Measuring Habitual Walking Speed of People With Type 2 Diabetes
Bibliographic record
Abstract
Revised physical activity guidelines for individuals with type 2 diabetes recommend at least 150 min/week of moderate-intensity aerobic physical activity (40–60% of V o2max or 50–70% of maximum heart rate) and/or 90 min/week of aerobic exercise (>60% of V o2max or >70% of maximum heart rate) (1). In a structured exercise program or in the laboratory setting, levels of physical activity can be closely monitored; however, self-directed walking is the most common and most acceptable form of physical activity (2,3) to people with type 2 diabetes, and little is known about self-paced walking speed (and therefore intensity). A walking speed of 4.0 km/h is widely accepted as moderately intense physical activity (4). Numerous studies examining the beneficial effects of physical activity for people with type 2 diabetes exist (rev. in 1,5). Few, however, have directly measured walking speed. Previous research (6,7) demonstrated efficacy in increasing physical activity of participants using the First Step Program (FSP), a pedometer-based, self-paced walking program designed to help people with type 2 diabetes increase their steps per day. Despite the increase in physical activity, improvements in health outcomes were modest. In contrast, the implementation of the FSP in a worksite setting involving healthy adults resulted in significant reductions in weight, BMI, waist girth, and resting heart rate (8). We hypothesize that a slower walking speed …
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".