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

Mobile Health (mhealth) (2974) Web-based and Mobile Health Interventions (3345) RCTs - Pilots/Feasibility Studies (eHealth) (84) mHealth for Wellness, Behavior Change and Prevention (2912) Health Coaching for Diabetes Patients (149) Diabetes Prevention (86)

Published on in Vol 20, No 2 (2018): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9723, first published .
A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial

A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial

A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial

Authors of this article:

Estelle Everett1 Author Orcid Image ;   Brian Kane2 Author Orcid Image ;   Ashley Yoo1 Author Orcid Image ;   Adrian Dobs1 Author Orcid Image ;   Nestoras Mathioudakis1 Author Orcid Image
Article Authors Cited by (56) Tweetations (31) Metrics

Journals

  1. Monteiro-Guerra F, Rivera-Romero O, Fernandez-Luque L, Caulfield B. Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review. IEEE Journal of Biomedical and Health Informatics 2020;24(6):1738 View
  2. Broome D, Hilton C, Mehta N. Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management. Current Diabetes Reports 2020;20(2) View
  3. Sforzo G, Kaye M, Harenberg S, Costello K, Cobus-Kuo L, Rauff E, Edman J, Frates E, Moore M. Compendium of Health and Wellness Coaching: 2019 Addendum. American Journal of Lifestyle Medicine 2020;14(2):155 View
  4. Van Rhoon L, Byrne M, Morrissey E, Murphy J, McSharry J. A systematic review of the behaviour change techniques and digital features in technology-driven type 2 diabetes prevention interventions. DIGITAL HEALTH 2020;6 View
  5. Sequi-Dominguez I, Alvarez-Bueno C, Martinez-Vizcaino V, Fernandez-Rodriguez R, del Saz Lara A, Cavero-Redondo I. Effectiveness of Mobile Health Interventions Promoting Physical Activity and Lifestyle Interventions to Reduce Cardiovascular Risk Among Individuals With Metabolic Syndrome: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2020;22(8):e17790 View
  6. Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. Journal of Medical Internet Research 2018;20(5):e10775 View
  7. Gimbel R, Rennert L, Crawford P, Little J, Truong K, Williams J, Griffin S, Shi L, Chen L, Zhang L, Moss J, Marshall R, Edwards K, Crawford K, Hing M, Schmeltz A, Lumsden B, Ashby M, Haas E, Palazzo K. Enhancing Patient Activation and Self-Management Activities in Patients With Type 2 Diabetes Using the US Department of Defense Mobile Health Care Environment: Feasibility Study. Journal of Medical Internet Research 2020;22(5):e17968 View
  8. Muralidharan S, Ranjani H, Mohan Anjana R, Jena S, Tandon N, Gupta Y, Ambekar S, Koppikar V, Jagannathan N, Allender S, Mohan V. Engagement and Weight Loss: Results from the Mobile Health and Diabetes Trial. Diabetes Technology & Therapeutics 2019;21(9):507 View
  9. Li J, Huang J, Zheng L, Li X. Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect. Frontiers in Public Health 2020;8 View
  10. Bradway M, Pfuhl G, Joakimsen R, Ribu L, Grøttland A, Årsand E, Borsci S. Analysing mHealth usage logs in RCTs: Explaining participants’ interactions with type 2 diabetes self-management tools. PLOS ONE 2018;13(8):e0203202 View
  11. Vehi J, Regincós Isern J, Parcerisas A, Calm R, Contreras I. Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study. JMIR mHealth and uHealth 2019;7(3):e11933 View
  12. Gershkowitz B, Hillert C, Crotty B. Digital Coaching Strategies to Facilitate Behavioral Change in Type 2 Diabetes: A Systematic Review. The Journal of Clinical Endocrinology & Metabolism 2021;106(4):e1513 View
