3369805

Original Citation

Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, and Tobias Kowatsch. Exploring the State-of-Receptivity for mHealth Interventions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (Ubicomp), volume 3, number 4, article 140, 27 pages. ACM, December 2019. doi:10.1145/3369805

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