Remote Measurement-Based Care Interventions for Mental Health: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Poor management of mental health conditions leads to reduced adherence to treatment, prolonged illness, unnecessary rehospitalization, and a significant financial burden to the health care system. Recognizing this, ecological momentary assessment (EMA) and remote measurement-based care (RMBC) interventions have emerged as promising strategies to address gaps in current care systems. They provide a convenient means to continuously monitor patient-reported outcomes, thereby informing clinical decision-making and potentially improving outcomes such as psychopathology, relapse, and quality of life. Objective: This systematic review and meta-analysis aims to comprehensively appraise and analyze the existing evidence on the use of EMA and RMBC for people living with mental illness. Methods: The study was conducted according to PRISMA-P (Preferred Reporting Items for Systematic Review and Explanation Meta-Analysis Protocols) guidelines and preregistered with the PROSPERO systematic review registry. A comprehensive search was conducted in 4 online databases using Medical Subject Headings terms related to mental disorders and digital technologies. Studies were included if they included adults with a formally diagnosed mental disorder and measured symptoms using EMA or RMBC. Studies were independently reviewed by subgroups of authors, and data were extracted focusing on symptom-focused or disease-specific outcomes, relapse, recovery-focused outcomes, global functioning, quality of life, and acceptability of the intervention. We performed a descriptive analysis of demographic variables and a meta-analysis of randomized controlled trials (RCTs). Risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials version 2 (RoB-2). Results: The systematic review included 103 studies, of which 15 used RMBC. Of these, 9 were RCTs that were meta-analyzed. RMBC interventions varied in effectiveness, generally showing small but significant effects on symptom-specific outcomes, with notable effects on mania symptoms and empowerment. The mean adherence rate across studies to all tracking items was 74.5% (SD 13.98; n=38). More prompts per day, but not more items per prompt, were associated with lower adherence. Adverse effects were infrequently reported and included technical problems and psychological distress. Concerns about bias were raised, particularly regarding participants' awareness of the interventions and potential deviations from the intended protocols. Conclusions: Although RMBC shows growing potential in improving and tailoring psychiatric care to individual needs, the evidence of its clinical effectiveness is still limited. However, we found potential effects on mania symptoms and empowerment. Overall, there were only a few RCTs with formal psychiatric diagnoses to be included in our analyses, and these had moderate risks of bias. Future studies assessing RMBC's effectiveness and long-term efficacy with larger populations are needed.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".