  13. Kaasalainen K, Kalmari J, Ruohonen T. Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs. Journal of Biomedical Informatics 2020;111:103577 View
  14. Sieczkowska S, de Lima A, Swinton P, Dolan E, Roschel H, Gualano B. Health Coaching Strategies for Weight Loss: A Systematic Review and Meta-Analysis. Advances in Nutrition 2021;12(4):1449 View
  15. Pham Q, Gamble A, Hearn J, Cafazzo J. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. Journal of Medical Internet Research 2021;23(2):e22320 View
  16. Nelson A, Moses O, Rea B, Morton K, Shih W, Alramadhan F, Singh P. Pilot Feasibility Study of Incorporating Whole Person Care Health Coaching Into an Employee Wellness Program. Frontiers in Public Health 2021;8 View
  17. 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
  18. 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
  19. Schneider-Kamp A. The Potential of AI in Care Optimization: Insights from the User-Driven Co-Development of a Care Integration System. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2021;58 View
  20. 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
  21. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  22. 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
  23. Berry M, Chwyl C, Metzler A, Sun J, Dart H, Forman E. Associations between behaviour change technique clusters and weight loss outcomes of automated digital interventions: a systematic review and meta-regression. Health Psychology Review 2023;17(4):521 View
  24. Sohl S, Duncan P, Thakur E, Puccinelli-Ortega N, Salsman J, Russell G, Pasche B, Wentworth S, Miller Jr D, Wagner L, Topaloglu U. Adaptation of a Personalized Electronic Care Planning Tool for Cancer Follow-up Care: Formative Study. JMIR Formative Research 2023;7:e41354 View
  25. Antoun J, Itani H, Alarab N, Elsehmawy A. The Effectiveness of Combining Nonmobile Interventions With the Use of Smartphone Apps With Various Features for Weight Loss: Systematic Review and Meta-analysis. JMIR mHealth and uHealth 2022;10(4):e35479 View
  26. Park G, Lee H, Lee M. Artificial Intelligence-based Healthcare Interventions: A Systematic Review. Korean Journal of Adult Nursing 2021;33(5):427 View
  27. Vairavasundaram S, Varadarajan V, Srinivasan D, Balaganesh V, Damerla S, Swaminathan B, Ravi L. Dynamic Physical Activity Recommendation Delivered through a Mobile Fitness App: A Deep Learning Approach. Axioms 2022;11(7):346 View
  28. Kulzer B. Künstliche Intelligenz (KI) in der Diabetologie – jetzt und in der Zukunft. Die Diabetologie 2023;19(1):35 View
  29. Arigo D, Lobo A, Ainsworth M, Baga K, Pasko K. Development and Initial Testing of a Personalized, Adaptive, and Socially Focused Web Tool to Support Physical Activity Among Women in Midlife: Multidisciplinary and User-Centered Design Approach. JMIR Formative Research 2022;6(7):e36280 View
  30. Graham S, Pitter V, Hori J, Stein N, Branch O. Weight loss in a digital app-based diabetes prevention program powered by artificial intelligence. DIGITAL HEALTH 2022;8:205520762211306 View
  31. Chen D, Zhang H, Cui N, Song F, Tang L, Shao J, Wu J, Guo P, Liu N, Wang X, Ye Z. Development of a behavior change intervention to improve physical activity adherence in individuals with metabolic syndrome using the behavior change wheel. BMC Public Health 2022;22(1) View
  32. Amagai S, Pila S, Kaat A, Nowinski C, Gershon R. Challenges in Participant Engagement and Retention Using Mobile Health Apps: Literature Review. Journal of Medical Internet Research 2022;24(4):e35120 View
  33. Sevilla-Gonzalez M, Bourguet-Ramirez B, Lazaro-Carrera L, Martagon-Rosado A, Gomez-Velasco D, Viveros-Ruiz T. Evaluation of a Web Platform to Record Lifestyle Habits in Subjects at Risk of Developing Type 2 Diabetes in a Middle-Income Population: Prospective Interventional Study. JMIR Diabetes 2022;7(1):e25105 View
