A Web-Based Intervention for Insufficiently Active College Students: Feasibility and Preliminary Efficacy
Bibliographic record
Abstract
Background: One third of college students do not achieve aerobic activity levels recommended for physical and mental health. The web-based “I Can Be Active!” intervention was designed to help college students increase their physical activity. The intervention was grounded in the Multi-Process Action Control (M-PAC) framework which emphasizes translating intention into sustainable action. Objective: The primary purpose was to evaluate the feasibility of the intervention with insufficiently active young adult college students. The secondary purposes were to describe the preliminary effects of the intervention on: (1) the M-PAC constructs and (2) physical activity. Methods: Twenty-one college students, ages 18 to 24, were enrolled in the pre-post quasi-experimental study to test the 8-week intervention during Spring 2021. Data were collected via self-report questionnaires, web-analytics, and interviews. Feasibility outcomes included recruitment, retention, acceptability, practicality, and implementation. Preliminary efficacy outcomes were self-report M-PAC constructs and physical activity. Data analyses included descriptive statistics, t tests, Wilcoxon signed rank tests, Hedge’s g, and thematic analysis. Results: Recruitment and retention rates were 70% and 71%, respectively. Participants reacted positively to the program, content, and features, except the manual entry step tracker and private social media group. Positive trends and significant increases were found in the regulatory and reflexive M-PAC constructs (self-regulation, habit, and identity) and physical activity. Conclusions: Findings support the feasibility and preliminary effects of the intervention for insufficiently active college students and highlight implications for intervention refinement. Future research will test intervention effectiveness using a randomized controlled trial with a larger diverse sample of college 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".