An exploratory study of two measures of free-living physical activity for people with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To examine the ability of two measures of physical activity (tri-axial accelerometer and activity diary) to discriminate among groups of inactive, moderately active and active individuals with multiple sclerosis and to explore the relationship between these two measures. DESIGN: Exploratory, descriptive study. SUBJECTS: Thirty individuals with multiple sclerosis and nine controls. PROTOCOL: Individuals with multiple sclerosis were recruited to inactive, moderately active and active groups as defined by Adjusted Activity Scores from the Human Activity Profile. Control participants were recruited to an active group. Free-living physical activity was recorded over four consecutive days. MAIN MEASURES: A TriTrac RT3 accelerometer and a self-report physical activity diary were used to measure activity. RESULTS: Thirty-six participants completed data collection. For the participants with multiple sclerosis, both the accelerometer (P = 0.004) and the diary (P = 0.006) detected significant differences between inactive and active groups. The accelerometer also detected a significant difference between moderately active and active groups (P = 0.04). In contrast, the diary detected a significant difference between inactive and moderately active groups (P = 0.05). Accelerometer and diary scores were significantly correlated (r = 0.59). Accelerometer scores were significantly correlated with neurological status (r(s) = -0.64). CONCLUSIONS: Both measures readily differentiated least active from most active groups. The accelerometer also differentiated moderately active from active groups, suggesting suitability for use in detecting change in more active client groups, while the diary differentiated inactive from moderately active groups, suggesting suitability for use in detecting change in less active groups.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".