Initial assessment of the StepWatch Activity Monitor™ to measure walking activity in Rett syndrome
Bibliographic record
Abstract
Purpose: In girls and women with Rett syndrome, we assessed the accuracy of the StepWatch Activity Monitor™ and investigated relationships between daily step counts, gross motor skills and age. Method: Twelve subjects (age 12.9 ± 8.0 years) participating in the Australian Rett Syndrome Database wore a StepWatch during a videoed session of activities to assess agreement with the criterion method of observation. Physical activity data were also collected over the course of 6 ± 1 whole days. Relationships between agreement, gross motor skills, average daily step count and age were analyzed. Results: The number of steps obtained using the StepWatch was similar to that viewed on video (mean difference = 0 steps per minute) and agreement did not differ with the level of general (p = 0.389) or complex gross motor skills (p = 0.221). Subjects were less active than their healthy peers (difference 6086 steps per day; p = 0.001), and physical activity was significantly greater in those who were younger and with greater levels of motor skill. Conclusions: The StepWatch provided accurate information on the physical activity of girls and women with Rett syndrome regardless of their level of gross motor function. Physical activity reduced with age despite the ability to walk. Advocacy for pro-active lifestyles is justified.Implications for RehabilitationPhysical activity can be objectively measured in girls and women with Rett syndrome.The amount of daily physical activity was less in those older and with less gross motor skill.Rehabilitation should consider strategies to maintain pro-active lifestyles over the life span.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".