Why are children sedentary: an examination using the International Study of Childhood Obesity, Lifestyle and the Environment
Bibliographic record
Abstract
Physical inactivity and sedentary behaviour have been independently associated with a wide range of negative health indicators including obesity, poor cardio-metabolic health, and poor psychosocial health. The overarching objective of this research was to gain a better understanding as to why children are sedentary and where we need to focus public health messages and interventions to reduce sedentary behaviour. Specifically, I aimed to provide insight on current awareness of sedentary behaviour guidelines, determine important correlates of total sedentary time (SED) and screen time (ST) in Canadian children, and understand correlates of SED and ST in a global context. The primary dataset used for this project was the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Background work was completed to review current literature on knowledge and awareness of Canadian physical activity and sedentary behaviour guidelines (in all age groups) and to understand the representativeness of the ISCOLE dataset. In addition to the 2 background papers, this dissertation includes 3 manuscripts, all prepared for submission in scientific, peer-reviewed journals: Canadian physical activity and screen time guidelines: do children know?, Correlates of objectively measured sedentary time and self-reported screen time in Canadian children, and Correlates of total sedentary time and screen time in 9-11 year-old children around the world: The International Study of Childhood Obesity, Lifestyle and the Environment. Overall, this work showed the majority of children around the world are accumulating large amounts of sedentary time, and exceeded current screen time guidelines. We found that the large majority of Canadian children are not aware of screen time guidelines; however, a greater proportion of children could identify physical activity guidelines. We also identified a number of correlates of SED and ST in Canadian children and in children around the world. The most common correlates included weight status and access to electronics in the house. Taken together, this work suggests that public health messaging should focus on increasing awareness of screen time guidelines. While increasing awareness of the guidelines, messaging can be tailored to promoting healthy weight status and reducing (or removing) children's access to electronic devices in hopes of reducing overall time spent sedentary.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".