Pub Date : 2024-06-11DOI: 10.20517/chatmed.2024.03
Cameron J. Leong, Sohat Sharma, Jayant Seth, Simon W. Rabkin
Aim: The objective of this systematic review and meta-analysis was to determine the diagnostic and prognostic utility of artificial intelligence/machine learning (AI/ML) algorithms in Brugada Syndrome (BrS). Methods: A systematic review and meta-analysis of the literature was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, EMBASE, SCOPUS, and WEB OF SCIENCE databases were searched for relevant articles. Abstract and title screening, full-text review, and data extraction were conducted independently by two of the authors. Conflicts were resolved via discussion among authors. A risk-of-bias assessment was performed using the QUADAS-2 tool for diagnostic studies and the PROBAST tool for prognostic studies. Forest plots and the summary area under the receiver operating characteristic (SAUROC) curve were done in R. Results: A total of 12 papers were included in our study. Among the best-performing diagnostic algorithms from each study, the sensitivity and specificity ranged from 0.80 to 0.89 and 0.74 to 0.97, respectively. In overall studies, sensitivity was 0.845 ± 0.014 and specificity was 0.892 ± 0.062 using a random effects model. A pooled analysis of the summary area under the receiver operating characteristic curve (SAUROC) was 0.77 for diagnostic studies. Prognostic studies showed good performance as well, with the AUC of the best-performing prognostic algorithms ranging from 0.71 to 0.90. Conclusions: Overall, AI/ML algorithms had high diagnostic and prognostic accuracy. These results highlight the potential of AI/ML algorithms for the diagnosis and prognosis of BrS and permit a choice of the best-performing ML algorithms.
目的：本系统综述和荟萃分析旨在确定人工智能/机器学习（AI/ML）算法在布鲁格达综合征（BrS）中的诊断和预后效用。方法：根据《系统综述和荟萃分析首选报告项目》（Preferred Reporting Items for Systematic reviews and Meta-Analyses，PRISMA）指南对文献进行系统综述和荟萃分析。在 MEDLINE、EMBASE、SCOPUS 和 WEB OF SCIENCE 数据库中检索了相关文章。摘要和标题筛选、全文审阅和数据提取由两位作者独立完成。作者之间的冲突通过讨论解决。诊断性研究使用 QUADAS-2 工具进行偏倚风险评估，预后性研究使用 PROBAST 工具进行偏倚风险评估。结果：我们的研究共纳入了 12 篇论文。在每项研究中表现最佳的诊断算法中，灵敏度和特异性分别为 0.80 至 0.89 和 0.74 至 0.97。采用随机效应模型，总体研究的灵敏度为 0.845 ± 0.014，特异度为 0.892 ± 0.062。诊断性研究的接收者操作特征曲线下的汇总面积（SAUROC）汇总分析为 0.77。预后研究也表现良好，表现最好的预后算法的AUC为0.71至0.90。结论：总体而言，人工智能/ML 算法具有很高的诊断和预后准确性。这些结果凸显了人工智能/ML 算法在 BrS 诊断和预后方面的潜力，并允许选择表现最佳的 ML 算法。
{"title":"Artificial intelligence streamlines diagnosis and assessment of prognosis in Brugada syndrome: a systematic review and meta-analysis","authors":"Cameron J. Leong, Sohat Sharma, Jayant Seth, Simon W. Rabkin","doi":"10.20517/chatmed.2024.03","DOIUrl":"https://doi.org/10.20517/chatmed.2024.03","url":null,"abstract":"Aim: The objective of this systematic review and meta-analysis was to determine the diagnostic and prognostic utility of artificial intelligence/machine learning (AI/ML) algorithms in Brugada Syndrome (BrS).\u0000 Methods: A systematic review and meta-analysis of the literature was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE, EMBASE, SCOPUS, and WEB OF SCIENCE databases were searched for relevant articles. Abstract and title screening, full-text review, and data extraction were conducted independently by two of the authors. Conflicts were resolved via discussion among authors. A risk-of-bias assessment was performed using the QUADAS-2 tool for diagnostic studies and the PROBAST tool for prognostic studies. Forest plots and the summary area under the receiver operating characteristic (SAUROC) curve were done in R.\u0000 Results: A total of 12 papers were included in our study. Among the best-performing diagnostic algorithms from each study, the sensitivity and specificity ranged from 0.80 to 0.89 and 0.74 to 0.97, respectively. In overall studies, sensitivity was 0.845 ± 0.014 and specificity was 0.892 ± 0.062 using a random effects model. A pooled analysis of the summary area under the receiver operating characteristic curve (SAUROC) was 0.77 for diagnostic studies. Prognostic studies showed good performance as well, with the AUC of the best-performing prognostic algorithms ranging from 0.71 to 0.90.\u0000 Conclusions: Overall, AI/ML algorithms had high diagnostic and prognostic accuracy. These results highlight the potential of AI/ML algorithms for the diagnosis and prognosis of BrS and permit a choice of the best-performing ML algorithms.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"81 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141359607","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}
Pub Date : 2024-05-24DOI: 10.20517/chatmed.2023.37
Vasileios C. Pezoulas, Dimitrios I. Fotiadis
Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant. Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models. Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain. Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.
