TY - JOUR

T1 - A Framework for Applying Natural Language Processing in Digital Health Interventions

AU - Funk, Burkhardt

AU - Sadeh-Sharvit, Shiri

AU - Fitzsimmons-Craft, Ellen E.

AU - Trockel, Mickey Todd

AU - Monterubio, Grace E

AU - Goel , Neha J

AU - Balantekin, Katherine N

AU - Eichen, Dawn M

AU - Flatt, Rachael E

AU - Firebaugh, Marie-Laure

AU - Jacobi, Corinna

AU - Graham, Andrea K.

AU - Hoogendoorn, Mark

AU - Wilfley, Denise E

AU - Taylor, C Barr

N1 - Publisher Copyright: © Burkhardt Funk, Shiri Sadeh-Sharvit, Ellen E Fitzsimmons-Craft, Mickey Todd Trockel, Grace E Monterubio, Neha J Goel, Katherine N Balantekin, Dawn M Eichen, Rachael E Flatt, Marie-Laure Firebaugh, Corinna Jacobi, Andrea K Graham, Mark Hoogendoorn, Denise E Wilfley, C Barr Taylor. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.02.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

PY - 2020/2/19

Y1 - 2020/2/19

N2 - 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.

AB - 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.

KW - Business informatics

KW - Digital Health Interventions Text Analytics (DHITA)

KW - digital health interventions

KW - eating disorders

KW - guided self-help

KW - natural language processing

KW - text mining

KW - Digital Health Interventions Text Analytics (DHITA)

KW - Text mining

KW - Digital health interventions

KW - Eating disorders

KW - Guided self-help

KW - Natural language processing

UR - http://www.scopus.com/inward/record.url?scp=85081043151&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b6140387-caaf-3fb9-a6da-caa0f3531f6b/

U2 - 10.2196/13855

DO - 10.2196/13855

M3 - Journal articles

C2 - 32130118

VL - 22

JO - Journal of Medical Internet Research

JF - Journal of Medical Internet Research

SN - 1439-4456

IS - 2

M1 - e13855

ER -