A combined health action process approach and mHealth intervention to reduce sedentary behaviour in university students – a randomized controlled trial
Bibliographic record
Abstract
Objective: This investigation evaluated the effectiveness of a Health Action Process Approach (HAPA) based planning intervention augmented with text messages to reduce student-related sitting time (primary outcome) and increase specific non-sedentary behaviours. Relationships between the HAPA volitional constructs and sedentary and non-sedentary behaviours were also explored. Design: University students (Mage = 21.13 y; SD = 4.81) were randomized into either a HAPA intervention (n = 28) or control (n = 33) condition. Main Outcome Measures: School-related sitting time, time spent in specific non-sedentary behaviours and HAPA volitional constructs were assessed at baseline, weeks 2, 4, 6 (post-intervention) and 8 (follow-up). Results: Significant group by time interaction effects favouring the intervention group were found for sitting time (p = 0.004, ɳp2 = 0.10), walking time (p = 0.021, ɳp2 = 0.06) and stretching time (p = 0.023, ɳp2 = 0.08), as well as for action planning (p < 0.001, ɳp2 = 0.17), coping planning (p < 0.001, ɳp2 = 0.20) and action control (p < 0.001, ɳp2 = 0.20). Significant correlations (p < 0.05) were also found between the HAPA constructs and sitting-related outcomes. Conclusions: Combining a HAPA-based planning intervention with text messages can reduce student-related sitting time in university students.Supplemental data for this article is available online at https://doi.org/10.1080/08870446.2021.1900574 .
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".