{"title":"The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study","authors":"Vasileios C. Pezoulas, Dimitrios I. Fotiadis","doi":"10.20517/chatmed.2023.37","DOIUrl":"https://doi.org/10.20517/chatmed.2023.37","url":null,"abstract":"Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant.\u0000 Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models.\u0000 Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain.\u0000 Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"74 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101766","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}
Pub Date : 2024-05-15DOI: 10.20517/chatmed.2023.30
Xinyue Song, Xiaorong Ding
Aim: We propose to examine the causal relationship between the noninvasive features represented by pulse transit time (PTT) and blood pressure (BP), with the aim of mitigating the impact of confounding factor(s) and thus improving the performance of BP estimation. Methods: We identified the causal graph of BP and the important noninvasive features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) via fast causal inference (FCI) algorithm, with the orientations of the edges in the causal graph being determined by the causal generative neural networks (CGNN) algorithm. With the knowledge obtained from the causal graph, we further used hierarchical regression model to estimate BP, and validated the proposed method on 17 subjects. Results: We found that the obtained causal graph was almost consistent with the prior knowledge, and heart rate (HR) was one of the main confounding factors of PTT and BP. Incorporating HR into the hierarchical regression model to eliminate its confounding effect on the PTT-based BP estimation, the mean value of SBP and DBP estimation was improved by 1.27 and 1.89 mmHg, respectively, and the mean absolute difference (MAD) was improved by 2.28 and 3.60 mmHg, respectively. Conclusion: Causal inference-based method has the potential to clarify the causal relationship between BP and related wearable noninvasive features, which can further shed light on developing new methods for cuffless BP with acceptable accuracy.
目的：我们建议研究以脉搏转运时间（PTT）为代表的无创特征与血压（BP）之间的因果关系，以减轻混杂因素的影响，从而提高血压估测的性能。方法我们通过快速因果推理（FCI）算法确定了血压的因果图以及从心电图（ECG）和血压计（PPG）中提取的重要无创特征，因果图中的边的方向由因果生成神经网络（CGNN）算法确定。利用从因果图中获得的知识，我们进一步使用层次回归模型来估计血压，并在 17 名受试者身上验证了所提出的方法。结果：我们发现所获得的因果图与先前的知识基本一致，而心率（HR）是 PTT 和 BP 的主要混杂因素之一。将心率纳入分层回归模型以消除其对基于 PTT 的血压估计的混杂影响，SBP 和 DBP 估计的平均值分别提高了 1.27 和 1.89 mmHg，平均绝对差值（MAD）分别提高了 2.28 和 3.60 mmHg。结论基于因果推理的方法有可能阐明血压与相关可穿戴无创特征之间的因果关系，从而进一步阐明如何开发具有可接受准确度的无袖带血压测量新方法。
{"title":"An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference","authors":"Xinyue Song, Xiaorong Ding","doi":"10.20517/chatmed.2023.30","DOIUrl":"https://doi.org/10.20517/chatmed.2023.30","url":null,"abstract":"Aim: We propose to examine the causal relationship between the noninvasive features represented by pulse transit time (PTT) and blood pressure (BP), with the aim of mitigating the impact of confounding factor(s) and thus improving the performance of BP estimation.\u0000 Methods: We identified the causal graph of BP and the important noninvasive features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) via fast causal inference (FCI) algorithm, with the orientations of the edges in the causal graph being determined by the causal generative neural networks (CGNN) algorithm. With the knowledge obtained from the causal graph, we further used hierarchical regression model to estimate BP, and validated the proposed method on 17 subjects.\u0000 Results: We found that the obtained causal graph was almost consistent with the prior knowledge, and heart rate (HR) was one of the main confounding factors of PTT and BP. Incorporating HR into the hierarchical regression model to eliminate its confounding effect on the PTT-based BP estimation, the mean value of SBP and DBP estimation was improved by 1.27 and 1.89 mmHg, respectively, and the mean absolute difference (MAD) was improved by 2.28 and 3.60 mmHg, respectively.\u0000 Conclusion: Causal inference-based method has the potential to clarify the causal relationship between BP and related wearable noninvasive features, which can further shed light on developing new methods for cuffless BP with acceptable accuracy.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976366","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}
Pub Date : 2024-03-19DOI: 10.20517/chatmed.2023.23
Yu Huang, Yonghu He, Zhengbi Song, Kun Gao, Yali Zheng
