Addressing the Nonexercise Part of the Activity Continuum: A More Realistic and Achievable Approach to Activity Programming for Adults With Mobility Disability?
Bibliographic record
Abstract
Participation in physical activity is fundamental for the maintenance of metabolic health and the prevention of major chronic diseases, particularly type 2 diabetes and cardiovascular disease. A whole-of-day approach to physical activity promotion is increasingly advocated and includes not only increasing moderate-intensity physical activity but also reducing sedentary time and increasing light-intensity activity (the "nonexercise" part of the activity continuum). This whole-of-day approach to tackling the challenge of inactivity may be particularly relevant for adults with mobility disabilities, who are among the most inactive segment of the population. Focusing on nonexercise activity by striving to reduce sedentary time and increase light-intensity activity may be a more successful place to begin to change behavior in someone with mobility disability. This article discusses what is known about the metabolic health consequences of sedentary behavior and light-intensity activity in adults with and without mobility disability. The concept of inactivity physiology is presented, along with possible applications or evidence from studies with adults with mobility disability. Mobility disability discussions and examples focus on stroke and spinal cord injury. Finally, clinical implications and future research directions related to sedentary behavior in adults with mobility disability are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".