Caucasian and south Asian men show equivalent improvements in surrogate biomarkers of cardiovascular and metabolic health following 6-weeks of supervised resistance training
- Knox, Allan, Sculthorpe, Nicholas, Grace, Fergal
- Authors: Knox, Allan , Sculthorpe, Nicholas , Grace, Fergal
- Date: 2018
- Type: Text , Journal article
- Relation: F1000Research Vol. 7, no. (2018), p. 1-16
- Full Text:
- Reviewed:
- Description: Background: The South Asian population have greater cardiovascular risk than their age-matched Caucasian counterparts, characterized by unfavorable biomarkers. South Asians may also be partially resistant to the pleiotropic benefits of physical activity on cardiovascular health. There is a current absence of studies that compare markers of cardio-metabolic health between Caucasians and South Asians employing resistance exercise. This study set out to compare the response in biomarkers of cardio-metabolic health in Caucasians and South Asians in response to resistance exercise. Methods: Caucasian (n=15, 25.5 ± 4.8 yrs) and South Asian (n=13, 25.4 ± 7.0 yrs) males completed a 6-week progressive resistance exercise protocol. Fasting blood glucose, insulin, and their product insulin resistance (HOMA-IR), triglycerides (TRIGS), low density lipoprotein (LDL), high density lipoprotein (HDL), total cholesterol (TC), vascular endothelial growth factor (VEGF), asymmetric dimythylarginine (ADMA), L-arginine (L-ARG) and C-reactive protein (CRP) were established at baseline and following resistance exercise. Results: There were significant improvements in fasting glucose, TC, LDL, HDL and VEGF in both groups following resistance exercise ( p<0.05, for all). No change was observed in insulin, HOMA-IR, TRIGS, ADMA, L-ARG following resistance exercise ( p>0.05, in both groups). CRP increased in the South Asian group ( p<0.05) but not the Caucasian group ( p>0.05) Conclusions: The cardio-metabolic response to resistance exercise is comparable in young Caucasian and South Asian males though inflammatory response to exercise may be prolonged in South Asians.
- Authors: Knox, Allan , Sculthorpe, Nicholas , Grace, Fergal
- Date: 2018
- Type: Text , Journal article
- Relation: F1000Research Vol. 7, no. (2018), p. 1-16
- Full Text:
- Reviewed:
- Description: Background: The South Asian population have greater cardiovascular risk than their age-matched Caucasian counterparts, characterized by unfavorable biomarkers. South Asians may also be partially resistant to the pleiotropic benefits of physical activity on cardiovascular health. There is a current absence of studies that compare markers of cardio-metabolic health between Caucasians and South Asians employing resistance exercise. This study set out to compare the response in biomarkers of cardio-metabolic health in Caucasians and South Asians in response to resistance exercise. Methods: Caucasian (n=15, 25.5 ± 4.8 yrs) and South Asian (n=13, 25.4 ± 7.0 yrs) males completed a 6-week progressive resistance exercise protocol. Fasting blood glucose, insulin, and their product insulin resistance (HOMA-IR), triglycerides (TRIGS), low density lipoprotein (LDL), high density lipoprotein (HDL), total cholesterol (TC), vascular endothelial growth factor (VEGF), asymmetric dimythylarginine (ADMA), L-arginine (L-ARG) and C-reactive protein (CRP) were established at baseline and following resistance exercise. Results: There were significant improvements in fasting glucose, TC, LDL, HDL and VEGF in both groups following resistance exercise ( p<0.05, for all). No change was observed in insulin, HOMA-IR, TRIGS, ADMA, L-ARG following resistance exercise ( p>0.05, in both groups). CRP increased in the South Asian group ( p<0.05) but not the Caucasian group ( p>0.05) Conclusions: The cardio-metabolic response to resistance exercise is comparable in young Caucasian and South Asian males though inflammatory response to exercise may be prolonged in South Asians.
A fuzzy derivative approach to classification of outcomes from the ADRAC database
- Mammadov, Musa, Saunders, Gary, Yearwood, John
- Authors: Mammadov, Musa , Saunders, Gary , Yearwood, John
- Date: 2004
- Type: Text , Journal article
- Relation: International Transactions in Operational Research Vol. 11, no. 2 (2004), p. 169-180
- Full Text: false
- Reviewed:
- Description: The Australian Adverse Drug Reaction Advisory Committee (ADRAC) database has been collected and maintained by the Therapeutic Goods Administration. In this paper we study a part of his database (Card2) which contains records having just reactions from the Cardiovascular group. Drug-reaction relationships are presented by a vector of degrees which shows the degree of association of a drug with each class of reactions. In this work we examine these relationships in the classification of reaction outcomes. A modified version of the fuzzy derivative method (FDM2) is used for classification.