Objective: This study aims to evaluate the effectiveness of deep learning techniques in estimating cuffless blood pressure (BP) across a diverse patient population in intensive care units (ICUs). Methods: A comprehensive ICU benchmarking dataset encompassing 2,154 patients with a wide demographic range (18-97 years old) and varied cardiovascular status was employed to validate several deep learning models in predicting continuous BP waveforms. Three methods were developed to enhance the model's generalizability to this heterogeneous dataset. Ten-fold subject-independent cross-validation was performed and the model performance was assessed through mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and compared with significance analysis. Results: The UTransBPNet_Demo_In model, which incorporated demographic and physiological signals as inputs, achieved a PCC of 0.89 and a MAE of 10.38 mmHg in predicting arterial BP waveforms, demonstrating the highest tracking capability among all models. Notably, the performance of UTransBPNet_Demo_In remained robust across variations in demographic and cardiovascular status. Conclusion: The UTransBPNet_Demo_In model demonstrates robust predictive capabilities across a broad spectrum of demographics and cardiovascular conditions. Although the performance still needs further improvement, this study offers a benchmark in the field of cuffless BP monitoring in critical care settings for future studies.
{"title":"Validation of deep learning models for cuffless blood pressure estimation on a large benchmarking dataset","authors":"Yu Huang, Yonghu He, Zhengbi Song, Kun Gao, Yali Zheng","doi":"10.20517/chatmed.2023.23","DOIUrl":"https://doi.org/10.20517/chatmed.2023.23","url":null,"abstract":"Objective: This study aims to evaluate the effectiveness of deep learning techniques in estimating cuffless blood pressure (BP) across a diverse patient population in intensive care units (ICUs).\u0000 Methods: A comprehensive ICU benchmarking dataset encompassing 2,154 patients with a wide demographic range (18-97 years old) and varied cardiovascular status was employed to validate several deep learning models in predicting continuous BP waveforms. Three methods were developed to enhance the model's generalizability to this heterogeneous dataset. Ten-fold subject-independent cross-validation was performed and the model performance was assessed through mean absolute error (MAE), Pearson’s correlation coefficient (PCC), and compared with significance analysis.\u0000 Results: The UTransBPNet_Demo_In model, which incorporated demographic and physiological signals as inputs, achieved a PCC of 0.89 and a MAE of 10.38 mmHg in predicting arterial BP waveforms, demonstrating the highest tracking capability among all models. Notably, the performance of UTransBPNet_Demo_In remained robust across variations in demographic and cardiovascular status.\u0000 Conclusion: The UTransBPNet_Demo_In model demonstrates robust predictive capabilities across a broad spectrum of demographics and cardiovascular conditions. Although the performance still needs further improvement, this study offers a benchmark in the field of cuffless BP monitoring in critical care settings for future studies.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"67 s96","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229744","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}
Pub Date : 2024-02-19DOI: 10.20517/chatmed.2023.11
Zi-Jun Liu, Ting Xiang, Ru-Shuang Zhou, Nan Ji, Yuan-ting Zhang
Aim: Photoplethysmography (PPG) has gained widespread popularity as a non-invasive method for potential cuff-less blood pressure (BP) measurement in smart devices. However, the accuracy of PPG-based devices is often hindered by motion artifacts, site variability, and inconsistent contact force (CF). This study aims to investigate the influence of CF variations on PPG signals. Methods: To address these challenges, we present a novel approach involving a multi-channel PPG array integrated with CF regulation in the form of a wearable wristband. This platform enables the visualization of regional PPG/BP distribution while simultaneously monitoring CF. Moreover, our research explored the relationship between PPG waveform characteristics and CF during wrist extension. Results: The results of this study reveal that the PPG amplitude (PPGA) and the b/a ratios, computed from the second derivative peaks of the PPG AC pulse wave, exhibit inconsistency in reaction to CF variations. Notably, a shape correlation coefficient of 0.65069 between normalized PPG and flipped CF sheds light on how changes in posture affect PPG measurements. Conclusions: The proposed platform shows promise in mitigating the effects of CF and spatial positioning on PPG, thereby improving measurement precision and offering a novel approach to image tonoarteriographic (TAG) activities for continuous hypertension management.
