Published on in Vol 4, No 3 (2016): Jul-Sept

Baseline Motivation Type as a Predictor of Dropout in a Healthy Eating Text Messaging Program

Baseline Motivation Type as a Predictor of Dropout in a Healthy Eating Text Messaging Program

Baseline Motivation Type as a Predictor of Dropout in a Healthy Eating Text Messaging Program

Authors of this article:

Kisha Coa 1 Author Orcid Image ;   Heather Patrick 2 Author Orcid Image

Journals

  1. Zarski A, Berking M, Reis D, Lehr D, Buntrock C, Schwarzer R, Ebert D. Turning Good Intentions Into Actions by Using the Health Action Process Approach to Predict Adherence to Internet-Based Depression Prevention: Secondary Analysis of a Randomized Controlled Trial. Journal of Medical Internet Research 2018;20(1):e9 View
  2. Pedersen D, Mansourvar M, Sortsø C, Schmidt T. Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors. Journal of Medical Internet Research 2019;21(9):e13617 View
  3. Rouf A, Nour M, Allman-Farinelli M. Improving Calcium Knowledge and Intake in Young Adults Via Social Media and Text Messages: Randomized Controlled Trial. JMIR mHealth and uHealth 2020;8(2):e16499 View
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  5. Grutzmacher S, Munger A, Speirs K, Vafai Y, Hilberg E, Braunscheidel Duru E, Worthington L, Lachenmayr L. Predicting Attrition in a Text-Based Nutrition Education Program: Survival Analysis of Text2BHealthy. JMIR mHealth and uHealth 2019;7(1):e9967 View
  6. Turchioe M, Heitkemper E, Lor M, Burgermaster M, Mamykina L. Designing for engagement with self-monitoring: A user-centered approach with low-income, Latino adults with Type 2 Diabetes. International Journal of Medical Informatics 2019;130:103941 View
  7. Issom D, Henriksen A, Woldaregay A, Rochat J, Lovis C, Hartvigsen G. Factors Influencing Motivation and Engagement in Mobile Health Among Patients With Sickle Cell Disease in Low-Prevalence, High-Income Countries: Qualitative Exploration of Patient Requirements. JMIR Human Factors 2020;7(1):e14599 View
  8. Heredia N, Lee M, Hwang K, Reininger B, Fernandez M, McNeill L. Health coaching to encourage obese adults to enroll in commercially-available weight management programs: The path to health study. Contemporary Clinical Trials 2019;83:1 View
  9. Harrington L. Electronic Person-Generated Health Data. AACN Advanced Critical Care 2019;30(3):217 View
  10. Nour M, Chen J, Allman-Farinelli M. Young Adults’ Engagement With a Self-Monitoring App for Vegetable Intake and the Impact of Social Media and Gamification: Feasibility Study. JMIR Formative Research 2019;3(2):e13324 View
  11. Radtke T, Ostergaard M, Cooke R, Scholz U. Web-Based Alcohol Intervention: Study of Systematic Attrition of Heavy Drinkers. Journal of Medical Internet Research 2017;19(6):e217 View
  12. Kwan R, Lee D, Lee P, Tse M, Cheung D, Thiamwong L, Choi K. Effects of an mHealth Brisk Walking Intervention on Increasing Physical Activity in Older People With Cognitive Frailty: Pilot Randomized Controlled Trial. JMIR mHealth and uHealth 2020;8(7):e16596 View
  13. Mooney J, Lipsky L, Liu A, Nansel T. Does stress attenuate motivation for healthful eating in pregnancy and postpartum?. Appetite 2021;163:105207 View
  14. Carlén K, Kylberg E. An intervention of sustainable weight change: Influence of self‐help group and expectations. Health Expectations 2021;24(4):1498 View
  15. Lee J, Turchioe M, Creber R, Biviano A, Hickey K, Bakken S. Phenotypes of engagement with mobile health technology for heart rhythm monitoring. JAMIA Open 2021;4(2) View
  16. Masterson Creber R, Turchioe M. Returning Cardiac Rhythm Data to Patients. Cardiac Electrophysiology Clinics 2021;13(3):555 View