- Description: C1
- Description: 2003000895
The lifestyle of our kids (LOOK) project : Outline of methods
- Telford, Richard, Bass, Shona, Budge, Marc, Byrne, Donald, Carlson, John, Coles, David, Cunningham, Ross, Daly, Robin, Dunstan, David, English, Rowena, Fitzgerald, Robert, Eser, Prisca, Gravenmaker, Karen, Haynes, Wayne, Hickman, Peter, Javaid, Ahmad, Jiang, Xiaoli, Lafferty, Tony, McGrath, Mark, Martin, Mary Kay, Naughton, Geraldine, Potter, Julia, Potter, Stacey, Prosser, Laurie, Pyne, David, Reynolds, Graham, Saunders, Philo, Seibel, Markus, Shaw, Jonathan, Southcott, Emma, Srikusalanukul, Wichat, Stuckey, Darryl, Telford, Rohan, Thomas, Kerry, Tallis, Ken, Waring, Paul
- Authors: Telford, Richard , Bass, Shona , Budge, Marc , Byrne, Donald , Carlson, John , Coles, David , Cunningham, Ross , Daly, Robin , Dunstan, David , English, Rowena , Fitzgerald, Robert , Eser, Prisca , Gravenmaker, Karen , Haynes, Wayne , Hickman, Peter , Javaid, Ahmad , Jiang, Xiaoli , Lafferty, Tony , McGrath, Mark , Martin, Mary Kay , Naughton, Geraldine , Potter, Julia , Potter, Stacey , Prosser, Laurie , Pyne, David , Reynolds, Graham , Saunders, Philo , Seibel, Markus , Shaw, Jonathan , Southcott, Emma , Srikusalanukul, Wichat , Stuckey, Darryl , Telford, Rohan , Thomas, Kerry , Tallis, Ken , Waring, Paul
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 12, no. 1 (2009), p. 156-163
- Full Text:
- Description: This methods paper outlines the overall design of a community-based multidisciplinary longitudinal study with the intent to stimulate interest and communication from scientists and practitioners studying the role of physical activity in preventive medicine. In adults, lack of regular exercise is a major risk factor in the development of chronic degenerative diseases and is a major contributor to obesity, and now we have evidence that many of our children are not sufficiently active to prevent early symptoms of chronic disease. The lifestyle of our kids (LOOK) study investigates how early physical activity contributes to health and development, utilizing a longitudinal design and a cohort of eight hundred and thirty 7-8-year-old (grade 2) school children followed to age 11-12 years (grade 6), their average family income being very close to that of Australia. We will test two hypotheses, that (a) the quantity and quality of physical activity undertaken by primary school children will influence their psychological and physical health and development; (b) compared with existing practices in primary schools, a physical education program administered by visiting specialists will enhance health and development, and lead to a more positive perception of physical activity. To test the first hypothesis we will monitor all children longitudinally over the 4 years. To test the second we will involve an intervention group of 430 children who receive two 50 min physical education classes every week from visiting specialists and a control group of 400 who continue with their usual primary school physical education with their class-room teachers. At the end of grades 2, 4, and 6 we will measure several areas of health and development including blood risk factors for chronic disease, cardiovascular structure and function, physical fitness, psychological characteristics and perceptions of physical activity, bone structure and strength, motor control, body composition, nutritional intake, influence of teachers and family, and academic performance. © 2007 Sports Medicine Australia.
- Authors: Telford, Richard , Bass, Shona , Budge, Marc , Byrne, Donald , Carlson, John , Coles, David , Cunningham, Ross , Daly, Robin , Dunstan, David , English, Rowena , Fitzgerald, Robert , Eser, Prisca , Gravenmaker, Karen , Haynes, Wayne , Hickman, Peter , Javaid, Ahmad , Jiang, Xiaoli , Lafferty, Tony , McGrath, Mark , Martin, Mary Kay , Naughton, Geraldine , Potter, Julia , Potter, Stacey , Prosser, Laurie , Pyne, David , Reynolds, Graham , Saunders, Philo , Seibel, Markus , Shaw, Jonathan , Southcott, Emma , Srikusalanukul, Wichat , Stuckey, Darryl , Telford, Rohan , Thomas, Kerry , Tallis, Ken , Waring, Paul
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 12, no. 1 (2009), p. 156-163
- Full Text:
- Description: This methods paper outlines the overall design of a community-based multidisciplinary longitudinal study with the intent to stimulate interest and communication from scientists and practitioners studying the role of physical activity in preventive medicine. In adults, lack of regular exercise is a major risk factor in the development of chronic degenerative diseases and is a major contributor to obesity, and now we have evidence that many of our children are not sufficiently active to prevent early symptoms of chronic disease. The lifestyle of our kids (LOOK) study investigates how early physical activity contributes to health and development, utilizing a longitudinal design and a cohort of eight hundred and thirty 7-8-year-old (grade 2) school children followed to age 11-12 years (grade 6), their average family income being very close to that of Australia. We will test two hypotheses, that (a) the quantity and quality of physical activity undertaken by primary school children will influence their psychological and physical health and development; (b) compared with existing practices in primary schools, a physical education program administered by visiting specialists will enhance health and development, and lead to a more positive perception of physical activity. To test the first hypothesis we will monitor all children longitudinally over the 4 years. To test the second we will involve an intervention group of 430 children who receive two 50 min physical education classes every week from visiting specialists and a control group of 400 who continue with their usual primary school physical education with their class-room teachers. At the end of grades 2, 4, and 6 we will measure several areas of health and development including blood risk factors for chronic disease, cardiovascular structure and function, physical fitness, psychological characteristics and perceptions of physical activity, bone structure and strength, motor control, body composition, nutritional intake, influence of teachers and family, and academic performance. © 2007 Sports Medicine Australia.
