Detecting Receptivity for mHealth Interventions in the Natural Environment
| Autor: | David Kotz, Florian Künzler, Tobias Kowatsch, Jan-Niklas Kramer, Varun Mishra, Elgar Fleisch |
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| Přispěvatelé: | University of Zurich, Mishra, Varun |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
digital health digital health intervention just-in-time adaptive intervention physical activity State of receptivity machine learning Receptivity Intervention Interruption Mobile Health Engagement Computer science Computer Networks and Communications Applied psychology Receptivity Psychological intervention 11549 Institute of Implementation Science in Health Care Computer Science - Human-Computer Interaction Context (language use) 610 Medicine & health computer science 02 engineering and technology computer.software_genre Chatbot Digital Health Intervention States of Receptivity Just-in-time adaptive interventions Article Human-Computer Interaction (cs.HC) 1709 Human-Computer Interaction field experiment Intervention (counseling) 1705 Computer Networks and Communications 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences mHealth 050107 human factors Wearable technology business.industry 1708 Hardware and Architecture health sciences 05 social sciences 020207 software engineering Computer Interaction information management Digital health Human-Computer Interaction Hardware and Architecture business computer social sciences Human |
| Zdroj: | Proc ACM Interact Mob Wearable Ubiquitous Technol arXiv Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (2) |
| ISSN: | 2474-9567 |
| Popis: | Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user’s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach – Ally – that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (2) ISSN:2474-9567 |
| Databáze: | OpenAIRE |
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