Cognitive and physical impact of cognitive-motor dual-task training in cognitively impaired older adults: An overview
Bibliographic record
Abstract
Ageing is associated with cognitive decline, ranging from normal to mild cognitive impairment or dementia. This leads to physical and cognitive impairments, which are risk factors for loss of autonomy. Therefore, cognitive and physical training are important for cognitively impaired older adults. The combination of both may represent an efficiency advantage. This overview aims to summarize the effectiveness of cognitive-motor dual-task (CMDT) interventions on cognitive, physical and dual-task functions in cognitively impaired older adults, as well as the safety, adherence, and retention of benefits of these interventions. We searched for systematic reviews or meta-analyses assessing the effects of CMDT interventions on cognitive or physical functions in older adults with mild cognitive impairment or dementia through eight databases (CDSR (Cochrane), MEDLINE, Scopus, EMBASE, CINAHL, PsycINFO, ProQuest and SportDiscus). Two reviewers independently performed the selection, data extraction and risk of bias evaluation. Nine reviews were included in this overview. CMDT interventions were found to be more effective than active control groups on cognitive and physical functions in older adults with cognitive impairment, irrespective of intervention dose and modalities; no information on dual-task functions was available. Retention of benefits, adherence, need for supervision and safety are still unclear. These results should be interpreted with caution, considering the low average methodological quality of included reviews. Future intervention research should follow more rigorous methodological standards and focus on other forms of CMDT.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".