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

Mobile Health (mhealth) (2939) mHealth for Wellness, Behavior Change and Prevention (2871) Use and User Demographics of mHealth (335)

Published on in Vol 18, No 6 (2016): Jun

Mining Health App Data to Find More and Less Successful Weight Loss Subgroups

Mining Health App Data to Find More and Less Successful Weight Loss Subgroups

Mining Health App Data to Find More and Less Successful Weight Loss Subgroups

Authors of this article:

Katrina J. Serrano1 Author Orcid Image ;   Mandi Yu1 Author Orcid Image ;   Kisha I. Coa2 Author Orcid Image ;   Linda M. Collins3 Author Orcid Image ;   Audie A. Atienza2 Author Orcid Image
Article Authors Cited by (38) Tweetations (27) Metrics

Journals

  1. Atienza A, Serrano K, Riley W, Moser R, Klein W. Advancing Cancer Prevention and Behavior Theory in the Era of Big Data. Journal of Cancer Prevention 2016;21(3):201 View
  2. Chen J, Berkman W, Bardouh M, Ng C, Allman-Farinelli M. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition 2019;57:208 View
  3. Brindal E, Hendrie G, Freyne J, Noakes M. Incorporating a Static Versus Supportive Mobile Phone App Into a Partial Meal Replacement Program With Face-to-Face Support: Randomized Controlled Trial. JMIR mHealth and uHealth 2018;6(4):e41 View
  4. Serrano K, Coa K, Yu M, Wolff-Hughes D, Atienza A. Characterizing user engagement with health app data: a data mining approach. Translational Behavioral Medicine 2017;7(2):277 View
  5. Caballé-Cervigón N, Castillo-Sequera J, Gómez-Pulido J, Gómez-Pulido J, Polo-Luque M. Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. Applied Sciences 2020;10(15):5135 View
  6. De Cock N, Vangeel J, Lachat C, Beullens K, Vervoort L, Goossens L, Maes L, Deforche B, De Henauw S, Braet C, Eggermont S, Kolsteren P, Van Camp J, Van Lippevelde W. Use of Fitness and Nutrition Apps: Associations With Body Mass Index, Snacking, and Drinking Habits in Adolescents. JMIR mHealth and uHealth 2017;5(4):e58 View
  7. Matthews P, Topham P, Caleb-Solly P. Interaction and Engagement with an Anxiety Management App: Analysis Using Large-Scale Behavioral Data. JMIR Mental Health 2018;5(4):e58 View
  8. Hendrie G, James-Martin G, Williams G, Brindal E, Whyte B, Crook A. The Development of VegEze: Smartphone App to Increase Vegetable Consumption in Australian Adults. JMIR Formative Research 2019;3(1):e10731 View
  9. Chen J, Gemming L, Hanning R, Allman-Farinelli M. Smartphone apps and the nutrition care process: Current perspectives and future considerations. Patient Education and Counseling 2018;101(4):750 View
  10. Pham Q, Graham G, Carrion C, Morita P, Seto E, Stinson J, Cafazzo J. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11941 View
  11. Martin Payo R, Fernandez Álvarez M, Blanco Díaz M, Cuesta Izquierdo M, Stoyanov S, Llaneza Suárez E. Spanish adaptation and validation of the Mobile Application Rating Scale questionnaire. International Journal of Medical Informatics 2019;129:95 View
  12. Jake-Schoffman D, Silfee V, Waring M, Boudreaux E, Sadasivam R, Mullen S, Carey J, Hayes R, Ding E, Bennett G, Pagoto S. Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps. JMIR mHealth and uHealth 2017;5(12):e190 View
  13. Idris I, Hampton J, Moncrieff F, Whitman M. Effectiveness of a Digital Lifestyle Change Program in Obese and Type 2 Diabetes Populations: Service Evaluation of Real-World Data. JMIR Diabetes 2020;5(1):e15189 View
  14. Painter S, Ahmed R, Hill J, Kushner R, Lindquist R, Brunning S, Margulies A. What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring. Journal of Medical Internet Research 2017;19(5):e160 View
  15. Timmins K, Green M, Radley D, Morris M, Pearce J. How has big data contributed to obesity research? A review of the literature. International Journal of Obesity 2018;42(12):1951 View
  16. Fuscà E, Bolzon A, Buratin A, Ruffolo M, Berchialla P, Gregori D, Perissinotto E, Baldi I. Measuring Caloric Intake at the Population Level (NOTION): Protocol for an Experimental Study. JMIR Research Protocols 2019;8(3):e12116 View
  17. Oka R, Nomura A, Yasugi A, Kometani M, Gondoh Y, Yoshimura K, Yoneda T. Study Protocol for the Effects of Artificial Intelligence (AI)-Supported Automated Nutritional Intervention on Glycemic Control in Patients with Type 2 Diabetes Mellitus. Diabetes Therapy 2019;10(3):1151 View
