How Active Are People With Stroke?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Accelerometers are a unique tool used to objectively measure free-living physical activity, but their reliability for people with stroke has not been established. The primary aim was to assess the day-to-day reliability of these instruments for the paretic and nonparetic hips. The secondary aims were to measure the amount of physical activity with accelerometers that people with stroke undertake in the community and its relationship with walking capacity (6-minute walk test distance). METHODS: Forty people with stroke wore one Actical accelerometer on each hip for 3 consecutive days at home and during the 6-minute walk test in the laboratory. The accelerometer measured physical activity using total activity counts per day and energy expenditure (kcal/d). RESULTS: Excellent intraclass correlation coefficients (ICCs) for the activity counts (paretic hip ICC([1,3])=0.95, nonparetic hip ICC([1,3])=0.94) and for the energy expenditure (paretic hip ICC([1,3])=0.95, nonparetic hip ICC([1,3])=0.95) were found across the 3 consecutive days at home. Excellent ICCs were also found between the paretic versus the nonparetic hips for the activity counts (ICC([1,3])=0.98) and for the energy expenditure (ICC([1,3])=0.96). Free-living physical activity was very low and 58% of the participants did not meet recommended physical activity levels. Only moderate correlations (r=0.6 to 0.73, P<0.001) were found between the 6-minute walk test distance in the laboratory and 3-day physical activity recording at home. CONCLUSIONS: The accelerometer was found to be a reliable objective instrument. The use of accelerometers quantified the low level of free-living physical activity of people with stroke.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".