Perceptions of Health Care Providers Regarding a Mobile Health Intervention to Manage Chronic Obstructive Pulmonary Disease: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Using a mobile health (mHealth) intervention, consisting of a smartphone and compatible medical device, has the potential to enhance chronic obstructive pulmonary disease (COPD) treatment outcomes while mitigating health care costs. OBJECTIVE: The aim of this study was to explore the potential facilitators and barriers among health care providers (HCPs) regarding the use of mHealth interventions for COPD management. METHODS: This was a qualitative study. Semistructured individual interviews were conducted with HCPs, including nurses, pharmacists, and physicians who work directly with patients with COPD. A flexible prompts guide was used to facilitate discussions. Interview topics included the following: demographics, mHealth usage, perceptions toward challenges of mHealth adoption, factors facilitating mHealth adoption, and preferences regarding features of the mHealth intervention for COPD management. Interviews were conversational in nature, and items were not asked verbatim or in the order presented. The interviews were transcribed verbatim and compared against the digital recordings to ensure the accuracy of the content. After creating a codebook for analysis, 2 researchers independently coded the remaining interview data using pattern coding. They discussed commonalities and differences in coding until a consensus was reached. RESULTS: A total of 30 nurses, physicians, and pharmacists participated. The main facilitators to mHealth adoption are possible health benefits for patients, ease of use, educating patients and their HCPs, credibility, and reducing cost to the health care system. Alternatively, the barriers to adoption are technical issues, privacy and confidentiality issues, lack of awareness, potential limited uptake from the elderly, potential limited connection between patients and HCPs, and finances. CONCLUSIONS: It is important to understand the perceptions of HCPs regarding the adoption of innovative mHealth interventions for COPD management. This study identifies some potential facilitators and barriers that may inform the successful development and implementation of mHealth interventions for COPD management.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".