Contagious diseases such as COVID-19 spread rapidly, forcing governments and policymakers to employ corrective measures. Contact tracing is one of the critical tools to identify whether individuals came into contact with infected persons. Many countries, including Australia, Singapore, and India, have released contact tracing apps to reduce the community spread. Such apps follow either a centralized or decentralized architecture; the former lets government agencies store and manage the user's data without privacy support, while the latter allows the user more control over their information, providing privacy. We analyze how the GDP and the democracy index influence the adoption of contact tracing applications. Our study analyzes COVID-19 contact tracing projects announced between February 2020 and August 2020 from 63 countries. The data indicates that countries with high GDP and democracy index tend to opt for decentralized architectures, while autocratic and low GDP countries tend to adopt centralized architectures.
{"title":"Poster: Centralized vs. Decentralized Contact Tracing: Do GDP and Democracy Index Influence Privacy Choices?","authors":"Nina Tanaka, G. Ramachandran, B. Krishnamachari","doi":"10.1145/3384420.3431777","DOIUrl":"https://doi.org/10.1145/3384420.3431777","url":null,"abstract":"Contagious diseases such as COVID-19 spread rapidly, forcing governments and policymakers to employ corrective measures. Contact tracing is one of the critical tools to identify whether individuals came into contact with infected persons. Many countries, including Australia, Singapore, and India, have released contact tracing apps to reduce the community spread. Such apps follow either a centralized or decentralized architecture; the former lets government agencies store and manage the user's data without privacy support, while the latter allows the user more control over their information, providing privacy. We analyze how the GDP and the democracy index influence the adoption of contact tracing applications. Our study analyzes COVID-19 contact tracing projects announced between February 2020 and August 2020 from 63 countries. The data indicates that countries with high GDP and democracy index tend to opt for decentralized architectures, while autocratic and low GDP countries tend to adopt centralized architectures.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115887314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we tested and analyzed the potential effectiveness of skilled nursing-facility (SNF) communication via a connected health app. We did this in efforts to discover an innovative approach to improve the communication systems of SNFs. We designed a 12-question questionnaire that would be utilized in phone surveys with 30 randomly selected participants above the age of 21. The data gathered was then analyzed to determine the effectiveness of the addition of a mobile application into a nursing home.
{"title":"Poster: Can a Mobile Application Aid in the Effectiveness of Skilled Nursing-Facility Communication Systems?","authors":"Ivy Shi, Allen Goodman","doi":"10.1145/3384420.3431780","DOIUrl":"https://doi.org/10.1145/3384420.3431780","url":null,"abstract":"In this study, we tested and analyzed the potential effectiveness of skilled nursing-facility (SNF) communication via a connected health app. We did this in efforts to discover an innovative approach to improve the communication systems of SNFs. We designed a 12-question questionnaire that would be utilized in phone surveys with 30 randomly selected participants above the age of 21. The data gathered was then analyzed to determine the effectiveness of the addition of a mobile application into a nursing home.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129443589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The human brain, containing billions of nerve cells (neurons) and their connecting fibers (Axons and Dendrons), is among the least understood organs in our body. A bundle of axon makes up a nerve tract that transmits electrical signals to other neurons, muscles and glands throughout the body. Over the years, clinical visualization techniques of brain nerve tracts have been developed to enhance the performance of medical diagnostics and neurosurgery [1] [2]. One of them is 2D MRI brain scans (raster) while another is the use of 2.5D visualization software, such as ExploreDTI11https://www.exploredti.com/, to connect nerve tract pathways and visualize off-axis viewpoints of tract trajectories (vector). The main objective of this project is to apply Virtual (VR), Augmented (AR) or Mixed Reality (MR) technologies to this problem and progress current standalone 2.5D brain tract visualisation tools to collaborative 3DVR visualisation and analysis of brain structures.
