This paper is in the following e-collection/theme issue:

mHealth for Wellness, Behavior Change and Prevention (2922) Fitness Trackers and Smart Pedometers/Accelerometers (571) Machine Learning (1501)

Published on in Vol 6, No 1 (2018): January

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Evaluating Machine Learning–Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial

Authors of this article:

Mo Zhou1 Author Orcid Image ;   Yoshimi Fukuoka2, 3 Author Orcid Image ;   Yonatan Mintz1 Author Orcid Image ;   Ken Goldberg1, 4 Author Orcid Image ;   Philip Kaminsky1 Author Orcid Image ;   Elena Flowers3, 5 Author Orcid Image ;   Anil Aswani1 Author Orcid Image
Article Authors Cited by (68) Tweetations (17) Metrics

Journals

  1. Liu Y, Avello M. Status of the research in fitness apps: A bibliometric analysis. Telematics and Informatics 2021;57:101506 View
  2. Ceasar J, Claudel S, Andrews M, Tamura K, Mitchell V, Brooks A, Dodge T, El-Toukhy S, Farmer N, Middleton K, Sabado-Liwag M, Troncoso M, Wallen G, Powell-Wiley T. Community Engagement in the Development of an mHealth-Enabled Physical Activity and Cardiovascular Health Intervention (Step It Up): Pilot Focus Group Study. JMIR Formative Research 2019;3(1):e10944 View
  3. Milne-Ives M, Lam C, De Cock C, Van Velthoven M, Meinert E. Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review. JMIR mHealth and uHealth 2020;8(3):e17046 View
  4. Gasparetti F, Aiello L, Quercia D. Personalized weight loss strategies by mining activity tracker data. User Modeling and User-Adapted Interaction 2020;30(3):447 View
  5. Forman E, Kerrigan S, Butryn M, Juarascio A, Manasse S, Ontañón S, Dallal D, Crochiere R, Moskow D. Can the artificial intelligence technique of reinforcement learning use continuously-monitored digital data to optimize treatment for weight loss?. Journal of Behavioral Medicine 2019;42(2):276 View
  6. Stuber J, Mackenbach J, de Boer F, de Bruijn G, Gillebaart M, Harbers M, Hoenink J, Klein M, Middel C, van der Schouw Y, Schuitmaker-Warnaar T, Velema E, Vos A, Waterlander W, Lakerveld J, Beulens J. Reducing cardiometabolic risk in adults with a low socioeconomic position: protocol of the Supreme Nudge parallel cluster-randomised controlled supermarket trial. Nutrition Journal 2020;19(1) View
  7. Hagen L, Jiang Y, Knäuper B, Uetake K, Yang N. Mobile Health Behavior Tracking: Health Effects of Tracking Consistency and Its Prediction. SSRN Electronic Journal 2020 View
  8. Aguilera A, Figueroa C, Hernandez-Ramos R, Sarkar U, Cemballi A, Gomez-Pathak L, Miramontes J, Yom-Tov E, Chakraborty B, Yan X, Xu J, Modiri A, Aggarwal J, Jay Williams J, Lyles C. mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study. BMJ Open 2020;10(8):e034723 View
  9. Zhou M, Fukuoka Y, Goldberg K, Vittinghoff E, Aswani A. Applying machine learning to predict future adherence to physical activity programs. BMC Medical Informatics and Decision Making 2019;19(1) View
  10. Ghelani D, Moran L, Johnson C, Mousa A, Naderpoor N. Mobile Apps for Weight Management: A Review of the Latest Evidence to Inform Practice. Frontiers in Endocrinology 2020;11 View
  11. Aswani A, Kaminsky P, Mintz Y, Flowers E, Fukuoka Y. Behavioral modeling in weight loss interventions. European Journal of Operational Research 2019;272(3):1058 View
