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Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis

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Abstract

Motivational interviewing (MI) is an evidence-based strategy for communicating with patients about behavior change. Although there is strong empirical evidence linking “MI-consistent” counselor behaviors and patient motivational statements (i.e., “change talk”), the specific counselor communication behaviors effective for eliciting patient change talk vary by treatment context and, thus, are a subject of ongoing research. An integral part of this research is the sequential analysis of pre-coded MI transcripts. In this paper, we evaluate the empirical effectiveness of the Hidden Markov Model, a probabilistic generative model for sequence data, for modeling sequences of behavior codes and closed frequent pattern mining, a method to identify frequently occurring sequential patterns of behavior codes in MI communication sequences to inform MI practice. We conducted experiments with 1,360 communication sequences from 37 transcribed audio recordings of weight loss counseling sessions with African-American adolescents with obesity and their caregivers. Transcripts had been previously annotated with patient-counselor behavior codes using a specialized codebook. Empirical results indicate that Hidden Markov Model and closed frequent pattern mining techniques can identify counselor communication strategies that are effective at eliciting patients’ motivational statements to guide clinical practice.

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Notes

  1. we used the implementation in the hmmlearn package publicly available at http://hmmlearn.readthedocs.io/

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Acknowledgements

We would like to thank the student assistants in the Department of Family Medicine and Public Health Sciences at Wayne State University School of Medicine for their help in developing the training dataset. The authors would like to thank Lisa Todd, JD, MA, for her thoughtful feedback and clinical expertise in the interpretation of these data.

Funding

This study was supported by a grant from the National Institutes of Health, NIDDK R21DK108071, Carcone and Kotov, MPIs.

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Authors and Affiliations

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

    Mehedi Hasan & Alexander Kotov

  2. Division of Behavioral Sciences, Department of Family Medicine and Public Health Sciences, Wayne State University School of Medicine, Detroit, MI, 48202, USA

    April Idalski Carcone

  3. Center for Translational Behavioral Research, Department of Behavioral Sciences and Social Medicine, Florida State University, Tallahassee, FL, 32306, USA

    Sylvie Naar

  4. Department of Oncology, Wayne State University/Karmanos Cancer Institute, Detroit, MI, 48201, USA

    Susan Eggly

  5. Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, 48202, USA

    Gwen L. Alexander

  6. Department of Humanities, Health and Society, Wertheim College of Medicine, Florida International University, Miami, FL, 33199, USA

    Kathryn E. Brogan Hartlieb

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  1. Mehedi Hasan
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  2. April Idalski Carcone
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  7. Alexander Kotov
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Correspondence to Mehedi Hasan.

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Hasan, M., Carcone, A.I., Naar, S. et al. Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis. J Healthc Inform Res 3, 86–106 (2019). https://doi.org/10.1007/s41666-018-0037-6

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  • DOI: https://doi.org/10.1007/s41666-018-0037-6

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