The Relationship Between Individual Characteristics and Interest in Using a Mobile Phone App for HIV Self-Management: Observational Cohort Study of People Living With HIV
Bibliographic record
Abstract
BACKGROUND: The human immunodeficiency virus (HIV) continues to be a major health issue in the United States, and an estimated 1.2 million people in the United States are living with HIV. As part of Healthy People 2020, the Office of Disease Prevention and Health Promotion has targeted the persistent demographic and geographic disparities in HIV prevalence and management. Preliminary evidence suggests that mobile health technology (smartphone apps) may be a promising way to support HIV self-management among vulnerable populations of people living with HIV (PLWH) who lack access to appropriate health care services. OBJECTIVE: This study examines the association between individual characteristics of PLWH and level of interest in using a free mobile phone app for HIV self-management. METHODS: This study was conducted using cross-sectional survey data collected in the Florida Cohort Study between 2014 and 2016 (N=766). Associations between individual characteristics of PLWH and level of interest in using a free mobile phone app for HIV self-management were examined using bivariate analysis and logistic regression. RESULTS: Overall, 85.5% (655/766) of respondents were interested in using a free mobile phone app that supports HIV self-management. Participants expressed the highest interest in app functions that facilitate communication with health care providers (568/740, 76.8%) or help to identify relevant health care services (556/745 74.6%). Age (OR 0.959, 95% CI 0.936-0.982), education (OR 1.281, 95% CI 1.027-1.598) and disability or inability to work (OR 0.296, 95% CI 0.145-0.606) were all significantly associated with being interested in using a free mobile phone app for HIV self-management. CONCLUSIONS: This study indicates that a majority of PLWH are interested in using a free mobile phone app to self-manage their condition. The findings can inform the development of mobile phone apps that support effective HIV self-management.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".