Prompting sedentary behaviour change in COPD: Acceptability and feasibility of wearable device
Bibliographic record
Abstract
Intro: Individuals with Chronic Obstructive Pulmonary Disease (COPD) experience limited physical activity (PA) and increased sedentary behaviour (SB). Physical inactivity and SB are predictors of mortality in COPD. Wearable technology providing movement prompts may be an effective strategy to decrease SB; however, the acceptability and feasibility of a FitBit device for reducing SB in COPD has not been investigated. Objective: To assess the acceptability and feasibility of a FitBit providing movement prompts for reducing SB in individuals with COPD. Methods: In this randomized crossover feasibility trial, individuals with stable COPD wear a FitBit with movement prompts enabled (vibration when <250 steps/hour detected) or movement prompts disabled for one week. Device acceptability in each condition is assessed. Preliminary results: Of the 12 participants contacted, eight were interested (67%). Of these, seven were eligible and agreed to participate (88%). To date, five participants completed the trial (two on-going). Compared to the no-vibrate condition, there are trends of increased self-efficacy for reducing SB (mean diff: 40%, scale 0-100%), positive attitudes for reducing SB (median diff: 1.0, scale 1-7), and intentions to monitor PA with the vibration device (median diff: 2.0, scale 1-7). Four (80%) identified the device as affordable. Conclusions: Preliminary evidence indicates that, in COPD, a FitBit providing movement prompts appears feasible and may be an effective method for improving participant self-efficacy for reducing SB, attitudes toward reducing SB, and intentions to monitor their PA. Future work assessing the effectiveness of this strategy for reducing SB is warranted.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".