Physiotherapy is Associated with Improvements in Gait and Balance in Individuals with Cognitive Impairment: A Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that individuals with cognitive impairment (CI) exhibit significant motor impairments; however, these motor impairments receive little treatment attention compared to more prominent CI. Moreover, there is a lack of evidence about physiotherapy (PT) in individuals with CI. OBJECTIVE: The purpose of this study was to determine if PT was effective at improving gait and balance in individuals with Alzheimer disease (AD), vascular dementia (VaD), dementia with Lewy bodies (DLB), and mild cognitive impairment (MCI). METHODS: Retrospective data of 173 individuals with CI conditions and an initial evaluation for PT were extracted from electronic records from January 2016 to December 2017 at a neuro-rehabilitation clinic. The individuals were grouped according to diagnosis (67 with AD, 34 with VaD, 35 with DLB, and 37 with MCI). The following outcomes before and after a month of PT were extracted from records: Montreal Cognitive Assessment (MoCA), MiniBESTest, 5 Times Sit-To-Stand, Timed Up and Go (TUG), TUG cognitive, preferred gait speed, fast gait speed, 6 Minute Walk Test, and the modified Fear of Falling Avoidance Behavior Questionnaire. RESULTS: The mean number of PT sessions over a month was 3.4 (±1.8). All groups showed improvements in balance and at least two gait outcome measures. Those with MCI improved in every gait and balance outcome measure. Lastly, MoCA scores improved in individuals in the AD, VaD, and MCI groups. CONCLUSIONS: Results of this study offer preliminary evidence that one month of PT may produce benefits to gait and balance in individuals with CI. Moreover, it may afford cognitive benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".