Determining what patients admitted with a chronic obstructive pulmonary disease (COPD) exacerbation will use for remote clinical monitoring: a patient engagement survey
Bibliographic record
Abstract
BACKGROUND: Remote monitoring may improve the health of people with chronic obstructive pulmonary disease (COPD) through earlier detection and intervention before conditions worsen, but there are major challenges in recruitment and retention in these research studies. There are also increasing technologies and the uptake of specific technologies in people with COPD is not well known. OBJECTIVE: The objective of this study was to identify remote monitoring interventions that people admitted to hospital with an exacerbation of COPD would be willing to use upon discharge and to identify factors that influenced their preferences. METHODS: We surveyed consecutive patients admitted to hospital with acute exacerbations of COPD. We asked participants how likely they would be willing to use 15 remote monitoring interventions and to explain the reasoning behind their preferences. We correlated demographic factors with willingness to use interventions. RESULTS: Out of the 88 people with COPD approached, we recruited 50 (57%). The average age was 72.5 years, and 48% were women. Patients were most willing to use in-home visits by nurses, remote monitoring of vital signs and reporting oximeter values through an app or a website. Least popular interventions were in-home cough, speech and activity monitoring. Perceived usefulness and previous positive experiences were reasons why participants would accept various interventions. Increased willingness to use remote monitoring was seen in women (p=0.02), people who spoke English as a primary language (p=0.005), people who did not rely on others for support (p=0.04) and those followed by a respirologist (p=0.02). CONCLUSIONS: Our survey of patients admitted with COPD exacerbations provides insight into the types of remote monitoring interventions patients will accept and who are more interested in participating. We also provide insight into equity concerns of remote monitoring technology by identifying demographic factors that may influence intervention use that could widen the digital divide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".