A Digital, Self-Management Behavior Change Intervention for People With Chronic Obstructive Pulmonary Disease: Cohort Study
Bibliographic record
Abstract
Background: People with chronic obstructive pulmonary disease (COPD) experience a range of limitations, which have a significant effect on their health. Self-management and pulmonary rehabilitation (PR) are key treatments for people with COPD; however, barriers often limit their uptake and adherence. Objective: To overcome these barriers, a digital self-management intervention called PocketMedic (PM) was developed and evaluated in people with COPD alongside, and in addition, to PR. Methods: A total of 53 participants were recruited to 1 of 3 groups: PM and PR, PM, or PR. Data were collected at baseline and 7 weeks (after the interventions had finished). Questionnaires on health-related quality of life, self-management knowledge, and disease knowledge were collected. Multivariate analysis of variances and ANOVAs were used to analyze the data. Results: The analyses found that the improvements in those receiving PM were not statistically significantly different from those receiving PR, indicating that PM may replicate the benefits underpinning self-management behaviors observed in those attending PR. However, there were no additional benefits when participants received PM and PR in combination. Conclusions: PM may be a useful treatment to support COPD self-management, especially when barriers prevent people with COPD receiving traditional services such as PR. The quantitative results suggest that PM may be less beneficial when delivered alongside PR. Feedback from participants indicated that they would prefer to receive PM while they were on the waiting list for PR, to support them during this time and alleviate the apprehensions associated with attending PR. Implications, limitations, and suggestions for future research are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".