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The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans
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Using alcohol consumption diary data from an internet intervention for outcome and predictive modeling: a validation

Philip Lindner1,2,3, Magnus Johansson4,5, Mikael Gajecki6,4

  • 1Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden. philip.lindner@ki.se.

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Summary

Adjusted alcohol consumption diary data approximates AUD treatment outcomes at a group level. However, early intervention data did not accurately predict individual treatment success due to missing data.

Keywords:
AlcoholCalendarClassificationDiaryMachine learningMeasurementPrediction

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Data Science

Background:

  • Alcohol Use Disorder (AUD) is common, with a significant treatment gap.
  • Internet interventions offer scalable solutions for AUD but face high attrition.
  • Continuous outcome measurement is rare, limiting understanding of treatment processes.

Purpose of the Study:

  • To assess if non-mandatory alcohol consumption diary data approximates reported drinks at follow-up in AUD internet interventions.
  • To determine if early intervention diary data can predict treatment success.

Main Methods:

  • 607 participants in an online AUD self-help trial used a non-mandatory consumption diary.
  • Multiple regression and generalized estimating equations adjusted diary data for missing entries.
  • Random forest models with 18 predictors from early diary data were used to assess predictive accuracy.

Main Results:

  • Adjusted diary data point-estimates overlapped with follow-up reported drinks at a group level.
  • Machine learning models achieved 64% accuracy for predicting non-hazardous drinking and 48% for AUD severity decrease.
  • Predictive models showed higher sensitivity than specificity.

Conclusions:

  • Adjusted alcohol consumption diary data can approximate AUD treatment outcomes at a group level.
  • This data may reveal treatment trajectories and potentially impute missing follow-up data.
  • Early intervention diary data lacked high individual-level predictive accuracy, likely due to missing data.