ABSTRACT
Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants’ self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.
- David J Albers, Matthew E Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, and George Hripcsak. 2018. Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. Journal of the American Medical Informatics Association 25, 10: 1392–1401. https://doi.org/10.1093/jamia/ocy106Google ScholarCross Ref
- American Diabetes Association. 2018. 4. Lifestyle Management:Standards of Medical Care in Diabetes-2018. Diabetes care 41, Suppl 1: S38–S50. https://doi.org/10.2337/dc18-S004Google ScholarCross Ref
- Adriana Arcia, Niurka Suero-Tejeda, Michael E. Bales, Jacqueline A. Merrill, Sunmoo Yoon, Janet Woollen, and Suzanne Bakken. 2016. Sometimes more is more: Iterative participatory design of infographics for engagement of community members with varying levels of health literacy. Journal of the American Medical Informatics Association 23, 1: 174–183. https://doi.org/10.1093/jamia/ocv079Google ScholarCross Ref
- Pablo Aschner. 2017. New IDF clinical practice recommendations for managing type 2 diabetes in primary care. Diabetes Research and Clinical Practice 132: 169–170. https://doi.org/10.1016/J.DIABRES.2017.09.002Google ScholarCross Ref
- Eric P.S. Baumer, Vera Khovanskaya, Mark Matthews, Lindsay Reynolds, Victoria Schwanda Sosik, and Geri Gay. 2014. Reviewing reflection. In Proceedings of the 2014 conference on Designing interactive systems - DIS ’14, 93–102. https://doi.org/10.1145/2598510.2598598Google 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 betweenWellbeing Data and Context in Natural Language to Promote Behavior Change. ACM Transactions on Computer-Human Interaction 20, 5: 1–27. https://doi.org/10.1145/2503823Google ScholarDigital Library
- Andrew B.L. Berry, Catherine Lim, Andrea L. Hartzler, Tad Hirsch, Evette Ludman, Edward H. Wagner, and James D. Ralston. 2017. Eliciting Values of Patients with Multiple Chronic Conditions: Evaluation of a Patient-centered Framework. AMIA ... Annual Symposium proceedings. AMIA Symposium 2017: 430–439. Retrieved September 15, 2020 from /pmc/articles/PMC5977727/?report=abstractGoogle Scholar
- Timothy Bickmore, Amanda Gruber, and Rosalind Picard. 2005. Establishing the computer–patient working alliance in automated health behavior change interventions. Patient Education and Counseling 59, 1: 21–30. https://doi.org/10.1016/J.PEC.2004.09.008Google ScholarCross Ref
- Timothy W. Bickmore, Rebecca A. Silliman, Kerrie Nelson, Debbie M. Cheng, Michael Winter, Lori Henault, and Michael K. Paasche-Orlow. 2013. A Randomized Controlled Trial of an Automated Exercise Coach for Older Adults. Journal of the American Geriatrics Society 61, 10: 1676–1683. https://doi.org/10.1111/jgs.12449Google ScholarCross Ref
- Timothy W Bickmore, Daniel Schulman, and Candace L Sidner. 2011. A reusable framework for health counseling dialogue systems based on a behavioral medicine ontology. Journal of Biomedical Informatics 44, 2: 183–197. https://doi.org/10.1016/j.jbi.2010.12.006Google ScholarDigital Library
- Thomas Bodenheimer, Kate Lorig, Halsted Holman, and Kevin Grumbach. 2002. Patient Self-management of Chronic Disease in Primary Care. JAMA 288, 19: 2469. https://doi.org/10.1001/jama.288.19.2469Google ScholarCross Ref
- Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2: 77–101. https://doi.org/10.1191/1478088706qp063oaGoogle ScholarCross Ref
- Marissa Burgermaster, Krzysztof Z. Gajos, Patricia Davidson, and Lena Mamykina. 2017. The Role of Explanations in Casual Observational Learning about Nutrition. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, 4097–4145. https://doi.org/10.1145/3025453.3025874Google ScholarDigital Library
