Subjective Memory Impairment and Gait Variability in Cognitively Healthy Individuals: Results from a Cross-Sectional Pilot Study
Bibliographic record
Abstract
BACKGROUND: Increased stride time variability has been associated with memory impairment in mild cognitive impairment. Subjective memory impairment (SMI) is considered the earliest clinical stage of Alzheimer's disease (AD). The association between increased stride time variability and SMI has not been reported. OBJECTIVE: This study aims to examine the association of stride time variability while performing single and dual tasking with SMI in cognitively healthy individuals (CHI). METHODS: A total of 126 CHI (15 without SMI, 69 with SMI expressed by participants, 10 with SMI expressed by participant's relative, and 32 with SMI expressed by both participants and their relatives) were included in this cross-sectional study. The coefficient of variation (CoV) of stride time and walking speed were recorded under usual condition and while counting backwards. Age, gender, body mass index, number of drugs taken daily, use of psychoactive drugs, fear of falling, history of previous falls, and walking speed were used as covariates. RESULTS: The multiple linear regression models showed that greater CoV of stride time while counting backwards, but not while single tasking, was associated with a participant's relative SMI (p = 0.038). CONCLUSION: This study found a specific association between SMI expressed by a participant's relative and a greater CoV of stride time (i.e., worse performance) while dual tasking, suggesting that the association between gait variability and memory may be present in the earliest stages of memory impairment. Thus, gait variability under dual-task in individuals with SMI expressed by their relatives can be a potential biomarker of AD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".