Published on in Vol 8, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24693, first published .
Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey

Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey

Attitudes Toward Using COVID-19 mHealth Tools Among Adults With Chronic Health Conditions: Secondary Data Analysis of the COVID-19 Impact Survey

Journals

  1. Cardona S, Calixte R, Rivera A, Islam J, Vidot D, Camacho-Rivera M. Perceptions and Patterns of Cigarette and E-Cigarette Use among Hispanics: A Heterogeneity Analysis of the 2017–2019 Health Information National Trends Survey. International Journal of Environmental Research and Public Health 2021;18(12):6378 View
  2. Blacklow S, Lisker S, Ng M, Sarkar U, Lyles C. Usability, inclusivity, and content evaluation of COVID-19 contact tracing apps in the United States. Journal of the American Medical Informatics Association 2021;28(9):1982 View
  3. Calixte R, Islam S, Osakwe Z, Rivera A, Camacho-Rivera M. Pattern of Use of Electronic Health Record (EHR) among the Chronically Ill: A Health Information National Trend Survey (HINTS) Analysis. International Journal of Environmental Research and Public Health 2021;18(14):7254 View
  4. Mehta S, Nugent M, Peynenburg V, Thiessen D, La Posta G, Titov N, Dear B, Hadjistavropoulos H. Internet-delivered cognitive behaviour therapy for chronic health conditions: self-guided versus team-guided. Journal of Behavioral Medicine 2022;45(5):674 View
  5. Wachira E, Laki K, Chavan B, Aidoo-Frimpong G, Kingori C. Factors Influencing COVID-19 Prevention Behaviors. Journal of Prevention 2023;44(1):35 View
  6. Ong A, Chuenyindee T, Prasetyo Y, Nadlifatin R, Persada S, Gumasing M, German J, Robas K, Young M, Sittiwatethanasiri T. Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”. International Journal of Environmental Research and Public Health 2022;19(10):6111 View
  7. Howell P, Abdelhamid M. Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study. JMIR Formative Research 2023;7:e36608 View
  8. Baek Y, Jeong K, Lee S, Kim H, Seo B, Jin H. Feasibility and Effectiveness of Assessing Subhealth Using a Mobile Health Management App (MibyeongBogam) in Early Middle-Aged Koreans: Randomized Controlled Trial. JMIR mHealth and uHealth 2021;9(8):e27455 View
  9. Chuenyindee T, Ong A, Prasetyo Y, Persada S, Nadlifatin R, Sittiwatethanasiri T. Factors Affecting the Perceived Usability of the COVID-19 Contact-Tracing Application “Thai Chana” during the Early COVID-19 Omicron Period. International Journal of Environmental Research and Public Health 2022;19(7):4383 View
  10. Alam M, Proteek S, Hoque I. A systematic literature review on mHealth related research during the COVID-19 outbreak. Health Education 2023;123(1):19 View
  11. Wileden L, Anthony D, Campos-Castillo C, Morenoff J. Resident Willingness to Participate in Digital Contact Tracing in a COVID-19 Hotspot: Findings From a Detroit Panel Study. JMIR Public Health and Surveillance 2023;9:e39002 View
  12. Rahimi R, Khoundabi B, fathian A. Investigating the effective factors of using mHealth apps for monitoring COVID-19 symptoms and contact tracing: A survey among Iranian citizens. International Journal of Medical Informatics 2021;155:104571 View
  13. Guillon M. Digital contact-tracing in France: uptake by COVID-19 risk factor and by exposure risk. Journal of Public Health 2022;44(3):e366 View
  14. Nguyen H, Tran K, Doan P, Nguyen T. Demand for Mobile Health in Developing Countries During COVID-19: Vietnamese’s Perspectives from Different Age Groups and Health Conditions. Patient Preference and Adherence 2022;Volume 16:265 View
  15. Jilani M, Moniruzzaman M, Dey M, Alam E, Uddin M. Strengthening the Trialability for the Intention to Use of mHealth Apps Amidst Pandemic: A Cross-Sectional Study. International Journal of Environmental Research and Public Health 2022;19(5):2752 View
  16. Katusiime J, Tumuhimbise W, Rwambuka Mugyenyi G, Kobutungi P, Mugaba A, Zender R, Pinkwart N, Musiimenta A. The role of mobile health technologies in promoting COVID-19 prevention: A narrative review of intervention effectiveness and adoption. DIGITAL HEALTH 2022;8:205520762211311 View
  17. Wachira E, Chavan B, Nganga-Good C, Kingori C, Rahman Q. The association between COVID-19 preventive behaviors and mental health conditions. PLOS ONE 2023;18(8):e0289533 View
  18. Sujarwoto S, Maharani A. Facilitators and barriers to the adoption of mHealth apps for COVID-19 contact tracing: a systematic review of the literature. Frontiers in Public Health 2023;11 View
  19. Chen Z, Siegel L, Prutzman Y, Wiseman K. Characterizing perceived usability and its correlation with smoking cessation: An analysis of user assessments of the smoking cessation app quitSTART. Internet Interventions 2024;35:100714 View
  20. Park N, Jang S. App-Based Digital Health Equity Determinants According to Ecological Models: Scoping Review. Sustainability 2024;16(6):2232 View
  21. Vincent W. Willingness to Use Digital Health Screening and Tracking Tools for Public Health in Sexual Minority Populations in a National Probability Sample: Quantitative Intersectional Analysis. Journal of Medical Internet Research 2024;26:e47448 View

Books/Policy Documents

  1. Arangurí M, Bravo J, Alarcón R, Rodriguez A, Correa D. Communication and Smart Technologies. View