Little attention has been given to factors contributing to firefighters' psychosomatic well-being. Objective The purpose of this descriptive study was to examine such contributing factors in a sample of professional firefighters. Methods Measures assessing sleep, depression, substance use, social bonding, and quality of life were examined in 112 firefighters. Results Overall, many firefighters reported sleep deprivation (59%), binge drinking behavior (58%), poor mental well-being (21%), current nicotine use (20%), hazardous drinking behavior (14%), depression (11%), poor physical well-being (8%), caffeine overuse (5%), or poor social bonding (4%). Conclusions Small-to-medium correlations were identified between sleep deprivation, depression, physical/mental well-being, and drinking behaviors. High-risk behaviors that impact psychosomatic well-being are prevalent in professional firefighters, which require environmental and individual-based health promotion interventions. The inter-correlation relationships between such behaviors, therefore, need to be explored in further details.
Epidemiological and biological plausibility studies support a cause-and-effect relationship between increased levels of physical activity or cardiorespiratory fitness and reduced coronary heart disease events. These data, plus the well-documented anti-aging effects of exercise, have likely contributed to the escalating numbers of adults who have embraced the notion that “more exercise is better.” As a result, worldwide participation in endurance training, competitive long distance endurance events, and high-intensity interval training has increased markedly since the previous American Heart Association statement on exercise risk. On the other hand, vigorous physical activity, particularly when performed by unfit individuals, can acutely increase the risk of sudden cardiac death and acute myocardial infarction in susceptible people. Recent studies have also shown that large exercise volumes and vigorous intensities are both associated with potential cardiac maladaptations, including accelerated coronary artery calcification, exercise-induced cardiac biomarker release, myocardial fibrosis, and atrial fibrillation. The relationship between these maladaptive responses and physical activity often forms a U- or reverse J-shaped dose-response curve. This scientific statement discusses the cardiovascular and health implications for moderate to vigorous physical activity, as well as high-volume, high-intensity exercise regimens, based on current understanding of the associated risks and benefits. The goal is to provide healthcare professionals with updated information to advise patients on appropriate preparticipation screening and the benefits and risks of physical activity or physical exertion in varied environments and during competitive events.
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.
Background The risk for cardiovascular events is higher for those with metabolic syndrome (MetS), and it is known that firefighters have a fourfold risk for cardiovascular events. The purpose of this study was to quantify MetS prevalence and evaluate the effect of a low glycemic nutritional fitness program on the reduction of MetS risk factors among firefighters. Methods Professional firefighters were screened for MetS then enrolled in a low glycemic nutritional fitness program for a 12-week period. Anthropometric and physiologic measurements were obtained at the start and end of the program. Subjects with ≥3 of the following were positive for MetS: waist ≥40 (men) or ≥35 inches (women), BP≥135 (systole) or ≥85 (diastole) mmHg, fasting blood sugar ≥100mg/dl, triglycerides ≥150mg/dl, and high-density lipoproteins <40 (men) or <50 mg/dl (women). Weekly training was provided with low glycemic nutrition and regular fitness and evaluation of individual progress. Results Seventy-five firefighters (age 42+8yrs, mostly Caucasian men) had a total MetS prevalence of 46.7% (p<0.05 vs normal population). One platoon (10 men, age 48±5yrs) was enrolled in the 12-week program. Most (7/10) had MetS at the baseline, but this prevalence decreased significantly after 12 weeks to 3 subjects (p=0.02). On average, subjects had 3.2±1.6 vs 1.9±1.7 MetS risk factors (p<0.01) at baseline and 12 week interval, respectively. Conclusions The prevalence of MetS and MetS risk factors are higher among professional firefighters compared to general population. A short-duration low glycemic fitness program can successfully improve anthropometric and physiologic measures and reduce the prevalence of MetS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.