Digital interventions for depression and anxiety in older adults: a systematic review of randomised controlled trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One in five older adults experience symptoms of depression and anxiety. Digital mental health interventions are promising in their ability to provide researchers, mental health professionals, clinicians, and patients with personalised tools for assessing their behaviour and seeking consultation, treatment, and peer support. This systematic review looks at existing randomised controlled trial studies on digital mental health interventions for older adults. Four factors have been found that contributed to the success of digital mental health interventions: (1) ease of use; (2) opportunities for social interactions; (3) having human support; and (4) having the digital mental health interventions tailored to the participants' needs. The findings also resulted in methodological considerations for future randomised controlled trials on digital mental health interventions: (1) having a healthy control group and an intervention group with clinical diagnoses of mental illness; (2) collecting data on the support given throughout the duration of the interventions; (3) obtaining qualitative and quantitative data to measure the success of the interventions; and (4) conducting follow-up interviews and surveys up to 1 year post-intervention to determine the long-term outcomes. The factors that were identified in this systematic review can provide future digital mental health interventions researchers, health professionals, clinicians, and patients with the tools to design, develop, and use successful interventions for older users.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it