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Exploring Challenges in Automated Just-In-Time Adaptive Food Choice Interventions

Published: 23 October 2017 Publication History

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.

References

[1]
Frank Bentley and Konrad Tollmar. 2013. The power of mobile notifications to increase wellbeing logging behavior In Proc. CHI'13. ACM, 1095--1098.
[2]
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.
[3]
Susanne Boll, Wilko Heuten, Jochen Meyer, and Jochen Meyer. 2015. From tracking to personal health. interactions, Vol. 23, 1 (2015), 72--75.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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).
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
[14]
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.pdf
[15]
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.
[16]
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.
[17]
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.
[18]
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.
[19]
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.
[20]
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.
[21]
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.
[22]
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.
[23]
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.
[24]
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).
[25]
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.
[26]
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.
[27]
Nanette Stroebele and John M. De Castro. 2004. Effect of ambience on food intake and food choice. Nutrition, Vol. 20, 9 (2004), 821--838.
[28]
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.
[29]
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.
[30]
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.

Cited By

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  • (2022)Human-AI Collaboration to Promote Trust, Engagement and Adaptation in the Process of Pro-environmental and Health Behaviour ChangeProceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022)10.1007/978-3-031-21333-5_38(381-392)Online publication date: 21-Nov-2022
  • (2021)Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice ConsortiumInternational Journal of Environmental Research and Public Health10.3390/ijerph1815787718:15(7877)Online publication date: 25-Jul-2021
  • (2021)Envirofy your Shop: Development of a Real-time Tool to Support Eco-friendly Food Purchases OnlineExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451713(1-10)Online publication date: 8-May-2021

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      cover image ACM Conferences
      MMHealth '17: Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care
      October 2017
      104 pages
      ISBN:9781450355049
      DOI:10.1145/3132635
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 23 October 2017

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      Author Tags

      1. adaptation
      2. context
      3. just-in-time intervention
      4. personalization
      5. sensing system

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      October 23, 2017
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      View all
      • (2022)Human-AI Collaboration to Promote Trust, Engagement and Adaptation in the Process of Pro-environmental and Health Behaviour ChangeProceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022)10.1007/978-3-031-21333-5_38(381-392)Online publication date: 21-Nov-2022
      • (2021)Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice ConsortiumInternational Journal of Environmental Research and Public Health10.3390/ijerph1815787718:15(7877)Online publication date: 25-Jul-2021
      • (2021)Envirofy your Shop: Development of a Real-time Tool to Support Eco-friendly Food Purchases OnlineExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451713(1-10)Online publication date: 8-May-2021

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