- Kerri L Cavanaugh. 2011. Health literacy in diabetes care: explanation, evidence and equipment. Diabetes management (London, England) 1, 2: 191–199. https://doi.org/10.2217/dmt.11.5Google ScholarCross Ref
- Centers for Disease Control and Prevention. 2018. Racial and Ethnic Approaches to Community Health | DNPAO. Retrieved January 3, 2019 from https://www.cdc.gov/nccdphp/dnpao/state-local-programs/reach/Google Scholar
- Beenish M. Chaudhry, Christopher Schaefbauer, Ben Jelen, Katie A. Siek, and Kay Connelly. 2016. Evaluation of a Food Portion Size Estimation Interface for a Varying Literacy Population. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI ’16, 5645–5657. https://doi.org/10.1145/2858036.2858554Google ScholarDigital Library
- Ming-Yuan Chih, Timothy Patton, Fiona M. McTavish, Andrew J. Isham, Chris L. Judkins-Fisher, Amy K. Atwood, and David H. Gustafson. 2014. Predictive modeling of addiction lapses in a mobile health application. Journal of Substance Abuse Treatment 46, 1: 29–35. https://doi.org/10.1016/J.JSAT.2013.08.004Google ScholarCross Ref
- 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. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems: 1143–1152. https://doi.org/10.1145/2556288.2557372Google ScholarDigital Library
- Chia-Fang Chung, Qiaosi Wang, Jessica Schroeder, Allison Cole, Jasmine Zia, James Fogarty, and Sean A. Munson. 2019. Identifying and Planning for Individualized Change. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 1: 1–27. https://doi.org/10.1145/3314394Google ScholarDigital Library
- Céline Clavel, Steve Whittaker, Anaïs Ana is Blacodon, and Jean-Claude Martin. 2018. WEnner: A Theoretically Motivated Approach for Tailored Coaching About Physical Activity. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - UbiComp ’18 (UbiComp ’18), 1669–1675. https://doi.org/10.1145/3267305.3274190Google ScholarDigital Library
- James Clawson, Jessica A. Pater, Andrew D. Miller, Elizabeth D. Mynatt, and Lena Mamykina. 2015. No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15, 647–658. https://doi.org/10.1145/2750858.2807554Google ScholarDigital Library
- Heather J. Cole-Lewis, Arlene M. Smaldone, Patricia R. Davidson, Rita Kukafka, Jonathan N. Tobin, Andrea Cassells, Elizabeth D. Mynatt, George Hripcsak, and Lena Mamykina. 2016. Participatory approach to the development of a knowledge base for problem-solving in diabetes self-management. International Journal of Medical Informatics 85, 1: 96–103. https://doi.org/10.1016/J.IJMEDINF.2015.08.003Google ScholarCross Ref
- Francis S. Collins and Harold Varmus. 2015. A New Initiative on Precision Medicine. New England Journal of Medicine 372, 9: 793–795. https://doi.org/10.1056/NEJMp1500523Google ScholarCross Ref
- Sunny Consolvo, Predrag Klasnja, David W. McDonald, and James A. Landay. 2009. Goal-setting considerations for persuasive technologies that encourage physical activity. In ACM International Conference Proceeding Series, 1. https://doi.org/10.1145/1541948.1541960Google ScholarDigital Library
- Felicia Cordeiro, Elizabeth Bales, Erin Cherry, and James Fogarty. 2015. Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15, 3207–3216. https://doi.org/10.1145/2702123.2702154Google ScholarDigital Library
- Nediyana Daskalova, Jina Yoon, Yibing Wang, Cintia Araujo, Guillermo Beltran, Nicole Nugent, John McGeary, Joseph Jay Williams, and Jeff Huang. 2020. SleepBandits: Guided Flexible Self-Experiments for Sleep. In Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3313831.3376584Google ScholarDigital Library
- Pooja M. Desai, Elliot G. Mitchell, Maria L. Hwang, Matthew E. Levine, David J. Albers, and Lena Mamykina. 2019. Personal health oracle: Explorations of personalized predictions in diabetes self-management. In Conference on Human Factors in Computing Systems - Proceedings, 1–13. https://doi.org/10.1145/3290605.3300600Google ScholarDigital Library
