A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Alcohol Abuse Prevention Among Fraternity Men: Addressing Individual Perceptions and Moderating Group Culture Determinants of High -Risk Drinking

149 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.Conclusions. Perceived subjective norms as defined by long standing behavior theory may provide an alternative and potentially more promising intervention target---for this high-risk student population---than the current focus on correcting students' errors in estimating the amount of alcohol their peers consume. Results of this study also suggest fraternity men may respond favorably to intervention messages emphasizing second hand consequences of alcohol use. Perceived second hand consequences offer another possible point of leverage for positive social culture change to reduce high-risk alcohol consumption patterns among fraternity men and other targeted populations.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD