Self-directed Technology-Based Therapeutic Methods for Adult Patients Receiving Mental Health Services: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Technological interventions used to treat illnesses and promote health are grouped under the umbrella term of digital therapeutics. The use of digital therapeutics is becoming increasingly common in mental health. Although many technologies are currently being implemented, research supporting their usability, efficacy, and risk requires further examination, especially for those interventions that can be used without support. OBJECTIVE: This review aims to identify the evidence-based, self-directed, technology-based methods of care that can be used in adult patients after they are discharged from mental health services. The interventions reviewed are automated with no human input required (either at the patient's or at the technology's end), so the patients can implement them without any support. METHODS: A systematic review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PROSPERO (International Prospective Register of Systematic Reviews) guidelines in 3 databases: PubMed, Web of Science, and OVID. The inclusion criteria were self-directed, automated, and technology-based interventions related to mental health, primarily for adults, having a solid evaluation process. The interventions had to be self-directed, in that the participants could use the technology without any external guidance. RESULTS: We identified 36 papers that met the inclusion criteria: 26 randomized controlled trials, 9 nonrandomized controlled trial quantitative studies, and 1 qualitative study. The technologies used included websites, automated text messaging, phone apps, videos, computer software, and integrated voice response. There were 22 studies focused on internet-based cognitive behavioral therapies as a therapeutic paradigm compared with the waitlist, web-based human-delivered therapy, and other interventions. Among these studies, 14 used paradigms other than the internet-based cognitive behavioral therapy. Of the 8 studies comparing guided and unguided digital care, 3 showed no differences, 3 favored guided interventions, and 2 favored unguided interventions. The research also showed that dropout rates were as high as 80%, citing potential problems with the acceptability of the suggested technologies. CONCLUSIONS: There is limited research on the efficacy and suitability of self-directed technology-based care options for mental health. Digital technologies have the potential to bridge the gap between ambulatory care and independent living. However, these interventions may need to be developed collaboratively with the users to encourage their acceptability and to avoid high dropout rates.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".