Published on in Vol 4 , No 1 (2016) :Jan-Mar

Health Behavior Theory in Popular Calorie Counting Apps: A Content Analysis

Health Behavior Theory in Popular Calorie Counting Apps: A Content Analysis

Health Behavior Theory in Popular Calorie Counting Apps: A Content Analysis

Journals

  1. Simoni J, Ronen K, Aunon F. Health Behavior Theory to Enhance eHealth Intervention Research in HIV: Rationale and Review. Current HIV/AIDS Reports 2018;15(6):423 View
  2. Mandracchia F, Llauradó E, Tarro L, Valls R, Solà R. Mobile Phone Apps for Food Allergies or Intolerances in App Stores: Systematic Search and Quality Assessment Using the Mobile App Rating Scale (MARS). JMIR mHealth and uHealth 2020;8(9):e18339 View
  3. Lupton D. ‘I Just Want It to Be Done, Done, Done!’ Food Tracking Apps, Affects, and Agential Capacities. Multimodal Technologies and Interaction 2018;2(2):29 View
  4. Grundy Q, Wang Z, Bero L. Challenges in Assessing Mobile Health App Quality. American Journal of Preventive Medicine 2016;51(6):1051 View
  5. Imschloss M, Lorenz J. How Mobile App Design Impacts User Responses to Mixed Self-Tracking Outcomes: Randomized Online Experiment to Explore the Role of Spatial Distance for Hedonic Editing. JMIR mHealth and uHealth 2018;6(4):e81 View
  6. Thomas J, Flay B, Cardinal B. Are Physical Activity Resources Understandable as Disseminated? A Meta-Analysis of Readability Studies. Quest 2018;70(4):492 View
  7. Grainger R, Townsley H, White B, Langlotz T, Taylor W. Apps for People With Rheumatoid Arthritis to Monitor Their Disease Activity: A Review of Apps for Best Practice and Quality. JMIR mHealth and uHealth 2017;5(2):e7 View
  8. Lim J, Lim C, Ibrahim I, Syahrul J, Mohamed Zabil M, Zakaria N, Daud Z. Limitations of Existing Dialysis Diet Apps in Promoting User Engagement and Patient Self-Management: Quantitative Content Analysis Study. JMIR mHealth and uHealth 2020;8(6):e13808 View
  9. Garnett C, Crane D, Brown J, Kaner E, Beyer F, Muirhead C, Hickman M, Redmore J, de Vocht F, Beard E, Michie S. Reported Theory Use by Digital Interventions for Hazardous and Harmful Alcohol Consumption, and Association With Effectiveness: Meta-Regression. Journal of Medical Internet Research 2018;20(2):e69 View
  10. Samoggia A, Riedel B. Assessment of nutrition-focused mobile apps' influence on consumers' healthy food behaviour and nutrition knowledge. Food Research International 2020;128:108766 View
  11. Lee Y, Cui Y, Tu M, Chen Y, Chang P. Mobile Health to Maintain Continuity of Patient-Centered Care for Chronic Kidney Disease: Content Analysis of Apps. JMIR mHealth and uHealth 2018;6(4):e10173 View
  12. Ferrara G, Kim J, Lin S, Hua J, Seto E. A Focused Review of Smartphone Diet-Tracking Apps: Usability, Functionality, Coherence With Behavior Change Theory, and Comparative Validity of Nutrient Intake and Energy Estimates. JMIR mHealth and uHealth 2019;7(5):e9232 View
  13. Maringer M, van’t Veer P, Klepacz N, Verain M, Normann A, Ekman S, Timotijevic L, Raats M, Geelen A. User-documented food consumption data from publicly available apps: an analysis of opportunities and challenges for nutrition research. Nutrition Journal 2018;17(1) View