Cardiovascular data analytics for real time patient monitoring
- Authors: Allami, Ragheed
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: Improvements in wearable sensor devices make it possible to constantly monitor physiological parameters such as electrocardiograph (ECG) signals for long periods. Remote patient monitoring with wearable sensors has an important role to play in health care, particularly given the prevalence of chronic conditions such as cardiovascular disease (CVD)—one of the prominent causes of morbidity and mortality worldwide. Approximately 4.2 million Australians suffer from long-term CVD with approximately one death every 12 minutes. The assessment of ECG features, especially heart rate variability (HRV), represents a non-invasive technique which provides an indication of the autonomic nervous system (ANS) function. Conditions such as sudden cardiac death, hypertension, heart failure, myocardial infarction, ischaemia, and coronary heart disease can be detected from HRV analysis. In addition, the analysis of ECG features can also be used to diagnose many types of life-threatening arrhythmias, including ventricular fibrillation and ventricular tachycardia. Non-cardiac conditions, such as diabetes, obesity, metabolic syndrome, insulin resistance, irritable bowel syndrome, dyspepsia, anorexia nervosa, anxiety, and major depressive disorder have also been shown to be associated with HRV. The analysis of ECG features from real time ECG signals generated from wearable sensors provides distinctive challenges. The sensors that receive and process the signals have limited power, storage and processing capacity. Consequently, algorithms that process ECG signals need to be lightweight, use minimal storage resources and accurately detect abnormalities so that alarms can be raised. The existing literature details only a few algorithms which operate within the constraints of wearable sensor networks. This research presents four novel techniques that enable ECG signals to be processed within the limitations of resource constraints on devices to detect some key abnormalities in heart function. - The first technique is a novel real-time ECG data reduction algorithm, which detects and transmits only those key points that are critical for the generation of ECG features for diagnoses. - The second technique accurately predicts the five-minute HRV measure using only three minutes of data with an algorithm that executes in real-time using minimal computational resources. - The third technique introduces a real-time ECG feature recognition system that can be applied to diagnose life threatening conditions such as premature ventricular contractions (PVCs). - The fourth technique advances a classification algorithm to enhance the performance of automated ECG classification to determine arrhythmic heart beats based on noisy ECG signals. The four novel techniques are evaluated in comparison with benchmark algorithms for each task on the standard MIT-BIH Arrhythmia Database and with data generated from patients in a major hospital using Shimmer3 wearable ECG sensors. The four techniques are integrated to demonstrate that remote patient monitoring of ECG using HRV and ECG features is feasible in real time using minimal computational resources. The evaluation show that the ECG reduction algorithm is significantly better than existing algorithms that can be applied within sensor nodes, such as time-domain methods, transformation methods and compressed sensing methods. Furthermore, the proposed ECG reduction is found to be computationally less complex for resource constrained sensors and achieves higher compression ratios than existing algorithms. The prediction of a common HRV measure, the five-minute standard deviation of inter-beat variations (SDNN) and the accurate detection of PVC beats was achieved using a Count Data Model, combined with a Poisson-generated function from three-minute ECG recordings. This was achieved with minimal computational resources and was well suited to remote patient monitoring with wearable sensors. The PVC beats detection was implemented using the same count data model together with knowledge-based rules derived from clinical knowledge. A real-time cardiac patient monitoring system was implemented using an ECG sensor and smartphone to detect PVC beats within a few seconds using artificial neural networks (ANN), and it was proven to provide highly accurate results. The automated detection and classification were implemented using a new wrapper-based hybrid approach that utilized t-distributed stochastic neighbour embedding (t-SNE) in combination with self-organizing maps (SOM) to improve classification performance. The t-SNE-SOM hybrid resulted in improved sensitivity, specificity and accuracy compared to most common hybrid methods in the presence of noise. It also provided a better, more accurate identification for the presence of many types of arrhythmias from the ECG recordings, leading to a more timely diagnosis and treatment outcome.
