@Article{info:doi/10.2196/16862，作者=“Petersen, Curtis Lee and Halter, Ryan and Kotz, David and Loeb, Lorie and Cook, Summer and Pidgeon, Dawna and Christensen, Brock C and Batsis, John A”，标题=“使用自然语言处理和情感分析增强传统的以用户为中心的设计:开发和可用性研究”，期刊=“JMIR Mhealth Uhealth”，年=“2020”，月=“8”，日=“7”，卷=“8”，数=“8”，页=“e16862”，关键词=“老年人;sarcopenia;遥感技术;远程医疗;背景:肌肉减少症，定义为与年龄相关的肌肉质量和力量的损失，可以通过基于阻力的身体活动有效缓解。家庭运动处方的依从性约为40%，实施遥感系统将帮助患者和临床医生更好地了解治疗进展并提高依从性。将终端用户纳入遥感系统移动应用程序的开发，可确保这些应用程序既方便用户使用，又便于遵守。随着自然语言处理(NLP)的进步，这些方法有可能用于通过以用户为中心的设计过程收集的数据。目的:本研究旨在通过以用户为中心的设计过程，与老年人和临床医生一起开发一种新型设备的移动应用程序，同时探索通过该过程收集的数据是否可以用于NLP和情绪分析。通过以用户为中心的设计过程，我们在开发老年人友好的蓝牙连接阻力运动带应用程序期间进行了半结构化访谈。我们在应用程序开发的第0,5和10周采访了患者和临床医生。 Each semistructured interview consisted of heuristic evaluations, cognitive walkthroughs, and observations. 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. Sentiment was defined as the sum of positive and negative words (each word with a +1 or −1 value). To assess utility, we used quantitative assessment questionnaires---System Usability Scale (SUS) and Usefulness, Satisfaction, and Ease of use (USE). Finally, we used multivariate linear models---adjusting for age, sex, subject group (clinician vs patient), and development---to explore the association between sentiment analysis and SUS and USE outcomes. Results: The mean age of the 22 participants was 68 (SD 14) years, and 17 (77{\%}) were female. The overall mean SUS and USE scores were 66.4 (SD 13.6) and 41.3 (SD 15.2), respectively. Both patients and clinicians provided valuable insights into the needs of older adults when designing and building an app. The mean positive-negative sentiment per sentence was 0.19 (SD 0.21) and 0.47 (SD 0.21) for patient and clinician interviews, respectively. 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). There was no significant association between sentiment and the USE score. The LDA analysis found no overlap between patients and clinicians in the 8 identified topics. Conclusions: 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. ", issn="2291-5222", doi="10.2196/16862", url="https://mhealth.www.mybigtv.com/2020/8/e16862", url="https://doi.org/10.2196/16862", url="http://www.ncbi.nlm.nih.gov/pubmed/32540843" }