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Detecting Receptivity for mHealth Interventions in the Natural Environment

Authors:

Florian Künzler,

Jan-Niklas Kramer,

Tobias Kowatsch,

David KotzAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 2

Article No.: 74, Pages 1 - 24

https://doi.org/10.1145/3463492

Published: 24 June 2021 Publication History

Abstract

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.

References

[1]

Apple. 2020. CoreML. https://developer.apple.com/documentation/coreml. [Online; accessed 04-February-2020].

[2]

Daniel Avrahami and Scott E Hudson. 2006. Responsiveness in instant messaging: predictive models supporting inter-personal communication. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 731--740.

Digital Library

[3]

Stephanie Bauer, Judith de Niet, Reinier Timman, and Hans Kordy. 2010. Enhancement of care through self-monitoring and tailored feedback via text messaging and their use in the treatment of childhood overweight. Patient education and counseling 79, 3 (2010), 315--319.

[4]

Dror Ben-Zeev, Christopher J Brenner, Mark Begale, Jennifer Duffecy, David C Mohr, and Kim T Mueser. 2014. Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia. Schizophrenia bulletin 40, 6 (2014), 1244--1253.

[5]

Yoav Benjamini and Yosef Hochberg. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57, 1 (1995), 289--300.

[6]

Woohyeok Choi, Sangkeun Park, Duyeon Kim, Youn-kyung Lim, and Uichin Lee. 2019. Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 2, Article 39 (June 2019), 26 pages. https://doi.org/10.1145/3328910

Digital Library

[7]

Kevin Coble. 2020. AIToolBox. https://github.com/KevinCoble/AIToolbox. [Online; accessed 04-February-2020; Git commit bada633].

[8]

Sunny Consolvo, David W McDonald, Tammy Toscos, Mike Y Chen, Jon Froehlich, Beverly Harrison, Predrag Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby, et al. 2008. Activity Sensing in the Wild: A Field Trial of UbiFit Garden. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 1797--1806.

Digital Library

[9]

Tilman Dingler, Dominik Weber, Martin Pielot, Jennifer Cooper, Chung-Cheng Chang, and Niels Henze. 2017. Language Learning On-the-Go: Opportune Moments and Design of Mobile Microlearning Sessions. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services (Vienna, Austria) (MobileHCI '17). Association for Computing Machinery, New York, NY, USA, Article 28, 12 pages. https://doi.org/10.1145/3098279.3098565

Digital Library

[10]

Andreas Filler, Tobias Kowatsch, Severin Haug, Fabian Wahle, Thorsten Staake, and Elgar Fleisch. 2015. MobileCoach: A novel open source platform for the design of evidence-based, scalable and low-cost behavioral health interventions: overview and preliminary evaluation in the public health context. In Wireless Telecommunications Symposium (WTS), 2015. IEEE, 1--6.

[11]

Joel E Fischer, Chris Greenhalgh, and Steve Benford. 2011. Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 181--190.

Digital Library

[12]

Koel Ghorai, Shahriar Akter, Fatema Khatun, and Pradeep Ray. 2014. mHealth for smoking cessation programs: a systematic review. Journal of personalized medicine 4, 3 (2014), 412--423.

[13]

David H Gustafson, Fiona M McTavish, Ming-Yuan Chih, Amy K Atwood, Roberta A Johnson, Michael G Boyle, Michael S Levy, Hilary Driscoll, Steven M Chisholm, Lisa Dillenburg, et al. 2014. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry 71, 5 (2014), 566--572.

[14]

Joyce Ho and Stephen S Intille. 2005. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 909--918.

Digital Library

[15]

Karen Hovsepian, Mustafa Al'Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (2015), 493--504. https://doi.org/10.1145/2750858.2807526

Digital Library

[16]

Donna M Kazemi, Brian Borsari, Maureen J Levine, Shaoyu Li, Katie A Lamberson, and Laura A Matta. 2017. A systematic review of the mHealth interventions to prevent alcohol and substance abuse. Journal of health communication 22, 5 (2017), 413--432.

