Feasibility And Effectiveness Of A 3-month Sedentary Behaviour Reduction Intervention In Sedentary Adults
Bibliographic record
Abstract
Adults engage in excessive amounts of sedentary time (ST; sitting/lying/reclining while awake), which increases their risk for poor health outcomes. International guidelines recommend spending <8 hours/day sedentary and to frequently break-up prolonged sitting periods. However, it is unknown if sedentary behaviour reduction interventions are effective at decreasing ST. PURPOSE: Test the hypothesis that a 3-month intervention would reduce daily ST in already sedentary adults (i.e., accumulated >8-hours ST per day). METHODS: 19 sedentary adults were randomly allocated to a Control (n = 10; 8♀; 21-89 years; 5 older adults) or Intervention (n = 9; 6♀; 20-93 years; 4 older adults) group. The Intervention group watched an educational video that highlighted the negative health consequences of excessive ST and received ~2 messages/week via text or email prompting them to decrease their ST. At baseline and 3-month follow-up, a thigh-worn activPAL inclinometer recorded habitual physical and sedentary activities for 7-days. RESULTS: ST increased in the Intervention group (10.0 ± 1.2 to 11.0 ± 1.5 hours/day, p = 0.034), whereby 7/9 Intervention participants increased their ST. However, there was no difference between time points in daily sedentary breaks (3.0 ± 1.0 to 2.7 ± 0.6 breaks/waking hour), light- (1.2 ± 0.6 to 1.2 ± 0.4 hours/day) or moderate-vigorous-intensity physical activity (0.4 ± 0.4 to 0.3 ± 0.3 hours/day), standing time (5.0 ± 1.7 to 5.1 ± 1.3 mins/day) or sleep time (8.3 ± 1.6 to 7.8 ± 1.1 hours/day). There were no differences in ST, sedentary breaks, physical activity, standing time, or sleep for the Control group between time points (all, p > 0.45) or compared to the Intervention group (all, p > 0.37). Based on follow-up phone calls with Intervention group participants, there were several life factors that may have contributed to increases in habitual behaviour including mental health, work/school schedule, and/or willingness to change. CONCLUSIONS: Despite a small sample size, the results of this feasibility study demonstrate that a single educational video and accompanying prompts were not effective at decreasing ST in adults. Future intervention may need to include a more intensive (e.g., more frequent prompt/phone calls, longer intervention duration) and individualized approach to reducing ST in adults.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".