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

mHealth for Wellness, Behavior Change and Prevention (2265) Innovations and Technology for Healthy Eating Education (317) Innovations and Technology for Physical Activity Education (537)

Published on in Vol 8 , No 1 (2020) :January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13273, first published .
Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study

Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study

Effect of mHealth With Offline Antiobesity Treatment in a Community-Based Weight Management Program: Cross-Sectional Study

Authors of this article:

Youngin Kim 1, 2 Author Orcid Image ;   Bumjo Oh 3 Author Orcid Image ;   Hyun-Young Shin 4 Author Orcid Image
Article Authors Cited by (12) Tweetations (8) Metrics

Journals

  1. Kim J, Kam H, Kim Y, Lee Y, Lee J. Understanding Time Series Patterns of Weight and Meal History Reports in Mobile Weight Loss Intervention Programs: Data-Driven Analysis. Journal of Medical Internet Research 2020;22(8):e17521 View
  2. Guzman A, Landers S, Nechitilo M, Garbers S, Catallozzi M, Gold M, Bell D, Rosenthal S. Acceptability of a Phone App-Based Motivational Interviewing Intervention for Young Men’s Health. Journal of Technology in Behavioral Science 2020;5(3):258 View
  3. Lugones-Sanchez C, Sanchez-Calavera M, Repiso-Gento I, Adalia E, Ramirez-Manent J, Agudo-Conde C, Rodriguez-Sanchez E, Gomez-Marcos M, Recio-Rodriguez J, Garcia-Ortiz L. Effectiveness of an mHealth Intervention Combining a Smartphone App and Smart Band on Body Composition in an Overweight and Obese Population: Randomized Controlled Trial (EVIDENT 3 Study). JMIR mHealth and uHealth 2020;8(11):e21771 View
  4. Kim H, Kim Y, Park Y. Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study. JMIR mHealth and uHealth 2021;9(3):e22183 View
  5. Kwon H, Kim H, An J, Lee J, Park Y. Lifelog Data-Based Prediction Model of Digital Health Care App Customer Churn: Retrospective Observational Study. Journal of Medical Internet Research 2021;23(1):e22184 View
  6. Kim S, Rhee S, Lee S. Effectiveness of Information and Communications Technology-Based Interventions for Obesity and Metabolic Syndrome. Journal of Obesity & Metabolic Syndrome 2022;31(3):201 View
  7. Szinay D, Perski O, Jones A, Chadborn T, Brown J, Naughton F. Perceptions of Factors Influencing Engagement With Health and Well-being Apps in the United Kingdom: Qualitative Interview Study. JMIR mHealth and uHealth 2021;9(12):e29098 View
  8. Zhou M, Zhang N, Zhang Y, Yan X, Li M, Guo W, Guo X, He H, Guo K, Ma G. Effect of Mobile-Based Lifestyle Intervention on Weight Loss among the Overweight and Obese Elderly Population in China: A Randomized Controlled Trial. International Journal of Environmental Research and Public Health 2021;18(16):8825 View
  9. De-Jongh González O, Tugault-Lafleur C, Buckler E, Hamilton J, Ho J, Buchholz A, Morrison K, Ball G, Mâsse L. The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes. Journal of Medical Internet Research 2022;24(6):e35285 View
  10. Kim H, Kim Y, Michaelides A, Park Y. Weight Loss Trajectories and Related Factors in a 16-Week Mobile Obesity Intervention Program: Retrospective Observational Study. Journal of Medical Internet Research 2022;24(4):e29380 View
  11. Ju H, Kang E, Kim Y, Ko H, Cho B. The Effectiveness of a Mobile Health Care App and Human Coaching Program in Primary Care Clinics: Pilot Multicenter Real-World Study. JMIR mHealth and uHealth 2022;10(5):e34531 View

Books/Policy Documents

  1. Bellinghausen M, Waerdt L, Baumeister H. Praxisbeispiele der Digitalisierung. View