[17]

Kevin Koch, Varun Mishra, Shu Liu, Thomas Berger, Elgar Fleisch, David Kotz, and Felix Wortmann. 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, Article 19 (March 2021), 30 pages. https://doi.org/10.1145/3448116

Digital Library

[18]

Tobias Kowatsch, Dirk Volland, Iris Shih, Dominik Rüegger, Florian Künzler, Filipe Barata, Andreas Filler, Dirk Büchter, Björn Brogle, Katrin Heldt, et al. 2017. Design and Evaluation of a Mobile Chat App for the Open Source Behavioral Health Intervention Platform MobileCoach. In International Conference on Design Science Research in Information Systems. Springer, 485--489.

[19]

Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Bastien Presset, David Kotz, Shawna Smith, Urte Scholz, and Tobias Kowatsch. 2019. Investigating intervention components and exploring states of receptivity for a smartphone app to promote physical activity: protocol of a microrandomized trial. JMIR research protocols 8, 1 (2019), e11540.

[20]

Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Shawna N. Smith, David Kotz, Urte Scholz, Elgar Fleisch, and Tobias Kowatsch. 2020. Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial. Annals of Behavioral Medicine (March 2020), 1--11. https://doi.org/10.1093/abm/kaaa002

[21]

Florian Künzler, Jan-Niklas Kramer, and Tobias Kowatsch. 2017. Efficacy of mobile context-aware notification management systems: A systematic literature review and meta-analysis. In Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 131--138.

[22]

Florian Künzler, Varun Mishra, Jan-Niklas Kramer, David Kotz, Elgar Fleisch, and Tobias Kowatsch. 2019. Exploring the State-of-Receptivity for MHealth Interventions. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT) 3, 4, Article 140 (Dec. 2019), 27 pages. https://doi.org/10.1145/3369805

Digital Library

[23]

Seth S Martin, David I Feldman, Roger S Blumenthal, Steven R Jones, Wendy S Post, Rebeccah A McKibben, Erin D Michos, Chiadi E Ndumele, Elizabeth V Ratchford, Josef Coresh, et al. 2015. mActive: a randomized clinical trial of an automated mHealth intervention for physical activity promotion. Journal of the American Heart Association 4, 11 (2015), e002239.

[24]

Afra Mashhadi, Akhil Mathur, and Fahim Kawsar. 2014. The myth of subtle notifications. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication. ACM, 111--114.

Digital Library

[25]

Abhinav Mehrotra, Sandrine R Müller, Gabriella M Harari, Samuel D Gosling, Cecilia Mascolo, Mirco Musolesi, and Peter J Rentfrow. 2017. Understanding the role of places and activities on mobile phone interaction and usage patterns. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1--22.

Digital Library

[26]

Abhinav Mehrotra, Mirco Musolesi, Robert Hendley, and Veljko Pejovic. 2015. Designing content-driven intelligent notification mechanisms for mobile applications. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 813--824.

Digital Library

[27]

Abhinav Mehrotra, Veljko Pejovic, Jo Vermeulen, Robert Hendley, and Mirco Musolesi. 2016. My phone and me: understanding people's receptivity to mobile notifications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 1021--1032.

Digital Library

[28]

Varun Mishra, Byron Lowens, Sarah Lord, Kelly Caine, and David Kotz. 2017. Investigating Contextual Cues As Indicators for EMA Delivery. In Proceedings of the International Workshop on Smart & Ambient Notification and Attention Management (UbiTtention). ACM, 935--940. https://doi.org/10.1145/3123024.3124571

Digital Library

[29]

Varun Mishra, Sougata Sen, Grace Chen, Tian Hao, Jeffrey Rogers, Ching-Hua Chen, and David Kotz. 2020. Evaluating the Reproducibility of Physiological Stress Detection Models. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) 4, 4, Article 147 (December 2020), 29 pages. https://doi.org/10.1145/3432220

Digital Library

[30]

Leanne G Morrison, Charlie Hargood, Veljko Pejovic, Adam WA Geraghty, Scott Lloyd, Natalie Goodman, Danius T Michaelides, Anna Weston, Mirco Musolesi, Mark J Weal, et al. 2017. The Effect of Timing and Frequency of Push Notifications on Usage of a Smartphone-Based Stress Management Intervention: An Exploratory Trial. PloS one 12, 1 (2017), e0169162.

