Augmenting Traditional User-Centered Design with Natural Language Processing and Sentiment Analysis

This paper includes patient and clinician feedback to better understand treatment progress and increase compliance in resistance-based physical activity to mitigate the effects of age-associated losses in muscle mass and strengths. This study aims to develop a mobile app for a novel device through a user-centered design process with both older adults and clinicians while exploring whether data collected through this process can be used in natural language processing (NLP) and sentiment analysis. We used the Bing sentiment library for a sentiment analysis of interview transcripts and then applied NLP-based latent Dirichlet allocation (LDA) topic modeling to identify differences and similarities in patient and clinician participant interviews. To assess utility, we used quantitative assessment questionnaires—System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). We found a positive association with positive sentiment in an interview and SUS score (ß=1.38; 95% CI 0.37 to 2.39; P=.01), but no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. Involving patients and clinicians allowed us to design and build an app that is user friendly for older adults while supporting compliance. This is the first analysis using NLP and usability questionnaires in the quantification of user-centered design of technology for older adults.

Curtis Lee Petersen, Ryan Halter, David Kotz, Lorie Loeb, Summer Cook, Dawna Pidgeon, Brock C. Christensen, and John A. Batsis. Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability StudyJMIR mHealth and uHealth 8(8), page Article#e16862 (13 pages), August 2020. JMIR Publications. DOI: 10.2196/16862