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Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.

Mehedi Hasan1, April Idalski Carcone2, Sylvie Naar3

  • 1Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI 48202.

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|October 12, 2019
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Summary
This summary is machine-generated.

Motivational Interviewing (MI) uses specific counselor behaviors to encourage patient change talk. New data analysis methods, Hidden Markov Models and closed frequent pattern mining, identify effective strategies for behavior change counseling.

Keywords:
closed frequent pattern mininghidden markov modelmotivational interviewingpediatric obesity

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Area of Science:

  • Behavioral Science
  • Health Psychology
  • Communication Science

Background:

  • Motivational Interviewing (MI) is an evidence-based strategy for behavior change.
  • Counselor behaviors linked to patient "change talk" are context-dependent.
  • Sequential analysis of MI transcripts is crucial for research.

Purpose of the Study:

  • Evaluate Hidden Markov Models (HMM) and closed frequent pattern mining for analyzing MI communication sequences.
  • Identify effective counselor communication strategies for eliciting patient change talk.
  • Inform MI practice through data-driven insights.

Main Methods:

  • Utilized 1,360 communication sequences from 37 transcribed weight loss counseling sessions.
  • Applied Hidden Markov Models for sequence data modeling.
  • Employed closed frequent pattern mining to identify behavior code patterns.

Main Results:

  • HMM and closed frequent pattern mining successfully modeled MI communication sequences.
  • Identified specific counselor communication strategies effective in eliciting patient change talk.
  • Demonstrated the utility of these methods for guiding clinical practice.

Conclusions:

  • Hidden Markov Models and closed frequent pattern mining are effective tools for analyzing MI communication.
  • These techniques can uncover actionable insights into effective counseling strategies.
  • Findings support the refinement of MI practice for behavior change interventions.