Clustering of (Un)Healthy Behaviours and Weight Status in New Zealand Adolescents
Bibliographic record
Abstract
PURPOSE: Forming healthy habits during adolescence is essential for setting the stage for healthy behaviours in adulthood. This study examined clustering of health behaviours (physical activity [PA] habits, screen time, fruit and vegetable intake) and weight status in New Zealand adolescents. METHODS: Adolescents from 9 secondary schools in Dunedin (New Zealand) (n=1,008; 45.6% male; age: 15.3±1.4 years) completed an online questionnaire. Participants self-reported PA, screen time outside of school, and fruit and vegetable intake. Height and weight were measured and weight status category was determined using international guidelines. Analysis included a two-step cluster analysis. RESULTS: On average, adolescents participated in ≥60 min of moderate-to-vigorous PA on 4.0±2.1 days/week, with only 16.4% meeting PA guidelines (≥60 min of moderate-to-vigorous PA every day). Adolescents reported 5.6±3.0 hours/day of screen time, with only 13.0% meeting screen time guidelines (≤2 hrs/day). More than half of students reported daily intake of fruit (56.0%) or vegetables (63.0%). However, only 28.6% of students met guidelines for both fruit and vegetable intake (more than once a day). Few students (2.6%) met all three guidelines, 10.3% met two, 29.5% met one and 57.5% did not meet any guideline. Students’ weight status was 2.9% underweight, 67.9% normal weight, 22.1% overweight and 7.1% obese. Six clusters were identified based on health behaviours, weight status (healthy/unhealthy) and gender: 1) non-compliant females with healthy weight (20.3%); 2) non-compliant males with healthy weight (18.5%); 3) non-compliant adolescents of both genders with unhealthy weight (18.8%); 4) healthy nutrition only with mostly healthy weight (17.6%); 5) meeting screen time guidelines, mostly inactive with healthy weight (12.5%); and 6) physically active, some eating healthy, and predominantly males with healthy weight (12.2%). Neither age nor socioeconomic status contributed meaningfully to cluster formation. CONCLUSION: More than half of adolescents are not meeting any of the recommended guidelines for PA, screen time and fruit and vegetable intake. Identifying clusters of adolescents based on relevant characteristics could help tailor interventions to promote healthy lifestyles in adolescents.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".