- Anind K. Dey. 2001. Understanding and using context. Personal and Ubiquitous Computing 5, 1: 4–7. https://doi.org/10.1007/s007790170019Google ScholarDigital Library
- The Diabetes Prevention Program (DPP) Research Diabetes Prevention Program (DPP) Research Group. 2002. The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes care 25, 12: 2165–71. https://doi.org/10.2337/diacare.25.12.2165Google ScholarCross Ref
- Sara Belle Donevant, Robin Dawson Estrada, Joan Marie Culley, Brian Habing, and Swann Arp Adams. 2018. Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocy104Google ScholarCross Ref
- David Elsweiler and Morgan Harvey. 2015. Towards Automatic Meal Plan Recommendations for Balanced Nutrition. Proceedings of the 9th ACM Conference on Recommender Systems: 313–316. https://doi.org/10.1145/2792838.2799665Google ScholarDigital Library
- David Elsweiler, Bernd Ludwig, Alan Said, Hanna Schaefer, and Christoph Trattner. 2016. Engendering Health with Recommender Systems. In Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 409–410. https://doi.org/10.1145/2959100.2959203Google ScholarDigital Library
- Daniel A. Epstein, Felicia Cordeiro, James Fogarty, Gary Hsieh, and Sean A. Munson. 2016. Crumbs: Lightweight Daily Food Challenges to Promote Engagement and Mindfulness. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems: 5632–5644. https://doi.org/10.1145/2858036.2858044Google ScholarDigital Library
- Daniel Epstein, Felicia Cordeiro, Elizabeth Bales, James Fogarty, and Sean Munson. 2014. Taming Data Complexity in Lifelogs: Exploring Visual Cuts of Personal Informatics Data. DIS ’14 Proceedings of the 2014 conference on Designing interactive systems: 667–676. https://doi.org/10.1145/2598510.2598558Google ScholarDigital Library
- Deborah Estrin. 2014. Small data, where n = me. Communications of the ACM 57, 4: 32–34. https://doi.org/10.1145/2580944Google ScholarDigital Library
- Alison B. Evert, Michelle Dennison, Christopher D. Gardner, W. Timothy Garvey, Ka Hei Karen Lau, Janice MacLeod, Joanna Mitri, Raquel F. Pereira, Kelly Rawlings, Shamera Robinson, Laura Saslow, Sacha Uelmen, Patricia B. Urbanski, and William S. Yancy. 2019. Nutrition therapy for adults with diabetes or prediabetes: A consensus report. https://doi.org/10.2337/dci19-0014Google ScholarCross Ref
- Daniel J Feller, Marissa Burgermaster, Matthew E Levine, Arlene Smaldone, Patricia G Davidson, David J Albers, and Lena Mamykina. 2018. A visual analytics approach for pattern-recognition in patient-generated data. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocy054Google ScholarCross Ref
- Kathleen Kara Fitzpatrick, Alison Darcy, and Molly Vierhile. 2017. Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR mental health 4, 2: e19. https://doi.org/10.2196/mental.7785Google ScholarCross Ref
- Patricia Fusch and Lawrence Ness. 2015. Are We There Yet? Data Saturation in Qualitative Research. The Qualitative Report 20, 9. Retrieved September 15, 2020 from https://nsuworks.nova.edu/tqr/vol20/iss9/3Google Scholar
- Andrea Grimes, Martin Bednar, Jay David Bolter, and Rebecca E. Grinter. 2008. EatWell: Sharing nutrition-related memories in a low-income community. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 87–96. https://doi.org/10.1145/1460563.1460579Google ScholarDigital Library
- Andrea Grimes, Vasudhara Kantroo, and Rebecca E. Grinter. 2010. Let's play! Mobile health games for adults. In UbiComp’10 - Proceedings of the 2010 ACM Conference on Ubiquitous Computing, 241–250. https://doi.org/10.1145/1864349.1864370Google ScholarDigital Library
- Lisa Grossman, Steven Feiner, Elliot Mitchell, and Ruth Masterson Creber. 2018. Leveraging Patient-Reported Outcomes Using Data Visualization. Applied Clinical Informatics 09, 03: 565–575. https://doi.org/10.1055/s-0038-1667041Google ScholarCross Ref
