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

Wearables and MHealth Reviews (276) mHealth for Treatment Adherence (404) mHealth for Wellness, Behavior Change and Prevention (2922) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428)

Published on in Vol 10, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33247, first published .
mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review

mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review

mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review

Authors of this article:

Maria Aguiar1, 2 Author Orcid Image ;   Maria Trujillo2 Author Orcid Image ;   Deisy Chaves2, 3 Author Orcid Image ;   Roberto Álvarez1, 4 Author Orcid Image ;   Gorka Epelde1, 4 Author Orcid Image
Article Authors Cited by (12) Tweetations (6) Metrics

Journals

  1. Sivakumar B, Lemonde M, Stein M, Mak S, Al-Hesayen A, Arcand J, White S. Patient perspectives on the use of mobile apps to support heart failure management: A qualitative descriptive study. PLOS ONE 2023;18(5):e0285659 View
  2. Uspenskiy V, Saprankov V, Mazin V, Filippov A, Boyarskaya N, Malashicheva A, Moiseeva O. Machine learning and artificial intelligence in the prediction, diagnosis and treatment of thoracic aortic diseases (literature review). Part 2. Russian Journal for Personalized Medicine 2023;3(3):132 View
  3. Metzendorf M, Wieland L, Richter B. Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity. Cochrane Database of Systematic Reviews 2024;2024(2) View
  4. Eaton C, McWilliams E, Yablon D, Kesim I, Ge R, Mirus K, Sconiers T, Donkoh A, Lawrence M, George C, Morrison M, Muther E, Oates G, Sathe M, Sawicki G, Snell C, Riekert K. Cross-Cutting mHealth Behavior Change Techniques to Support Treatment Adherence and Self-Management of Complex Medical Conditions: Systematic Review. JMIR mHealth and uHealth 2024;12:e49024 View
  5. Benthem de Grave R, Bull C, Monjardino de Souza Monteiro D, Margariti E, McMurchy G, Hutchinson J, Smeddinck J. Smartphone Apps for Food Purchase Choices: Scoping Review of Designs, Opportunities, and Challenges. Journal of Medical Internet Research 2024;26:e45904 View
  6. Bucher A, Blazek E, Symons C. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review. Mayo Clinic Proceedings: Digital Health 2024;2(3):375 View
  7. Reen J, Chan G, Orji R. iCare: Insights from the Evaluation of an App for Managing Stress Among Working-Class Indian Women. International Journal of Human–Computer Interaction 2024:1 View
  8. Saliasi I, Lan R, Rhanoui M, Fraticelli L, Viennot S, Tardivo D, Clément C, du Sartz de Vigneulles B, Bernard S, Darlington-Bernard A, Dussart C, Bourgeois D, Carrouel F. French Version of the User Mobile Application Rating Scale: Adaptation and Validation Study. JMIR mHealth and uHealth 2024;12:e63776 View
  9. Cheng M, Dai Q, Liu Z, Wang Y, Zhou C. New progress in pediatric allergic rhinitis. Frontiers in Immunology 2024;15 View
  10. Oliveira A, Alfouzan N, Yu J, Yahya A, Lammy K, Wright M, Reinhold D, Peterson L, Brewer A, Liechty J, Nakamura M. Feasibility and acceptability pilot study of an online weight loss program in rural, underserved communities. PeerJ 2024;12:e18268 View
  11. Oliveira A, Wolff J, Alfouzan N, Yu J, Yahya A, Lammy K, Nakamura M. A Novel Web App for Dietary Weight Management: Development, Implementation, and Usability Study. JMIR Formative Research 2024;8:e58363 View
  12. Martin-Payo R, González-Nuevo-Vázquez C, Álvarez-Gómez E, Surendran S, Cachero-Rodríguez J, Fernandez-Alvarez M, Cudejko T. Spanish versión of The App Behavior Change Scale (ABACUS-Es): Adaptation and validation study. PLOS ONE 2024;19(12):e0314753 View