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

Depression and Mood Disorders; Suicide Prevention (1687) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2109) Anxiety and Stress Disorders (1326) Diagnostic Tools in Mental Health (326) Wearable Devices and Sensors (1133) Digital Biomarkers and Digital Phenotyping (283) Psychiatry (94) Mobile Health in Psychiatry (527) Telepsychiatry (212) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1730) Mobile Health (mhealth) (3461)

Published on in Vol 9, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35549, first published .
Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Authors of this article:

Taylor A Braund1, 2 Author Orcid Image ;   May The Zin2 Author Orcid Image ;   Tjeerd W Boonstra1, 2, 3 Author Orcid Image ;   Quincy J J Wong1, 4 Author Orcid Image ;   Mark E Larsen1, 2 Author Orcid Image ;   Helen Christensen1, 2 Author Orcid Image ;   Gabriel Tillman5 Author Orcid Image ;   Bridianne O’Dea1, 2 Author Orcid Image
Article Authors Cited by (17) Tweetations (14) Metrics

Journals

  1. Bjella T, Collier Høegh M, Holmstul Olsen S, Aminoff S, Barrett E, Ueland T, Icick R, Andreassen O, Nerhus M, Myhre Ihler H, Hagen M, Busch-Christensen C, Melle I, Lagerberg T. Developing “MinDag” – an app to capture symptom variation and illness mechanisms in bipolar disorder. Frontiers in Medical Technology 2022;4 View
  2. Braund T, O’Dea B, Bal D, Maston K, Larsen M, Werner-Seidler A, Tillman G, Christensen H. Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study. JMIR Mental Health 2023;10:e44986 View
  3. Lukka L, Palva J. The Development of Game-Based Digital Mental Health Interventions: Bridging the Paradigms of Health Care and Entertainment. JMIR Serious Games 2023;11:e42173 View
  4. Shin J, Bae S. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984 View
  5. 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
  6. Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
  7. Evans H, Ryu M, Hsieh T, Zhou J, Xu K, Akers K, Sherrill A, Arriaga R. Using Sensor-Captured Patient-Generated Data to Support Clinical Decision-making in PTSD Therapy. Proceedings of the ACM on Human-Computer Interaction 2024;8(CSCW1):1 View
  8. Janssen Daalen J, van den Bergh R, Prins E, Moghadam M, van den Heuvel R, Veen J, Mathur S, Meijerink H, Mirelman A, Darweesh S, Evers L, Bloem B. Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art. npj Digital Medicine 2024;7(1) View
  9. Aledavood T, Luong N, Baryshnikov I, Darst R, Heikkilä R, Holmén J, Ikäheimonen A, Martikkala A, Riihimäki K, Saleva O, Triana A, Isometsä E. Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study. JMIR Mental Health 2025;12:e63622 View
  10. Sidani L, Nadar S, Tfaili J, El Rayes S, Sharara F, Elhage J, Fakhoury M. Digital Psychiatry: Opportunities, Challenges, and Future Directions. Journal of Psychiatric Practice 2024;30(6):400 View
  11. Gubin D, Weinert D, Stefani O, Otsuka K, Borisenkov M, Cornelissen G. Wearables in Chronomedicine and Interpretation of Circadian Health. Diagnostics 2025;15(3):327 View
  12. Linardon J, Chen K, Gajjar S, Eadara A, Wang S, Flathers M, Burns J, Torous J. Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features. Psychiatry Research 2025;348:116483 View
  13. Piotr Łapiński , Mateusz Świątko , Anna Tokarska , Marcin Grzebyk , Aleksandra Arnista , Joanna Rybak , Katarzyna Gawrońska , Agnieszka Waszczuk , Aleksandra Kołodziejczyk , Paweł Sosnowski . PREDICTING AFFECTIVE EPISODES THROUGH DIGITAL MONITORING OF CIRCADIAN RHYTHM DISTURBANCES: A SYSTEMATIC REVIEW OF MODERN TECHNOLOGY APPLICATIONS. International Journal of Innovative Technologies in Social Science 2025;(2(46)) View
  14. Amin R, Schreynemackers S, Oppenheimer H, Petrovic M, Hegerl U, Reich H. Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review. Journal of Medical Internet Research 2025;27:e57418 View
  15. Clemens J, Mühlbauer E, Reinhard I, Bauer M, Neubauer A, Ritter P, Ludwig V, Severus W, Ebner-Priemer U, Schmitz S. Circadian rhythm parameters differentiate euthymic, manic and depressive mood states in bipolar disorders – an explorative pilot study. International Journal of Bipolar Disorders 2025;13(1) View
  16. Tlachac M, Heinz M, Bryan A, LaPreay A, Dimas G, Zhao T, Jacobson N, Ogden S. Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review. IEEE Transactions on Affective Computing 2025;16(4):2599 View

Conference Proceedings

  1. Agudelo Y, Espinosa M, Cukic M. 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT). Cloud aggregation of sensor data: an application on mood disorder analysis View