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Chorleywood, United Kingdom

Jones A.,University College London | McMillan M.R.,University College London | Jones R.W.,Chorleywood Health Center | Jones R.W.,Brunel University | And 6 more authors.
Stress | Year: 2013

In contrast to heavy alcohol consumption, which is harmful, light to moderate drinking has been linked to reduced cardiovascular morbidity and mortality. Effects on lipid status or clotting do not fully explain these benefits. Exaggerated cardiovascular responses to mental stress are detrimental to cardiovascular health. We hypothesized that habitual alcohol consumption might reduce these responses, with potential benefits. Advanced magnetic resonance techniques were used to accurately measure cardiovascular responses to an acute mental stressor (Montreal Imaging Stress Task) in 88 healthy adults (∼1:1 male:female). Salivary cortisol and task performance measures were used to assess endocrine and cognitive responses. Habitual alcohol consumption and confounding factors were assessed by questionnaire. Alcohol consumption was inversely related to responses of heart rate (HR) (r=-0.31, p=0.01), cardiac output (CO) (r=-0.32, p=0.01), vascular resistance (r=0.25, p=0.04) and mean blood pressure (r=-0.31, p=0.01) provoked by stress, but not to stroke volume (SV), or arterial compliance changes. However, high alcohol consumers had greater cortisol stress responses, compared to moderate consumers (3.5 versus 0.7nmol/L, p=0.04). Cognitive measures did not differ. Findings were not explained by variations in age, sex, social class, ethnicity, physical activity, adrenocortical activity, adiposity, smoking, menstrual phase and chronic stress. Habitual alcohol consumption is associated with reduced cardiac responsiveness during mental stress, which has been linked to lower risk of hypertension and vascular disease. Consistent with established evidence, our findings suggest a mechanism by which moderate alcohol consumption might reduce cardiovascular disease, but not high consumption, where effects such as greater cortisol stress responses may negate any benefits. © 2013 Informa UK Ltd.

Jones A.,University College London | McMillan M.R.,University College London | Jones R.W.,Chorleywood Health Center | Jones R.W.,Brunel University | And 6 more authors.
PLoS ONE | Year: 2012

Obesity and mental stress are potent risk factors for cardiovascular disease but their relationship with each other is unclear. Resilience to stress may differ according to adiposity. Early studies that addressed this are difficult to interpret due to conflicting findings and limited methods. Recent advances in assessment of cardiovascular stress responses and of fat distribution allow accurate assessment of associations between adiposity and stress responsiveness. We measured responses to the Montreal Imaging Stress Task in healthy men (N = 43) and women (N = 45) with a wide range of BMIs. Heart rate (HR) and blood pressure (BP) measures were used with novel magnetic resonance measures of stroke volume (SV), cardiac output (CO), total peripheral resistance (TPR) and arterial compliance to assess cardiovascular responses. Salivary cortisol and the number and speed of answers to mathematics problems in the task were used to assess neuroendocrine and cognitive responses, respectively. Visceral and subcutaneous fat was measured using T2*-IDEAL. Greater BMI was associated with generalised blunting of cardiovascular (HR:β = -0.50 bpm.unit-1, P = 0.009; SV:β = -0.33 mL.unit-1, P = 0.01; CO:β = -61 mL.min-1.unit-1, P = 0.002; systolic BP:β = -0.41 mmHg.unit-1, P = 0.01; TPR:β = 0.11 WU.unit-1, P = 0.02), cognitive (correct answers: r = -0.28, P = 0.01; time to answer: r = 0.26, P = 0.02) and endocrine responses (cortisol: r = -0.25, P = 0.04) to stress. These associations were largely determined by visceral adiposity except for those related to cognitive performance, which were determined by both visceral and subcutaneous adiposity. Our findings suggest that adiposity is associated with centrally reduced stress responsiveness. Although this may mitigate some long-term health risks of stress responsiveness, reduced performance under stress may be a more immediate negative consequence. © 2012 Jones et al.

