{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:13:11Z","timestamp":1755839591341,"version":"3.41.0"},"reference-count":7,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2023,8,3]]},"abstract":"<jats:p>Just-In-Time Adaptive Interventions (JITAI) have the potential to provide effective support for health behavior by delivering the right type and amount of intervention at the right time. The timing of interventions is crucial to ensure that users are receptive and able to use the support provided. Previous research has explored the association of context and user-specific traits on receptivity and built machine-learning models to detect receptivity after the study was completed. However, for effective intervention delivery, JITAI systems need to make in-the-moment decisions about a user's receptivity. In this study, we deployed machinelearning models in a chatbot-based digital coach to predict receptivity for physical-activity interventions. We included a static model that was built before the study and an adaptive model that continuously updated itself during the study. Compared to a control model that sent intervention messages randomly, the machine-learning models improved receptivity by up to 36%. Receptivity to messages from the adaptive model increased over time.<\/jats:p>","DOI":"10.1145\/3614214.3614221","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T10:25:18Z","timestamp":1691231118000},"page":"23-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Detecting Receptivity for mHealth Interventions"],"prefix":"10.1145","volume":"27","author":[{"given":"Varun","family":"Mishra","sequence":"first","affiliation":[{"name":"Northeastern University, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"K\u00fcnzler","sequence":"additional","affiliation":[{"name":"Nash Exchange"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan-Niklas","family":"Kramer","sequence":"additional","affiliation":[{"name":"CSS Health Insurance, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elgar","family":"Fleisch","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias","family":"Kowatsch","sequence":"additional","affiliation":[{"name":"University of Zurich, University of St. Gallen, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Kotz","sequence":"additional","affiliation":[{"name":"Dartmouth College, Hanover, NH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapsychiatry.2013.4642"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 5(1).","author":"Koch Kevin","year":"2021","unstructured":"Kevin Koch , Varun Mishra , Shu Liu , Thomas Berger , Elgar Fleisch , David Kotz , and Felix Wortmann . March 2021 . When do drivers interact with in-vehicle well-being interventions? An exploratory analysis of a longitudinal study on public roads . Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 5(1). Kevin Koch, Varun Mishra, Shu Liu, Thomas Berger, Elgar Fleisch, David Kotz, and Felix Wortmann. March 2021. When do drivers interact with in-vehicle well-being interventions? An exploratory analysis of a longitudinal study on public roads. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 5(1)."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/11540"},{"volume-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT), 3(4.","author":"K\u00fcnzler Florian","key":"e_1_2_1_4_1","unstructured":"Florian K\u00fcnzler , Varun Mishra , Jan-Niklas Kramer , David Kotz , Elgar Fleisch , and Tobias Kowatsch . Exploring the state-of-receptivity for mhealth interventions. December 2019 . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT), 3(4. Florian K\u00fcnzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, and Tobias Kowatsch. Exploring the state-of-receptivity for mhealth interventions. December 2019. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT), 3(4."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3463492"},{"key":"e_1_2_1_6_1","article-title":"Just-in-time adaptive interventions (JITAIS) in mobile health: Key components and design principles for ongoing health behavior support","author":"Nahum-Shani Inbal","year":"2016","unstructured":"Inbal Nahum-Shani , Shawna N. Smith , Bonnie J. Spring , Linda M. Collins , Katie Witkiewitz , Ambuj Tewari , and Susan A. Murphy . 2016 . Just-in-time adaptive interventions (JITAIS) in mobile health: Key components and design principles for ongoing health behavior support . Annals of Behavioral Medicine, pages 1--17. Inbal Nahum-Shani, Shawna N. Smith, Bonnie J. Spring, Linda M. Collins, Katie Witkiewitz, Ambuj Tewari, and Susan A. Murphy. 2016. Just-in-time adaptive interventions (JITAIS) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, pages 1--17.","journal-title":"Annals of Behavioral Medicine, pages 1--17."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2636082"}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3614214.3614221","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3614214.3614221","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:45:47Z","timestamp":1750178747000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3614214.3614221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,3]]},"references-count":7,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,8,3]]}},"alternative-id":["10.1145\/3614214.3614221"],"URL":"https:\/\/doi.org\/10.1145\/3614214.3614221","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"type":"print","value":"2375-0529"},{"type":"electronic","value":"2375-0537"}],"subject":[],"published":{"date-parts":[[2023,8,3]]},"assertion":[{"value":"2023-08-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}