Privacy and Confidentiality Concerns Related to the Use of mHealth Apps for HIV Prevention Efforts Among Malaysian Men Who Have Sex With Men: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The use of mobile health (mHealth), including smartphone apps, can improve the HIV prevention cascade for key populations such as men who have sex with men (MSM). In Malaysia, where stigma and discrimination toward MSM are high, the mHealth platform has the potential to open new frontiers for HIV prevention efforts. However, little guidance is available to inform researchers about privacy and confidentiality concerns unique to the development and implementation of app-based HIV prevention programs. OBJECTIVE: Given the lack of empirical data in this area, we aim to understand the privacy and confidentiality concerns associated with participation in a hypothetical app-based research study for HIV prevention efforts. METHODS: A cross-sectional, web-based survey was conducted between June and July 2020 among 355 Malaysian MSM. The survey included demographic and sexual health questions and a series of short videos describing a hypothetical app-based HIV prevention program, followed by questions related to privacy and confidentiality concerns in each step of the app-based program (ie, recruitment, clinical interaction, risk assessment, and weekly reminder). Multivariable logistic regression models were used to identify the correlates of willingness to use such an app-based program. RESULTS: Most of the participants (266/355, 74.9%) indicated their willingness to participate in a hypothetical mHealth app-based HIV prevention program. Participants expressed concerns about privacy, confidentiality, data security, and risks and benefits of participating in all stages of the app-based HIV research process. Multivariate analyses indicated that participants who had a higher degree of perceived participation benefits (adjusted odds ratio [aOR] 1.873; 95% CI 1.274-2.755; P=.001) were more willing to participate. In contrast, participants who had increased concerns about app-based clinical interaction and e-prescription (aOR 0.610; 95% CI 0.445-0.838; P=.002) and those who had a higher degree of perceived risks of participating (aOR 0.731; 95% CI 0.594-0.899; P=.003) were less willing to participate. CONCLUSIONS: Overall, our results indicate that mHealth app-based HIV prevention programs are acceptable for future research on Malaysian MSM. The findings further highlighted the role of privacy and confidentiality, as well as the associated risks and benefits associated with participation in such a program. Given the ever-evolving nature of such technological platforms and the complex ethical-legal landscape, such platforms must be safe and secure to ensure widespread public trust and uptake.
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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 | 0.010 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".