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 -Ally - 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.
Supplemental Material
Available for Download
Supplemental movie, appendix, image and software files for, Exploring the State-of-Receptivity for mHealth Interventions
- Ionut Andone, Konrad Blaszkiewicz, Mark Eibes, Boris Trendafilov, Christian Montag, and Alexander Markowetz. 2016. How Age and Gender Affect Smartphone Usage. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (UbiComp '16). ACM, New York, NY, USA, 9--12. https://doi.org/10.1145/2968219.2971451Google ScholarDigital Library
- Adrienne Andrew, Gaetano Borriello, and James Fogarty. 2007. Toward a Systematic Understanding of Suggestion Tactics in Persuasive Technologies. In Persuasive Technology, Yvonne de Kort, Wijnand IJsselsteijn, Cees Midden, Berry Eggen, and B. J. Fogg (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 259--270.Google Scholar
- Apple. 2018. CMMotionActivityManager. https://developer.apple.com/documentation/coremotion/cmmotionactivitymanager. (2018). [Online; accessed 11-February-2019].Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Google Developers. 2018. Activity Recognition API. https://developers.google.com/location-context/activity-recognition. (2018). [Online; accessed 11-February-2019].Google Scholar
- Alexandra Ehrenberg, Suzanna Juckes, Katherine M White, and Shari P Walsh. 2008. Personality and self-esteem as predictors of young people's technology use. Cyberpsychology & behavior 11, 6 (2008), 739--741.Google Scholar
- 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.Google ScholarCross Ref
- 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 International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI). ACM, 181--190.Google ScholarDigital Library
- Joel E Fischer, Nick Yee, Victoria Bellotti, Nathan Good, Steve Benford, and Chris Greenhalgh. 2010. Effects of content and time of delivery on receptivity to mobile interruptions. In Proceedings of the International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI). ACM, 103--112.Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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 (CHI). ACM, 909--918.Google ScholarDigital Library
- 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/10.1145/2750858.2807526Google ScholarDigital Library
- Abby C King, Eric B Hekler, Lauren A Grieco, Sandra J Winter, Jylana L Sheats, Matthew P Buman, Banny Banerjee, Thomas N Robinson, and Jesse Cirimele. 2013. Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PloS one 8, 4 (2013), e62613.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Jan-Niklas Kramer, Florian Künzler, Varun Mishra, Bastien Presset, David Kotz, Shawna Smith, Urte Scholz, and Tobias Kowatsch. 2018. Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Study Protocol of the ALLY Micro-Randomized Trial. JMIR Research Protocols, forthcoming (2018).Google Scholar
- 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.Google Scholar
- 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.Google ScholarDigital Library
- Abhinav Mehrotra, Mirco Musolesi, Robert Hendley, and Veljko Pejovic. 2015. Designing content-driven intelligent notification mechanisms for mobile applications. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 813--824.Google ScholarDigital Library
- Abhinav Mehrotra, Veljko Pejovic, Jo Vermeulen, Robert Hendley, and Mirco Musolesi. 2016. My phone and me: understanding people's receptivity to mobile notiications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI). ACM, 1021--1032.Google ScholarDigital Library
- 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/10.1145/3123024.3124571Google ScholarDigital Library
- Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. 2018. The Case for a Commodity Hardware Solution for Stress Detection. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct (UbiComp'18). ACM. https://doi.org/10.1145/3267305.3267538Google ScholarDigital Library
- Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. 2019. Continuous Detection of Physiological Stress with Commodity Hardware. ACM Transactions on Computing for Healthcare (HEALTH) 1, 1 (2019). https://doi.org/10.1145/3361562Google Scholar
- Raul Montoliu, Jan Blom, and Daniel Gatica-Perez. 2013. Discovering places of interest in everyday life from smartphone data. Multimedia Tools and Applications 62, 1 (01 Jan 2013), 179--207. https://doi.org/10.1007/s11042-011-0982-zGoogle Scholar
- 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.Google ScholarCross Ref
- 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.Google Scholar
- 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 52, 6 (2016), 446--462.Google ScholarCross Ref
- Heather L. O'Brien and Elaine G. Toms. 2008. What is User Engagement? A Conceptual Framework for Defining User Engagement with Technology. J. Am. Soc. Inf. Sci. Technol. 59, 6 (April 2008), 938--955. https://doi.org/10.1002/asi.v59:6Google ScholarDigital Library
- Mikio Obuchi, Wataru Sasaki, Tadashi Okoshi, Jin Nakazawa, and Hideyuki Tokuda. 2016. Investigating interruptibility at activity breakpoints using smartphone activity recognition API. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 1602--1607.Google ScholarDigital Library
- 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 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 475--486.Google ScholarDigital Library
- Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: designing intelligent prompting mechanisms for pervasive applications. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 897--908.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 825--836.Google ScholarDigital Library
- Beatrice Rammstedt and Oliver P John. 2007. Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of research in Personality 41, 1 (2007), 203--212.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarDigital Library
- Ginka Toegel and Jean-Louis Barsoux. 2012. How to Become a Better Leader. MIT Sloan Management Review 53, 3 (2012), 51--60.Google Scholar
- 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.Google ScholarDigital Library
Index Terms
- Exploring the State-of-Receptivity for mHealth Interventions
Recommendations
Detecting Receptivity for mHealth Interventions in the Natural Environment
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 ...
Multi-Stage Receptivity Model for Mobile Just-In-Time Health Intervention
A critical aspect of mobile just-in-time (JIT) health intervention is proper delivery timing, which correlates with successfully promoting target behaviors. Despite extensive prior studies on interruptibility, however, our understanding of the ...
Effects of content and time of delivery on receptivity to mobile interruptions
MobileHCI '10: Proceedings of the 12th international conference on Human computer interaction with mobile devices and servicesIn this paper we investigate effects of the content of interruptions and of the time of interruption delivery on mobile phones. We review related work and report on a naturalistic quasi-experiment using experience-sampling that showed that the ...
Comments