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Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone

Mehrab Beikzadeh1, Ian W Holloway2, Kimmo Kärkkäinen3

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Passively collected mobile phone data can identify substance use and sexual risks in young sexual and gender minority (SGM) individuals. This technology can personalize HIV and STI prevention interventions.

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Area of Science:

  • Digital epidemiology
  • Mobile health interventions
  • Machine learning in public health

Background:

  • Sexual and gender minority (SGM) individuals face higher risks for substance use and sexually transmitted infections (STIs).
  • Passive mobile phone data collection offers a novel approach for personalized intervention by identifying behavioral risks without direct user input.

Purpose of the Study:

  • To determine if passively sensed mobile phone data can identify substance use and sexual risk behaviors among young men who have sex with men (MSM) within the SGM population.
  • To assess the predictive accuracy of identified outcomes and identify the most predictive passive data sources for these behaviors.

Main Methods:

  • A mobile app collected participants' messaging, location, and app usage data.
  • Machine learning models (logistic regression, gradient boosting) were trained to predict substance use and sexual behaviors, validated against self-report questionnaires.
  • F1-scores quantified prediction accuracy; independent t-tests analyzed differences in data domains by outcome.

Main Results:

  • The model achieved high accuracy (F1-scores up to 0.83) in predicting methamphetamine use and having six or more sexual partners.
  • Predictive accuracy for condomless anal sex was lower (highest F1-score 0.38).
  • Text-based features were most predictive, with app use and location data enhancing predictions, particularly for multiple sexual partners. Methamphetamine use and higher numbers of sexual partners were associated with specific app usage patterns and language use.

Conclusions:

  • Passively collected mobile phone data show promise for detecting sexual risk behaviors among SGM individuals.
  • Expanding data collection could improve prediction accuracy for less common behaviors like injection drug use.
  • These predictive models can inform personalized interventions for substance use harm reduction and STI/HIV prevention.