Smart Health最新文献

{"title":"German mHealth App Usability Questionnaire (G-MAUQ) and short version (G-MAUQ-S): Translation and validation study","authors":"Marvin Kopka ,&nbsp;Anna Slagman ,&nbsp;Claudia Schorr ,&nbsp;Henning Krampe ,&nbsp;Maria Altendorf ,&nbsp;Felix Balzer ,&nbsp;Myrto Bolanaki ,&nbsp;Doreen Kuschick ,&nbsp;Martin Möckel ,&nbsp;Hendrik Napierala ,&nbsp;Lennart Scatturin ,&nbsp;Konrad Schmidt ,&nbsp;Alica Thissen ,&nbsp;Malte L. Schmieding","doi":"10.1016/j.smhl.2024.100517","DOIUrl":"10.1016/j.smhl.2024.100517","url":null,"abstract":"<div><h3>Background</h3><p>The use of mobile health applications is increasingly common among the general public and in healthcare systems. With such apps percolating into the classic healthcare sector, the necessity of sound and standardized evaluation grows. The mHealth App Usability Questionnaire (MAUQ) provides a novel and custom-tailored psychometrically validated instrument to capture users’ perception of the usefulness and usability of an mHealth application. So far, this questionnaire is only available in English, Malay and Chinese. The aim of this study was to translate and validate a German version of the MAUQ (G-MAUQ). Further, we developed a short scale with 6 items (G-MAUQ-S) in German.</p></div><div><h3>Methods</h3><p>We used the Translation, Review, Adjudication, Pretest and Documentation (TRAPD) method to translate the MAUQ. Subsequently, we assessed content validity with 15 expert ratings and face validity with 15 German speaking mHealth users. To further validate the questionnaire, we used data from 148 participants of an RCT examining symptom checkers in the Emergency Department to assess convergent validity by correlating the G-MAUQ with the German version of the System Usability Scale and discriminant validity by correlating the G-MAUQ with other unrelated questionnaires. Lastly, we developed a short version by assessing item discrimination, factor loadings, correlation with the full scale and construct validity.</p></div><div><h3>Results</h3><p>All but one item showed sufficient content validity with item-level content validity index values between CVI-I = 0.8 and 1.0. Face validity was excellent with item-level face validity index values ranging from FVI-I = 0.87 to 1. Convergent validity was sufficient with r = 0.769, and discriminant validity was sufficient with values between r = −0.014 and r = 0.220. An internal consistency of Cronbach's α = 0.93 demonstrated high reliability. The short scale showed sufficient convergent validity (r = 0.762) and discriminant validity (r between −0.012 and 0.201).</p></div><div><h3>Conclusions</h3><p>A validated and reliable G-MAUQ can be used by researchers and practitioners to assess the usability of mHealth interventions. We also provide the German mHealth App Usability Questionnaire – Short Version (G-MAUQ-S) with six questions to quickly assess the usability of an intervention.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000734/pdfft?md5=695393762eb37c368501d137f28a9fa2&pid=1-s2.0-S2352648324000734-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach to predict core temperature during heat stress among firefighters","authors":"Cory J. Coehoorn,&nbsp;Jonathan Teran,&nbsp;Patrick St Martin,&nbsp;Hannah Cowart,&nbsp;Kylie Dufrene","doi":"10.1016/j.smhl.2024.100518","DOIUrl":"10.1016/j.smhl.2024.100518","url":null,"abstract":"<div><div>This study aimed to create a novel, non-invasive approach to predict core temperature (Tc) during heat stress among firefighters.</div></div><div><h3>Background</h3><div>The direct measure of Tc is typically performed through invasive techniques (rectal, esophageal, or intestinal). Existing predictive methods involve complex systems with multiple pieces of impractical equipment or are otherwise unsuitable for the work environment. Here, we hypothesized that a novel, non-invasive algorithm using variables collected from a single piece of commercially available equipment could effectively predict Tc.</div></div><div><h3>Methods</h3><div>The participants performed a steady-state exercise protocol in an environmental chamber (35 °C, 45% humidity) while donning firefighter personal protective equipment. The variables collected were skin temperature (Tsk), heart rate (HR), time, respiratory rate (RR), and rate of skin temperature acquisition per minute (Tsk/min).</div></div><div><h3>Results</h3><div>Of the variables collected, all contributed to the multiple regression model, except HR. Tsk/min was calculated using Tsk and time. The initial model created in this study predicted Tc with a standard error of the estimate (SEE) of 0.23 °C and an adjusted R² of 0.897. Following a \"leave-one-out\" bootstrap method, a robust equation was created using mean coefficients. This robust equation predicted Tc with a SEE of 0.23 and an R² of 0.902.</div></div><div><h3>Discussion</h3><div>This paper provides a practical, non-invasive model to predict Tc with minimal resources. This method has the potential to provide continuous monitoring of firefighters in the field and can be used as a metric to withdraw firefighters when under detrimental physiological stress. Ultimately, this could improve the health and longevity of firefighters.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416358","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}

