Efficacy of virtual interventions for reducing symptoms of depression in community-dwelling older adults: a systematic review
Bibliographic record
Abstract
BACKGROUND: Older adults experience symptoms of depression, leading to suffering and increased morbidity and mortality. Although we have effective depression therapies, physical distancing and other public health measures have severely limited access to in-person interventions. OBJECTIVE: To describe the efficacy of virtual interventions for reducing symptoms of depression in community-dwelling older adults. DESIGN: Systematic review. SETTING: We searched MEDLINE, EMBASE, Cochrane Libraries, PsycINFO, and gray literature from inception to July 5, 2021. PARTICIPANTS AND INTERVENTIONS: We included randomized trials (RCTs) comparing the efficacy of virtual interventions to any other virtual intervention or usual care in community-dwelling adults ≥60 years old experiencing symptoms of depression or depression as an outcome. MEASUREMENTS: The primary outcome was change in symptoms of depression measured by any depression scale. RESULTS: We screened 12,290 abstracts and 830 full text papers. We included 15 RCTs (3100 participants). Five RCTs examined persons with depression symptoms at baseline and ten examined depression as an outcome only. Included studies demonstrated feasibility of interventions such as internet or telephone cognitive behavioral therapy with some papers showing statistically significant improvement in depressive symptoms. CONCLUSIONS: There is a paucity of studies examining virtual interventions in older adults with depression. Given difficulty in accessing in-person therapies in a pandemic and poor access for people living in rural and remote regions, there is an urgent need to explore efficacy, effectiveness, and implementation of virtual therapies.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".