{"title":"Towards Diagnostic Applications of Virtual/Mixed Reality to Brain Scans*","authors":"Fatemeh Naderifar, Basil Lim, J. Carswell","doi":"10.1145/1234567890","DOIUrl":"https://doi.org/10.1145/1234567890","url":null,"abstract":"The human brain, containing billions of nerve cells (neurons) and their connecting fibers (Axons and Dendrons), is among the least understood organs in our body. A bundle of axon makes up a nerve tract that transmits electrical signals to other neurons, muscles and glands throughout the body. Over the years, clinical visualization techniques of brain nerve tracts have been developed to enhance the performance of medical diagnostics and neurosurgery [1] [2]. One of them is 2D MRI brain scans (raster) while another is the use of 2.5D visualization software, such as ExploreDTI11https://www.exploredti.com/, to connect nerve tract pathways and visualize off-axis viewpoints of tract trajectories (vector). The main objective of this project is to apply Virtual (VR), Augmented (AR) or Mixed Reality (MR) technologies to this problem and progress current standalone 2.5D brain tract visualisation tools to collaborative 3DVR visualisation and analysis of brain structures.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129781226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this poster, we position and present preliminary work of the design of camera based virtual hand rehabilitation. We position the proposed system as an application that can be hosted on any mobile device such as smartphone, tablet or a personal computer. The primarily focus of the application is to serve as a self-monitoring tool for home-based exercise programs for personnel experiencing stroke recovery. This is made possible by using a mobile camera to capture and measure hand movements during the prescribed rehabilitation exercise performance and provide real-time feedback to the user. Through this approach, we posit that our visual feedback-based approach and the innovative use of mobile camera technology for rehabilitation, has the potential to revolutionise home-based exercise programs for individuals with chronic disabilities.
{"title":"Poster: Virtual Hand Rehabilitation using a Mobile Camera","authors":"A. Ashok, Sutanuka Bhattacharjya","doi":"10.1145/3384420.3431771","DOIUrl":"https://doi.org/10.1145/3384420.3431771","url":null,"abstract":"In this poster, we position and present preliminary work of the design of camera based virtual hand rehabilitation. We position the proposed system as an application that can be hosted on any mobile device such as smartphone, tablet or a personal computer. The primarily focus of the application is to serve as a self-monitoring tool for home-based exercise programs for personnel experiencing stroke recovery. This is made possible by using a mobile camera to capture and measure hand movements during the prescribed rehabilitation exercise performance and provide real-time feedback to the user. Through this approach, we posit that our visual feedback-based approach and the innovative use of mobile camera technology for rehabilitation, has the potential to revolutionise home-based exercise programs for individuals with chronic disabilities.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134045316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wrist-worn devices afford convenient and unobtrusive heart rate sensing, however, motion artifacts can lead to unreliable data recordings. This paper evaluates heart rate estimates acquired during treadmill walking and 12 hours of everyday living from a medical-grade Empatica E4 data streaming wristband wearable compared to a Polar H10 chest strap ECG sensor. For treadmill walking, heart rate Mean Absolute Percentage Errors (MAPEs) were between 7.2% and 29.2%, and IntraClass Correlations (ICCs) between 0.6 and −0.5, indicating moderate agreement and strong disagreement, respectively. During 12-hour everyday living acquisitions, heart rate estimate MAPEs were between 5.3% and 13.5% and ICCs between 0.7 and 0.1, indicating good to poor agreements. CCS CONCEPTS • Applied computing → Consumer health.
{"title":"Poster: Heart Rate Performance of a Medical-Grade Data Streaming Wearable Device","authors":"Tendai Rukasha, Sandra I. Woolley, Tim Collins","doi":"10.1145/3384420.3431776","DOIUrl":"https://doi.org/10.1145/3384420.3431776","url":null,"abstract":"Wrist-worn devices afford convenient and unobtrusive heart rate sensing, however, motion artifacts can lead to unreliable data recordings. This paper evaluates heart rate estimates acquired during treadmill walking and 12 hours of everyday living from a medical-grade Empatica E4 data streaming wristband wearable compared to a Polar H10 chest strap ECG sensor. For treadmill walking, heart rate Mean Absolute Percentage Errors (MAPEs) were between 7.2% and 29.2%, and IntraClass Correlations (ICCs) between 0.6 and −0.5, indicating moderate agreement and strong disagreement, respectively. During 12-hour everyday living acquisitions, heart rate estimate MAPEs were between 5.3% and 13.5% and ICCs between 0.7 and 0.1, indicating good to poor agreements. CCS CONCEPTS • Applied computing → Consumer health.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117290941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ramesh Kumar Sah, H. Ghasemzadeh, Assal Habibi, M. McDonell, Patricia Pendry, M. Cleveland
Alcohol related disorder has increasingly become a serious public health issue. Stress detection and intervention is considered a key element in a treatment strategy towards preventing alcohol dependent individuals from relapsing. In this paper, we present a proof-of-concept approach to study the usability of a wearable device and viability of a mobile health application to prevent alcohol relapse by detecting moments of stress and providing adaptive interventions in real-time.