[31]

Inbal Nahum-Shani, Eric B Hekler, and Donna Spruijt-Metz. 2015. Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychology 34, S (2015), 1209.

[32]

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 (2016), 1-17

[33]

Tadashi Okoshi, Julian Ramos, Hiroki Nozaki, Jin Nakazawa, Anind K Dey, and Hideyuki Tokuda. 2015. Reducing users' perceived mental effort due to interruptive notifications in multi-device mobile environments. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 475--486.

Digital Library

[34]

T. Okoshi, K. Tsubouchi, M. Taji, T. Ichikawa, and H. Tokuda. 2017. Attention and engagement-awareness in the wild: A large-scale study with adaptive notifications. In 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom). 100--110. https://doi.org/10.1109/PERCOM.2017.7917856

[35]

Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 897--908.

Digital Library

[36]

Martin Pielot, Bruno Cardoso, Kleomenis Katevas, Joan Serrà, Aleksandar Matic, and Nuria Oliver. 2017. Beyond interruptibility: Predicting opportune moments to engage mobile phone users. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 91.

Digital Library

[37]

Martin Pielot, Tilman Dingler, Jose San Pedro, and Nuria Oliver. 2015. When attention is not scarce-detecting boredom from mobile phone usage. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, 825--836.

Digital Library

[38]

William Riley, Jami Obermayer, and Jersino Jean-Mary. 2008. Internet and mobile phone text messaging intervention for college smokers. Journal of American College Health 57, 2 (2008), 245--248.

[39]

Hillol Sarker, Moushumi Sharmin, Amin Ahsan Ali, Md Mahbubur Rahman, Rummana Bari, Syed Monowar Hossain, and Santosh Kumar. 2014. Assessing the availability of users to engage in just-in-time intervention in the natural environment. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 909--920.

Digital Library

[40]

Chen-Hsuan Shih, Naofumi Tomita, Yanick X Lukic, Álvaro Hernández Reguera, Elgar Fleisch, and Tobias Kowatsch. 2019. Breeze: Smartphone-based Acoustic Real-time Detection of Breathing Phases for a Gamified Biofeedback Breathing Training. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (2019), 1--30.

Digital Library

[41]

Michael R. Smith, Tony Martinez, and Christophe Giraud-Carrier. 2014. An instance level analysis of data complexity. Machine Learning 95, 2 (01 May 2014), 225--256. https://doi.org/10.1007/s10994-013-5422-z

Digital Library

[42]

Vincent W. S. Tseng, Michael Merrill, Franziska Wittleder, Saeed Abdullah, Min Hane Aung, and Tanzeem Choudhury. 2016. Assessing Mental Health Issues on College Campuses: Preliminary Findings from a Pilot Study. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (Heidelberg, Germany) (UbiComp '16). ACM, New York, NY, USA, 1200--1208. https://doi.org/10.1145/2968219.2968308

Digital Library

[43]

Aku Visuri, Niels [van Berkel], Tadashi Okoshi, Jorge Goncalves, and Vassilis Kostakos. 2019. Understanding smartphone notifications' user interactions and content importance. International Journal of Human-Computer Studies 128 (2019), 72 - 85. https://doi.org/10.1016/j.ijhcs.2019.03.001

Digital Library

[44]

Rui Wang, Min S. H. Aung, Saeed Abdullah, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A. Scherer, and et al. 2016. CrossCheck: Toward Passive Sensing and Detection of Mental Health Changes in People with Schizophrenia. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, Germany) (UbiComp '16). ACM, New York, NY, USA, 886--897. https://doi.org/10.1145/2971648.2971740

Digital Library

[45]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T. Campbell. 2014. StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students Using Smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Seattle, Washington) (UbiComp '14). ACM, New York, NY, USA, 3--14. https://doi.org/10.1145/2632048.2632054

Digital Library

[46]

Tilo Westermann, Ina Wechsung, and Sebastian Möller. 2016. Smartphone Notifications in Context: A Case Study on Receptivity by the Example of an Advertising Service. In Proceedings of the CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2355--2361.

