Overcoming the Digital Divide for Older Patients With Respiratory Disease: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: The need for and adoption of eHealth programs are growing worldwide. However, access can be limited among patients with low socioeconomic backgrounds, often resulting in a so-called "digital divide" due to a mismatch between eHealth and target populations that can gain benefit. This digital divide can result in unsuccessful eHealth implementations, which is of critical importance to health care. OBJECTIVE: This study evaluated the opinions of elderly patients with asthma and chronic obstructive pulmonary disease (COPD) about an existing pharmacy-based personalized patient web portal that provides medication overview and information on associated diagnoses. The aim was to obtain insights on the common barriers of elderly people when using health-related websites, which can help to improve accessibility. METHODS: This was a cross-sectional qualitative study of a patient panel of the Groningen Research Institute for Asthma and COPD in primary care. Participants were required to be older than 55 years, be Dutch speaking, have no prior experience with the study website, and be diagnosed with a chronic respiratory illness. Two focus groups were created, and they completed a 45-minute session for testing the website and a 120-minute session for semistructured interviews. The focus group sessions were recorded, transcribed verbatim, and analyzed by content analysis. RESULTS: We enrolled 11 patients (9 women) with a mean age of 66 (SD 9) years. Of these, 5 had asthma, 3 had COPD, 2 had asthma-COPD overlap syndrome, and 1 had bronchiectasis. Participants were generally positive about the website, especially the areas providing disease-related information and the medication overview. They appreciated that the website would enable them to share this information with other health care providers. However, some difficulties were reported with navigation, such as opening a new tab, and others reported that the layout of the website was difficult either because of visual impairments or problems with navigation. It was also felt that monitoring would only be relevant if it is also checked by health care professionals as part of a treatment plan. Participants mentioned few privacy or safety concerns. CONCLUSIONS: It is feasible to develop websites for elderly patients; however, developers must take the specific needs and limitations of elderly people into account (eg, navigation problems, poor vision, or poor hand-eye coordination). The provision of information appears to be the most important aspect of the website, and as such, we should endeavor to ensure that the layout and navigation remain basic and accessible. Patients are only motivated to use self-management applications if they are an integrated part of their treatment. The usability of the website can be improved by including older people during development and by implementing design features that can improve accessibility in this group.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".