- Greg Guest, Arwen Bunce, and Laura Johnson. 2006. How Many Interviews Are Enough? Field Methods 18, 1: 59–82. https://doi.org/10.1177/1525822X05279903Google ScholarCross Ref
- Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C. Nelson, Jessica L. Mega, and Dale R. Webster. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA - Journal of the American Medical Association 316, 22: 2402–2410. https://doi.org/10.1001/jama.2016.17216Google ScholarCross Ref
- Ankit Gupta, Tim Heng, Chris Shaw, Linda Li, and Lynne Feehan. 2018. Towards developing an e-coach to support arthritis patients in maintaining a physically active lifestyle. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare - PervasiveHealth ’18, 392–395. https://doi.org/10.1145/3240925.3240954Google ScholarDigital Library
- Mark D. Hayward, Toni P. Miles, Eileen M. Crimmins, and Yu Yang. 2000. The Significance of Socioeconomic Status in Explaining the Racial Gap in Chronic Health Conditions. American Sociological Review 65, 6: 910. https://doi.org/10.2307/2657519Google ScholarCross Ref
- Victoria Hollis, Artie Konrad, Aaron Springer, Matthew Antoun, Christopher Antoun, Rob Martin, and Steve Whittaker. 2017. What Does All This Data Mean for My Future Mood? Actionable Analytics and Targeted Reflection for Emotional Well-Being. Human-Computer Interaction 32, 5–6: 208–267. https://doi.org/10.1080/07370024.2016.1277724Google ScholarDigital Library
- Paris Hsu, Jingshu Zhao, Kehan Liao, Tianyi Liu, and Chen Wang. 2017. AllergyBot: A Chatbot technology intervention for young adults with food allergies Dining out. In Conference on Human Factors in Computing Systems - Proceedings, 74–79. https://doi.org/10.1145/3027063.3049270Google ScholarDigital Library
- Bart A. Kamphorst. 2017. E-coaching systems: What they are, and what they aren't. Personal and Ubiquitous Computing 21, 4: 625–632. https://doi.org/10.1007/s00779-017-1020-6Google ScholarDigital Library
- Ravi Karkar, Jasmine Zia, Jessica Schroeder, Daniel A. Epstein, Laura R. Pina, Jeffrey Scofield, James Fogarty, Julie A. Kientz, Sean A. Munson, and Roger Vilardaga. 2017. TummyTrials: A Feasibility Study of Using Self-Experimentation to Detect Individualized Food Triggers. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17, 6850–6863. https://doi.org/10.1145/3025453.3025480Google ScholarDigital Library
- Ravi Karkar, Jasmine Zia, Roger Vilardaga, Sonali R Mishra, James Fogarty, Sean A Munson, and Julie A Kientz. 2016. A framework for self-experimentation in personalized health. Journal of the American Medical Informatics Association 23, 3: 440–448. https://doi.org/10.1093/jamia/ocv150Google ScholarCross Ref
- Shigeko Kato, Kayo Waki, Sadako Nakamura, Sanae Osada, Haruka Kobayashi, Hideo Fujita, Takashi Kadowaki, and Kazuhiko Ohe. 2016. Validating the use of photos to measure dietary intake: the method used by DialBetics, a smartphone-based self-management system for diabetes patients. Diabetology International 7, 3: 244–251. https://doi.org/10.1007/s13340-015-0240-0Google ScholarCross Ref
- Yoojung Kim, Sookyoung Ji, Hyunjeong Lee, Jeong-Whun Kim, Sooyoung Yoo, and Joongseek Lee. 2016. “My Doctor is Keeping an Eye on Me!”: Exploring the Clinical Applicability of a Mobile Food Logger. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems - CHI ’16, 5620–5631. https://doi.org/10.1145/2858036.2858145Google ScholarDigital Library
- Predrag Klasnja, Sunny Consolvo, and Wanda Pratt. 2011. How to evaluate technologies for health behavior change in HCI research. In Conference on Human Factors in Computing Systems - Proceedings, 3063–3072. https://doi.org/10.1145/1978942.1979396Google ScholarDigital Library