  12. Sporrel K, Nibbeling N, Wang S, Ettema D, Simons M. Unraveling Mobile Health Exercise Interventions for Adults: Scoping Review on the Implementations and Designs of Persuasive Strategies. JMIR mHealth and uHealth 2021;9(1):e16282 View
  13. Hunter R, Gough A, Murray J, Tang J, Brennan S, Chrzanowski-Smith O, Carlin A, Patterson C, Longo A, Hutchinson G, Prior L, Tully M, French D, Adams J, McIntosh E, Xin Y, Kee F. A loyalty scheme to encourage physical activity in office workers: a cluster RCT. Public Health Research 2019;7(15):1 View
  14. Cox D. The Many Functions of Quantitative Modeling. Computational Brain & Behavior 2019;2(3-4):166 View
  15. Sporrel K, De Boer R, Wang S, Nibbeling N, Simons M, Deutekom M, Ettema D, Castro P, Dourado V, Kröse B. The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining. Frontiers in Public Health 2021;8 View
  16. Davis A, Sweigart R, Ellis R. A systematic review of tailored mHealth interventions for physical activity promotion among adults. Translational Behavioral Medicine 2020;10(5):1221 View
  17. Wiemeyer J. Evaluation of mobile applications for fitness training and physical activity in healthy low-trained people - A modular interdisciplinary framework. International Journal of Computer Science in Sport 2019;18(3):12 View
  18. Wilson‐Barnes S, Gymnopoulos L, Dimitropoulos K, Solachidis V, Rouskas K, Russell D, Oikonomidis Y, Hadjidimitriou S, María Botana J, Brkic B, Mantovani E, Gravina S, Telo G, Lalama E, Buys R, Hassapidou M, Balula Dias S, Batista A, Perone L, Bryant S, Maas S, Cobello S, Bacelar P, Lanham‐New S, Hart K. PeRsOnalised nutriTion for hEalthy livINg: The PROTEIN project. Nutrition Bulletin 2021;46(1):77 View
  19. Figueroa C, Vittinghoff E, Aguilera A, Fukuoka Y. Differences in objectively measured daily physical activity patterns related to depressive symptoms in community dwelling women – mPED trial. Preventive Medicine Reports 2021;22:101325 View
  20. Chew H, Ang W, Lau Y. The potential of artificial intelligence in enhancing adult weight loss: a scoping review. Public Health Nutrition 2021;24(8):1993 View
  21. Tong H, Quiroz J, Kocaballi A, Fat S, Dao K, Gehringer H, Chow C, Laranjo L. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Preventive Medicine 2021;148:106532 View
  22. Daryabeygi-Khotbehsara R, Shariful Islam S, Dunstan D, McVicar J, Abdelrazek M, Maddison R. Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review. Journal of Medical Internet Research 2021;23(9):e26315 View
  23. Walsh J, Richmond J, Mc Sharry J, Groarke A, Glynn L, Kelly M, Harney O, Groarke J. Examining the Impact of an mHealth Behavior Change Intervention With a Brief In-Person Component for Cancer Survivors With Overweight or Obesity: Randomized Controlled Trial. JMIR mHealth and uHealth 2021;9(7):e24915 View
  24. Thomas Craig K, Morgan L, Chen C, Michie S, Fusco N, Snowdon J, Scheufele E, Gagliardi T, Sill S. Systematic review of context-aware digital behavior change interventions to improve health. Translational Behavioral Medicine 2021;11(5):1037 View
  25. Gong J, Zhao L. Dynamic Behavioral Analytics in Weight-Loss Incentive Design Based on Personal Health Data. Procedia Computer Science 2021;192:3822 View
  26. Kyung N, Kwon H. Rationally trust, but emotionally? The roles of cognitive and affective trust in laypeople's acceptance of AI for preventive care operations. Production and Operations Management 2022 View
  27. Zhou Z, Athey S, Wager S. Offline Multi-Action Policy Learning: Generalization and Optimization. Operations Research 2023;71(1):148 View