{"title":"A multi-channel photoplethysmography array with contact-force regulation for tonoarteriographic imaging","authors":"Zi-Jun Liu, Ting Xiang, Ru-Shuang Zhou, Nan Ji, Yuan-ting Zhang","doi":"10.20517/chatmed.2023.11","DOIUrl":"https://doi.org/10.20517/chatmed.2023.11","url":null,"abstract":"Aim: Photoplethysmography (PPG) has gained widespread popularity as a non-invasive method for potential cuff-less blood pressure (BP) measurement in smart devices. However, the accuracy of PPG-based devices is often hindered by motion artifacts, site variability, and inconsistent contact force (CF). This study aims to investigate the influence of CF variations on PPG signals.\u0000 Methods: To address these challenges, we present a novel approach involving a multi-channel PPG array integrated with CF regulation in the form of a wearable wristband. This platform enables the visualization of regional PPG/BP distribution while simultaneously monitoring CF. Moreover, our research explored the relationship between PPG waveform characteristics and CF during wrist extension.\u0000 Results: The results of this study reveal that the PPG amplitude (PPGA) and the b/a ratios, computed from the second derivative peaks of the PPG AC pulse wave, exhibit inconsistency in reaction to CF variations. Notably, a shape correlation coefficient of 0.65069 between normalized PPG and flipped CF sheds light on how changes in posture affect PPG measurements.\u0000 Conclusions: The proposed platform shows promise in mitigating the effects of CF and spatial positioning on PPG, thereby improving measurement precision and offering a novel approach to image tonoarteriographic (TAG) activities for continuous hypertension management.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"170 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451520","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}
Pub Date : 2023-12-29DOI: 10.20517/chatmed.2023.09
Rajshree Thapa, W. Takele, Amanda Thrift, Aysegul Zengin
Hypertension is a major public health problem, accounting for 7.5 million deaths and 57 million disability-adjusted life years annually worldwide. The majority of hypertension-related deaths occur in low- and middle-income countries (LMICs). Despite the escalating prevalence of hypertension in many LMICs, only one-third of those affected are aware of their hypertension status. The rapid proliferation of eHealth technologies presents an opportunity to improve the detection and management of hypertension. Many LMICs face a critical shortage of physicians, and their services often come at a considerable cost to the health system. Non-physician health workers could be a cost-effective alternative to improve the detection and management of hypertension, particularly in LMICs. In this systematic review, we aim to synthesize and evaluate the effectiveness of interventions that integrated eHealth technologies with non-physician health workers to reduce blood pressure. A diverse range of eHealth technologies, such as mobile-based applications, telemonitoring, short text messaging and electronic decision support systems, are being used by non-physician health workers for the management of hypertension. We found that eHealth technologies integrated with non-physician health workers reduced overall mean systolic blood pressure by -4.09 mmHg (95%CI: -5.87 to -2.32) compared to usual care. Similarly, such an integrated approach also reduced diastolic blood pressure by -1.25 mmHg (-2.31 to -0.81) in the intervention group than usual care. Therefore, leveraging the use of cost-effective eHealth technologies to support task-sharing with non-physicians presents an effective strategy for enhancing blood pressure management, applicable to both high- and low-income countries.
