Technology-Enhanced Health Promotion for College Students: A Seed Development Project
Bibliographic record
Abstract
Obesity is an issue for young adults in the U.S. This population is particularly vulnerable to weight gain as they move from adolescence to young adulthood, especially as they transition from high school to college. Adopting a health promotion approach, a university-based cluster of researchers, community advocates, and a technology partner embarked on a two-year seed development project that focused on development, implementation, and evaluation of a web-based healthy lifestyle intervention for college students. Using a mixed-method design, two convenience samples of residential university students were recruited to participate in a 4-week intervention called Eat, Move, Live, in which they interacted with a newly-created comprehensive website about management of a healthy lifestyle. Participants' post-intervention readiness for change increased by 15% (eating and life balance behaviors) to 23% (moving behaviors). Participants reported increased awareness of eating behaviors, and feelings of engagement in tracking their fruit and vegetable consumption. Findings suggest that technology may be utilized to enhance the effectiveness and efficiency of achieving students' individual goals related to healthy living. These preliminary findings have implications for increasing the development and implementation of technological approaches to health promotion for young adult students.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".