  18. Goldstein S, Thomas J, Foster G, Turner-McGrievy G, Butryn M, Herbert J, Martin G, Forman E. Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes. Health Informatics Journal 2020;26(4):2315 View
  19. Hicks J, Althoff T, Sosic R, Kuhar P, Bostjancic B, King A, Leskovec J, Delp S. Best practices for analyzing large-scale health data from wearables and smartphone apps. npj Digital Medicine 2019;2(1) View
  20. Hendrie G, Hussain M, Brindal E, James-Martin G, Williams G, Crook A. Impact of a Mobile Phone App to Increase Vegetable Consumption and Variety in Adults: Large-Scale Community Cohort Study. JMIR mHealth and uHealth 2020;8(4):e14726 View
  21. Painter S, Lu W, Schneider J, James R, Shah B. Drivers of weight loss in a CDC-recognized digital diabetes prevention program. BMJ Open Diabetes Research & Care 2020;8(1):e001132 View
  22. Solar C, Halat A, MacLean R, Rajeevan H, Williams D, Krein S, Heapy A, Bair M, Kerns R, Higgins D. Predictors of engagement in an internet-based cognitive behavioral therapy program for veterans with chronic low back pain. Translational Behavioral Medicine 2021;11(6):1274 View
  23. Spaulding E, Marvel F, Piasecki R, Martin S, Allen J. User Engagement With Smartphone Apps and Cardiovascular Disease Risk Factor Outcomes: Systematic Review. JMIR Cardio 2021;5(1):e18834 View
  24. Gómez-Pulido J, Gómez-Pulido J, Rodríguez-Puyol D, Polo-Luque M, Vargas-Lombardo M. Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers. International Journal of Environmental Research and Public Health 2021;18(5):2364 View
  25. Espel-Huynh H, Goldstein C, Finnegan O, Elwy A, Wing R, Thomas J. Primary Care Clinicians’ Perspectives on Clinical Decision Support to Enhance Outcomes of Online Obesity Treatment in Primary Care: a Qualitative Formative Evaluation. Journal of Technology in Behavioral Science 2021;6(3):515 View
  26. Radcliffe E, Lippincott B, Anderson R, Jones M. A Pilot Evaluation of mHealth App Accessibility for Three Top-Rated Weight Management Apps by People with Disabilities. International Journal of Environmental Research and Public Health 2021;18(7):3669 View
  27. 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
  28. Ku J, Sim I. Mobile Health: making the leap to research and clinics. npj Digital Medicine 2021;4(1) View
  29. Shah L, Ding J, Spaulding E, Yang W, Lee M, Demo R, Marvel F, Martin S. Sociodemographic Characteristics Predicting Digital Health Intervention Use After Acute Myocardial Infarction. Journal of Cardiovascular Translational Research 2021;14(5):951 View
  30. Wang T, Zheng X, Liang J, An K, He Y, Nuo M, Wang W, Lei J. Use of Machine Learning to Mine User-Generated Content From Mobile Health Apps for Weight Loss to Assess Factors Correlated With User Satisfaction. JAMA Network Open 2022;5(5):e2215014 View
  31. Martin-Vicario L, Bustos Díaz J, Nicolas-Sans R, Yan Z. Weight Loss App Descriptors in App Stores: A Qualitative Analysis of the Spanish Market. Human Behavior and Emerging Technologies 2023;2023:1 View
  32. Martin-Vicario L, Bustos Díaz J, Martínez-Sánchez M, Nicolas-Sans R. Mobile applications for weight-loss in the Spanish-speaking market: Usability and engagement. Obesity Medicine 2023;41:100499 View
  33. Richards R, Wren G, Campion P, Whitman M. Service evaluation of a remotely-delivered, semaglutide-supported specialist weight management program: Preliminary findings (Preprint). JMIR Formative Research 2023 View
  34. Richards R, Wren G, Whitman M. The Potential of a Digital Weight Management Program to Support Specialist Weight Management Services in the UK National Health Service: Retrospective Analysis. JMIR Diabetes 2024;9:e52987 View
  35. Wang T, Wang W, Feng J, Fan X, Guo J, Lei J. A novel user-generated content-driven and Kano model focused framework to explore the impact mechanism of continuance intention to use mobile APPs. Computers in Human Behavior 2024;157:108252 View

Books/Policy Documents

  1. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. View
  2. Hocke-Bolte Z, Peters B, Haunit T. Forschungsmethoden in der Gesundheitsförderung und Prävention. View