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

mHealth for Wellness, Behavior Change and Prevention (2728) mHealth for Data Collection and Research (907) Sleep Monitoring, Sleep Quality, Sleep Disorders (167)

Published on in Vol 7, No 5 (2019): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13421, first published .
Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

Validation of the Mobile App–Recorded Circadian Rhythm by a Digital Footprint

Authors of this article:

Yu-Hsuan Lin1, 2, 3, 4 Author Orcid Image ;   Bo-Yu Wong1 Author Orcid Image ;   Yuan-Chien Pan1, 5 Author Orcid Image ;   Yu-Chuan Chiu6 Author Orcid Image ;   Yang-Han Lee7 Author Orcid Image
Article Authors Cited by (13) Tweetations (4) Metrics

Journals

  1. Lin Y, Chen S, Lin P, Tai A, Pan Y, Hsieh C, Lin S. Assessing User Retention of a Mobile App: Survival Analysis. JMIR mHealth and uHealth 2020;8(11):e16309 View
  2. Aubourg T, Demongeot J, Vuillerme N. Novel statistical approach for assessing the persistence of the circadian rhythms of social activity from telephone call detail records in older adults. Scientific Reports 2020;10(1) View
  3. Aubourg T, Demongeot J, Provost H, Vuillerme N. Exploitation of Outgoing and Incoming Telephone Calls in the Context of Circadian Rhythms of Social Activity Among Elderly People: Observational Descriptive Study. JMIR mHealth and uHealth 2020;8(11):e13535 View
  4. Chen I, Chen Y, Liao S, Lin Y. Development of Digital Biomarkers of Mental Illness via Mobile Apps for Personalized Treatment and Diagnosis. Journal of Personalized Medicine 2022;12(6):936 View
  5. Cay G, Ravichandran V, Sadhu S, Zisk A, Salisbury A, Solanki D, Mankodiya K. Recent Advancement in Sleep Technologies: A Literature Review on Clinical Standards, Sensors, Apps, and AI Methods. IEEE Access 2022;10:104737 View
  6. Ren B, Xia C, Gehrman P, Barnett I, Satterthwaite T. Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study. JMIR Formative Research 2022;6(9):e33890 View
  7. Varma P, Postnova S, Phillips A, Knock S, Howard M, Rajaratnam S, Sletten T. Pilot feasibility testing of biomathematical model recommendations for personalising sleep timing in shift workers. Journal of Sleep Research 2023 View
  8. Lin C, Chen I, Chuang H, Wang Z, Lin H, Lin Y. Examining Human-Smartphone Interaction as a Proxy for Circadian Rhythm in Patients With Insomnia: Cross-Sectional Study. Journal of Medical Internet Research 2023;25:e48044 View
  9. Chen H, Lu H, Weng W, Lin Y. Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study. Journal of Medical Internet Research 2023;25:e48834 View
  10. Jaiswal S, Pawelek J, Warshawsky S, Quer G, Trieu M, Pandit J, Owens R. Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies. Current Sleep Medicine Reports 2024;10(1):82 View
  11. Chuang H, Lin C, Lee L, Chang H, She G, Lin Y. Comparing Human-Smartphone Interactions and Actigraphy Measurements for Circadian Rhythm Stability and Adiposity: Algorithm Development and Validation Study. Journal of Medical Internet Research 2024;26:e50149 View
  12. Chen H, Lin C, Chang H, Chang J, Chuang H, Lin Y. Developing Methods for Assessing Mental Activity Using Human-Smartphone Interactions: Comparative Analysis of Activity Levels and Phase Patterns in General Mental Activities, Working Mental Activities, and Physical Activities. Journal of Medical Internet Research 2024;26:e56144 View

Books/Policy Documents

  1. Nawi A, Hussin Z, Ren C, Norsaidi N, Mohd Pozi M. Digital Libraries at Times of Massive Societal Transition. View