Gait Patterns as Digital Markers of Post‐Stroke Cognitive Impairment: A Comparative Analysis of Real‐World and Laboratory Assessments
Bibliographic record
Abstract
BACKGROUND: Transient ischemic attack (TIA), or ministroke, is a major risk factor for dementia, with post-TIA dementia risks reaching 20% at 5 years, 30% at 15 years, and 48% at 25 years. Given this high risk, health authorities emphasize the need for prediction and early detection. Subtle gait changes, emerging as early cognitive decline markers, are often assessed in laboratory settings. However, leveraging passively collected data from wearable technology enables continuous, real-world monitoring of mobility and cognitive health, offering an ecologically valid approach for early dementia detection. OBJECTIVE: To compare gait kinematic parameters between healthy individuals and TIA patients and assess the similarities in real-world and laboratory settings. METHOD: Gait data were collected from three TIA patients (67.67±2.52 years) and four healthy controls (67.75±4.19 years) using lumbar-worn wearable sensors. Real-world gait monitoring spanned 21 days; laboratory-based assessments included the Timed Up and Go (TUG) test and dual-task gait test. Key gait parameters were analyzed including step length, gait speed, stride length and cadence. Statistical comparisons were performed using independent t-tests (p < 0.05). RESULT: In real-world settings, TIA patients showed significantly larger vertical axis stride-step regularity difference (-2.71±0.07 vs. -2.57±0.03, p = 0.03), step length (0.91±0.02 m vs. 0.66±0.05 m, p = 0. 0.0009), stride length (1.82±0.04 m vs. 1.33±0.1 m, p = 0.001), gait speed (1.71±0.05 m/s vs. 1.19±0.08 m/s, p = 0.0004), and cadence (115.22±3.65 vs. 107.06±2.36, p = 0.029) compared to healthy controls. Between the real-world and laboratory settings in TIA patients, the absolute difference between stride and step regularity for the vertical axis with significantly larger in real life than in the lab (0.06±0.01 vs. 0.03±0.01, p = 0.014). While there was no significant difference between the two settings in healthy controls. CONCLUSION: Preliminary results suggest that TIA significantly affects some gait parameters, particularly in real-world settings. These findings demonstrate that real-world gait measurements may serve as sensitive and scalable markers of mobility decline, highlighting the potential of wearable technology for early detection and continuous monitoring of TIA. However, larger studies are needed to validate these findings due to the current small sample size.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".