Published on in Vol 7, No 1 (2019): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11267, first published .
A Mobile Phone-Based Program to Promote Healthy Behaviors Among Adults With Prediabetes Who Declined Participation in Free Diabetes Prevention Programs: Mixed-Methods Pilot Randomized Controlled Trial

A Mobile Phone-Based Program to Promote Healthy Behaviors Among Adults With Prediabetes Who Declined Participation in Free Diabetes Prevention Programs: Mixed-Methods Pilot Randomized Controlled Trial

A Mobile Phone-Based Program to Promote Healthy Behaviors Among Adults With Prediabetes Who Declined Participation in Free Diabetes Prevention Programs: Mixed-Methods Pilot Randomized Controlled Trial

Journals

  1. Nhim K, Gruss S, Porterfield D, Jacobs S, Elkins W, Luman E, Van Aacken S, Schumacher P, Albright A. Using a RE-AIM framework to identify promising practices in National Diabetes Prevention Program implementation. Implementation Science 2019;14(1) View
  2. Toro-Ramos T, Michaelides A, Anton M, Karim Z, Kang-Oh L, Argyrou C, Loukaidou E, Charitou M, Sze W, Miller J. Mobile Delivery of the Diabetes Prevention Program in People With Prediabetes: Randomized Controlled Trial. JMIR mHealth and uHealth 2020;8(7):e17842 View
  3. Nittas V. Developing mobile self-tracking for chronic disease prevention: Why listening to users matters?. International Journal of Public Health 2020;65(3):323 View
  4. Lau N, O'Daffer A, Colt S, Yi-Frazier J, Palermo T, McCauley E, Rosenberg A. Android and iPhone Mobile Apps for Psychosocial Wellness and Stress Management: Systematic Search in App Stores and Literature Review. JMIR mHealth and uHealth 2020;8(5):e17798 View
  5. Kaufman N, Mel E. Using Digital Health Technology to Prevent and Treat Diabetes. Diabetes Technology & Therapeutics 2020;22(S1):S-63 View
  6. Malipatil N, Fachim H, Siddals K, Geary B, Wark G, Porter N, Anderson S, Donn R, Harvie M, Whetton A, Gibson M, Heald A. Data Independent Acquisition Mass Spectrometry Can Identify Circulating Proteins That Predict Future Weight Loss with a Diet and Exercise Programme. Journal of Clinical Medicine 2019;8(2):141 View
  7. Ringeval M, Wagner G, Denford J, Paré G, Kitsiou S. Fitbit-Based Interventions for Healthy Lifestyle Outcomes: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2020;22(10):e23954 View
  8. Xu Z, Geng J, Zhang S, Zhang K, Yang L, Li J, Li J. A Mobile-Based Intervention for Dietary Behavior and Physical Activity Change in Individuals at High Risk for Type 2 Diabetes Mellitus: Randomized Controlled Trial. JMIR mHealth and uHealth 2020;8(11):e19869 View
  9. Barengo N, Diaz Valencia P, Apolina L, Estrada Cruz N, Fernández Garate J, Correa González R, Cinco Gonzalez C, Gómez Rodriguez J, González N, Arellano Flores M, Ledesma Muñoz M, González Sotelo D, Davila Maldonado O, Gomez Garcia J, Laureano Hernandez F, Zarazua Jimenez J, Pulido Garcia B, Rodriguez Vazquez H, Ramirez Dorantes A, Gonzalez Fierro L, Hernandez Hernandez J, Zenil Perez J. Mobile Health Technology in the Primary Prevention of Type 2 Diabetes: a Systematic Review. Current Diabetes Reports 2022;22(1):1 View
  10. Zare H, Delgado P, Spencer M, Thorpe R, Thomas L, Gaskin D, Werrell L, Carter E. Using Community Health Workers to Address Barriers to Participation and Retention in Diabetes Prevention Program: A Concept Paper. Journal of Primary Care & Community Health 2022;13:215013192211345 View
  11. 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
  12. Salas-Groves E, Galyean S, Alcorn M, Childress A. Behavior Change Effectiveness Using Nutrition Apps in People With Chronic Diseases: Scoping Review. JMIR mHealth and uHealth 2023;11:e41235 View
  13. 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
  14. Yoon S, Wee S, Loh D, Bee Y, Thumboo J. Facilitators and Barriers to Uptake of Community-Based Diabetes Prevention Program Among Multi-Ethnic Asian Patients With Prediabetes. Frontiers in Endocrinology 2022;13 View
  15. Summers C, Tobin S, Unwin D. Evaluation of the Low Carb Program Digital Intervention for the Self-Management of Type 2 Diabetes and Prediabetes in an NHS England General Practice: Single-Arm Prospective Study. JMIR Diabetes 2021;6(3):e25751 View
  16. Yang Y, Boulton E, Todd C. Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework. Journal of Medical Internet Research 2022;24(6):e30817 View
  17. Kondo M, Okitsu T, Waki K, Yamauchi T, Nangaku M, Ohe K. Effect of Information and Communication Technology–Based Self-management System DialBeticsLite on Treating Abdominal Obesity in the Specific Health Guidance in Japan: Randomized Controlled Trial. JMIR Formative Research 2022;6(3):e33852 View
  18. Jeem Y, Andriani R, Nabila R, Emelia D, Lazuardi L, Koesnanto H. The Use of Mobile Health Interventions for Outcomes among Middle-Aged and Elderly Patients with Prediabetes: A Systematic Review. International Journal of Environmental Research and Public Health 2022;19(20):13638 View
  19. Hospodková P, Berežná J, Barták M, Rogalewicz V, Severová L, Svoboda R. Change Management and Digital Innovations in Hospitals of Five European Countries. Healthcare 2021;9(11):1508 View
  20. Hazrati‐Meimaneh Z, Zamanian H, Shalchi Oghli S, Moradnejad S, Karkehabadi F, Pourabbasi A, Amini‐Tehrani M. Treatment self‐regulation questionnaire across three self‐care behaviours: An instrument validation study in Iranian patients with type 2 diabetes mellitus. Nursing Open 2022;9(4):2084 View
  21. 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
  22. Heisler M, Dyer W, Finertie H, Stoll S, Wiley D, Turner C, Sedgwick T, Kullgren J, Richardson C, Hedderson M, Schmittdiel J. Using Peer Support to Prevent Diabetes: Results of a Pragmatic RCT. American Journal of Preventive Medicine 2023;65(2):239 View
  23. Arora S, Lam C, Burner E, Menchine M. Implementation and Evaluation of an Automated Text Message–Based Diabetes Prevention Program for Adults With Pre-diabetes. Journal of Diabetes Science and Technology 2023:193229682311626 View
  24. Griauzde D, Hershey C, Michaels J, Evans R, Richardson C, Heisler M, Kullgren J, Saslow L. A very low-carbohydrate diabetes prevention program for veterans with prediabetes: a single-arm mixed methods pilot study. Frontiers in Nutrition 2023;10 View
  25. Buss V, Barr M, Parker S, Kabir A, Lau A, Liaw S, Stocks N, Harris M. Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study. JMIR mHealth and uHealth 2024;12:e45942 View
  26. 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 View

Books/Policy Documents

  1. Persson D, Yoo S, Bardram J, Skinner T, Bækgaard P. HCI International 2023 – Late Breaking Papers. View