- Predrag Klasnja and Wanda Pratt. 2012. Healthcare in the pocket: Mapping the space of mobile-phone health interventions. Journal of Biomedical Informatics 45, 1: 184–198. https://doi.org/10.1016/J.JBI.2011.08.017Google ScholarDigital Library
- Rafal Kocielnik and Gary Hsieh. 2017. New Opportunities for Dialogue-based Interaction in Behavior Change Domain. In CSCW 2017 workshop on Talking with Conversational Agents in Collaborative Action. Retrieved October 30, 2019 from https://talkingwithagents.files.wordpress.com/2017/02/7-kocielnik1.pdfGoogle Scholar
- Rafal Kocielnik, Lillian Xiao, Daniel Avrahami, and Gary Hsieh. 2018. Reflection Companion: A Conversational System for Engaging Users in Reflection on Physical Activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2: 1–26. https://doi.org/10.1145/3214273Google ScholarDigital Library
- Elizabeth V. Korinek, Sayali S. Phatak, Cesar A. Martin, Mohammad T. Freigoun, Daniel E. Rivera, Marc A. Adams, Pedja Klasnja, Matthew P. Buman, and Eric B. Hekler. 2018. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. Journal of Behavioral Medicine 41, 1: 74–86. https://doi.org/10.1007/s10865-017-9878-3Google ScholarCross Ref
- Mark Kutner, Elizabeth Greenberg, and Justin Baer. 2006. A First Look at the Literacy of America's Adults in the 21st Century. NCES 2006-470. National Center for Education Statistics. Retrieved August 23, 2018 from https://eric.ed.gov/?id=ED489066Google Scholar
- Liliana Laranjo, Annie Lau, and Enrico Coiera. 2017. Design and Implementation of Behavioral Informatics Interventions. . Springer, Cham, 13–42. https://doi.org/10.1007/978-3-319-51732-2_2Google ScholarCross Ref
- Amanda Lazar, Christian Koehler, Joshua Tanenbaum, and David H. Nguyen. 2015. Why we use and abandon smart devices. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15, 635–646. https://doi.org/10.1145/2750858.2804288Google ScholarDigital Library
- Jisoo Lee, Eric B. Hekler, Emil Chiauzzi, Auriell Towner, and Marcy Fitz-Randolph. 2016. Helping Users Set Rules for Defining Short-Term Activity Goals. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA ’16, 2178–2184. https://doi.org/10.1145/2851581.2892488Google ScholarDigital Library
- Huitian Lei, Ambuj Tewari, and Susan A. Murphy. 2017. An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions. Retrieved January 31, 2019 from http://arxiv.org/abs/1706.09090Google Scholar
- Ian Li, Anind Dey, and Jodi Forlizzi. 2010. A stage-based model of personal informatics systems. Proceedings of the 28th international conference on Human factors in computing systems CHI 10: 557. https://doi.org/10.1145/1753326.1753409Google ScholarDigital Library
- Ian Li, Anind K. Dey, and Jodi Forlizzi. 2011. Understanding my data, myself: supporting self-reflection with ubicomp technologies. In Proceedings of the 13th international conference on Ubiquitous computing - UbiComp ’11, 405. https://doi.org/10.1145/2030112.2030166Google ScholarDigital Library
- Lena Mamykina, Matthew E Levine, Patricia G Davidson, Arlene M Smaldone, Noemie Elhadad, and David J Albers. 2016. Data-driven health management: reasoning about personally generated data in diabetes with information technologies. Journal of the American Medical Informatics Association 23, 3: 526–531. https://doi.org/10.1093/jamia/ocv187Google ScholarCross Ref
- Lena Mamykina, Elizabeth Mynatt, Patricia Davidson, and David Greenblatt. 2008. MAHI: Investigation of social scaffolding for reflective thinking in diabetes management. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’08), 477–486. https://doi.org/10.1145/1357054.1357131Google ScholarDigital Library
- Lena Mamykina, Drashko Nakikj, and Noemie Elhadad. 2015. Collective sensemaking in online health forums. In Conference on Human Factors in Computing Systems - Proceedings, 3217–3226. https://doi.org/10.1145/2702123.2702566Google ScholarDigital Library
