Quantitative Research on Digitalized Treatment Options for Older Adults With Mental Illness: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Older adults with mental illness face specific physical and psychosocial challenges and inequities, reflected in limited access to advanced technology. This digital divide is alarming as mental health interventions increasingly depend on both patients' and clinicians' access to technology. However, digitalized treatments also present opportunities to enhance accessibility, effectiveness, and equity across age groups. OBJECTIVE: This scoping review charted the state of quantitative research on digitalized treatment options for older people with mental illness. We focused specifically on how technology is integrated into existing nonpharmacological mental health interventions or leveraged to create new ones. We also summarized the state of the art on the feasibility and effectiveness of these interventions for various mental illnesses. METHODS: This review was conducted in compliance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic scoping reviews. A PubMed search conducted in April 2024 and updated in April 2025 identified 64 studies (15,644 participants; aged 40-97 years). Included studies were original quantitative studies or reviews of these studies looking into nonpharmacological treatments for older adults with a psychiatric diagnosis using any kind of technology. RESULTS: The technologies examined ranged from web-based psychotherapy platforms and digital devices for daily challenges to robots for social interaction. Few studies (5/64, 7%) examined the newest advances in digital mental health, such as artificial intelligence or virtual reality. Most studies (37/64, 58%) evaluated dementia-related interventions using small, nonrandomized samples and uncontrolled designs. CONCLUSIONS: The current state of the field, despite the promises of technology to reduce inequities between age groups, still largely excludes older adults from research on technological advances in mental health and their benefits. The field needs to overcome this selective bias and fight the "digital gray divide" in mental health.
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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 | 0.056 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".