{"title":"Poster: Mobile Health for Alcohol Recovery and Relapse Prevention","authors":"Ramesh Kumar Sah, H. Ghasemzadeh, Assal Habibi, M. McDonell, Patricia Pendry, M. Cleveland","doi":"10.1145/3384420.3431779","DOIUrl":"https://doi.org/10.1145/3384420.3431779","url":null,"abstract":"Alcohol related disorder has increasingly become a serious public health issue. Stress detection and intervention is considered a key element in a treatment strategy towards preventing alcohol dependent individuals from relapsing. In this paper, we present a proof-of-concept approach to study the usability of a wearable device and viability of a mobile health application to prevent alcohol relapse by detecting moments of stress and providing adaptive interventions in real-time.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114794338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Zhuang, M. Hassan, Chad M. Aldridge, Xuwang Yin, T. McMurry, A. Southerland, G. Rohde
The BANDIT - Brain Attack Neurological Deficit Identification Tool - project aims at developing an automatic tool to quantitatively assess stroke-related neurological deficits such as facial weakness and limb drift. In this paper, we first introduce the BANDIT project by describing the main framework and then present a patient video data acquisition protocol that was conducted in a real-world hospital setting. We also discuss the inherent bias and variations within our dataset that may create challenges for the algorithm design. The experiences and lessons gained from our study could be beneficial for other researchers who conduct camera-based research in the connected health area.
{"title":"Poster: A Pilot Study On Camera-based Neurological Deficit Detection","authors":"Yan Zhuang, M. Hassan, Chad M. Aldridge, Xuwang Yin, T. McMurry, A. Southerland, G. Rohde","doi":"10.1145/3384420.3431778","DOIUrl":"https://doi.org/10.1145/3384420.3431778","url":null,"abstract":"The BANDIT - Brain Attack Neurological Deficit Identification Tool - project aims at developing an automatic tool to quantitatively assess stroke-related neurological deficits such as facial weakness and limb drift. In this paper, we first introduce the BANDIT project by describing the main framework and then present a patient video data acquisition protocol that was conducted in a real-world hospital setting. We also discuss the inherent bias and variations within our dataset that may create challenges for the algorithm design. The experiences and lessons gained from our study could be beneficial for other researchers who conduct camera-based research in the connected health area.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129413711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Interest in blockchain applications for supply chain management is on the rise. Recent regulatory relief enabling telehealth expansion across the United States, coupled with drug tracking requirements mandated by the Drug Supply Chain Security Act (DSCSA), necessitates an evidence-based understanding of feasible blockchain applications for the DSCSA-compliant tracking of drugs prescribed to remote patients who use personal devices to access telehealth. The purpose of this project is twofold: first, to identify use cases for DSCSA-compliant blockchain applications for telehealth and second, to formulate stakeholder recommendations with regard to policy and technical issues surrounding the current regulatory framework for blockchain applications in health care.
{"title":"Poster: Blockchain Application Feasibility in Telehealth for DSCSA Compliance","authors":"Stephanie Zawada","doi":"10.1145/3384420.3431772","DOIUrl":"https://doi.org/10.1145/3384420.3431772","url":null,"abstract":"Interest in blockchain applications for supply chain management is on the rise. Recent regulatory relief enabling telehealth expansion across the United States, coupled with drug tracking requirements mandated by the Drug Supply Chain Security Act (DSCSA), necessitates an evidence-based understanding of feasible blockchain applications for the DSCSA-compliant tracking of drugs prescribed to remote patients who use personal devices to access telehealth. The purpose of this project is twofold: first, to identify use cases for DSCSA-compliant blockchain applications for telehealth and second, to formulate stakeholder recommendations with regard to policy and technical issues surrounding the current regulatory framework for blockchain applications in health care.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131182638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Petersen, Colin M. Minor, Suehayla Mohieldin, Linda G. Park, R. Halter, J. Batsis
Sarcopenia is the age-related loss of muscle mass and strength that is associated with adverse health outcomes. Resistance-based exercises are effective for mitigation and enhancement of strength; however, adherence is low and challenging to measure when patients are at home. In a single-arm, pilot study of seven older adults, we conducted a field-based usability study evaluating the feasibility and acceptability of using a system consisting of a Bluetooth-connected resistance exercise band and tablet-based app which together we call BandPass in completing four different home-based exercises. The system measured a total of 147 exercises by participants with a mean duration of 94±66 seconds, completing an average of 30±20 repetitions. Though not all patients completed each exercise type, patients were positive about use: patient activation measure: 80.7±14; system usability scale: 6.9±2.9; and confidence in use: 7.7±2.7. The BandPass system demonstrated its ability to collect data on exercise type, force during an exercise, and duration of exercise when older adults use it for monitoring exercise at home.