Lagani V.,Foundation for Research and Technology Hellas | Chiarugi F.,Foundation for Research and Technology Hellas | Manousos D.,Foundation for Research and Technology Hellas | Verma V.,Brunel University | And 4 more authors.
Journal of Diabetes and its Complications | Year: 2015

Aim We present a computerized system for the assessment of the long-term risk of developing diabetes-related complications. Methods The core of the system consists of a set of predictive models, developed through a data-mining/machine-learning approach, which are able to evaluate individual patient profiles and provide personalized risk assessments. Missing data is a common issue in (electronic) patient records, thus the models are paired with a module for the intelligent management of missing information. Results The system has been deployed and made publicly available as Web service, and it has been fully integrated within the diabetes-management platform developed by the European project REACTION. Preliminary usability tests showed that the clinicians judged the models useful for risk assessment and for communicating the risk to the patient. Furthermore, the system performs as well as the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine when both systems are tested on an independent cohort of UK diabetes patients. Conclusions Our work provides a working example of risk-stratification tool that is (a) specific for diabetes patients, (b) able to handle several different diabetes related complications, (c) performing as well as the widely known UKPDS Risk Engine on an external validation cohort. © 2015 Elsevier Inc.

Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2009.5.1 | Award Amount: 16.32M | Year: 2010

The REACTION project will develop an integrated approach to improved long term management of diabetes; continuous blood glucose monitoring, clinical monitoring and intervention strategies, monitoring and predicting related disease indicators, complemented by education on life style factors such as obesity and exercise and, ultimately, automated closed-loop delivery of insulin.\nThe REACTION platform will feature an interoperable peer-to-peer communication platform based on a (SoA) service oriented architecture all functionalities, including devices, are represented as services and applications consisting of a series of services orchestrated to perform a desired workflow. The REACTION platform also features a Model Drive Application Development environment based on extensive use of dynamic ontologies and advanced Data Management capabilities with algorithms for clinical assessment and rule-based data processing.\nThe intelligent, interoperable platform developed by REACTION will provide integrated, professional, management and therapy services to diabetes patients in different healthcare regimes across Europe, including 1) professional decision support for in-hospital environments, 2) safety monitoring for dosage and compliance, 3) long term management of outpatients in clinical schemes, 4) care of acute diabetic conditions and 5) support for self management and life-style changes for diabetic patients.\nA range of REACTION services will be developed targeted to insulin-dependent type 1 diabetic patients. The services aim to improve continuous blood glucose monitoring (CGM) and insulin therapy, by both basal dose adjustment and contextualised glycaemic control based on patient activity, nutrition, stress level, etc. Decision support will assist healthcare professionals, patients and informal carers to better manage diabetes therapy and make correct choices about e.g. good blood glucose control, nutrition and exercise.

de Folter J.,Brunel University | Gokalp H.,Brunel University | Fursse J.,Chorleywood Health Center | Sharma U.,Brunel University | Clarke M.,Brunel University
BMC medical informatics and decision making | Year: 2014

BACKGROUND: Changes in daily habits can provide important information regarding the overall health status of an individual. This research aimed to determine how meaningful information may be extracted from limited sensor data and transformed to provide clear visualization for the clinicians who must use and interact with the data and make judgments on the condition of patients. We ascertained that a number of insightful features related to habits and physical condition could be determined from usage and motion sensor data.METHODS: Our approach to the design of the visualization follows User Centered Design, specifically, defining requirements, designing corresponding visualizations and finally evaluating results. This cycle was iterated three times.RESULTS: The User Centered Design method was successfully employed to converge to a design that met the main objective of this study. The resulting visualizations of relevant features that were extracted from the sensor data were considered highly effective and intuitive to the clinicians and were considered suitable for monitoring the behavior patterns of patients.CONCLUSIONS: We observed important differences in the approach and attitude of the researchers and clinicians. Whereas the researchers would prefer to have as many features and information as possible in each visualization, the clinicians would prefer clarity and simplicity, often each visualization having only a single feature, with several visualizations per page. In addition, concepts considered intuitive to the researchers were not always to the clinicians.

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