Digital Library

Cited By

Musthafa HWalker JMehta MBordbar SMalhotra DVaishnavi (2024)Strategic Integration of Diversity, Equity, Inclusion (DEI) and Artificial Intelligence (AI) in Human Resource Management (HRM)Modern Trends and Future Innovation in Human Resource Management10.4018/979-8-3693-6402-4.ch006(173-196)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-6402-4.ch006
Pinge AGad VJaisighani DGhosh SSen S(2024)Detection and monitoring of stress using wearables: a systematic reviewFrontiers in Computer Science10.3389/fcomp.2024.14788516Online publication date: 18-Dec-2024
https://doi.org/10.3389/fcomp.2024.1478851
Suh JHowe ELewis RHernandez JSaha KAlthoff TCzerwinski M(2024)Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention ImplementationJMIR Mental Health10.2196/4897411(e48974)Online publication date: 12-Sep-2024
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Index Terms

Detecting Receptivity for mHealth Interventions in the Natural Environment
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Human-centered computing
  1. Ubiquitous and mobile computing

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

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 2

June 2021

932 pages

EISSN:2474-9567

DOI:10.1145/3472726

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Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 24 June 2021

Published in IMWUT Volume 5, Issue 2

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Cited By

Musthafa HWalker JMehta MBordbar SMalhotra DVaishnavi (2024)Strategic Integration of Diversity, Equity, Inclusion (DEI) and Artificial Intelligence (AI) in Human Resource Management (HRM)Modern Trends and Future Innovation in Human Resource Management10.4018/979-8-3693-6402-4.ch006(173-196)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-6402-4.ch006
Pinge AGad VJaisighani DGhosh SSen S(2024)Detection and monitoring of stress using wearables: a systematic reviewFrontiers in Computer Science10.3389/fcomp.2024.14788516Online publication date: 18-Dec-2024
https://doi.org/10.3389/fcomp.2024.1478851
Suh JHowe ELewis RHernandez JSaha KAlthoff TCzerwinski M(2024)Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention ImplementationJMIR Mental Health10.2196/4897411(e48974)Online publication date: 12-Sep-2024
https://doi.org/10.2196/48974
Brons AWang SVisser BKröse BBakkes SVeltkamp R(2024)Machine Learning Methods to Personalize Persuasive Strategies in mHealth Interventions That Promote Physical Activity: Scoping Review and Categorization OverviewJournal of Medical Internet Research10.2196/4777426(e47774)Online publication date: 15-Nov-2024
https://doi.org/10.2196/47774
King ZYu HVaessen TMyin-Germeys ISano A(2024)Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing StudyJMIR mHealth and uHealth10.2196/4634712(e46347)Online publication date: 7-Feb-2024
https://doi.org/10.2196/46347
Lehmann MJones LSchirmann F(2024)App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World DataJournal of Medical Internet Research10.2196/4546926(e45469)Online publication date: 7-Jun-2024
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Mishra VHong SKotz DKostakos VKay JHoang T(2024)Exploring the Relationship Between Intrinsic Motivation and Receptivity to mHealth InterventionsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678498(437-443)Online publication date: 5-Oct-2024
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Bhattacharya SMajethia RChoube AMishra VKostakos VKay JHoang T(2024)Imputation Strategies for Longitudinal Behavioral Studies: Predicting Depression Using GLOBEM DatasetsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678424(736-742)Online publication date: 5-Oct-2024
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Bedmutha MKarumudi SPatrick KRataj HWeibel NKostakos VKay JHoang T(2024)Exploring User Willingness towards Mobile Sensing and Intervention: A Case Study on Mental Health of Undergraduate College StudentsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678422(721-728)Online publication date: 5-Oct-2024
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Adler DChoudhury TKostakos VKay JHoang T(2024)Ubiquitous Computing in Action: Infrastructure to Support Sensing and Mental Health Research in PracticeCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678372(301-306)Online publication date: 5-Oct-2024
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