  28. Li Y, Chang M, Zhao H, Jiang C, Xu S. Anxiety only makes it worse: Exploring the impact mechanisms of app‐based learning on performance progress. Journal of Computer Assisted Learning 2023;39(1):63 View
  29. Lauffenburger J, Yom-Tov E, Keller P, McDonnell M, Bessette L, Fontanet C, Sears E, Kim E, Hanken K, Buckley J, Barlev R, Haff N, Choudhry N. REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial. BMJ Open 2021;11(12):e052091 View
  30. Figueroa C, Luo T, Jacobo A, Munoz A, Manuel M, Chan D, Canny J, Aguilera A. Conversational Physical Activity Coaches for Spanish and English Speaking Women: A User Design Study. Frontiers in Digital Health 2021;3 View
  31. Alhasani M, Mulchandani D, Oyebode O, Baghaei N, Orji R. A Systematic and Comparative Review of Behavior Change Strategies in Stress Management Apps: Opportunities for Improvement. Frontiers in Public Health 2022;10 View
  32. Diaz C, Caillaud C, Yacef K. Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review. JMIR Medical Informatics 2023;11:e41153 View
  33. Stecher C, Pfisterer B, Harden S, Epstein D, Hirschmann J, Wunsch K, Buman M. Assessing the Pragmatic Nature of Mobile Health Interventions Promoting Physical Activity: Systematic Review and Meta-analysis. JMIR mHealth and uHealth 2023;11:e43162 View
  34. Vos A, de Bruijn G, Klein M, Lakerveld J, Boerman S, Smit E. SNapp, a Tailored Smartphone App Intervention to Promote Walking in Adults of Low Socioeconomic Position: Development and Qualitative Pilot Study. JMIR Formative Research 2023;7:e40851 View
  35. Dias S, Oikonomidis Y, Diniz J, Baptista F, Carnide F, Bensenousi A, Botana J, Tsatsou D, Stefanidis K, Gymnopoulos L, Dimitropoulos K, Daras P, Argiriou A, Rouskas K, Wilson-Barnes S, Hart K, Merry N, Russell D, Konstantinova J, Lalama E, Pfeiffer A, Kokkinopoulou A, Hassapidou M, Pagkalos I, Patra E, Buys R, Cornelissen V, Batista A, Cobello S, Milli E, Vagnozzi C, Bryant S, Maas S, Bacelar P, Gravina S, Vlaskalin J, Brkic B, Telo G, Mantovani E, Gkotsopoulou O, Iakovakis D, Hadjidimitriou S, Charisis V, Hadjileontiadis L. Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach. Frontiers in Nutrition 2022;9 View
  36. Li Z, Das S, Codella J, Hao T, Lin K, Maduri C, Chen C. An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity. IEEE Journal of Biomedical and Health Informatics 2019;23(3):999 View
  37. Park G, Lee H, Lee M. Artificial Intelligence-based Healthcare Interventions: A Systematic Review. Korean Journal of Adult Nursing 2021;33(5):427 View
  38. Yang N. Victim of Your (Customer's) Own Success. SSRN Electronic Journal 2019 View
  39. Collombon E, Bolman C, Peels D, de Bruijn G, de Groot R, Lechner L. Adding Mobile Elements to Online Physical Activity Interventions Targeted at Adults Aged 50 Years and Older: Protocol for a Systematic Design. JMIR Research Protocols 2022;11(7):e31677 View
  40. Fukuoka Y, Haskell W, Vittinghoff E. Mechanisms of an App-Based Physical Activity Intervention and Maintenance in Community-Dwelling Women. Journal of Cardiovascular Nursing 2023;38(2):E61 View
  41. Xiong S, Lu H, Peoples N, Duman E, Najarro A, Ni Z, Gong E, Yin R, Ostbye T, Palileo-Villanueva L, Doma R, Kafle S, Tian M, Yan L. Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries. npj Digital Medicine 2023;6(1) View
