ABSTRACT
A healthy diet lowers the risk of developing diseases like diabetes, obesity and different types of cancers and cardiovascular conditions. Persuasive systems have already shown promise in changing user's nutrition through the strategy of monitoring and retrospectively visualizing (bad) eating behavior. In contrast emerged the idea of systems proactively offering help before such behavior even occurs, i.e. before a food choice has been made. Recent advances within the sensor-enrichment of smartphones and wearable technologies have made it possible to develop new behavior change intervention techniques, such as Just-In-Time Adaptive Interventions (JITAI). Within this work, we discuss challenges towards technology-supported, completely automated JITAIs to support healthy food choices. We derive the challenges based on existing literature, and discuss future research opportunities that would benefit users towards achieving a healthier eating behavior.
- Frank Bentley and Konrad Tollmar. 2013. The power of mobile notifications to increase wellbeing logging behavior In Proc. CHI'13. ACM, 1095--1098. Google ScholarDigital Library
- Frank Bentley, Konrad Tollmar, Peter Stephenson, Laura Levy, Brian Jones, Scott Robertson, Ed Price, Richard Catrambone, and Jeff Wilson. 2013. Health Mashups: Presenting statistical patterns between wellbeing data and context in natural language to promote behavior change. ACM TOCHI'13, Vol. 20, 5 (2013), 30. Google ScholarDigital Library
- Susanne Boll, Wilko Heuten, Jochen Meyer, and Jochen Meyer. 2015. From tracking to personal health. interactions, Vol. 23, 1 (2015), 72--75. Google ScholarDigital Library
- Erin A. Carroll, Mary Czerwinski, Asta Roseway, Ashish Kapoor, Paul Johns, Kael Rowan, and M. C. Schraefel. 2013. Food and mood: Just-in-time support for emotional eating Affective Computing and Intelligent Interaction (ACII), 2013. IEEE, 252--257. Google ScholarDigital Library
- Eun Kyoung Choe, Nicole B. Lee, Bongshin Lee, Wanda Pratt, and Julie A. Kientz. 2014. Understanding quantified-selfers' practices in collecting and exploring personal data In Proc. CHI'14. ACM, 1143--1152. Google ScholarDigital Library
- Sunny Consolvo, Katherine Everitt, Ian Smith, and James A. Landay. 2006. Design requirements for technologies that encourage physical activity In Proc. CHI'06. ACM, 457--466. Google ScholarDigital Library
- Felicia Cordeiro, Daniel A. Epstein, Edison Thomaz, Elizabeth Bales, Arvind K. Jagannathan, Gregory D. Abowd, and James Fogarty. 2015. Barriers and negative nudges: Exploring challenges in food journaling In Proc. CHI'15. ACM, 1159--1162. Google ScholarDigital Library
- Laura Dennison, Leanne Morrison, Gemma Conway, and Lucy Yardley. 2013. Opportunities and challenges for smartphone applications in supporting health behavior change: qualitative study. Journal of medical Internet research Vol. 15, 4 (2013).Google ScholarCross Ref
- Thomas Fritz, Elaine M. Huang, Gail C. Murphy, and Thomas Zimmermann. 2014. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. In In Proc. CHI'14. ACM, 487--496. Google ScholarDigital Library
- Rebecca Gulotta, Jodi Forlizzi, Rayoung Yang, and Mark Wah Newman. 2016. Fostering Engagement with Personal Informatics Systems Proceedings of the 2016 ACM Conference on Designing Interactive Systems. ACM, 286--300. Google ScholarDigital Library
- Katrin Hänsel, Natalie Wilde, Hamed Haddadi, and Akram Alomainy. 2015. Challenges with current wearable technology in monitoring health data and providing positive behavioural support. In In Proc. MobiHealth'15. 158--161. Google ScholarDigital Library
- Eric B. Hekler, Predrag Klasnja, Jon E. Froehlich, and Matthew P. Buman. 2013. Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research In Proc. CHI'13. ACM, 3307--3316. Google ScholarDigital Library
- Anne Hsu, Jing Yang, Yigit Han Yilmaz, Md Sanaul Haque, Cengiz Can, and Ann E. Blandford. 2014. Persuasive technology for overcoming food cravings and improving snack choices In Proc. CHI'14. ACM, 3403--3412. Google ScholarDigital Library
