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

Web-based and Mobile Health Interventions (3582) Fitness Trackers and Smart Pedometers/Accelerometers (604) mHealth for Screening (251) Depression and Mood Disorders; Suicide Prevention (1498) Diagnostic Tools in Mental Health (277) Mobile Health in Psychiatry (473)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22634, first published .
Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method

Authors of this article:

Olga Minaeva1 Author Orcid Image ;   Harriëtte Riese1 Author Orcid Image ;   Femke Lamers2 Author Orcid Image ;   Niki Antypa3 Author Orcid Image ;   Marieke Wichers1 Author Orcid Image ;   Sanne H Booij4, 5 Author Orcid Image
Article Authors Cited by (18) Tweetations (8) Metrics

Journals

  1. George S, Kunkels Y, Booij S, Wichers M. Uncovering complexity details in actigraphy patterns to differentiate the depressed from the non-depressed. Scientific Reports 2021;11(1) View
  2. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  3. Wüthrich F, Nabb C, Mittal V, Shankman S, Walther S. Actigraphically measured psychomotor slowing in depression: systematic review and meta-analysis. Psychological Medicine 2022;52(7):1208 View
  4. De Calheiros Velozo J, Habets J, George S, Niemeijer K, Minaeva O, Hagemann N, Herff C, Kuppens P, Rintala A, Vaessen T, Riese H, Delespaul P. Designing daily-life research combining experience sampling method with parallel data. Psychological Medicine 2024;54(1):98 View
  5. Abd-alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. Journal of Medical Internet Research 2023;25:e42672 View
  6. Liu Y, Kang K, Doe M. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123 View
  7. Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Frontiers in Psychiatry 2022;13 View
  8. Holm A, Johnson A, Clockston R, Oselinsky K, Lundeberg P, Rand K, Graham D. Intersectional health disparities: the relationships between sex, race/ethnicity, and sexual orientation and depressive symptoms. Psychology & Sexuality 2022;13(4):1068 View
  9. Pieters L, Deenik J, de Vet S, Delespaul P, van Harten P. Combining actigraphy and experience sampling to assess physical activity and sleep in patients with psychosis: A feasibility study. Frontiers in Psychiatry 2023;14 View
  10. Van Assche E, Antoni Ramos-Quiroga J, Pariante C, Sforzini L, Young A, Flossbach Y, Gold S, Hoogendijk W, Baune B, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100 View
  11. Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine 2023;6(1) View
  12. Punturieri C, Duncan W, Greenstein D, Shandler G, Zarate C, Evans J. An exploration of actigraphy in the context of ketamine and treatment‐resistant depression. International Journal of Methods in Psychiatric Research 2024;33(1) View
  13. Poon C, Cheng Y, Wong V, Tam H, Chung K, Yeung W, Ho F. Directional associations among real-time activity, sleep, mood, and daytime symptoms in major depressive disorder using actigraphy and ecological momentary assessment. Behaviour Research and Therapy 2024;173:104464 View
  14. Ho F, Poon C, Wong V, Chan K, Law K, Yeung W, Chung K. Actigraphic monitoring of sleep and circadian rest-activity rhythm in individuals with major depressive disorder or depressive symptoms: A meta-analysis. Journal of Affective Disorders 2024;361:224 View
  15. Sideri K, van Dijk N. The techno-politics of computing the mind: Opening the black box of digital psychiatry. Social Studies of Science 2024 View
  16. Lucasius C, Ali M, Patel T, Kundur D, Szatmari P, Strauss J, Battaglia M. A procedural overview of why, when and how to use machine learning for psychiatry. Nature Mental Health 2025;3(1):8 View
  17. Nejadshamsi S, Karami V, Ghourchian N, Armanfard N, Bergman H, Grad R, Wilchesky M, Khanassov V, Vedel I, Abbasgholizadeh Rahimi S. Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study. JMIR Aging 2025;8:e67715 View

Books/Policy Documents

  1. Verhagen S, van Os J, Delespaul P. Mental Health in a Digital World. View