Sedentary behaviour among university students: A mobile app pilot intervention
Bibliographic record
Abstract
Sedentary behaviour (SB) poses a number of health risks (Katzmarzyk et al., 2009) and university students in particular are at risk of engaging in prolonged SB. Due to the pervasiveness of smartphones, mobile apps may be used to encourage less SB in this population. The purpose of this study was to pilot a SB app among undergraduates. Participants (n=177) first completed an online survey that included self-reported levels of SB and experiences with apps. Following this, participants were asked to participate in a follow-up study and were randomly assigned to a trial group (used the app Rise & Recharge® for 2 weeks; n=53) or a control group (n=74). After 2 weeks, participants in trial (n=18) and control groups (n=38) completed a second online survey that repeated the self-report SB questions. Participants in the trial group responded to additional questions about their app experience. A two-way mixed ANOVA was conducted on data for participants who had SB data at both time points. This yielded a significant interaction between group and time (F(1,35)=5.59, p=0.02, np2=0.14) in which the trial group (n=11) had lower SB at Time 2 than the control (n=26) group. Despite this, participants in the trial group rated the app as only 'slightly influential'. Further, students' open-ended responses showed that they perceive a lack of control over their own SB due to the demands of university. Overall, this study provides insight into SB among university students, and sheds light on the potential of using apps to influence this behaviour.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".