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

mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (995) Telehealth and Telemonitoring (3141) mHealth in a Clinical Setting (1197) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (2428) mHealth for Treatment Adherence (628) Infectious Diseases (non-STD/STI) (1945) Medication Adherence (178)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57991, first published .
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Effectiveness of a Mobile Health Intervention (DOT Selfie) in Increasing Treatment Adherence Monitoring and Support for Patients With Tuberculosis in Uganda: Randomized Controlled Trial

Effectiveness of a Mobile Health Intervention (DOT Selfie) in Increasing Treatment Adherence Monitoring and Support for Patients With Tuberculosis in Uganda: Randomized Controlled Trial

Authors of this article:

Juliet Nabbuye Sekandi1, 2 Author Orcid Image ;   Esther Buregyeya3 Author Orcid Image ;   Sarah Zalwango3, 4 Author Orcid Image ;   Damalie Nakkonde3 Author Orcid Image ;   Patrick Kaggwa1, 2 Author Orcid Image ;   Trang Ho Thu Quach2 Author Orcid Image ;   David Asiimwe3, 5 Author Orcid Image ;   Lynn Atuyambe3 Author Orcid Image ;   Kevin Dobbin1 Author Orcid Image
Article Authors Cited by (10) Tweetations (1) Metrics

Journals

  1. Kėvelaitienė K, Davidavičienė V, Danila E. Tuberculosis treatment failure: what are the risk factors? A comprehensive literature review. Multidisciplinary Respiratory Medicine 2025;20(Vol. 20 (2025)) View
  2. Cheng Q, Chen P, Dai R, Jia Q, Bai X, Cao Q, Li Q, Wu Y, Huang Y. Comparative Effectiveness of Digital Health Technologies in Tuberculosis Treatment: Systematic Review and Network Meta-Analysis of Randomized Controlled Trials. JMIR mHealth and uHealth 2025;13:e75424 View
  3. Faye L, Dlatu N, Hosu M, Chitha W, Apalata T. Community-engaged clinical governance and machine learning for optimizing tuberculosis management in rural Eastern Cape. Frontiers in Public Health 2026;13 View
  4. Ahmad R, Ramos J, Duarte R, Lange C, Van Rest J. Enhancing tuberculosis care in lower and middle-income countries through digital adherence technologies. Breathe 2026;22(1):250173 View
  5. Mangela J, Muhammad Jusman Rau , Arwan , Rasyika Nurul Fadjriah , Ni Wayan Sridani . Effect of self-efficacy and social support on medication compliance for tuberculosis patients. Jurnal Ilmiah Kesehatan Sandi Husada 2026;15(1):10 View
  6. Sacramento D, Mendonça S, de Farias A, Sampaio V. Digital solutions for tuberculosis surveillance and control in primary health care: a scoping review. Epidemiologic Reviews 2026;48(1) View
  7. Bwambale K, Amutuheire D, Nakkonde D, Kaggwa P, Buregyeya E, Zalwango S, Nabukeera S, Isunju J, Sekandi J. Time to first and recurrent missed video directly observed therapy sessions and associated factors among people with Tuberculosis in Kampala, Uganda. Frontiers in Digital Health 2026;8 View
  8. Asrianto L, Susliyanti Hasiu T, Nusyuhada W, Noor A, Harfan D. Community-based social network analysis measuring tuberculosis control intervention impact: A quasi-experimental study. Jurnal Ilmiah Kesehatan Sandi Husada 2026;15(2):672 View

Books/Policy Documents

  1. Thakur A, Kumar A, Patel D, Priya N, Mitra D, Bhatt T. Advanced Tuberculosis Infection Prevention and Control Practices. View

Conference Proceedings

  1. Wang Y, Kaggwa P, Jolly H, Bhandarkar S, Sekandi J, Sun J. 2026 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). When AI Watches the Dose: Can Vision-Language Models Perform TB Medication Adherence Assessments Like Clinicians? View