Web-enabled knowledge-based analysis of genetic data

We present a web-based implementation of GenePath, an intelligent assistant tool for data analysis in functional genomics. GenePath considers mutant data and uses expert-defined patterns to find gene-to-gene or gene-to-outcome relations. It presents the results of analysis as genetic networks, wherein a set of genes has various influence on one another and on a biological outcome. In the paper, we particularly focus on its web-based interface and explanation mechanisms

ActivityAware: an App for Real-Time Daily Activity Level Monitoring on the Amulet Wrist-Worn Device

Physical activity helps reduce the risk of cardiovascular disease, hypertension and obesity. The ability to monitor a person\u27s daily activity level can inform self-management of physical activity and related interventions. For older adults with obesity, the importance of regular, physical activity is critical to reduce the risk of long-term disability. In this work, we present ActivityAware, an application on the Amulet wrist-worn device that measures daily activity levels (sedentary, moderate and vigorous) of individuals, continuously and in real-time. The app implements an activity-level detection model, continuously collects acceleration data on the Amulet, classifies the current activity level, updates the day\u27s accumulated time spent at that activity level, logs the data for later analysis, and displays the results on the screen. We developed an activity-level detection model using a Support Vector Machine (SVM). We trained our classifiers using data from a user study, where subjects performed the following physical activities: sit, stand, lay down, walk and run. With 10-fold cross validation and leave-one-subject-out (LOSO) cross validation, we obtained preliminary results that suggest accuracies up to 98%, for n=14 subjects. Testing the ActivityAware app revealed a projected battery life of up to 4 weeks before needing to recharge. The results are promising, indicating that the app may be used for activity-level monitoring, and eventually for the development of interventions that could improve the health of individuals

Development of a ‘Smart’ Resistance Exercise Band to Assess Strength

https://digitalcommons.dartmouth.edu/wetterhahnsymposium-2018/1004/thumbnail.jp

Use of Amulet in behavioral change for geriatric obesity management

Background: Obesity in older adults is a significant public health concern. Weight-loss interventions are known to improve physical function but risk the development of sarcopenia. Mobile health devices have the potential to augment existing interventions and, if designed accordingly, could improve one’s physical activity and strength in routine physical activity interventions. Methods and results: We present Amulet, a mobile health device that has the capability of engaging patients in physical activity. The purpose of this article is to discuss the development of applications that are tailored to older adults with obesity, with the intention to engage and improve their health. Conclusions: Using a team-science approach, Amulet has the potential, as an open-source mobile health device, to tailor activity interventions to older adults

Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study

Background: Sarcopenia, defined as the age-associated loss of muscle mass and strength, can be effectively mitigated through resistance-based physical activity. With compliance at approximately 40% for home-based exercise prescriptions, implementing a remote sensing system would help patients and clinicians to better understand treatment progress and increase compliance. The inclusion of end users in the development of mobile apps for remote-sensing systems can ensure that they are both user friendly and facilitate compliance. With advancements in natural language processing (NLP), there is potential for these methods to be used with data collected through the user-centered design process. Objective: 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 NLP and sentiment analysis. Methods: Through a user-centered design process, we conducted semistructured interviews during the development of a geriatric-friendly Bluetooth-connected resistance exercise band app. We interviewed patients and clinicians at weeks 0, 5, and 10 of the app development. 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

Detection and Monitoring of Repetitions Using an mHealth-Enabled Resistance Band

Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years and older and recorded 4 specific, physical therapist-led exercises. Three blinded reviewers examined the findings and we also applied a peak finding algorithm to the data. There were 16.6 repetitions per exercise across reviewers, with an intraclass correlation of 0.912 (95%CI: $0.853-0.953$, $p\u3c0.001$) between reviewers and the algorithm. Using this novel resistance band, we feasibly detected repetition of exercises in older adults. Sarcopenia is defined as an age-related loss of muscle mass and strength which impairs physical function leading to disability and frailty. Resistance exercises are effective treatments for sarcopenia and are critical in mitigating weight-loss induced sarcopenia in older adults attempting to lose weight. Yet, adherence to home-based regimens, which is a cornerstone to lifestyle therapies, is poor and cannot be ascertained by clinicians as no objective methods exist to determine patient compliance outside of a supervised setting. Our group developed a Bluetooth connected resistance band that tests the ability to detect exercise repetitions. We recruited 6 patients aged 65 years and older and recorded 4 specific, physical therapist-led exercises. Three blinded reviewers examined the findings and we also applied a peak finding algorithm to the data. There were 16.6 repetitions per exercise across reviewers, with an intraclass correlation of 0.912 (95%CI: $0.853-0.953$, $p\u3c0.001$) between reviewers and the algorithm. Using this novel resistance band, we feasibly detected repetition of exercises in older adults

Aging, the Medical Subspecialties, and Career Development: Where We Were, Where We Are Going

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136733/1/jgs14708_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136733/2/jgs14708.pd

Development and Usability Assessment of a Connected Resistance Exercise Band Application for Strength-Monitoring

Resistance exercise bands are a core component of any physical activity strengthening program. Strength training can mitigate the development of sarcopenia, the loss of muscle mass or strength and function with aging. Yet, the adherence of such behavioral exercise strategies in a home-based setting are fraught with issues of monitoring and compliance. Our group developed a Bluetooth-enabled resistance exercise band capable of transmitting data to an open-source platform. In this work, we developed an application to capture this information in real-time, and conducted three usability studies in two mixed-aged groups of participants (n=6 each) and a group of older adults with obesity participating in a weight-loss intervention (n=20). The system was favorable, acceptable and provided iterative information that could assist in future deployment on ubiquitous platforms. Our formative work provides the foundation to deliver home-based monitoring interventions in a high-risk, older adult population

Breakpoint mapping of 13 large parkin deletions/duplications reveals an exon 4 deletion and an exon 7 duplication as founder mutations

Early-onset Parkinson’s disease (EOPD) has been associated with recessive mutations in parkin (PARK2). About half of the mutations found in parkin are genomic rearrangements, i.e., large deletions or duplications. Although many different rearrangements have been found in parkin before, the exact breakpoints involving these rearrangements are rarely mapped. In the present study, the exact breakpoints of 13 different parkin deletions/duplications, detected in 13 patients out of a total screened sample of 116 EOPD patients using Multiple Ligation Probe Amplification (MLPA) analysis, were mapped using real time quantitative polymerase chain reaction (PCR), long-range PCR and sequence analysis. Deletion/duplication-specific PCR tests were developed as a rapid and low cost tool to confirm MLPA results and to test family members or patients with similar parkin deletions/duplications. Besides several different deletions, an exon 3 deletion, an exon 4 deletion and an exon 7 duplication were found in multiple families. Haplotype analysis in four families showed that a common haplotype of 1.2 Mb could be distinguished for the exon 7 duplication and a common haplotype of 6.3 Mb for the deletion of exon 4. These findings suggest common founder effects for distinct large rearrangements in parkin