Exploring the State-of-Receptivity for mHealth Interventions

Item Type Journal paper
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 notifications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors affecting 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 define several metrics to gauge receptivity towards the interventions, and found that (1) several participant-specific characteristics (age, personality, and device type) show significant 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 significant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the effectiveness 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 classifier.

Authors Künzler, Florian; Varun, Mishra; Kramer, Jan-Niklas; Kotz, David; Fleisch, Elgar & Kowatsch, Tobias
Journal or Publication Title Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)
Language English
Subjects computer science
information management
social sciences
HSG Classification contribution to scientific community
HSG Profile Area SoM - Business Innovation
Refereed Yes
Date December 2019
Publisher ACM
Place of Publication New York, USA
Volume 3
Number 4
Page Range Article 140
Publisher DOI 10.1145/3369805
Official URL https://doi.org/10.1145/3369805
Depositing User Prof. Dr. Tobias Kowatsch
Date Deposited 12 Dec 2019 14:44
Last Modified 25 Feb 2020 18:34
URI: https://www.alexandria.unisg.ch/publications/258698

Download

[img] Text
Kuenzler et al 2019 Exploring States of Receptivity mHealth.pdf

Download (4MB)

Citation

Künzler, Florian; Varun, Mishra; Kramer, Jan-Niklas; Kotz, David; Fleisch, Elgar & Kowatsch, Tobias (2019) Exploring the State-of-Receptivity for mHealth Interventions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 3 (4). Article 140.

Statistics

https://www.alexandria.unisg.ch/id/eprint/258698
Edit item Edit item
Feedback?