Medical Data Analysis

@article{pmid31156454,

title = {Mental Workload Alters Heart Rate Variability, Lowering Non-linear Dynamics},

author = {Stéphane Delliaux and Alexis Delaforge and Jean-Claude Deharo and Guillaume Chaumet},

doi = {10.3389/fphys.2019.00565},

issn = {1664-042X},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Front Physiol},

volume = {10},

pages = {565},

abstract = {Mental workload is known to alter cardiovascular function leading to increased cardiovascular risk. Nevertheless, there is no clear autonomic nervous system unbalance to be quantified during mental stress. We aimed to characterize the mental workload impact on the cardiovascular function with a focus on heart rate variability (HRV) non-linear indexes. A 1-h computerized switching task (letter recognition) was performed by 24 subjects while monitoring their performance (accuracy, response time), electrocardiogram and blood pressure waveform (finger volume clamp method). The HRV was evaluated from the beat-to-beat RR intervals (RRI) in time-, frequency-, and informational- domains, before (Control) and during the task. The task induced a significant mental workload (visual analog scale of fatigue from 27 ± 26 to 50 ± 31 mm,  < 0.001, and NASA-TLX score of 56 ± 17). The heart rate, blood pressure and baroreflex function were unchanged, whereas most of the HRV parameters markedly decreased. The maximum decrease occurred during the first 15 min of the task (P1), before starting to return to the baseline values reached at the end of the task (P4). The RRI dimension correlation (D2) decrease was the most significant (P1 vs. Control: 1.42 ± 0.85 vs. 2.21 ± 0.8,  < 0.001) and only D2 lasted until the task ended (P4 vs. Control: 1.96 ± 0.9 vs. 2.21 ± 0.9,  < 0.05). D2 was identified as the most robust cardiovascular variable impacted by the mental workload as determined by posterior predictive simulations ( = 0.9). The Spearman correlation matrix highlighted that D2 could be a marker of the generated frustration ( = -0.61,  < 0.01) induced by a mental task, as well as the myocardial oxygen consumption changes assessed by the double product ( = -0.53,  < 0.05). In conclusion, we showed that mental workload sharply lowered the non-linear RRI dynamics, particularly the RRI correlation dimension.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid31281261b,

title = {High Bubble Grade After Diving: The Role of the Blood Pressure Regimen},

author = {Alain Boussuges and Guillaume Chaumet and Nicolas Vallée and Jean Jacques Risso and Jean Michel Pontier},

doi = {10.3389/fphys.2019.00749},

issn = {1664-042X},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Front Physiol},

volume = {10},

pages = {749},

abstract = { Previous studies have suggested that the circulatory system was involved in the production of circulatory bubbles after diving. This study was designed to research the cardio-vascular function characteristics related to the production of high bubble grades after diving.  Thirty trained divers were investigated both at baseline and after a 30-msw SCUBA dive. At baseline, the investigations included blood pressure measurement, echocardiography, and assessment of aerobic fitness using VO peak measurement. Blood samples were taken at rest, to measure the plasma concentration of NOx and endothelin-1. After diving, circulating bubbles were detected in the pulmonary artery by pulsed Doppler at 20-min intervals during the 90 min after surfacing. The global bubble quantity production was estimated by the KISS index.  Divers with a high bubble grade (KISS > 7.5) had systolic blood pressure, pulse pressure, weight, and height significantly higher than divers with a low bubble grade. By contrast, total arterial compliance, plasma NOx level, and percentage of predicted value of peak oxygen uptake were significantly lower in divers with a high bubble grade. Cardiac dimensions, left ventricular function, and plasma endothelin-1 concentration were not significantly different between groups. The multivariate analysis identified blood pressure as the main contributor of the quantity of bubble production. The model including pulse pressure, plasma NOx level, and percentage of predicted value of peak oxygen uptake has an explanatory power of 49.22%.  The viscoelastic properties of the arterial tree appeared to be an important contributor to the circulating bubble production after a dive.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid29721761,

title = {A Cross-Sectional Study Assessing the Contributions of Body Fat Mass and Fat-Free Mass to Body Mass Index Scores in Male Youth Rugby Players},

author = {Olivier Gavarry and Gregory Lentin and Patrick Pezery and Anne Delextrat and Guillaume Chaumet and Alain Boussuges and Julien Piscione},

doi = {10.1186/s40798-018-0130-7},

issn = {2199-1170},

year  = {2018},

date = {2018-05-01},

urldate = {2018-05-01},

journal = {Sports Med Open},

volume = {4},

number = {1},

pages = {17},

abstract = {BACKGROUND: In some sports such as rugby, a large body size is an advantage, and the desire to gain weight can bring young players to become overweight or obese. The aim of this study was to evaluate the prevalence of overweight and obesity and the contribution of body fat mass index (BFMI) and fat-free mass index (FFMI) to body mass index (BMI) changes among young male rugby players (15-a-side rugby).



METHODS: The criteria of the International Obesity Task Force were used to define overweight and obesity from BMI. The method of skinfold thickness was used to assess percentage of body fat (%BF), BFMI, and FFMI. Excess body fat was defined by using BFMI and %BF above the 75th percentile. Data were grouped according to the age categories of the French Rugby Federation (U11, under 11 years; U13, under 13 years; U15, under 15 years) and to BMI status (NW normal-weight versus OW/OB overweight/obese).



RESULTS: Overall, 32.8% of the young players were overweight, and 13.8% were obese. However, 53% of young players classified as obese and overweight by BMI had an excess body fat by using BFMI above the 75th percentile. FFMI increased significantly between U11 and U13 in both groups, without significant change in BMI and BFMI. Both groups had similar significant gains in BMI and FFMI between U13 and U15, while BFMI only increased significantly in OW/OB (+ 18.5%). The strong correlations between BMI and %BF were systematically lower than those between BMI and BFMI. FFMI was strongly or moderately associated with BFMI.



CONCLUSIONS: Chart analysis of BFMI and FFMI could be used to distinguish changes in body composition across age categories in young male rugby players classified as normal-weight, overweight, and obese by BMI.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}