- Description: Doctor of Philosophy
- Authors: Allami, Ragheed
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: Improvements in wearable sensor devices make it possible to constantly monitor physiological parameters such as electrocardiograph (ECG) signals for long periods. Remote patient monitoring with wearable sensors has an important role to play in health care, particularly given the prevalence of chronic conditions such as cardiovascular disease (CVD)—one of the prominent causes of morbidity and mortality worldwide. Approximately 4.2 million Australians suffer from long-term CVD with approximately one death every 12 minutes. The assessment of ECG features, especially heart rate variability (HRV), represents a non-invasive technique which provides an indication of the autonomic nervous system (ANS) function. Conditions such as sudden cardiac death, hypertension, heart failure, myocardial infarction, ischaemia, and coronary heart disease can be detected from HRV analysis. In addition, the analysis of ECG features can also be used to diagnose many types of life-threatening arrhythmias, including ventricular fibrillation and ventricular tachycardia. Non-cardiac conditions, such as diabetes, obesity, metabolic syndrome, insulin resistance, irritable bowel syndrome, dyspepsia, anorexia nervosa, anxiety, and major depressive disorder have also been shown to be associated with HRV. The analysis of ECG features from real time ECG signals generated from wearable sensors provides distinctive challenges. The sensors that receive and process the signals have limited power, storage and processing capacity. Consequently, algorithms that process ECG signals need to be lightweight, use minimal storage resources and accurately detect abnormalities so that alarms can be raised. The existing literature details only a few algorithms which operate within the constraints of wearable sensor networks. This research presents four novel techniques that enable ECG signals to be processed within the limitations of resource constraints on devices to detect some key abnormalities in heart function. - The first technique is a novel real-time ECG data reduction algorithm, which detects and transmits only those key points that are critical for the generation of ECG features for diagnoses. - The second technique accurately predicts the five-minute HRV measure using only three minutes of data with an algorithm that executes in real-time using minimal computational resources. - The third technique introduces a real-time ECG feature recognition system that can be applied to diagnose life threatening conditions such as premature ventricular contractions (PVCs). - The fourth technique advances a classification algorithm to enhance the performance of automated ECG classification to determine arrhythmic heart beats based on noisy ECG signals. The four novel techniques are evaluated in comparison with benchmark algorithms for each task on the standard MIT-BIH Arrhythmia Database and with data generated from patients in a major hospital using Shimmer3 wearable ECG sensors. The four techniques are integrated to demonstrate that remote patient monitoring of ECG using HRV and ECG features is feasible in real time using minimal computational resources. The evaluation show that the ECG reduction algorithm is significantly better than existing algorithms that can be applied within sensor nodes, such as time-domain methods, transformation methods and compressed sensing methods. Furthermore, the proposed ECG reduction is found to be computationally less complex for resource constrained sensors and achieves higher compression ratios than existing algorithms. The prediction of a common HRV measure, the five-minute standard deviation of inter-beat variations (SDNN) and the accurate detection of PVC beats was achieved using a Count Data Model, combined with a Poisson-generated function from three-minute ECG recordings. This was achieved with minimal computational resources and was well suited to remote patient monitoring with wearable sensors. The PVC beats detection was implemented using the same count data model together with knowledge-based rules derived from clinical knowledge. A real-time cardiac patient monitoring system was implemented using an ECG sensor and smartphone to detect PVC beats within a few seconds using artificial neural networks (ANN), and it was proven to provide highly accurate results. The automated detection and classification were implemented using a new wrapper-based hybrid approach that utilized t-distributed stochastic neighbour embedding (t-SNE) in combination with self-organizing maps (SOM) to improve classification performance. The t-SNE-SOM hybrid resulted in improved sensitivity, specificity and accuracy compared to most common hybrid methods in the presence of noise. It also provided a better, more accurate identification for the presence of many types of arrhythmias from the ECG recordings, leading to a more timely diagnosis and treatment outcome.
- Description: Doctor of Philosophy
Analysis of cardiovascular adverse drug reactions from the ADRAC database
- Mammadov, Musa, Saunders, Gary
- Authors: Mammadov, Musa , Saunders, Gary
- Date: 2003
- Type: Text , Conference paper
- Relation: Paper presented at the APAC Conference and Exhibition on Advanced Computing, Grid Applications and eResearch, Gold Coast, Queensland : 29th September, 2003
- Full Text: false
- Reviewed:
- Description: E1
- Description: 2003000342
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