{"title":"Gamifying the exploration of home mobility barriers for individuals with limited mobility: Scoping review","authors":"Luis Villarreal Laguna ,&nbsp;Carla Sílvia Fernandes ,&nbsp;Joana Campos ,&nbsp;Marta Campos Ferreira","doi":"10.1016/j.smhl.2024.100523","DOIUrl":"10.1016/j.smhl.2024.100523","url":null,"abstract":"<div><div>As advancements in the health sector continue to improve, people are living longer and increasingly aging in place. However, aging is often accompanied by disabilities and mobility issues. Whether these issues develop gradually or suddenly, many homes are not equipped to accommodate such changes, resulting in significant mobility barriers. This document presents a systematic review focusing on three key areas: “Home Barriers and Modification”, “Accessibilities and Disabilities”, and “Gamification and Assistive Technologies”. The aim is to synthesize existing knowledge and explore the interconnections among these topics. The primary objective of this review is to examine how gamification can be utilized to identify barriers within the homes of individuals with disabilities. Despite numerous advancements and available technologies, the review reveals a paucity of research on the application of gamification in this context, highlighting a promising area for future investigation. Additionally, the review underscores the benefits of home modifications to enhance accessibility, emphasizing the potential for significant improvements in the quality of life for individuals with disabilities.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100523"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards accurate Diabetic Foot Ulcer image classification: Leveraging CNN pre-trained features and extreme learning machine","authors":"Fitri Arnia ,&nbsp;Khairun Saddami ,&nbsp;Roslidar Roslidar ,&nbsp;Rusdha Muharar ,&nbsp;Khairul Munadi","doi":"10.1016/j.smhl.2024.100502","DOIUrl":"10.1016/j.smhl.2024.100502","url":null,"abstract":"<div><p>Diabetes mellitus (DM) can cause irreversible tissue damage in the legs, leading to foot ulcers that are difficult to heal. Early detection is crucial in preventing further complications. This study proposes a detection system for foot ulcers using a hybrid approach that combines deep convolutional neural networks (CNN) with an extreme learning machine (ELM). We explore the features of popular pre-trained models, including ResNet101, DenseNet201, MobileNetv2, EfficientNetB0, InceptionResNetv2, and NasNet mobile. Given the challenge of a limited dataset, traditional data augmentation may introduce inter-class bias. Therefore, we adopt a fusion of CNN and ELM to mitigate this issue. The experiments show promising results, with ResNet101, DenseNet201, InceptionResNetv2, MobileNetV2, NasNet mobile, and EfficientNetB0 achieving accuracies of 80%, 76.67%, 80%, 83.34%, 80%, and 80%, respectively. Our analysis reveals that MobileNetV2 provides the best feature representation, achieving the highest accuracy rate of 83.34% with zero false positives. Based on the findings, we suggest that the proposed hybrid method can accurately recognize DM foot images, providing a potential tool for early diagnosis and treatment of foot ulcers.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100502"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000588/pdfft?md5=fd85ad0912474ec33c2bdf458506b97e&pid=1-s2.0-S2352648324000588-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons","authors":"Jingxiao Tian ,&nbsp;Patrick Mercier ,&nbsp;Christopher Paolini","doi":"10.1016/j.smhl.2024.100498","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100498","url":null,"abstract":"<div><p>This work focuses on the development and manufacturing of a wireless, wearable, low-power fall detection sensor (FDS) designed to predict and detect falls in elderly at-risk individuals. Unintentional falls are a significant risk in this demographic, often resulting from diminished physical capabilities such as reduced hand grip strength and complications from conditions like arthritis, vertigo, and neuromuscular issues. To address this, we utilize advanced low-power field-programmable gate arrays (FPGAs) to implement a fixed-function neural network capable of categorizing activities of daily life (ADLs), including the detection of falls. This system employs a Convolutional Neural Network (CNN) model, trained and validated using the Caffe deep learning framework with data collected from human subjects experiments. This system integrates an ST Microelectronics LSM6DSOX inertial measurement unit (IMU) sensor, embedded with an ultra-low-power Lattice iCE40UP FPGA, which samples and stores joint acceleration and orientation rate. Additionally, we have acquired and published a dataset of 3D accelerometer and gyroscope measurements from predefined ADLs and falls, using volunteer human subjects. This innovative approach aims to enhance the safety and well-being of older adults by providing timely and accurate fall detection and prediction.</p><p>In this paper, we present an innovative approach to utilizing a compact Convolutional Neural Network (CNN) core for accelerating convolutional operations on a machine learning model, suitable for deployment on an ultra-low power FPGA.