The impact of digitally-supported personalised goals to reduce sedentary behaviour in a clinically obese population.
Bibliographic record
Abstract
Background: According to the World Health Organisation (WHO), 39% of the global population are overweight and 13% are obese and the annual cost of identifying and treating obesity-related diseases in Ireland alone is €1.13 billion (WHO, 2015). The aim of this study was to examine the effect of weekly personalised goals and feedback on reductions in sedentary behaviour in clinically obese patients (BMI>30) attending the CLANN lifestyle programme. Methods: A sample of 78 obese men and women participating in the CLANN 10-week lifestyle program were randomised to control or experimental conditions in a 2 (control/experimental) x 2 (baseline/follow-up) design. Clinical (BMI, sedentary behaviour) and psychological variables (anxiety, distress, self-efficacy and social support) were assessed at baseline and again at 10-week follow-up. Participants were fitted with activPALTM physical activity monitors to monitor sedentary behaviour. The experimental group received weekly personalised sedentary behaviour goals (calculated by subtracting 10% from previous week) and feedback via the activPALTM data on their progress. The control group received general care for the duration of the programme. Findings: Significant improvements were observed within 10 weeks with reductions in sitting/lying time (p<.05), increases in up/down transitions (p<.05) and increases in selfefficacy (p<.05) in the experimental group compared with controls. Discussion: Personalised goal-setting with frequent feedback and monitoring was effective in reducing sedentary behaviour in a clinically obese population. Settling realistic, attainable goals increased the likelihood of success in achieving targets resulting in increased selfefficacy and yielding significant reductions in sedentary 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".