Adverse Events of Mood Monitoring and Ambulatory Assessment in Depression and Bipolar Disorder: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Mood monitoring and ambulatory assessment offer improvements in measuring mood and behavior for mental health research and clinical practice. However, concerns about adverse effects and usability may hinder their implementation. Objective: This systematic review and meta-analysis assessed the prevalence of adverse events, barriers and facilitators to use, and suggestions for improvement in quantitative mood monitoring studies involving people with depression and bipolar disorder. Methods: We conducted a systematic review and meta-analysis of 77 quantitative studies that used mood monitoring or ambulatory assessment in depression and bipolar disorder, assessing adverse events, barriers and facilitators to use, and suggestions for improvement. Adverse events data were pooled to identify prevalence. Results: Of the 77 studies, 15 (19%) reported adverse events, and 20 (26%) reported usability issues. Pooled prevalence of adverse events was 0.04 (95% CI 0.03-0.06; P<.001). Specific adverse effects included increased burden or stress (0.04, 95% CI 0.02-0.07; P<.001), mood worsening (0.02, 95% CI 0.01-0.02; P=.001), self-harm (0.05, 95% CI-0.02 to 0.10; P=.007), and hospitalization (0.06, 95% CI 0.04-0.09; P=.26). The top facilitators were perceived helpfulness and ease of use, the top barriers included technical challenges and the time-consuming nature of the interventions, and the top suggested improvement was personalization. Conclusions: A small number of mood monitoring or ambulatory assessment users experienced negative psychological effects; however, we were unable to infer causality. Due to the severe underreporting of adverse events as well as heterogeneity and publication bias in the included studies, there was limited certainty in the prevalence, duration, and severity of these adverse events. More systematic monitoring of adverse events is needed to optimize safety and usability. Many mood monitoring protocols may require additional development to decrease adverse events and improve acceptability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".