ArticleTranslational psychiatry2020
Daily estimates of clinical severity of symptoms in bipolar disorder from smartphone-based self-assessments.
Article in Translational psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Ecological assessment of transdiagnostic clinical symptoms in serious mental illness with daily smartphone surveys.Translational psychiatry · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Circadian rhythm parameters differentiate euthymic, manic and depressive mood states in bipolar disorders - an explorative pilot study.International journal of bipolar disorders · 2025Article
- Ecological Assessment of Transdiagnostic Clinical Symptoms in Serious Mental Illness with Daily Smartphone Surveys.medRxiv : the preprint server for health sciences · 2025Article
- Sleep and circadian disruption in bipolar disorders: From psychopathology to digital phenotyping in clinical practice.Psychiatry and clinical neurosciences · 2024Review
- 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 · 2024Article
- Real-time monitoring of increases in restlessness to assess idiographic risk of recurrence of depressive symptoms.Psychological medicine · 2023Article
- Behavioral and Self-reported Data Collected From Smartphones for the Assessment of Depressive and Manic Symptoms in Patients With Bipolar Disorder: Prospective Observational Study.Journal of medical Internet research · 2022Observational
- Precision Psychiatry: The Future Is Now.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2022Article
- Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Currently, the golden standard for assessing the severity of depressive and manic symptoms in patients with bipolar disorder (BD) is clinical evaluations using validated rating scales such as the Hamilton Depression Rating Scale 17-items (HDRS) and the Young Mania Rating Scale (YMRS). Frequent automatic estimation of symptom severity could potentially help support monitoring of illness activity and allow for early treatment intervention between outpatient visits. The present study aimed (1) to assess the feasibility of producing daily estimates of clinical rating scores based on smartphone-based self-assessments of symptoms collected from a group of patients with BD; (2) to demonstrate how these estimates can be utilized to compute individual daily risk of relapse scores. Based on a total of 280 clinical ratings collected from 84 patients with BD along with daily smartphone-based self-assessments, we applied a hierarchical Bayesian modelling approach capable of providing individual estimates while learning characteristics of the patient population. The proposed method was compared to common baseline methods. The model concerning depression severity achieved a mean predicted R
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