Treatments for Depression for Older Adults Living in Long-Term Care: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the comparative efficacy of interventions on depressive symptoms and disorders in older adults living in long-term care (LTC). DESIGN: Systematic review and network meta-analysis. SETTING AND PARTICIPANTS: Older adults living in LTC or equivalent settings. METHODS: We searched 6 electronic databases and gray literature sources to identify randomized controlled trials describing pharmacologic or nonpharmacologic interventions. Studies had to measure depression as an outcome in persons living in LTC. Study inclusion and study quality were assessed in duplicate. Population characteristics, descriptions of intervention and control treatments, and end-point depression outcomes for each treatment were extracted from included studies. A network meta-analysis using the standardized mean difference (SMD) of depression scores was completed using a random effects model. RESULTS: A total of 182 studies were included in the review. The network meta-analysis was completed with 147 studies and included 31 treatment conditions. Compared with usual care, horticulture therapy (SMD, -6.85; 95% Credibility Interval, -8.49 to -5.22) and cognitive behavioral therapy (SMD, -1.98; 95% Credibility Interval, -2.91 to -1.05) were the most efficacious treatments. Animal therapy, group reminiscence therapy, multicomponent nonpharmacologic treatments, exercise, and socialization interventions also significantly improved depressive symptoms compared with usual care. CONCLUSIONS AND IMPLICATIONS: Many nonpharmacologic treatments for depression in LTC have been studied and are found to be efficacious. The low-risk and cost-effective nature of many of the nonpharmacologic treatments makes them ideal for use in LTC. More studies of pharmacologic treatments are needed to inform prescribing for depression in the LTC population. The range of treatments available for depression may help clinicians select therapies individualized to resident needs.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".