Exercise Intervention for Late-Life Depression
Bibliographic record
Abstract
OBJECTIVE: To quantify the association between physical exercise intervention (PEI) and reduction in depressive symptoms in older adults. DATA SOURCES: MEDLINE, PsycINFO, and EMBASE were searched from inception through December 2018 with no language restrictions using keywords related to exercise, depression, elderly adults, and randomized controlled trials. STUDY SELECTION: Randomized controlled trials comparing a sedentary control group, with no physically active intervention, to a supervised, moderate-to-vigorous PEI with participants aged ≥ 60 years and having a primary outcome of depressive symptoms were included. DATA EXTRACTION: Data on pre- and post-intervention scores on scales measuring depressive symptoms were extracted using a standard form. Random-effects models were used to pool standardized mean differences (Hedges g) in depressive symptoms across studies. DATA SYNTHESIS: Nine studies involving 1,308 participants were included; mean participant age was 82 years. Moderate-to-vigorous PEI was associated with a medium effect size of 0.64 (95% CI, 0.27 to 1.01; z = 3.38; P < .001) in reducing depressive symptoms. However, there was considerable heterogeneity (T² = 0.22, Q = 36.34, P < .0001; I² = 78.0%) in the effect of PEI across included studies. Age > 80 years, Mini-Mental State Examination (MMSE) score < 23, and no depressive symptoms at baseline contributed to heterogeneity. Fitness metrics and adherence to exercise were inconsistently reported, and 5 of 9 studies were deemed at high risk of bias. CONCLUSIONS: A moderate reduction in depressive symptoms was seen with PEI among older adults. Nevertheless, more work is needed to support PEI for late-life depression in adults over age 80 years or with MMSE scores < 23 suggestive of cognitive decline.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".