The Impact of Habit Trackers, Healthy Lifestyles and Exercise Motives of University Students in Toronto
Bibliographic record
Abstract
ABSTRACT: Encouraging a healthy and active lifestyle is an important aspect of societal institutions. In spite of these efforts, young adults are still not meeting physical activity guidelines, leading to serious health problems. This study looked to determine the exercise motivations of university students and worked to help academics understand and determine whether a self-reported, healthy-lifestyle habit tracker can improve an individual’s health, generate greater awareness of the benefits of being physically active—including academic benefits of living a healthy lifestyle; and change their behaviors. With this in mind, students from a large downtown Toronto-based university were recruited for this study and were required to answer two surveys, six weeks apart after receiving a healthy lifestyle tracking tool. The questionnaires measured individuals’ healthy lifestyle behaviors by using a modified Healthy Lifestyle Scale for University Students (HLSUS) and exercise motivations by using the Exercise Motivations Index-2 (EMI-2). Our research suggests that exercise motivations of university-aged students are similar, but that there are significant differences between gender, race, and age group. The study results also indicated that using the physical habit tracker was not correlated with increased healthy lifestyle behaviors but did increase awareness of the academic benefits of living a healthy lifestyle. KEYWORDS: motivations, behaviors, healthy lifestyle, exercise
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".