  42. Mintz Y, Aswani A, Kaminsky P, Flowers E, Fukuoka Y. Behavioral analytics for myopic agents. European Journal of Operational Research 2023;310(2):793 View
  43. Irvin L, Madden L, Marshall P, Vince R. Digital Health Solutions for Weight Loss and Obesity: A Narrative Review. Nutrients 2023;15(8):1858 View
  44. Antwi J. Precision Nutrition to Improve Risk Factors of Obesity and Type 2 Diabetes. Current Nutrition Reports 2023;12(4):679 View
  45. An R, Shen J, Wang J, Yang Y. A scoping review of methodologies for applying artificial intelligence to physical activity interventions. Journal of Sport and Health Science 2024;13(3):428 View
  46. Shao Z, Xu Y. Moving towards carbon neutral lifestyle through FinTech social media platform: a case study of Ant Forest. Frontiers in Environmental Science 2023;11 View
  47. Jiang Y, Uetake K, Yang N. Does Premium Version Adoption in mHealth Improve User Engagement and Health-Related Outcomes?. SSRN Electronic Journal 2022 View
  48. Ren Z, Zhou Z. Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits. Management Science 2024;70(2):1315 View
  49. Yfantidou S, Sermpezis P, Vakali A. 14 Years of Self-Tracking Technology for mHealth—Literature Review: Lessons Learned and the PAST SELF Framework. ACM Transactions on Computing for Healthcare 2023;4(3):1 View
  50. Lee S, Kim S, Park S. Dietary Management of Obesity. The Korean Journal of Gastroenterology 2024;83(3):87 View
  51. Lee J, Lim J, Han B, Seok S, Yoo H. Development of AI Web Service for Quantification of Dental Plaque. International Journal of Clinical Preventive Dentistry 2024;20(1):27 View
  52. Ramalho A, Paulo R, Duarte-Mendes P, Serrano J, Petrica J. Age Unplugged: A Brief Narrative Review on the Intersection of Digital Tools, Sedentary and Physical Activity Behaviors in Community-Dwelling Older Adults. Healthcare 2024;12(9):935 View
  53. Bucher A, Blazek E, Symons C. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):375 View
  54. Brown C, Richardson K, Halil-Pizzirani B, Hughes S, Atkins L, Pitt J, Yücel M, Segrave R. PEAK Mood, Mind, and Marks: a pilot study of an intervention to support university students’ mental and cognitive health through physical exercise. Frontiers in Psychiatry 2024;15 View
  55. Coppens I, De Pessemier T, Martens L. Exploring the added effect of three recommender system techniques in mobile health interventions for physical activity: a longitudinal randomized controlled trial. User Modeling and User-Adapted Interaction 2024;34(5):1835 View
  56. Asgari Mehrabadi M, Khatibi E, Jimah T, Labbaf S, Borg H, Narvaez L, Pimentel P, Turner A, Dutt N, Guo Y, Rahmani A. PERFECT: Personalized Exercise Recommendation Framework and architECTure. ACM Transactions on Computing for Healthcare 2024;5(4):1 View
  57. de Castro B, Levens S, Sullivan M, Shaw G. Recommender systems use in weight management mHealth interventions: A scoping review. Obesity Reviews 2024 View
  58. Doherty C, Lambe R, O’Grady B, O’Reilly-Morgan D, Smyth B, Lawlor A, Hurley N, Tragos E. An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial. JMIR mHealth and uHealth 2024;12:e49443 View

Books/Policy Documents

  1. John M, Kleppisch M. Prävention und Gesundheitsförderung. View
  2. John M, Kleppisch M. Prävention und Gesundheitsförderung. View
  3. Ulmer T, Baldauf M. Human-Computer Interaction. User Experience and Behavior. View
  4. Hao L, Goetze S, Hawley M. HCI International 2023 – Late Breaking Papers. View