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100498"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000540/pdfft?md5=436ed873a93f5155977c1d04d19122da&pid=1-s2.0-S2352648324000540-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bio-inspired model for robust navigation assistive devices","authors":"Simon L. Gay ,&nbsp;Edwige Pissaloux ,&nbsp;Jean-Paul Jamont","doi":"10.1016/j.smhl.2024.100484","DOIUrl":"10.1016/j.smhl.2024.100484","url":null,"abstract":"<div><p>This paper proposes a new implementation and evaluation in a real-world environment of a bio-inspired predictive navigation model for mobility control, suitable especially for assistance of visually impaired people and autonomous mobile systems. This bio-inspired model relies on the interactions between formal models of three types of neurons identified in the mammals’ brain implied in navigation tasks, namely place cells, grid cells, and head direction cells, to construct a topological model of the environment under the form of a decentralized navigation graph. Previously tested in virtual environments, this model demonstrated a high tolerance to motion drift, making possible to map large environments without the need to correct it to handle such drifts, and robustness to environment changes. The presented implementation is based on a stereoscopic camera, and is evaluated on its possibilities to map and guide a person or an autonomous mobile robot in an unknown real environment. The evaluation results confirm the effectiveness of the proposed bio-inspired navigation model to build a path map, localize and guide a person through this path. The model predictions remain robust to environment changes, and allow to estimate traveled distances with an error rate below 3% over test paths, up to 100m. The tests performed on a robotic platform also demonstrated the pertinence of navigation data produced by this navigation model to guide an autonomous system. These results open the way toward efficient wearable assistive devices for visually impaired people independent navigation.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100484"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000400/pdfft?md5=da26293fcfb66b6f6d83277cab8ac5b3&pid=1-s2.0-S2352648324000400-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of medical artificial intelligence technology in sub-Saharan Africa: Prospects for medical laboratories","authors":"Richard Kobina Dadzie Ephraim ,&nbsp;Gabriel Pezahso Kotam ,&nbsp;Evans Duah ,&nbsp;Frank Naku Ghartey ,&nbsp;Evans Mantiri Mathebula ,&nbsp;Tivani Phosa Mashamba-Thompson","doi":"10.1016/j.smhl.2024.100505","DOIUrl":"10.1016/j.smhl.2024.100505","url":null,"abstract":"<div><p>The widespread adoption of artificial intelligence (AI) technology globally has brought significant changes to various sectors. AI-assisted algorithms have notably improved decision-making, operational efficiency, and productivity, especially in healthcare and medicine. However, in low and middle-income countries (LMICs), particularly in sub-Saharan Africa (SSA), the integration of medical AI has faced delays and challenges, slowing its acceptance and implementation in medical interventions. This thematic narrative critically explores the current trends and patterns in applying medical AI in SSA, with a specific focus on its potential impact on medical laboratories. The review covers the general use of medical AI in SSA, examining factors like enablers, challenges, and opportunities that influence healthcare systems. Additionally, it looks into the implications of medical AI for medical laboratories and suggests context-specific and practical recommendations for potential integration. We highlight various challenges, including data availability, security concerns, resource limitations, regulatory gaps, poor internet connectivity, and digital literacy issues, contributing to the slow integration of AI in healthcare systems in SSA. Despite challenges, the adoption of medical AI in SSA medical laboratories holds latent potential for improving diagnostic accuracy, streamlining workflows, and enhancing patient care. Further exploration and careful consideration are necessary to unlock these possibilities.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000618/pdfft?md5=5ebc9f63c766918d348d9c6ec4b33b87&pid=1-s2.0-S2352648324000618-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifying ambulation patterns in institutional settings","authors":"Jose-Valentin Sera-Josef ,&nbsp;Joseph J. LaViola ,&nbsp;Mary Elizabeth Bowen","doi":"10.1016/j.smhl.2024.100503","DOIUrl":"10.1016/j.smhl.2024.100503","url":null,"abstract":"<div><p>Observational studies of older adults show pacing, lapping, and stationary ambulation patterns can be associated with an increased risks for falls or an early sign of an acute or other health event in long-term care. The aim of this study is to use classical machine learning algorithms to automate the process of recognizing these patterns with the goal of assisting health care staff in monitoring the health and well-being of their residents. This study utilized data from six residents whose movements were tracked with a real-time locating system while performing everyday activities of daily living for up to 1.9 years. No residents exhibited lapping patterns over the course of the study. Machine learning statistical techniques recognized stationary and pacing with accuracy≥70%, with indirect and direct patterns having an accuracy of around 50% due to environmental constraints. Study findings suggest automated methods may be used with real-time locating data to recognize ambulation patterns that have been associated with poor health in this population. Study findings may be utilized by health care staff to tailor resident care plans and develop timely interventions that may affect falls and provide for the earlier recognition of acute events in this population.