Action observation combined with gait training to improve gait and cognition in elderly with mild cognitive impairment A randomized controlled trial
Bibliographic record
Abstract
Owing to advancement of medical technology and current knowledge, the population has a longer life expectancy, leading to an increase in the proportion of elderly. OBJECTIVE: The study aimed to investigate the effect of action observation (AO) combined with gait training on gait and cognition in elderly with mild cognitive impairment (MCI). METHODS: Thirty-three participants were randomly allocated to action observation with gait training (AOGT), gait training (GT), and control (CT) groups. The AOGT and GT groups received a program of observation and gait training protocol with the same total duration of 65 min for 12 sessions. For the observation, the AGOT group watched a video of normal gait movement, while the GT group watched an abstract picture and the CT group received no training program. All participants were assessed for gait parameters during single- and dual-tasks using an electronic gait mat system and were assessed for cognitive level using the Montreal Cognitive Assessment (MoCA) at baseline, after training and at 1-month follow-up. RESULTS: The results showed that the AOGT group had significant improvements in gait speeds during single- and dual-tasks, as well as better MoCA score, while the GT group had significant improvement only in gait speed. CONCLUSION: The adjunct treatment of AO with gait training provides greater benefits for both gait and cognitive performances in elderly with MCI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".