The Effectiveness of Technology‐Based Interventions for Mental Health and Well‐Being: A Systematic Review
Bibliographic record
Abstract
While technology‐based interventions can effectively promote mental health and well‐being, their effectiveness remains unclear. Gaining more insight into the characteristics of various technology‐based interventions aimed at improving mental health is crucial to understanding why some are effective while others are not. This study aims to review the literature on technology‐based mental health interventions (TMHIs) to investigate 1) whether there is a relationship between TMHI design features/strategies and their effectiveness and 2) highlighting and summarizing emerging trends in the technological intervention design, research method, target mental health issues, persuasive strategies employed in TMHIs, and dropout rate of participants. We provide an empirical review of 18 years (from 2003 to 2020) of TMHI studies. The study found that most studies on TMHIs have reported successful outcomes, suggesting that when combined with the right persuasive strategy, they can promote mental and emotional health. The most common target populations are adults and young adults, with mobile applications being the most common. Despite only three studies using behavioral theories, they were found to be more effective. Finally, we identified the pitfalls and gaps in the literature that could inform the direction of future research in this area. In conclusion, TMHIs are promising tools for improving mental health. Numerous factors can influence their effectiveness.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".