The impact of technology systems and level of support in digital mental health interventions: a secondary meta-analysis
Bibliographic record
Abstract
BACKGROUND: The majority of people with a chronic disease (e.g., diabetes, hypertension, COPD) have more than one concurrent condition and are also at higher risk for developing comorbidities in mental health, including anxiety and depression. There is an urgent need for more relevant and accurate data on digital interventions in this area to prepare for an increase demand for mental health services. The aim of this study was to conduct a meta-analysis of the digital mental health interventions for people with comorbid physical and mental chronic diseases to compare the effect of technology systems and level of support. METHODS: This secondary meta-analysis follows a rapid review of systematic reviews, a virtual workshop with knowledge users to identify research questions and a modified Delphi study to guide research methods: What types of digital health interventions (according to a recognized categorization) are the most effective for the management of concomitant mental health and chronic disease conditions in adults? We conducted a secondary analysis of the primary studies identified in the rapid review. Two reviewers independently screened the titles and abstracts and applied inclusion criteria: RCT design using a digital mental health intervention in a population of adults with another chronic condition, published after 2010 in French or English, and including an outcome measurement of anxiety or depression. RESULTS: Seven hundred eight primary studies were extracted from the systematic reviews and 84 primary studies met the inclusion criteria Digital mental health interventions were significantly more effective than in-person care for both anxiety and depression outcomes. Online messaging was the most effective technology to improve anxiety and depression scores; however, all technology types were effective. Interventions partially supported by healthcare professionals were more effective than self-administered. CONCLUSIONS: While our meta-analysis identifies digital intervention's characteristics are associated with better effectiveness, all technologies and levels of support could be used considering implementation context and population. TRIAL REGISTRATION: The protocol for this review is registered in the National Collaborating Centre for Methods and Tools (NCCMT) COVID-19 Rapid Evidence Service (ID 75).
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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 | 0.035 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.098 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".