A Combined Health Action Process Approach and mHealth Intervention to Increase Non‐Sedentary Behaviours in Office‐Working Adults—A Randomised Controlled Trial
Bibliographic record
Abstract
Background Office‐working adults represent an at‐risk population for high levels of sedentary behaviour (SB), which has been associated with an increased risk for numerous chronic diseases. This study examined the effectiveness of a Health Action Process Approach (HAPA) based planning intervention augmented with tailored text messages to reduce workplace sitting time (primary outcome) and increase specific non‐SBs (i.e. standing time, walking time, stretching time, break frequency, break duration). A secondary purpose was to examine relationships among HAPA volitional constructs and sedentary and non‐SBs. Methods Full‐time office workers ( M age = 45.18 ± 11.33 years) from Canada were randomised into either a HAPA intervention ( n = 29) or control ( n = 31) condition. Workplace sitting time, time spent in specific non‐SBs, 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, that favoured the intervention group, were found for sitting time ( p = .003, ɳ p 2 = .07), standing time ( p = .019, ɳ p 2 = .05), and stretching time ( p = .001, ɳ p 2 = .08) as well as for action planning ( p < .001, ɳ p 2 = .20), coping planning ( p < .001, ɳ p 2 = .18), and action control ( p < .001, ɳ p 2 = .15). Significant correlations ( p < .05) were also found between the HAPA constructs and time spent sitting, standing, walking, as well as break frequency. Conclusions Augmenting a HAPA‐based planning intervention with text messages can reduce workplace sitting time in office workers. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03461926.
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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 | 0.004 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".