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Summary
This summary is machine-generated.

Machine learning accurately predicts HIV pre-exposure prophylaxis (PrEP) use in young sexual and gender minorities by analyzing mobile app data. This supports personalized interventions to increase PrEP uptake among app users.

Keywords:
MHealthMachine learning modelPre-exposure prophylaxis (PrEP)Sexual and gender minoritiesYoung adults

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Area of Science:

  • Digital Health
  • Machine Learning in Public Health
  • HIV Prevention

Background:

  • Pre-exposure prophylaxis (PrEP) is a critical HIV prevention tool but faces underutilization, especially among young sexual and gender minorities (SGM).
  • Dating and social media apps are popular among young SGM, presenting an opportunity for targeted health interventions.
  • Leveraging mobile technology can bridge gaps in accessing and adhering to HIV prevention methods.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) model for predicting PrEP use among young SGM.
  • To assess the predictive accuracy of ML models trained on various mobile app usage data.
  • To inform the development of data-driven interventions for improving PrEP uptake and adherence.

Main Methods:

  • Adapted the eWellness Android app to passively collect mobile app usage, keystroke patterns, and GPS location data from participants (2021-2024).
  • Trained an ML model using collected data to predict self-reported PrEP use status.
  • Evaluated model accuracy using F1 scores, comparing models based on different data feature combinations.

Main Results:

  • The ML model incorporating data from all mobile apps (messaging, dating, social media) achieved high predictive accuracy (F1 scores of 0.84 for PrEP use, 0.82 for non-use).
  • Models using only social media usage, risk behavior language, or location monitoring showed lower predictive accuracy.
  • Combining all mobile app usage data yielded superior predictive performance compared to individual data types or other combinations.

Conclusions:

  • Machine learning can accurately predict PrEP use status in young SGM individuals based on their mobile app activity.
  • Findings support the development of personalized PrEP promotion strategies targeting young SGM who utilize social media and dating apps.
  • Future research should explore model adaptability across diverse SGM subgroups to refine intervention strategies.