{"title":"在高强度间歇训练期间,对基于光电血压计的脉率、搏动间隔和血氧饱和度监测进行评估。","authors":"Tara Vijgeboom, Marjolein Muller, Kambiz Ebrahimkheil, Casper van Eijck, Eelko Ronner","doi":"10.1186/s12938-024-01309-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Heart disease patients necessitate precise monitoring to ensure the safety and efficacy of their physical activities when managing conditions such as hypertension or heart failure. This study, therefore, aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of pulse rate (PR), interbeat-intervals (IB-I) and oxygen saturation (SpO2) during high-intensity interval training (HIIT).</p><p><strong>Methods: </strong>Between January and March 2024, healthy volunteers were subjected to a cycling HIIT workout with bike resistance increments to evaluate performance within different heart rate ranges. To determine the accuracy of PPG-based measurements for PR, IB-I, and SpO2 using the CardioWatch 287-2 (Corsano Health, the Netherlands), measurements throughout these ranges were compared to paired reference values from the Covidien Nellcor pulse oximeter (PM10N) and Vivalink's wearable ECG patch monitor. Subgroups were defined for Fitzpatrick skin type and gender.</p><p><strong>Results: </strong>In total, 35 healthy individuals participated, resulting in 7183 paired measurements for PR, 22,713 for IB-I, and 41,817 for SpO2. The PR algorithm showed an average root mean square (Arms) of 2.51 beats per minute (bpm), bias at 0.05 bpm, and limits of agreement (LoA) from -4.87 to 4.97 bpm. The IB-I algorithm achieved an Arms of 23.00 ms, a bias of 1.00 ms, and LoA from -43.82 to 46.21 ms. Finally, the SpO2 algorithm showed an Arms of 1.28%, a bias of 0.13%, and LoA from -2.37% to 2.62%. The results were consistent across different demographic subgroups.</p><p><strong>Conclusions: </strong>This study demonstrates that the PPG-based CardioWatch 287-2 can accurately monitor PR, IB-I, and SpO2 during HIIT. However, further research is recommended to evaluate the algorithm's performance in heart disease patients during demanding exercise.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"23 1","pages":"114"},"PeriodicalIF":2.9000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552347/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluation of photoplethysmography-based monitoring of pulse rate, interbeat-intervals, and oxygen saturation during high-intensity interval training.\",\"authors\":\"Tara Vijgeboom, Marjolein Muller, Kambiz Ebrahimkheil, Casper van Eijck, Eelko Ronner\",\"doi\":\"10.1186/s12938-024-01309-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Heart disease patients necessitate precise monitoring to ensure the safety and efficacy of their physical activities when managing conditions such as hypertension or heart failure. This study, therefore, aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of pulse rate (PR), interbeat-intervals (IB-I) and oxygen saturation (SpO2) during high-intensity interval training (HIIT).</p><p><strong>Methods: </strong>Between January and March 2024, healthy volunteers were subjected to a cycling HIIT workout with bike resistance increments to evaluate performance within different heart rate ranges. To determine the accuracy of PPG-based measurements for PR, IB-I, and SpO2 using the CardioWatch 287-2 (Corsano Health, the Netherlands), measurements throughout these ranges were compared to paired reference values from the Covidien Nellcor pulse oximeter (PM10N) and Vivalink's wearable ECG patch monitor. Subgroups were defined for Fitzpatrick skin type and gender.</p><p><strong>Results: </strong>In total, 35 healthy individuals participated, resulting in 7183 paired measurements for PR, 22,713 for IB-I, and 41,817 for SpO2. The PR algorithm showed an average root mean square (Arms) of 2.51 beats per minute (bpm), bias at 0.05 bpm, and limits of agreement (LoA) from -4.87 to 4.97 bpm. The IB-I algorithm achieved an Arms of 23.00 ms, a bias of 1.00 ms, and LoA from -43.82 to 46.21 ms. Finally, the SpO2 algorithm showed an Arms of 1.28%, a bias of 0.13%, and LoA from -2.37% to 2.62%. The results were consistent across different demographic subgroups.</p><p><strong>Conclusions: </strong>This study demonstrates that the PPG-based CardioWatch 287-2 can accurately monitor PR, IB-I, and SpO2 during HIIT. However, further research is recommended to evaluate the algorithm's performance in heart disease patients during demanding exercise.</p>\",\"PeriodicalId\":8927,\"journal\":{\"name\":\"BioMedical Engineering OnLine\",\"volume\":\"23 1\",\"pages\":\"114\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11552347/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BioMedical Engineering OnLine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1186/s12938-024-01309-w\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedical Engineering OnLine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12938-024-01309-w","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Evaluation of photoplethysmography-based monitoring of pulse rate, interbeat-intervals, and oxygen saturation during high-intensity interval training.
Background: Heart disease patients necessitate precise monitoring to ensure the safety and efficacy of their physical activities when managing conditions such as hypertension or heart failure. This study, therefore, aimed to evaluate the accuracy of photoplethysmography (PPG)-based monitoring of pulse rate (PR), interbeat-intervals (IB-I) and oxygen saturation (SpO2) during high-intensity interval training (HIIT).
Methods: Between January and March 2024, healthy volunteers were subjected to a cycling HIIT workout with bike resistance increments to evaluate performance within different heart rate ranges. To determine the accuracy of PPG-based measurements for PR, IB-I, and SpO2 using the CardioWatch 287-2 (Corsano Health, the Netherlands), measurements throughout these ranges were compared to paired reference values from the Covidien Nellcor pulse oximeter (PM10N) and Vivalink's wearable ECG patch monitor. Subgroups were defined for Fitzpatrick skin type and gender.
Results: In total, 35 healthy individuals participated, resulting in 7183 paired measurements for PR, 22,713 for IB-I, and 41,817 for SpO2. The PR algorithm showed an average root mean square (Arms) of 2.51 beats per minute (bpm), bias at 0.05 bpm, and limits of agreement (LoA) from -4.87 to 4.97 bpm. The IB-I algorithm achieved an Arms of 23.00 ms, a bias of 1.00 ms, and LoA from -43.82 to 46.21 ms. Finally, the SpO2 algorithm showed an Arms of 1.28%, a bias of 0.13%, and LoA from -2.37% to 2.62%. The results were consistent across different demographic subgroups.
Conclusions: This study demonstrates that the PPG-based CardioWatch 287-2 can accurately monitor PR, IB-I, and SpO2 during HIIT. However, further research is recommended to evaluate the algorithm's performance in heart disease patients during demanding exercise.
期刊介绍:
BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering.
BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to:
Bioinformatics-
Bioinstrumentation-
Biomechanics-
Biomedical Devices & Instrumentation-
Biomedical Signal Processing-
Healthcare Information Systems-
Human Dynamics-
Neural Engineering-
Rehabilitation Engineering-
Biomaterials-
Biomedical Imaging & Image Processing-
BioMEMS and On-Chip Devices-
Bio-Micro/Nano Technologies-
Biomolecular Engineering-
Biosensors-
Cardiovascular Systems Engineering-
Cellular Engineering-
Clinical Engineering-
Computational Biology-
Drug Delivery Technologies-
Modeling Methodologies-
Nanomaterials and Nanotechnology in Biomedicine-
Respiratory Systems Engineering-
Robotics in Medicine-
Systems and Synthetic Biology-
Systems Biology-
Telemedicine/Smartphone Applications in Medicine-
Therapeutic Systems, Devices and Technologies-
Tissue Engineering