{"title":"Interventions of eHealth technologies integrated with non-physician health workers for improving management of hypertension: Systematic review and meta-analysis","authors":"Rajshree Thapa, W. Takele, Amanda Thrift, Aysegul Zengin","doi":"10.20517/chatmed.2023.09","DOIUrl":"https://doi.org/10.20517/chatmed.2023.09","url":null,"abstract":"Hypertension is a major public health problem, accounting for 7.5 million deaths and 57 million disability-adjusted life years annually worldwide. The majority of hypertension-related deaths occur in low- and middle-income countries (LMICs). Despite the escalating prevalence of hypertension in many LMICs, only one-third of those affected are aware of their hypertension status. The rapid proliferation of eHealth technologies presents an opportunity to improve the detection and management of hypertension. Many LMICs face a critical shortage of physicians, and their services often come at a considerable cost to the health system. Non-physician health workers could be a cost-effective alternative to improve the detection and management of hypertension, particularly in LMICs. In this systematic review, we aim to synthesize and evaluate the effectiveness of interventions that integrated eHealth technologies with non-physician health workers to reduce blood pressure. A diverse range of eHealth technologies, such as mobile-based applications, telemonitoring, short text messaging and electronic decision support systems, are being used by non-physician health workers for the management of hypertension. We found that eHealth technologies integrated with non-physician health workers reduced overall mean systolic blood pressure by -4.09 mmHg (95%CI: -5.87 to -2.32) compared to usual care. Similarly, such an integrated approach also reduced diastolic blood pressure by -1.25 mmHg (-2.31 to -0.81) in the intervention group than usual care. Therefore, leveraging the use of cost-effective eHealth technologies to support task-sharing with non-physicians presents an effective strategy for enhancing blood pressure management, applicable to both high- and low-income countries.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144253","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}
Pub Date : 2023-12-25DOI: 10.20517/chatmed.2023.08
K. Kario, M. Nishizawa, Nobuhiko Yasui, Takahiro Fujiwara, Takahiro Kunigita, N. Harada, S. Hoshide
Aim: The DICAP feasibility study aims to determine chronological blood pressure (BP) control status and BP variability up to the end of life in different life settings in the community and their clinical implications. Methods: A simple, easy-to-use automated hybrid BP telemonitoring system combined cellular and Bluetooth BP monitors, the DICAP (DIgital Cardiovascular Prevention) system, was devised to obtain all the different BP values measured in a time series in different settings in 500 community-dwelling individuals in their homes and local elderly care facilities. Expected results and Perspectives: This study will confirm the feasibility of collecting BP variability over time until the end of life for the management of hypertension in all community-dwelling patients, including those unfamiliar with digital technology and those in diverse residential settings, such as elderly care facilities. This feasibility study has the potential to serve as a basis for future community and disaster medicine initiatives worldwide.
{"title":"A protocol for digital cardiovascular prevention feasibility study using hybrid home blood pressure telemonitoring system","authors":"K. Kario, M. Nishizawa, Nobuhiko Yasui, Takahiro Fujiwara, Takahiro Kunigita, N. Harada, S. Hoshide","doi":"10.20517/chatmed.2023.08","DOIUrl":"https://doi.org/10.20517/chatmed.2023.08","url":null,"abstract":"Aim: The DICAP feasibility study aims to determine chronological blood pressure (BP) control status and BP variability up to the end of life in different life settings in the community and their clinical implications. Methods: A simple, easy-to-use automated hybrid BP telemonitoring system combined cellular and Bluetooth BP monitors, the DICAP (DIgital Cardiovascular Prevention) system, was devised to obtain all the different BP values measured in a time series in different settings in 500 community-dwelling individuals in their homes and local elderly care facilities. Expected results and Perspectives: This study will confirm the feasibility of collecting BP variability over time until the end of life for the management of hypertension in all community-dwelling patients, including those unfamiliar with digital technology and those in diverse residential settings, such as elderly care facilities. This feasibility study has the potential to serve as a basis for future community and disaster medicine initiatives worldwide.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"15 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139158785","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}
Pub Date : 2023-09-27DOI: 10.20517/chatmed.2023.04
Xinrui Wang, Bee Luan Khoo, Shih-Chi Chen
:
{"title":"Advancing cardiovascular disease prediction: portable or wearable devices for automatic and rapid blood sample collection for biomarker detection with simultaneous physiological marker measurement","authors":"Xinrui Wang, Bee Luan Khoo, Shih-Chi Chen","doi":"10.20517/chatmed.2023.04","DOIUrl":"https://doi.org/10.20517/chatmed.2023.04","url":null,"abstract":":","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579981","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}
Pub Date : 2023-09-13DOI: 10.20517/chatmed.2023.03
Zi-Jun Liu, Ting Xiang, Nan Ji, Yuan-Ting Zhang
The measurement and monitoring of continuous arterial blood pressure (BP) have undergone significant evolution over the past 170 years, transitioning from ancient invasive approaches, like kymograph, to modern non-invasive and unobtrusive technologies such as tonoarteriography (TAG). This progressive shift has revolutionized the way we track BP, providing safer, more accurate, and convenient methods for monitoring BP. This paper aims to provide some historical perspectives on the development of continuous BP technology, highlight key milestones that have shaped the field, discuss the state-of-the-art two-dimensional TAG imaging, and address challenges for future unobtrusive BP measurements. In addition to presenting a concise review of the progression of continuous BP measurement technologies, this article also emphasizes the importance of adopting more precise, convenient and affordable approaches for personalized BP monitoring at home and patient care optimizations at hospitals, thereby empowering healthcare professionals to enhance pervasive hypertension management anywhere.
