SynchroHealth, a startup company launched with technology originating in the Amulet project, recently received a grant of almost $225,000 from the National Institute on Aging (NIA), part of the National Institutes of Health (NIH). SynchroHealth aims to develop hardware and software solutions for non-invasive detection and acquisition of remote healthcare data.
Amulet’s Ryan Halter, co-founder of SynchroHealth, says “there’s a huge disparity in what clinicians and physical therapists think goes on in at-home rehabilitation programs and what actually happens. This disconnect […] could ultimately lead to unnecessary interventions that increase costs and potential risks to the patient. We’re aiming to close that gap.”
SynchroHealth is continuing the development of their ‘BandPass’ technology for the treatment of sarcopenia – loss of muscle mass and strength due to aging. BandPassis capable of monitoring, evaluating, and guiding patients in upper-body strength training in real-time. It includes sensors equipped to an exercise band with custom-designed electronics for the wireless transmission of patient data. It is unique in that the data collected could be monitored by a physician to aid in proper interventions.
Along with continuing the development of the BandPass, the team will develop a mobile application and cloud-based service for data transmission, processing, and storage. Later this year, the team will test their device on a cohort of 16 patients to obtain feedback.
This work is being supported under Award Number R41AG071290 by the National Institute On Aging of the National Institutes of Health. To learn more about BandPass and SynchroHealth, check out their website here. The Dartmouth Engineering article on this work can be found here.
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 Study. JMIR mHealth and uHealth 8(8), page Article#e16862 (13 pages), August 2020. JMIR Publications. DOI: 10.2196/16862