  34. MacPherson M, Merry K, Locke S, Jung M. How Can We Keep People Engaged in the Behavior Change Process? An Exploratory Analysis of Two mHealth Applications. Journal of Technology in Behavioral Science 2022;7(3):337 View
  35. Teo J, Ramachandran H, Jiang Y, Seah C, Lim S, Nguyen H, Wang W. The characteristics and acceptance of Technology‐Enabled diabetes prevention programs (t‐DPP) amongst individuals with prediabetes: A scoping review. Journal of Clinical Nursing 2023;32(17-18):5562 View
  36. Johannessen E, Johansson J, Hartvigsen G, Horsch A, Årsand E, Henriksen A. Collecting health-related research data using consumer-based wireless smart scales. International Journal of Medical Informatics 2023;173:105043 View
  37. Alsayed A, Ismail N, Hasan L, Syed A, Embarak F, Da'u A. A systematic literature review for understanding the effectiveness of advanced techniques in diabetes self-care management. Alexandria Engineering Journal 2023;79:274 View
  38. Salinari A, Machì M, Armas Diaz Y, Cianciosi D, Qi Z, Yang B, Ferreiro Cotorruelo M, Villar S, Dzul Lopez L, Battino M, Giampieri F. The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases 2023;11(3):97 View
  39. Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Reports Medicine 2023;4(10):101213 View
  40. Berry M, Taylor L, Huang Z, Chwyl C, Kerrigan S, Forman E. Automated Messaging Delivered Alongside Behavioral Treatment for Weight Loss: Qualitative Study. JMIR Formative Research 2023;7:e50872 View
  41. Rivera-Romero O, Gabarron E, Ropero J, Denecke K. Designing personalised mHealth solutions: An overview. Journal of Biomedical Informatics 2023;146:104500 View
  42. Jahan E, Almansour R, Ijaz K, Baptista S, Giordan L, Ronto R, Zaman S, O'Hagan E, Laranjo L. Smartphone Applications to Prevent Type 2 Diabetes: A Systematic Review and Meta-Analysis. American Journal of Preventive Medicine 2024;66(6):1060 View
  43. Stowell M, Dobson R, Garner K, Baig M, Nehren N, Whittaker R, Klisic A. Digital interventions for self-management of prediabetes: A scoping review. PLOS ONE 2024;19(5):e0303074 View
  44. Park G, Lee H, Lee Y, Kim M, Jung S, Khang A, Yi D. Automated Personalized Self-care Program for Patients With Type 2 Diabetes Mellitus: A Pilot Trial. Asian Nursing Research 2024;18(2):114 View
  45. Abusamaan M, Ballreich J, Dobs A, Kane B, Maruthur N, McGready J, Riekert K, Wanigatunga A, Alderfer M, Alver D, Lalani B, Ringham B, Vandi F, Zade D, Mathioudakis N. Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial. Trials 2024;25(1) View
  46. 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
  47. McMullen B, Duncanson K, Collins C, MacDonald‐Wicks L. A systematic review of the mechanisms influencing engagement in diabetes prevention programmes for people with pre‐diabetes. Diabetic Medicine 2024;41(8) View
  48. Sheng B, Pushpanathan K, Guan Z, Lim Q, Lim Z, Yew S, Goh J, Bee Y, Sabanayagam C, Sevdalis N, Lim C, Lim C, Shaw J, Jia W, Ekinci E, Simó R, Lim L, Li H, Tham Y. Artificial intelligence for diabetes care: current and future prospects. The Lancet Diabetes & Endocrinology 2024;12(8):569 View
  49. Weingott S, Parkinson J. The application of artificial intelligence in health communication development: A scoping review. Health Marketing Quarterly 2024:1 View

Books/Policy Documents

  1. Kloek C. Geriatrie in de fysiotherapie en kinesitherapie. View
  2. Colberg S, Scheiner G. Diabetes Digital Health and Telehealth. View
  3. Ahmad A, Mohamed A. Artificial Intelligence and Autoimmune Diseases. View