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Substance Use Disorders Affecting Sleep01:24

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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
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Personalizing ecological momentary intervention for substance use disorders through data-driven decision rules.

Mina Kwon1, Joo Yun Song1, Jae Yeon Hwang1

  • 1Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.

Frontiers in Psychiatry
|March 18, 2026
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Summary
This summary is machine-generated.

Ecological momentary interventions (EMIs) show promise for substance use disorders (SUDs) but yield mixed results. A data-driven approach using personalized decision rules can improve real-time support for SUDs.

Keywords:
ecological momentary intervention (EMI)personalized interventionreal-time interventionreal-time risk detectionsubstance use disorders (SUDs)

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Computational Psychiatry

Background:

  • Substance use disorders (SUDs) are a major public health concern with low treatment engagement.
  • Ecological momentary interventions (EMIs) offer real-time support but have inconsistent outcomes.
  • Current EMIs often use static rules, failing to capture individual variability in SUD risk.

Purpose of the Study:

  • To propose a data-driven framework for developing personalized, context-aware EMIs for SUDs.
  • To address the heterogeneity of SUDs by tailoring interventions to individual risk patterns.
  • To improve the reliability and effectiveness of EMIs by optimizing decision rules.

Main Methods:

  • Collect multimodal data (lab, smartphone, wearables, offline periods) to capture diverse contexts.
  • Develop context-aware prediction models to estimate momentary SUD risk.
  • Implement real-time, adaptive decision rules based on individual risk profiles and contextual factors.

Main Results:

  • Mixed findings in current EMI research highlight the need for improved intervention strategies.
  • A data-driven approach can integrate diverse data sources to overcome limitations of individual data types.
  • Validated predictors across contexts and modalities allow for flexible signal translation.
  • Personalized, context-sensitive decision rules can optimize intervention timing and delivery.

Conclusions:

  • A data-driven, personalized approach to EMIs is crucial for effectively managing SUDs.
  • Optimizing decision rules within EMIs can reduce outcome variability and enhance treatment efficacy.
  • This framework offers a practical pathway to delivering more reliable and personalized support for SUDs.