- César A. Martín, Daniel E. Rivera, Eric B. Hekler, William T. Riley, Matthew P. Buman, Marc A. Adams, and Alicia B. Magann. 2020. Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions. IEEE Transactions on Control Systems Technology 28, 2: 331–346. https://doi.org/10.1109/TCST.2018.2873538Google ScholarCross Ref
- Nirupa R Matthan, Lynne M Ausman, Huicui Meng, Hocine Tighiouart, and Alice H Lichtenstein. 2016. Estimating the reliability of glycemic index values and potential sources of methodological and biological variability. The American journal of clinical nutrition 104, 4: 1004–1013. https://doi.org/10.3945/ajcn.116.137208Google ScholarCross Ref
- Mollie McKillop, Lena Mamykina, and Noémie Elhadad. 2018. Designing in the Dark. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18, 1–15. https://doi.org/10.1145/3173574.3174139Google ScholarDigital Library
- Susan Michie, Michelle Richardson, Marie Johnston, Charles Abraham, Jill Francis, Wendy Hardeman, Martin P. Eccles, James Cane, and Caroline E. Wood. 2013. The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions. Annals of Behavioral Medicine 46, 1: 81–95. https://doi.org/10.1007/s12160-013-9486-6Google ScholarCross Ref
- Elliot G Mitchell, Esteban G Tabak, Matthew E Levine, Lena Mamykina, and David J Albers. 2021. Enabling personalized decision support with patient-generated data and attributable components. Journal of Biomedical Informatics 113, 103639: 103639. https://doi.org/10.1016/j.jbi.2020.103639Google ScholarDigital Library
- Sean Munson and Sunny Consolvo. 2012. Exploring Goal-setting, Rewards, Self-monitoring, and Sharing to Motivate Physical Activity. In Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare. https://doi.org/10.4108/icst.pervasivehealth.2012.248691Google ScholarCross Ref
- Aanand D. Naik, Nynikka Palmer, Nancy J. Petersen, Richard L. Street, Radha Rao, Maria Suarez-Almazor, and Paul Haidet. 2011. Comparative Effectiveness of Goal Setting in Diabetes Mellitus Group Clinics. Archives of Internal Medicine 171, 5: 453–459. https://doi.org/10.1001/archinternmed.2011.70Google ScholarCross Ref
- Brunilda Nazario. 2013. Portion Size Plate | Recommended Serving Sizes for Portion Control. Retrieved April 15, 2018 from https://www.webmd.com/diet/healthtool-portion-size-plateGoogle Scholar
- Jasmin Niess and Paweı W. Wozniak. 2018. Supporting Meaningful Personal Fitness: the Tracker Goal Evolution Model. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18, 1–12. https://doi.org/10.1145/3173574.3173745Google ScholarDigital Library
- Jon Noronha, Eric Hysen, Haoqi Zhang, and Krzysztof Z. Gajos. 2011. Platemate: Crowdsourcing Nutritional Analysis from Food Photographs. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, 1–12. https://doi.org/10.1145/2047196.2047198Google ScholarDigital Library
- Aisling Ann O'Kane, Sun Young Park, Helena Mentis, Ann Blandford, and Yunan Chen. 2016. Turning to Peers: Integrating Understanding of the Self, the Condition, and Others’ Experiences in Making Sense of Complex Chronic Conditions. Computer Supported Cooperative Work: CSCW: An International Journal 25, 6: 477–501. https://doi.org/10.1007/s10606-016-9260-yGoogle ScholarDigital Library
- Monica E Peek, Algernon Cargill, and Elbert S Huang. 2007. Diabetes health disparities: a systematic review of health care interventions. Medical care research and review: MCRR 64, 5 Suppl: 101S–56S. https://doi.org/10.1177/1077558707305409Google ScholarCross Ref
- Stephen Purpura, Victoria Schwanda, Kaiton Williams, William Stubler, and Phoebe Sengers. 2011. Fit4Life: The design of a persuasive technology promoting healthy behavior and ideal weight. In Conference on Human Factors in Computing Systems - Proceedings, 423–432. https://doi.org/10.1145/1978942.1979003Google ScholarDigital Library