- World Cancer Research Fund International. 2014. The link between food, nutrition, diet and non-communicable diseases. (2014). http://www.wcrf.org/sites/default/files/PPA_NCD_Alliance_Nutrition.pdfGoogle Scholar
- Azusa Kadomura, Cheng-Yuan Li, Koji Tsukada, Hao-Hua Chu, and Itiro Siio. 2014. Persuasive technology to improve eating behavior using a sensor-embedded fork In Proc. UBICOMP'14. ACM, 319--329. Google ScholarDigital Library
- Nicholas D. Lane, Mu Lin, Mashfiqui Mohammod, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, Ethan Berke, Andrew T. Campbell, et al. 2014. Bewell: Sensing sleep, physical activities and social interactions to promote wellbeing. Mobile Networks and Applications Vol. 19, 3 (2014), 345--359. Google ScholarDigital Library
- Jisoo Lee, Erin Walker, Winslow Burleson, Matthew Kay, Matthew Buman, and Eric B. Hekler. 2017. Self-experimentation for behavior change: Design and formative evaluation of two approaches In Proc. CHI'17. ACM, 6837--6849. Google ScholarDigital Library
- 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, Vol. 34, S (2015), 1209.Google ScholarCross Ref
- Zhibo Pang, Lirong Zheng, Junzhe Tian, Sharon Kao-Walter, Elena Dubrova, and Qiang Chen. 2015. Design of a terminal solution for integration of in-home health care devices and services towards the Internet-of-Things. Enterprise Information Systems Vol. 9, 1 (2015), 86--116. Google ScholarDigital Library
- Benjamin Poppinga, Wilko Heuten, and Susanne Boll. 2014. Sensor-based identification of opportune moments for triggering notifications. IEEE Pervasive Computing Vol. 13, 1 (2014), 22--29. Google ScholarDigital Library
- Tauhidur Rahman, Mary Czerwinski, Ran Gilad-Bachrach, and Paul Johns. 2016. Predicting About-to-Eat Moments for Just-in-Time Eating Intervention In Proc. Digital Health'16. ACM, 141--150. Google ScholarDigital Library
- 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 In Proc. UBICOMP'14. ACM, 909--920. Google ScholarDigital Library
- Hillol Sarker, Matthew Tyburski, Md Mahbubur Rahman, Karen Hovsepian, Moushumi Sharmin, David H. Epstein, Kenzie L. Preston, C. Debra Furr-Holden, Adam Milam, Inbal Nahum-Shani, et al. 2016. Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In In Proc. CHI'16. ACM, 4489--4501. Google ScholarDigital Library
- Patrick C. Shih, Kyungsik Han, Erika Shehan Poole, Mary Beth Rosson, and John M. Carroll. 2015. Use and adoption challenges of wearable activity trackers. In Proc. IConference 2015 (2015).Google Scholar
- Joshua M. Smyth and Kristin E. Heron. 2016. Is providing mobile interventions "just-in-time" helpful? an experimental proof of concept study of just-in-time intervention for stress management. Wireless Health. 89--95.Google Scholar
- Donna Spruijt-Metz, Cheng K. F. Wen, Gillian O'Reilly, Ming Li, Sangwon Lee, B. A. Emken, Urbashi Mitra, Murali Annavaram, Gisele Ragusa, and Shrikanth Narayanan. 2015. Innovations in the use of interactive technology to support weight management. Current obesity reports Vol. 4, 4 (2015), 510--519.Google Scholar
- Nanette Stroebele and John M. De Castro. 2004. Effect of ambience on food intake and food choice. Nutrition, Vol. 20, 9 (2004), 821--838.Google ScholarCross Ref
- Eric M. VanEpps, Julie S. Downs, and George Loewenstein. 2016. Advance Ordering for Healthier Eating? Field Experiments on the Relationship Between the Meal Order-Consumption Time Delay and Meal Content. Journal of Marketing Research Vol. 53, 3 (2016), 369--380.Google ScholarCross Ref
- Brian Wansink and Katherine Abowd Johnson. 2015. The clean plate club: about 92% of self-served food is eaten. International Journal of Obesity Vol. 39, 2 (2015), 371--374.Google ScholarCross Ref
- Shibo Zhang, Rawan Alharbi, William Stogin, Mohamad Pourhomayun, Bonnie Spring, and Nabil Alshurafa. 2016. Food watch: detecting and characterizing eating episodes through feeding gestures Proceedings of the 11th EAI International Conference on Body Area Networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 91--96. Google ScholarDigital Library
Index Terms
- Exploring Challenges in Automated Just-In-Time Adaptive Food Choice Interventions
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