Engaging technology for encouraging physical activity in COPD patients: A first user test
Bibliographic record
Abstract
It is known that a physically active lifestyle can improve quality of life and help in managing COPD. It is important for patients to be aware of how physically active they are. Our aim was to test usage, usability, acceptance, and compliance of a web-based physical activity interface (PAI) in COPD patients. 14 COPD patients were recruited (age: 60.9±8.8yrs; 5M, 9F) at the CIRO+ Expertise Center (Horn, Netherlands). Patients were provided a Direct Life (Philips) activity monitor (AM) and physical activity (PA) was monitored for 2 weeks. Patients were provided with the PAI and were instructed to upload their PA data recorded by the AM, review it, plan and mark as complete their activities each day. Patients were not encouraged or asked to perform any additional PA and were interviewed about their experiences and acceptance of the PAI. Compliance to wearing the AM was 88.2±20.5% of time and patients showed an average daily active energy expenditure of 398.5±191.6 kcal. Patients logged on daily with a minimum of 2 sessions but with lower usage during weekends. The average daily time spent on the PAI was 11 minutes. Patients planned 2.68±1.89 tasks a day with 1.70±1.66 tasks marked as complete. There was a significant correlation between the number of planned and completed tasks (r=0.69; p=0.0042). The PAI scored high (>80%) on Usefulness, Satisfaction, and Ease of use questionnaire. Patients were highly engaged in the PAI indicated by the high compliance to activity monitoring and planning tasks. Although patients indicated a beneficial value for managing their PA, further studies are needed to prove the efficacy of a PAI and verify whether such tool could be used in improving/maintaining PA.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".