Usefulness and Relevance of an eHealth Tool in Supporting the Self-Management of Chronic Obstructive Pulmonary Disease: Explorative Qualitative Study of a Cocreative Process
Bibliographic record
Abstract
BACKGROUND: New strategies are urgently needed to support self-management for people with chronic obstructive pulmonary disease (COPD) in primary care. The use of electronic health (eHealth) solutions is promising. However, there is a lack of knowledge about how such eHealth tools should be designed in order to be perceived as relevant and useful and meet the needs and expectations of the health professionals as well as people with COPD and their relatives. OBJECTIVE: The objective of this study was to explore the aspects of an eHealth tool design and content that make it relevant and useful for supporting COPD-related self-management strategies from the perspective of health care professionals, people with COPD and their relatives, and external researchers. METHODS: Data were collected during the development of an eHealth tool. A cocreation process was carried out with participants from two primary care units in northern Sweden and external researchers. Individual interviews were performed with health care professionals (n=13) as well as people with COPD (n=6) and their relatives (n=2), and focus group discussions (n=9) were held with all groups of participants. Data were analyzed using qualitative content analysis. RESULTS: The overarching theme, reinforcing existing support structures, reflects participant views that the eHealth tool needs to be directly applicable and create a sense of commitment in users. Moreover, participants felt that the tool needs to fit with existing routines and contexts and preferably should not challenge existing hierarchies between health care professionals and people with COPD. Important content for health care professionals and people with COPD included knowledge about self-management strategies. Videos were regarded as the most effective method for communicating such knowledge. CONCLUSIONS: The cocreation in the development process enables participant perspectives and priorities to be built into the eHealth tool. This is assumed to contribute to a tool that is useful and relevant and, therefore, adopted into clinical practice and everyday life. Findings from this study can inform the development of eHealth tools for people with COPD in other contexts, as well as the development of eHealth tools for self-management support of other chronic diseases.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".