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

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