Using wearables and self-management apps in patients with COPD: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Technology such as wearable technology and self-management applications could improve the care of patients with chronic obstructive pulmonary disease (COPD) by real-time continuous monitoring, early detection of COPD and improved self-management. However, patients have not been willing to use technology when it is too difficult to use, interferes with their daily lives or threatens their identity, independence and self-care. METHODS: We conducted a qualitative study to determine what patients with COPD would like to see in a wearable device and a mobile application to help manage their condition. Semi-structured interviews were conducted, recorded and transcribed. Thematic analysis was used to identify themes and concepts. RESULTS: We interviewed 14 people with COPD with an average age of 69 years. Participants perceived that the technology could improve their ability to manage their condition both in daily life and during exacerbations by connecting how they feel and by knowing their oxygen saturation, heart rate and activity. The technology may help them address feelings of fear and panic associated with exacerbations and may provide reassurance and connectedness. Some people with COPD wanted their healthcare providers to have access to their data, while others were concerned about inundating them with too much information. Of note, people wanted to maintain control of the information; to make connections with the data, but also in order to be alerted when a possible exacerbation occurs. CONCLUSION: Patients perceived significant potential for wearables and apps to help manage their condition.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".