Building a Motivational Interviewing Dataset.

This paper contributes a novel psychological dataset consisting of counselors’ behaviors during Motivational Interviewing encounters. Annotations were conducted using the Motivational Interviewing Integrity Treatment (MITI). We describe relevant aspects associated with the construction of a dataset that relies on behavioral coding such as data acquisition, transcription, expert data annotations, and reliability assessments. The dataset contains a total of 22,719 counselor utterances extracted from 277 motivational interviewing sessions that are annotated with 10 counselor behavioral codes. The reliability analysis showed that annotators achieved excellent agreement at session level, with Intraclass Correlation Coefficient (ICC) scores in the range of 0.75 to 1, and fair to good agreement at utterance level, with Cohen’s Kappa scores ranging from 0.31 to 0.64. Behavioral interventions are a promising approach to address public health issues such as smoking cessation, increasing physical activity, and reducing substance abuse, among others (Resnicow et al., 2002). In particular, Motivational Interviewing (MI), a client centered psychotherapy style, has been receiving increasing attention from the clinical psychology community due to its established efficacy for treating addiction and other behaviors (Moyers et al., 2009; Apodaca et al., 2014; Barnett et al., 2014; Catley et al., 2012). Despite its potential benefits in combating addiction and in providing broader disease prevention and management, implementing MI counseling at larger scale or in other domains is limited by the need for human-based evaluations. Currently, this requires a human either watching or listening to video-tapes and then providing evaluative feedback. Recently, computational approaches have been proposed to aid the MI evaluation process (Atkins et al., 2014; Xiao et al., 2014; Klonek et al., 2015). However, learning resources for this task are not readily available. Having such resources will enable the application of data-driven strategies for the automatic coding of counseling behaviors, thus providing researchers with automatic means for the evaluation of MI. Moreover, this can also be useful to explore how MI works by relating MI behaviors to health outcomes, and to provide counselors with evaluative feedback that helps them improve their MI skills. In this paper, we present the construction and validation of a dataset annotated with counselor verbal behaviours using the Motivational Interviewing Treatment Integrity 4.0 (MITI), which is the current gold standard for MI-based psychology interventions. The dataset is derived from 277 MI sessions containing a total of 22,719 coded utterances. 1 Motivational Interviewing Miller and Rollnick define MI as a collaborative, goal-oriented style of psychotherapy with particular attention to the language of change (Miller and Rollnick, 2013). MI has been widely used as a treatment method in clinical trials on psychotherapy research to address addictive behaviors such as alcohol, tobacco and drug use; promote healthier habits such as nutrition and fitness; and help clients with