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Smartphone and wearable data, with artificial intelligence/machine learning, can predict violence. Methodological challenges and ethical considerations were identified in studies with trauma-exposed adults and couples experiencing intimate partner violence.

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Area of Science:

  • Digital health
  • Computational social science
  • Behavioral prediction

Background:

  • Predicting violence, anger, and aggression is crucial for public health.
  • Smartphone and wearable devices offer novel data streams for behavioral analysis.
  • Limited understanding exists regarding the methodology for using such data to predict violence.

Purpose of the Study:

  • To report methodological lessons learned from two studies using smartphone and wearable data.
  • To explore the potential of artificial intelligence/machine learning in predicting violence-related emotions and behaviors.
  • To identify participant, technical, data, and ethical challenges in this research area.

Main Methods:

  • Two independent studies were conducted with adults exposed to trauma and adult couple dyads experiencing intimate partner violence.
  • Real-world data collection using smartphone and wearable devices, including self-report, physiological, and GPS data.
  • Analysis focused on leveraging collected data to predict anger, aggression, and violence.

Main Results:

  • At-risk populations are willing to provide valid data on sensitive behaviors for violence prediction.
  • Significant participant recruitment and retention challenges were encountered.
  • Technical and data-related issues, including data quality and processing, posed difficulties.
  • Ethical considerations regarding data privacy and consent require careful management.

Conclusions:

  • Methodological insights gained have implications for future research in predicting violence.
  • Despite challenges, smartphone and wearable data hold promise for understanding and predicting at-risk behaviors.
  • Addressing technical, data, and ethical hurdles is essential for advancing this field.