Quantitative gait analysis under dual-task in older people with mild cognitive impairment: a reliability study
Bibliographic record
Abstract
BACKGROUND: Reliability of quantitative gait assessment while dual-tasking (walking while doing a secondary task such as talking) in people with cognitive impairment is unknown. Dual-tasking gait assessment is becoming highly important for mobility research with older adults since better reflects their performance in the basic activities of daily living. Our purpose was to establish the test-retest reliability of assessing quantitative gait variables using an electronic walkway in older adults with mild cognitive impairment (MCI) under single and dual-task conditions. METHODS: The gait performance of 11 elderly individuals with MCI was evaluated using an electronic walkway (GAITRite System) in two sessions, one week apart. Six gait parameters (gait velocity, step length, stride length, step time, stride time, and double support time) were assessed under two conditions: single-task (sG: usual walking) and dual-task (dG: counting backwards from 100 while walking). Test-retest reliability was determined using intra-class correlation coefficient (ICC). Gait variability was measured using coefficient of variation (CoV). RESULTS: Eleven participants (average age = 76.6 years, SD = 7.3) were assessed. They were high functioning (Clinical Dementia Rating Score = 0.5) with a mean Mini-Mental Status Exam (MMSE) score of 28 (SD = 1.56), and a mean Montreal Cognitive Assessment (MoCA) score of 22.8 (SD = 1.23). Under dual-task conditions, mean gait velocity (GV) decreased significantly (sGV = 119.11 +/- 20.20 cm/s; dGV = 110.88 +/- 19.76 cm/s; p = 0.005). Additionally, under dual-task conditions, higher gait variability was found on stride time, step time, and double support time. Test-retest reliability was high (ICC>0.85) for the six parameters evaluated under both conditions. CONCLUSION: In older people with MCI, variability of time-related gait parameters increased with dual-tasking suggesting cognitive control of gait performance. Assessment of quantitative gait variables using an electronic walkway is highly reliable under single and dual-task conditions. The presence of cognitive impairment did not preclude performance of dual-tasking in our sample supporting that this methodology can be reliably used in cognitive impaired older individuals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".