Lifestyle Behavior Patterns and Their Association with Active Commuting to School Among Spanish Adolescents: A Cluster Analysis
Bibliographic record
Abstract
Objectives: We aimed to identify clustering patterns of the device-measured physical activity (PA) levels (i.e., light PA and moderate-to-vigorous PA) and sedentary time (ST), screen time, sleep duration, and breakfast consumption of Spanish adolescents and their associations with the mode of commuting to and from schools (i.e., active and passive). Methods: A total of 151 adolescents aged 14.4 ± 0.6 years (53.64% girls) were included in this study. Participants wore an accelerometer device during seven consecutive days to measure PA levels and ST levels. Screen time, sleep duration, breakfast consumption, and the mode of commuting to and from school were self-reported by the participants. A two-step cluster analysis was performed to examine the different lifestyle behavior patterns (defined as data-driven groupings of daily behaviors identified through cluster analysis). Logistic regression models were used to determine the associations among the lifestyle behavior patterns and the mode of commuting to and from school. Results: The main characteristics of the three identified clusters were as follows: (active) high PA levels and low ST (38.4%); (inactive) high sleep duration and daily breakfast consumption, but low PA levels and high ST and screen time (37.2%); and (unhealthy) low PA levels and sleep duration, high ST and screen time, and usually skip breakfast (24.4%). No associations were found between these clusters and the mode of commuting to and from school (all, p > 0.05). Conclusions: Three different lifestyle behavior patterns were identified among Spanish adolescents, but no associations were found between these patterns and their mode of commuting to and from school.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".