Public mobile chronic obstructive pulmonary disease applications for self-management: Patients and healthcare professionals’ perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Poorly controlled chronic obstructive pulmonary disease (COPD) can negatively impact quality of life but mobile applications (apps) are popular digital tools that may mitigate these support needs. However, it is unclear if public mobile COPD apps are acceptable to healthcare professionals and patients, people living with COPD. Objectives: The primary objective is to determine people with COPD and healthcare professionals' perspectives on the appropriateness of public mobile COPD apps for supporting individuals’ needs. The secondary objectives were to identify the ideal features and styles of mobile COPD apps for COPD self-management; and to identify the facilitators, barriers and needs for future COPD app research and development. Methods: Public mobile COPD apps were rated by questionnaires administered before and after focus group meetings. Ratings were reported as medians with interquartile ranges and median scores were categorized into three levels of appropriateness: 1-3 for inappropriate; 4-6 for uncertain; and 7-9 for appropriate. Results: A total of 6 people with COPD (mean age 68.2 ± 4.8years) and 22 healthcare professionals (mean age 45 ± 8.3years) completed this study. People with COPD identified one and healthcare professionals identified three public mobile COPD apps to be appropriate. They had different preferences for features and engagement styles but similar preferences for facilitators and barriers to use. Stakeholders mutually rated one public mobile COPD app as appropriate for self-management and emphasized the need for apps to be supplementary and customizable, rather than replacements for clinical management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it