{"title":"Some perspectives of continuous arterial blood pressure measurements: from kymograph to tonoarteriographic imaging","authors":"Zi-Jun Liu, Ting Xiang, Nan Ji, Yuan-Ting Zhang","doi":"10.20517/chatmed.2023.03","DOIUrl":"https://doi.org/10.20517/chatmed.2023.03","url":null,"abstract":"The measurement and monitoring of continuous arterial blood pressure (BP) have undergone significant evolution over the past 170 years, transitioning from ancient invasive approaches, like kymograph, to modern non-invasive and unobtrusive technologies such as tonoarteriography (TAG). This progressive shift has revolutionized the way we track BP, providing safer, more accurate, and convenient methods for monitoring BP. This paper aims to provide some historical perspectives on the development of continuous BP technology, highlight key milestones that have shaped the field, discuss the state-of-the-art two-dimensional TAG imaging, and address challenges for future unobtrusive BP measurements. In addition to presenting a concise review of the progression of continuous BP measurement technologies, this article also emphasizes the importance of adopting more precise, convenient and affordable approaches for personalized BP monitoring at home and patient care optimizations at hospitals, thereby empowering healthcare professionals to enhance pervasive hypertension management anywhere.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135742186","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}
Pub Date : 2023-01-01DOI: 10.20517/chatmed.2022.025
Theodore Curran, Daniel McDuff, Xin Liu, Girish Narayanswamy, Chengqian Ma, Shwetak Patel, Eugene Yang
Telehealth has seen rapid adoption in the past three years as a direct result of the COVID-19 pandemic. Conventional methods for the measurement of vital signs are neither optimized for remote care nor highly scalable. Blood pressure is a critical vital parameter that currently cannot be measured remotely. Cameras are versatile and capable sensors that can be repurposed to measure vital signs. In this article, we review the use of cameras for remote photoplethysmography and assessment of blood pressure. We discuss the principles behind this technology and the current evidence for blood pressure measurement. We also explore future applications and potential challenges to provide a roadmap for researchers, clinicians, and regulators considering this new technology.
{"title":"Camera-based remote photoplethysmography for blood pressure measurement: current evidence, clinical perspectives, and future applications","authors":"Theodore Curran, Daniel McDuff, Xin Liu, Girish Narayanswamy, Chengqian Ma, Shwetak Patel, Eugene Yang","doi":"10.20517/chatmed.2022.025","DOIUrl":"https://doi.org/10.20517/chatmed.2022.025","url":null,"abstract":"Telehealth has seen rapid adoption in the past three years as a direct result of the COVID-19 pandemic. Conventional methods for the measurement of vital signs are neither optimized for remote care nor highly scalable. Blood pressure is a critical vital parameter that currently cannot be measured remotely. Cameras are versatile and capable sensors that can be repurposed to measure vital signs. In this article, we review the use of cameras for remote photoplethysmography and assessment of blood pressure. We discuss the principles behind this technology and the current evidence for blood pressure measurement. We also explore future applications and potential challenges to provide a roadmap for researchers, clinicians, and regulators considering this new technology.","PeriodicalId":72693,"journal":{"name":"Connected health and telemedicine","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135686411","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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