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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) Digital Biomarkers and Digital Phenotyping (191) Ecological Momentary Assessment (EMA) (309) Psychotic Disorders (160)

Published on in Vol 8, No 4 (2020): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15028, first published .
Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

Authors of this article:

Jonas Busk1 Author Orcid Image ;   Maria Faurholt-Jepsen2 Author Orcid Image ;   Mads Frost3 Author Orcid Image ;   Jakob E Bardram4 Author Orcid Image ;   Lars Vedel Kessing2, 5 Author Orcid Image ;   Ole Winther1, 6, 7 Author Orcid Image
Article Authors Cited by (28) Tweetations (5) Metrics

Journals

  1. Manchia M, Vieta E, Smeland O, Altimus C, Bechdolf A, Bellivier F, Bergink V, Fagiolini A, Geddes J, Hajek T, Henry C, Kupka R, Lagerberg T, Licht R, Martinez-Cengotitabengoa M, Morken G, Nielsen R, Pinto A, Reif A, Rietschel M, Ritter P, Schulze T, Scott J, Severus E, Yildiz A, Kessing L, Bauer M, Goodwin G, Andreassen O. Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action. European Neuropsychopharmacology 2020;36:121 View
  2. Marsch L. Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology 2021;46(1):191 View
  3. Faurholt-Jepsen M, Busk J, Vinberg M, Christensen E, Þórarinsdóttir H, Frost M, Bardram J, Kessing L. Daily mobility patterns in patients with bipolar disorder and healthy individuals. Journal of Affective Disorders 2021;278:413 View
  4. Orsolini L, Fiorani M, Volpe U. Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?. International Journal of Molecular Sciences 2020;21(20):7684 View
  5. Chan E, Sun Y, Aitchison K, Sivapalan S. Mobile App–Based Self-Report Questionnaires for the Assessment and Monitoring of Bipolar Disorder: Systematic Review. JMIR Formative Research 2021;5(1):e13770 View
  6. Sükei E, Norbury A, Perez-Rodriguez M, Olmos P, Artés A. Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach. JMIR mHealth and uHealth 2021;9(3):e24465 View
  7. Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840 View
  8. Saccaro L, Amatori G, Cappelli A, Mazziotti R, Dell'Osso L, Rutigliano G. Portable technologies for digital phenotyping of bipolar disorder: A systematic review. Journal of Affective Disorders 2021;295:323 View
  9. Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Mental Health 2023;10:e42045 View
  10. Ortiz A, Maslej M, Husain M, Daskalakis Z, Mulsant B. Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction. Journal of Affective Disorders 2021;295:1190 View
  11. White K, Williamson C, Bergou N, Oetzmann C, de Angel V, Matcham F, Henderson C, Hotopf M. A systematic review of engagement reporting in remote measurement studies for health symptom tracking. npj Digital Medicine 2022;5(1) View
  12. Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
  13. 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
  14. Bougeard A, Guay Hottin1 R, Houde V, Jean T, Piront T, Potvin S, Bernard P, Tourjman V, De Benedictis L, Orban P. Le phénotypage digital pour une pratique clinique en santé mentale mieux informée. Santé mentale au Québec 2021;46(1):135 View
  15. Llamocca P, López V, Čukić M. The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection. Frontiers in Physiology 2022;12 View
  16. Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E, Narasimhan P. Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digital Health 2022;1(8):e0000079 View
  17. Birk R, Samuel G. Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems. Current Psychiatry Reports 2022;24(10):523 View
  18. Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2023;25:e46778 View
  19. Wu C, Hsu J, Liou C, Su H, Lin E, Chen P. Automatic Bipolar Disorder Assessment Using Machine Learning With Smartphone-Based Digital Phenotyping. IEEE Access 2023;11:121845 View
  20. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  21. Luo Y, Deznabi I, Shaw A, Simsiri N, Rahman T, Fiterau M. Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data. Scientific Reports 2024;14(1) View
  22. Halabi R, Mulsant B, Alda M, DeShaw A, Hintze A, Husain M, O'Donovan C, Patterson R, Ortiz A. Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder. Journal of Psychiatric Research 2024;174:326 View
  23. Bilal A, Pagoni K, Iliadis S, Papadopoulos F, Skalkidou A, Öster C. Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study. JMIR Formative Research 2024;8:e53508 View
  24. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View

Books/Policy Documents

  1. Kolenik T. Integrating Artificial Intelligence and IoT for Advanced Health Informatics. View
  2. Hindley G, Smeland O, Frei O, Andreassen O. Mental Health in a Digital World. View
  3. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View
  4. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View