Effects of multicomponent and dual-task exercise on falls in nursing homes: The AgeingOn Dual-Task study
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of a multicomponent exercise program and a dual-task exercise program on the number of falls (fall rate) and number of fallers (fall incidence) and on parameters associated with fall risk in older adults living in long-term nursing homes (LTNH). STUDY DESIGN: This is a secondary analysis of a single-blind randomized controlled trial involving 85 older adults in nine LTNHs (Gipuzkoa, Spain). Participants allocated to the multicomponent group underwent a twice-a-week 3-month individualized and progressive resistance and balance program. The dual-task group performed simultaneous cognitive training with the same multicomponent exercises. MAIN OUTCOMES: Fall rate and incidence were analyzed using Poisson regression (adjusting for cognitive function and previous fall rate) and Kaplan-Meier analysis, respectively. Handgrip asymmetry, single- and dual-task TUG velocity and cost were assessed using two-way ANOVA for repeated measures and paired Student's t-tests. RESULTS: The dual-task group showed a 3.8 times greater risk of falling than the multicomponent group during the intervention, and a 2.59 times greater risk during the 12-month follow-up. There were no between-group differences in fall incidence. There were between-group differences in handgrip strength asymmetry in favor of the multicomponent group. While only the multicomponent group improved on the TUG test, the dual-task group improved on dual-task cost. CONCLUSIONS: Compared with the dual-task program, the multicomponent exercise program showed more benefits in reducing falls and in parameters associated with fall risk in LTNH residents. Future studies are warranted to confirm our results and continue to explore physical and cognitive interventions to prevent falls in LTNHs. Australian New Zealand Clinical Trials Registry ACTRN12618000536268.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".