Feasibility and Acceptability of a Digital Health Portal to Improve HIV Care Engagement Among Kenyan Youth: Mixed Methods Study
Bibliographic record
Abstract
Background: Adolescents and young people aged 15-24 years in Kenya bear a disproportionate burden of HIV, necessitating innovative, youth-friendly approaches to improve care engagement. Digital solutions such as patient health portals (PHPs) offer scalable ways to enhance access, understanding, and support. Objective: This study aims to evaluate the feasibility and acceptability of a customized PHP tailored for Kenyan adolescents and young people living with HIV, and to explore participant preferences for features, content delivery modes, and privacy concerns. Methods: A cross-sectional, mixed methods feasibility study was conducted among 163 adolescents and young people recruited during psychosocial support group meetings across seven high-volume clinics in Kiambu and Kirinyaga counties in August 2021. Participants interacted with a prototype PHP through guided demonstrations and completed structured questionnaires assessing health literacy, digital readiness, feature preferences, emotional needs, and data security perceptions. Quantitative data were analyzed using descriptive and bivariate statistics. While qualitative data were also collected to inform portal development, this paper focuses exclusively on quantitative findings. Results: The median age was 18 (IQR 15-24) years, with 60% (98/163) female participants. Most participants were students (73/163, 45%) and had secondary-level education (83/163, 51%). Approximately 17% (27/163) participants reported difficulty understanding written medical information. A large majority (146/160, 91%) expressed interest in using a web-based portal, with 78% (124/159) participants rating it as easy to use. Weekly use was anticipated by 56% (90/161) of participants. Top-rated features included appointment scheduling (129/163, 79%), access to test results (129/163, 79%), and communication with the doctor (117/163, 72%). Emotional health tools like a mood tracker and PHQ-9 (Patient Health Questionnaire-9) screening were highly valued, with 59% (92/156) reporting difficulties coping emotionally with HIV. Concerns about data privacy were minimal, and 62% (99/159) were willing to share access with a trusted family member. No statistically significant associations were observed between portal preference and age, sex, education level, or employment status. Conclusions: The study demonstrates the strong feasibility and acceptability of a digital health portal among Kenyan adolescents and young people. High readiness for digital health, combined with clearly expressed content and feature preferences, underscores the potential for such tools to improve engagement in HIV care. These findings support the need for co-designed, youth-centered digital interventions that address both medical and psychosocial needs in resource-limited settings.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".