SynthesisFrontiers in psychiatry2022
Digital phenotype of mood disorders: A conceptual and critical review.
Synthesis in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of machine learning in migraine classification: a call for study design standardization and global collaboration.The journal of headache and pain · 2025Pooled it
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- Interpretable machine learning for depression symptom classification in NHANES: Performance in a curated high-confidence corpus and the full survey population.IBRO neuroscience reports · 2026Article
- The 'UPIC' cohort: a nationwide prospective study of mental health among adolescents and young adults in Sweden - a study protocol.BMJ open · 2026Article
- Did national implementation of interpersonal counseling improve adolescent depression treatment pathways in primary care? Protocol for a longitudinal cohort study.BMC public health · 2026Article
- Article
- Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.JMIR medical informatics · 2025Article
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology.Acta psychiatrica Scandinavica · 2025Article
- Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study.JMIR mental health · 2025Observational
- Developing a suicide risk prediction model for hospitalized adolescents with depression in China.Frontiers in psychiatry · 2025Article
- Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study.Journal of medical Internet research · 2024Observational
- Latent class analysis of actigraphy within the depression early warning (DEW) longitudinal clinical youth cohort.Child and adolescent psychiatry and mental health · 2024Article
- Integration of passive sensing technology to enhance delivery of psychological interventions for mothers with depression: the StandStrong study.Scientific reports · 2024Article
- Increasing psychopharmacology clinical trial success rates with digital measures and biomarkers: Future methods.NPP - digital psychiatry and neuroscience · 2024Review
- Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2024Review
- A translationally informed approach to vital signs for psychiatry: a preliminary proof of concept.NPP - digital psychiatry and neuroscience · 2024Article
- Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.Journal of medical Internet research · 2023Article
- Optomyography-based sensing of facial expression derived arousal and valence in adults with depression.Frontiers in psychiatry · 2023Article
- Remembering Paul E. Meehl: Historical Contributions to Predictive Modeling in Human Behavior.Harvard review of psychiatryArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care. Objective: The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders. Methods: We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. Results: Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). Conclusion: The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
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