The Cost-Effectiveness of Digitally Supported Mental Well-Being Prevention and Promotion Targeting Nonclinical Adult Populations: Systematic Review
Bibliographic record
Abstract
Background: In recent years, policymakers worldwide have been increasingly concerned with promoting public mental well-being. While digitally supported well-being interventions seem effective in general nonclinical populations, their cost-effectiveness remains unclear. Objective: This study aims to systematically synthesize evidence on the cost-effectiveness of digitally supported mental well-being interventions targeting the general population or adults with subclinical mental health symptoms. Methods: PubMed, Embase, Scopus, and Web of Science were systematically searched for health economic or cost-minimization studies. Eligibility criteria included interventions in the general population or adults showing risk factors or subclinical mental health symptoms, with at least 1 digital component. Study quality was comprehensively assessed using the Consensus Health Economic Criteria list. Results: Of 3455 records identified after duplicate removal, 12 studies were included: 3 studies evaluated universal prevention, 3 investigated selective prevention, and 6 covered indicated prevention. Six studies applied a societal perspective. Incremental cost-utility ratios were reported in 6 of the included studies and varied from dominant to €18,710 (US $ 23,185) per quality-adjusted life year. In general, digitally supported well-being interventions in nonclinical adults, and particularly indicated prevention strategies, seemed to generate improved health outcomes at lower costs from a societal perspective. The quality appraisal highlighted several shortcomings of the available literature. Conclusions: Overall, the use of digital tools for mental well-being prevention and promotion in nonclinical adult populations has the potential to be cost-effective. Nevertheless, to adequately guide policymaking, more evidence is still needed. Future studies should ensure valid argumentation for the applied time horizon and perspective, alongside rigorous sensitivity analyses in accordance with best practices to improve cost-effectiveness evidence. Furthermore, assessment methods more sensitive to changes in well-being such as the EQ Health and Well-being instrument could be considered.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".