- Aare Puussaar, Adrian K. Clear, and Peter Wright. 2017. Enhancing personal informatics through social sensemaking. In Conference on Human Factors in Computing Systems - Proceedings, 6936–6942. https://doi.org/10.1145/3025453.3025804Google ScholarDigital Library
- Mashfiqui Rabbi, Min Hane Aung, Mi Zhang, and Tanzeem Choudhury. 2015. MyBehavior: Automatic personalized health feedback from user behaviors and preferences using smartphones. In UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 707–718. https://doi.org/10.1145/2750858.2805840Google ScholarDigital Library
- Shriti Raj, Kelsey Toporski, Ashley Garrity, Joyce M. Lee, and Mark W. Newman. 2019. “My blood sugar is higher on the weekends”: Finding a role for context and context-awareness in the design of health self-management technology. In Conference on Human Factors in Computing Systems - Proceedings, 1–13. https://doi.org/10.1145/3290605.3300349Google ScholarDigital Library
- Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg S. Corrado, and Jeffrey Dean. 2018. Scalable and accurate deep learning with electronic health records. npj Digital Medicine 1, 1: 18. https://doi.org/10.1038/s41746-018-0029-1Google ScholarCross Ref
- Meghan Reading Turchioe, Marissa Burgermaster, Elliot G. Mitchell, Pooja M. Desai, and Lena Mamykina. 2020. Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes. Journal of Biomedical Informatics 110, 103572. https://doi.org/10.1016/j.jbi.2020.103572Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems: Introduction and Challenges. In Recommender Systems Handbook. Springer US, Boston, MA, 1–34. https://doi.org/10.1007/978-1-4899-7637-6_1Google ScholarCross Ref
- Darius A. Rohani, Andrea Quemada Lopategui, Nanna Tuxen, Maria Faurholt-Jepsen, Lars V. Kessing, and Jakob E. Bardram. 2020. MUBS: A Personalized Recommender System for Behavioral Activation in Mental Health. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376879Google ScholarDigital Library
- Hanna Schäfer. 2016. Personalized Support for Healthy Nutrition Decisions. In Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 455–458. https://doi.org/10.1145/2959100.2959105Google ScholarDigital Library
- Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, and Christoph Trattner. 2017. Towards Health (Aware) Recommender Systems. In Proceedings of the 2017 International Conference on Digital Health - DH ’17, 157–161. https://doi.org/10.1145/3079452.3079499Google ScholarDigital Library
- Jessica Schroeder, Ravi Karkar, James Fogarty, Julie A. Kientz, Sean A. Munson, and Matthew Kay. 2018. A Patient-Centered Proposal for Bayesian Analysis of Self-Experiments for Health. Journal of Healthcare Informatics Research: 1–32. https://doi.org/10.1007/s41666-018-0033-xGoogle ScholarCross Ref
- Jessica Schroeder, Ravi Karkar, Natalia Murinova, James Fogarty, and Sean A. Munson. 2019. Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4: 1–26. https://doi.org/10.1145/3369809Google ScholarDigital Library
- Jessica Schroeder, Chelsey Wilks, Kael Rowan, Arturo Toledo, Ann Paradiso, Mary Czerwinski, Gloria Mark, and Marsha M. Linehan. 2018. Pocket Skills: A Conversational Mobile Web App To Support Dialectical Behavioral Therapy. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2018): 1–15. https://doi.org/10.1145/3173574.3173972Google ScholarDigital Library
- Elizabeth Stowell, Mercedes C. Lyson, Herman Saksono, Reneé C. Wurth, Holly Jimison, Misha Pavel, and Andrea G. Parker. 2018. Designing and Evaluating mHealth Interventions for Vulnerable Populations. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18, 1–17. https://doi.org/10.1145/3173574.3173589Google ScholarDigital Library
- Kirsten Swearingen and R. Sinha. 2001. Beyond Algorithms: An HCI Perspective on Recommender Systems. ACM SIGIR 2001 Workshop on Recommender Systems (2001): 1–11. https://doi.org/10.1.1.23.9764Google Scholar
