Exploring factors influencing the adoption and use of digital health technology for HIV management among older adults living with HIV: A qualitative study
Bibliographic record
Abstract
Background: The intersection of aging and HIV presents unique challenges in healthcare, with older adults living with HIV experiencing compounded health issues. Advances in technology, including digital health tools, offer opportunities to improve self-management and care delivery. However, older adults living with HIV face barriers in adopting these digital health tools due to socio-cultural factors and technological challenges. Objective: This study explores the factors influencing the adoption of digital health technologies for HIV management among older adults, aiming to identify strategies to improve accessibility and effectiveness of these tools. Methods: A qualitative descriptive study using interviews was conducted within a larger research program on virtual care for socio-culturally diverse older adults. Data were analyzed using the Theoretical Domains Framework (TDF) to identify barriers and facilitators to technology adoption. Results: Fourteen older adults living with HIV (mean age 58.7, SD 6.5) participated in the study. Key themes included self-management, perceived usefulness, and ease of use, with older adults living with HIV using technology for health tracking and symptom management. Barriers such as affordability, linguistic diversity, and complex user interfaces were identified, along with concerns about privacy and stigma. Facilitators included peer influence, perceived utility of tools, and ease of navigation. Participants emphasized the importance of transparency in consent processes and the need for technology to accommodate cognitive and sensory impairments. Conclusion: This study highlights the need for tailored digital health interventions that address the unique challenges of older adults living with HIV. Future technologies should prioritize user-friendly interfaces, accessibility, and clear consent processes to enhance adoption and engagement.
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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 | 0.008 | 0.011 |
| 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 | 0.003 |
| 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".