Mobility disability and the pattern of accelerometer-derived sedentary and physical activity behaviors in people with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Low physical activity and high sedentary behavior levels are major concerns in persons with multiple sclerosis (MS) and these differ depending on the level of mobility disability. However, the manner in which daily activity is accumulated is currently unknown in this population. METHODS: A secondary analysis was performed on a combined data set of persons with MS from two previous investigations of physical activity and symptomatic or quality of life outcomes in the United States over a two year period (2007-2009). Mobility disability status was determined using the Patient Determined Disease Steps (PDDS) while activity behavior was objectively monitored using an ActiGraph accelerometer for 7 days. RESULTS: Persons with MS who have mobility disability were involved in sedentary behavior, light and moderate intensity activity for 65%, 34% and 1% of the day, respectively compared to 60%, 37%, and 3%, respectively in those without mobility disability (p < 0.05). Breaks in sedentary time did not differ by mobility disability status. Compared to those without mobility disability, the average number of sedentary bouts longer than 30 min was greater in those with mobility disability (p = 0.016). CONCLUSION: Persons with MS with mobility disability are less active, engage in more sedentary behavior and accumulate prolonged sedentary bouts.
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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 | 0.003 |
| 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 | 0.000 | 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".