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

Wearables and MHealth Reviews (284) mHealth for Data Collection and Research (1080) Sleep Monitoring, Sleep Quality, Sleep Disorders (227) mHealth for Wellness, Behavior Change and Prevention (3096) Quality Evaluation and Descriptive Analysis/Reviews of Multiple Existing Mobile Apps (394)

Published on in Vol 11 (2023)

This is a member publication of University of Bristol (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44123, first published .
The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review

The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review

The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review

Authors of this article:

Doaa Alamoudi1 Author Orcid Image ;   Emma Breeze2 Author Orcid Image ;   Esther Crawley3 Author Orcid Image ;   Ian Nabney1 Author Orcid Image
Article Authors Cited by (11) Tweetations (5) Metrics

Journals

  1. Fainardi V, Capoferri G, Tornesello M, Pisi G, Esposito S. Telemedicine and Its Application in Cystic Fibrosis. Journal of Personalized Medicine 2023;13(7):1041 View
  2. Negev M, Magal T, Kaphzan H. Attitudes of psychiatrists toward telepsychiatry: A policy Delphi study. DIGITAL HEALTH 2023;9 View
  3. Montag C, Hall B. Enhancing real-time digital surveillance can guide evidence-based policymaking to improve global mental health. Nature Mental Health 2023;1(10):697 View
  4. Alamoudi D, Nabney I, Crawley E. Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances. Sensors 2024;24(3):722 View
  5. Corda E, Massa S, Riboni D. Context-Aware Behavioral Tips to Improve Sleep Quality via Machine Learning and Large Language Models. Future Internet 2024;16(2):46 View
  6. Zhao M, Wang R, Zhao Z, Li L, Luo H, Wu L. The relationship between boredom proneness, the behavioral inhibition system, and anxiety in college students: variable-centered and person-centered analytic approaches. Frontiers in Psychology 2024;15 View
  7. Kim S. Trends and Perspectives of mHealth in Obesity Control. Applied Sciences 2024;15(1):74 View
  8. Stojchevska M, Van Der Donckt J, Vandenbussche N, De Brouwer M, Paemeleire K, Ongenae F, Van Hoecke S. Uncovering the potential of smartphones for behavior monitoring during migraine follow-up. BMC Medical Informatics and Decision Making 2025;25(1) View
  9. Wan Q, Liu K, Bo Y, Yuan X, Li M, Wang X, Chen C, Liu L, Wu W. Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning. IEEE Access 2025;13:45964 View
  10. Gao T, Xiang H, Wu Q, Zhu L, Pei W, Fu W, Chou T. Advances in the research of comorbid insomnia and depression: mechanisms, impacts, and interventions. Frontiers in Psychiatry 2025;16 View

Books/Policy Documents

  1. Rodriguez-Tenorio J, Borja V, Ramírez-Reivich A. 7th EAI International Conference on Computer Science and Engineering in Health Services. View