The health outcomes and physical activity in preschoolers (HOPP) study: rationale and design
Bibliographic record
Abstract
BACKGROUND: The early years are the period of growth for which we know the least about the impact of physical activity. In contrast, we know that more than 90 % of school-aged Canadian children, for example, are not meeting physical activity recommendations. Such an activity crisis is a major contributor to recent trends in childhood obesity, to which preschoolers are not immune. The World Health Organization estimated that more than 42 million children under the age of 5 years were overweight world-wide in 2010. If an activity crisis exists during the preschool years, we should also be concerned about its broader impact on health. Unfortunately, the relationship between physical activity and health during the early years is poorly understood. The goal of the Health Outcomes and Physical activity in Preschoolers (HOPP) study is to describe how the prevalence and patterns of physical activity in preschoolers are associated with indices of health. METHODS: The HOPP study is a prospective cohort study. We aim to recruit 400 3- to 5-year-old children (equal number of boys and girls) and test them once per year for 3 years. Each annual assessment involves 2 laboratory visits and 7 consecutive days of physical activity monitoring with protocols developed in our pilot work. At visit 1, we assess body composition, aerobic fitness, short-term muscle power, motor skills, and have the parents complete a series of questionnaires related to their child's physical activity, health-related quality of life and general behaviour. Over 7 consecutive days each child wears an accelerometer on his/her waist to objectively monitor physical activity. The accelerometer is programmed to record movement every 3 s, which is needed to accurately capture the intensity of physical activity. At visit 2, we assess vascular structure and function using ultrasound. To assess the associations between physical activity and health outcomes, our primary analysis will involve mixed-effects models for longitudinal analyses. DISCUSSION: The HOPP study addresses a significant gap in health research and our findings will hold the potential to shape public health policy for active living during the early years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".