{"title":"Remote Rehabilitation: A Field-Based Feasibility Study of an mHealth Resistance Exercise Band","authors":"C. Petersen, Colin M. Minor, Suehayla Mohieldin, Linda G. Park, R. Halter, J. Batsis","doi":"10.1145/3384420.3431773","DOIUrl":"https://doi.org/10.1145/3384420.3431773","url":null,"abstract":"Sarcopenia is the age-related loss of muscle mass and strength that is associated with adverse health outcomes. Resistance-based exercises are effective for mitigation and enhancement of strength; however, adherence is low and challenging to measure when patients are at home. In a single-arm, pilot study of seven older adults, we conducted a field-based usability study evaluating the feasibility and acceptability of using a system consisting of a Bluetooth-connected resistance exercise band and tablet-based app which together we call BandPass in completing four different home-based exercises. The system measured a total of 147 exercises by participants with a mean duration of 94±66 seconds, completing an average of 30±20 repetitions. Though not all patients completed each exercise type, patients were positive about use: patient activation measure: 80.7±14; system usability scale: 6.9±2.9; and confidence in use: 7.7±2.7. The BandPass system demonstrated its ability to collect data on exercise type, force during an exercise, and duration of exercise when older adults use it for monitoring exercise at home.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117255564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The absence of highly predictive and readily applicable biomarkers for Parkinson's disease (PD) significantly hinders the diagnosis and subsequent monitoring of the condition. Since up to 90% of PD patients exhibit speech aberrations, however, the use of patient voice as a rapid diagnostic measure has shown significant promise. Past research towards creating voice-based automated diagnostic tools has relied on expert handcrafted audio feature sets that capture patient articulation, phonation, and prosody properties. Not only is there a limited consensus on the ideal contents of a PD audio diagnostic feature set, but also manually selected features may not fully exploit the predictive power of the underlying data. In this study, we demonstrate the benefit of employing VGGish embeddings, a more generalizable and higher throughput feature extraction strategy, for voice-based PD diagnosis. Our top VGGish-based model achieved 87% accuracy for detecting PD and significantly outperformed models trained on multiple handcrafted feature sets, a mel-frequency cepstral coefficient set, as well as an ImageNet pretrained convolutional neural network extraction strategy. VGGish models were also highly competitive with clinically determined UPDRS III–18 speech deterioration ratings for PD diagnosis. These results demonstrate the potential of VGGish embeddings for creating fast and accurate voice-based PD classification models.
{"title":"Poster: Vggish Embeddings Based Audio Classifiers to Improve Parkinson's Disease Diagnosis","authors":"Sruthi Kurada, Abhinav Kurada","doi":"10.1145/3384420.3431775","DOIUrl":"https://doi.org/10.1145/3384420.3431775","url":null,"abstract":"The absence of highly predictive and readily applicable biomarkers for Parkinson's disease (PD) significantly hinders the diagnosis and subsequent monitoring of the condition. Since up to 90% of PD patients exhibit speech aberrations, however, the use of patient voice as a rapid diagnostic measure has shown significant promise. Past research towards creating voice-based automated diagnostic tools has relied on expert handcrafted audio feature sets that capture patient articulation, phonation, and prosody properties. Not only is there a limited consensus on the ideal contents of a PD audio diagnostic feature set, but also manually selected features may not fully exploit the predictive power of the underlying data. In this study, we demonstrate the benefit of employing VGGish embeddings, a more generalizable and higher throughput feature extraction strategy, for voice-based PD diagnosis. Our top VGGish-based model achieved 87% accuracy for detecting PD and significantly outperformed models trained on multiple handcrafted feature sets, a mel-frequency cepstral coefficient set, as well as an ImageNet pretrained convolutional neural network extraction strategy. VGGish models were also highly competitive with clinically determined UPDRS III–18 speech deterioration ratings for PD diagnosis. These results demonstrate the potential of VGGish embeddings for creating fast and accurate voice-based PD classification models.","PeriodicalId":193143,"journal":{"name":"2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124052893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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