Weight Training to Activities of Daily Living: Helping Older Adults Make a Connection
Bibliographic record
Abstract
PURPOSE: To compare a weight training alone treatment (WT) to an innovative WT plus education treatment (WT + ED) about the use of strength-training gains when performing activities of daily living (ADL) with respect to their effects on ADL self-efficacy and performance. METHODS: Twenty-three men and 41 women (mean age = 74.4 +/- 3.7 yr) were randomly assigned to WT or WT + ED. Both groups performed 12 wk (two sessions per week) of WT targeting eight major muscle groups. WT + ED received behavioral training and associated written materials emphasizing the link between WT and ADL. WT received a placebo educational intervention. Baseline and posttest measures were collected for self-efficacy for performing eight lab-based ADL tasks and performance of the eight ADL tasks. A manipulation check compared participants' knowledge of ADL that might be improved through WT. RESULTS: The WT + ED treatment listed more ADL that could be improved with WT and had greater posttest self-efficacy for performing the ADL lab tasks than the WT treatment. Greater ADL self-efficacy did not translate into better ADL performance. CONCLUSIONS: A targeted educational intervention can help older adults generalize the benefits and confidence obtained through WT to their performance of ADL. Further research is needed to determine the behavioral and psychosocial impact of enhanced ADL self-efficacy on older adults.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".