Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

Daily estimates of clinical severity of symptoms in bipolar disorder from smartphone-based self-assessments

Research output: Contribution to journalJournal articleResearchpeer-review

  1. Systemic DNA and RNA damage from oxidation after serotonergic treatment of unipolar depression

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Genetic association study of childhood aggression across raters, instruments, and age

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Pharmacogenetic genotype and phenotype frequencies in a large Danish population-based case-cohort sample

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Serotonin transporter availability increases in patients recovering from a depressive episode

    Research output: Contribution to journalJournal articleResearchpeer-review

  1. Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Digital innovation giver nye perspektiver i psykiatrien

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

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 R2 of 0.57 (SD = 0.10) and RMSE of 3.85 (SD = 0.47) on the HDRS, while the model concerning mania severity achieved a mean predicted R2 of 0.16 (SD = 0.25) and RMSE of 3.68 (SD = 0.54) on the YMRS. In both cases, smartphone-based self-reported mood was the most important predictor variable. The present study shows that daily smartphone-based self-assessments can be utilized to automatically estimate clinical ratings of severity of depression and mania in patients with BD and assist in identifying individuals with high risk of relapse.

Original languageEnglish
Article number194
JournalTranslational psychiatry
Volume10
Issue number1
Pages (from-to)194
ISSN2158-3188
DOIs
Publication statusPublished - 18 Jun 2020

ID: 60949487