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100503"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842205","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}

{"title":"Digital native students using nutritional apps: are they more adherent to a mediterranean diet model? Results from the good APPetite survey","authors":"Silvia Marconi,&nbsp;Elisa Carrara,&nbsp;Giulia Gilberti,&nbsp;Maurizio Castellano,&nbsp;Barbara Zanini","doi":"10.1016/j.smhl.2024.100497","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100497","url":null,"abstract":"<div><p>Reading and understanding food labels are crucial steps in healthy dietary choices. Nutrition-related applications (n-apps) have increased in the recent years and the aim of this study was to assess the use and the perception of n-apps among a population of university students, also investigating the attitude and relationship with reading food labels and adherence to the Mediterranean diet (Medi-Lite score).</p><p>In 2023, 316 students, mainly attending the courses of Medicine, Pharmacy and Dietetics at the University of Brescia, Italy, completed an anonymous and specifically designed survey. 33.9% of the students stated that they use or have used n-apps. The most used apps were Yuka, MyFitnessPal, Fat Secret and Yazio, especially for the ease of use, speed, nutritional values estimation and barcode reading. 53.2% and 53.5% of the students declared to be food information and nutrition label readers respectively and the Medi-Lite mean value was 9.98 ± 2.46. N-app-users were significantly more attentive to food information and nutrition label than app not-users (both p &lt; 0.0001) and recorded a Medi-Lite score significantly higher (p = 0.0131).</p><p>The present study found for the first time an extensive correlation between the use of n-apps, the food labels awareness and healthy eating habits in a digitally native population.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100497"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291547","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}

{"title":"iSecureHealth: An efficient and secure technique to exchange health data using IoMT devices","authors":"Chayan Kanti Dhar,&nbsp;Abhishek Majumder","doi":"10.1016/j.smhl.2024.100504","DOIUrl":"10.1016/j.smhl.2024.100504","url":null,"abstract":"<div><p>The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT), which consists of internet-connected medical devices, hardware, and software applications that facilitate healthcare information technology. Transformation of the healthcare sector through the adoption of IoMT devices offers significant benefits, including efficient and timely medical interventions based on real-time monitoring of patients’ vitals. Security, authentication and privacy safeguards are the key hurdles in adopting medical-grade IoMT deployment. To address these critical hurdles, a lightweight, efficient and reliable key exchange scheme, termed iSecureHealth, has been proposed. The proposed system incorporates a security control node outside the User-IoMT-Gateway paradigm to enforce end-to-end secure data transactions for a medical-grade IoMT-based patient monitoring Environment. The secure data transaction techniques and key management comprise an authentication, authorization, and access (AAA) control layer, ensuring a secure data channel between IoMT sensors and the Gateway node (GNo) paradigm. Elliptic Curve Cryptography (ECC)-based key management, using the Elliptic Curve Diffie–Hellman Key Exchange technique, provides a secure, end-to-end private health data transmission through authorized IoMT devices. We used HMACSHA256 for JWT session key generation to design a lightweight automatic authentication scheme for iSecureHealth. For mutual authentication validation, a well-known BAN-Logic is applied. We considered the widely accepted random Oracle-based Real-Or-Random (ROR) model and Dolev–Yao (DY) logic for formal and informal security analysis, respectively. A generic ESP32/ESP-32S development board connected with a multisensory (MAX30102) was used for implementation. The publisher–subscriber-based lightweight Secure Message Queuing Telemetry Transport (SMQTT) protocol demonstrates real-time streaming of sensor-acquired data over the secure transport layer. Our experiments and results show that the performance of the proposed technique is better compared to the baselines.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100504"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953185","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}