Interventions incorporating physical and cognitive elements to reduce falls risk in cognitively impaired older adults
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is a risk factor for falls. Older adults with cognitive impairment (such as dementia) have an increased risk of falling compared with age-matched individuals without a cognitive impairment. To reduce falls in this population, interventions could theoretically target and train both physical and cognitive abilities. Combining and addressing cognitive components in falls rehabilitation is a novel and emerging area of healthcare. OBJECTIVES: The objective of this review was to identify the effectiveness of combined cognitive and physical interventions on the risk of falls in cognitively impaired older adults. INCLUSION CRITERIA TYPES OF PARTICIPANTS: Older persons who were 65 years or older and identified as having a cognitive impairment either through diagnosis or assessment of global cognition. TYPES OF INTERVENTION(S): Multifactorial or multiple interventions where physical and cognitive elements were combined was compared against standard care or a single element intervention. TYPES OF STUDIES: Randomized controlled trials (RCTs), controlled clinical trials and experimental studies in which randomization was used. OUTCOMES: Outcomes related to falls, including falls rate, specific falls risk measures (i.e. Physiological Profile Assessment) or related clinical outcome measures (i.e. Timed Up and Go test, Tinetti and gait speed). SEARCH STRATEGY: A three-step search strategy was utilized in this review, including search of electronic databases: CENTRAL, JBISRIR, MEDLINE, EMBASE, AMED, CINAHL and PsychINFO. Initial keywords used were dementia, cognitive impairment, memory loss, exercise, rehabilitation and accidental falls. Grey literature (Google Scholar) and trials registers (Current Controlled Trials) searches were also completed. METHODOLOGICAL QUALITY: The methodological quality of included studies was assessed using Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) software. DATA EXTRACTION: Data was extracted from articles included in the review using the standardized data extraction tool from JBI-MAStARI. DATA SYNTHESIS: A quantitative meta-analysis was performed where possible. Otherwise, data synthesis was in the form of narrative review. Sub-group analysis according to level of cognitive impairment was completed where possible. RESULTS: Eight RCTs were included in this review; they evaluated the effectiveness of multicomponent exercise programs, including physical and cognitive activities, music-based group exercise and mind-body tai chi on falls related outcomes. Most of the studies were of good quality with an average quality score of 7.5. Four studies reported effectiveness based on the number of falls, half of which reported a significant difference between the groups, but pooling of results into meta-analysis was not possible because of differences in reporting of the outcome. Falls related outcomes that were combined in meta-analysis included balance (Berg balance scale), functional mobility (Timed Up and Go) and gait speed (m/s). There was a statistically significant improvement in balance and gait speed following the intervention; however, the studies were too heterogeneous to be included in the analysis from the functional mobility results. CONCLUSION: Overall, multicomponent interventions incorporating both physical and cognitive components demonstrated positive effects on balance, functional mobility and gait speed when compared with a control and had significantly better effect on balance and gait speed within mild cognitive impairment populations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| 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) | 0.005 | 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".