- Esteban G. Tabak and Giulio Trigila. 2018. Explanation of Variability and Removal of Confounding Factors from Data through Optimal Transport. Communications on Pure and Applied Mathematics 71, 1: 163–199. https://doi.org/10.1002/cpa.21706Google ScholarCross Ref
- United States Department of Agriculture (USDA). ChooseMyPlate. Retrieved September 16, 2020 from https://www.choosemyplate.gov/Google Scholar
- Tiffany C. Veinot, Jessica S. Ancker, Heather Cole-Lewis, Elizabeth D. Mynatt, Andrea G. Parker, Katie A. Siek, and Lena Mamykina. 2019. Leveling Up. Medical Care 57: S108–S114. https://doi.org/10.1097/MLR.0000000000001032Google ScholarCross Ref
- Tiffany C Veinot, Hannah Mitchell, and Jessica S Ancker. 2018. Good intentions are not enough: how informatics interventions can worsen inequality. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocy052Google ScholarCross Ref
- Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019. Designing theory-driven user-centric explainable AI. In Conference on Human Factors in Computing Systems - Proceedings, 1–15. https://doi.org/10.1145/3290605.3300831Google ScholarDigital Library
- M L Wheeler, A Daly, A Evert, and others. 2014. Choose Your Foods, Food Lists for Diabetes. Chicago, IL: Academy of Nutrition and Dietetics/American Diabetes Association.Google Scholar
- Longqi Yang, Cheng-Kang Hsieh, Hongjian Yang, Nicola Dell, Serge Belongie, Curtis Cole, and Deborah Estrin. 2016. Yum-me: A Personalized Nutrient-based Meal Recommender System. ACM Transactions on Information Systems 36, 1: 7. https://doi.org/10.1145/3072614Google ScholarDigital Library
- David Zeevi, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, Dar Lador, Tali Avnit-Sagi, Maya Lotan-Pompan, Jotham Suez, Jemal Ali Mahdi, Elad Matot, Gal Malka, Noa Kosower, Michal Rein, Gili Zilberman-Schapira, Lenka Dohnalová, Meirav Pevsner-Fischer, Rony Bikovsky, Zamir Halpern, Eran Elinav, and Eran Segal. 2015. Personalized Nutrition by Prediction of Glycemic Responses. Cell 163, 5: 1079–1095. https://doi.org/10.1016/j.cell.2015.11.001Google ScholarCross Ref
- Brian J Zikmund-Fisher, Aaron M Scherer, Holly O Witteman, Jacob B Solomon, Nicole L Exe, Beth A Tarini, and Angela Fagerlin. 2016. Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results. Journal of the American Medical Informatics Association 24, 3: ocw169. https://doi.org/10.1093/jamia/ocw169Google ScholarCross Ref
- Facts Up Front. Retrieved September 15, 2018 from http://www.factsupfront.org/Google Scholar
Index Terms
- From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations
Recommendations
Goals for Goal Setting: A Scoping Review on Personal Informatics
DIS '23: Proceedings of the 2023 ACM Designing Interactive Systems ConferenceResearch has extensively explored how personal informatics tools can support people's health goal setting practices. To understand the current state and reflect on the future of goal setting in personal informatics, we report the results of a scoping ...
Understanding self-reflection: how people reflect on personal data through visual data exploration
PervasiveHealth '17: Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for HealthcareRapid advancements in consumer technologies enable people to collect a wide range of personal data. With a proper means for people to ask questions and explore their data, longitudinal data feeds from multiple self-tracking tools pose great ...
Examining Opportunities for Goal-Directed Self-Tracking to Support Chronic Condition Management
Although self-tracking offers potential for a more complete, accurate, and longer-term understanding of personal health, many people struggle with or fail to achieve their goals for health-related self-tracking. This paper investigates how to address ...
Comments