Perspectives and Needs Regarding Remote Monitoring Technologies Among South Asian Individuals Living With Long-Term Conditions in the United Kingdom: Semistructured Interview and Focus Group Study
Bibliographic record
Abstract
Abstract Background South Asian individuals face a higher burden of long-term conditions while also experiencing more inequities in health care access and outcomes. Despite the potential of remote monitoring technologies to improve management of long-term conditions, South Asian individuals are less likely to engage with digital health interventions and are underrepresented in health research, partly due to language barriers. Objective This study explored the perspectives and needs regarding remote monitoring technologies of South Asian individuals living with a long-term condition in the United Kingdom who did not have English as their first language. We used Pakistanis as an example subgroup of South Asian individuals and rheumatoid arthritis and early inflammatory arthritis as example long-term conditions. Methods We conducted semistructured interviews and a focus group discussion with Pakistani adults diagnosed with rheumatoid or early inflammatory arthritis who did not have English as their first language. Audio-recordings were transcribed verbatim, deidentified, and analyzed thematically. Results Seventeen adults participated in this study (n=9, 53% in an individual interview and n=8, 47% in the focus group); none of them had previous experience of remote monitoring technologies. We identified three themes: (1) the perceived value and challenges of using remote monitoring technologies for disease self-management, (2) differences in perceived needs and capacity for using remote monitoring technologies between first- and later-generation immigrants related to social determinants, and (3) the role of community and family support in using remote monitoring technologies. Participants perceived remote monitoring technologies as useful, particularly where they were dissatisfied with current health care services. Language and the role of family and community members in supporting technology use were considered important factors, but needs in these areas varied between first-generation (migrated to the United Kingdom) and second- or third-generation immigrants (born in the United Kingdom to parents or grandparents who migrated to the United Kingdom). For first-generation immigrants, these factors intersected with other social and digital determinants, such as gender and literacy, resulting in additional requirements. Conclusions Addressing language and literacy barriers, alongside leveraging family and community support, will contribute to equitable remote monitoring technologies to facilitate self-managing long-term conditions among South Asian ethnic minority groups. Future efforts should focus on developing tailored, culturally responsive approaches, particularly for first-generation immigrants, to ensure remote monitoring technologies decrease rather than exacerbate existing ethnic health inequities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".