Positive Health and Health Assets: Re-analysis of Longitudinal Datasets

Most approaches to health over the centuries have focused on the absence of illness. In contrast, we are investigating Positive Health —well-being beyond the mere absence of disease. In this article, we describe our theoretical framework and empirical work to date on Positive Health. Positive Health empirically identifies health assets by determining factors that predict health and illness over and above conventional risk factors. Biological health assets might include, for example, high heart rate variability, high levels of HDL, and cardiorespiratory fitness. Subjective health assets might include positive emotions, life satisfaction, hope, optimism, and a sense of meaning and purpose. Functional health assets might include close friends and family members; a stable marriage; meaningful work; participation in a social community; and the ability to carry out work, family, and social roles

The exact Darwin Lagrangian

Darwin (1920) noted that when radiation can be neglected it should be possible to eliminate the radiation degrees-of-freedom from the action of classical electrodynamics and keep the discrete particle degrees-of-freedom only. Darwin derived his well known Lagrangian by series expansion in $v/c$ keeping terms up to order $(v/c)^2$. Since radiation is due to acceleration the assumption of low speed should not be necessary. A Lagrangian is suggested that neglects radiation without assuming low speed. It cures deficiencies of the Darwin Lagrangian in the ultra-relativistic regime.Comment: 2.5 pages, no figure

Influential Periods in Longitudinal Clinical Cardiovascular Health Scores

The prevalence of ideal cardiovascular health (CVH) among adults in the United States is low and decreases with age. Our objective was to identify specific age windows when the loss of CVH accelerates, to ascertain preventive opportunities for intervention. Data were pooled from 5 longitudinal cohorts (Project Heartbeat!, Cardiovascular Risk in Young Finns Study, The Bogalusa Heart Study, Coronary Artery Risk Development in Young Adults, Special Turku Coronary Risk Factor Intervention Project) from the United States and Finland from 1973 to 2012. Individuals with clinical CVH factors (i.e., body mass index, blood pressure, cholesterol, blood glucose) measured from ages 8 to 55 years were included. These factors were categorized and summed into a clinical CVH score ranging from 0 (worst) to 8 (best). Adjusted, segmented, linear mixed models were used to estimate the change in CVH over time. Among the 18,343 participants, 9,461 (52%) were female and 12,346 (67%) were White. The baseline mean (standard deviation) clinical CVH score was 6.9 (1.2) at an average age of 17.6 (8.1) years. Two inflection points were estimated: at 16.9 years (95% confidence interval: 16.4, 17.4) and at 37.2 years (95% confidence interval: 32.4, 41.9). Late adolescence and early middle age appear to be influential periods during which the loss of CVH accelerates.publishedVersionPeer reviewe

Influential Periods in Longitudinal Clinical Cardiovascular Health Scores

The prevalence of ideal cardiovascular health (CVH) among adults in the United States is low and decreases with age. Our objective was to identify specific age windows when the loss of CVH accelerates, to ascertain preventive opportunities for intervention. Data were pooled from 5 longitudinal cohorts (Project Heartbeat!, Cardiovascular Risk in Young Finns Study, The Bogalusa Heart Study, Coronary Artery Risk Development in Young Adults, Special Turku Coronary Risk Factor Intervention Project) from the United States and Finland from 1973 to 2012. Individuals with clinical CVH factors (i.e., body mass index, blood pressure, cholesterol, blood glucose) measured from ages 8 to 55 years were included. These factors were categorized and summed into a clinical CVH score ranging from 0 (worst) to 8 (best). Adjusted, segmented, linear mixed models were used to estimate the change in CVH over time. Among the 18,343 participants, 9,461 (52%) were female and 12,346 (67%) were White. The baseline mean (standard deviation) clinical CVH score was 6.9 (1.2) at an average age of 17.6 (8.1) years. Two inflection points were estimated: at 16.9 years (95% confidence interval: 16.4, 17.4) and at 37.2 years (95% confidence interval: 32.4, 41.9). Late adolescence and early middle age appear to be influential periods during which the loss of CVH accelerates. </p

The U.S. Army Person-Event Data Environment: A Military–Civilian Big Data Enterprise

This report describes a groundbreaking military–civilian collaboration that benefits from an Army and Department of Defense (DoD) big data business intelligence platform called the Person-Event Data Environment (PDE). The PDE is a consolidated data repository that contains unclassified but sensitive manpower, training, financial, health, and medical records covering U.S. Army personnel (Active Duty, Reserve, and National Guard), civilian contractors, and military dependents. These unique data assets provide a veridical timeline capturing each soldier’s military experience from entry to separation from the armed forces. The PDE was designed to afford unprecedented cost-efficiencies by bringing researchers and military scientists to a single computerized repository rather than porting vast data resources to individual laboratories. With funding from the Robert Wood Johnson Foundation, researchers from the University of Pennsylvania Positive Psychology Center joined forces with the U.S. Army Research Facilitation Laboratory, forming the scientific backbone of the military–civilian collaboration. This unparalleled opportunity was necessitated by a growing need to learn more about relations between psychological and health assets and health outcomes, including healthcare utilization and costs—issues of major importance for both military and civilian population health. The PDE represents more than 100 times the population size and many times the number of linked variables covered by the nation’s leading sources of population health data (e.g., the National Health and Nutrition Examination Survey). Following extensive Army vetting procedures, civilian researchers can mine the PDE’s trove of information using a suite of statistical packages made available in a Citrix Virtual Desktop. A SharePoint collaboration and governance management environment ensures user compliance with federal and DoD regulations concerning human subjects’ protections and also provides a secure portal for multisite collaborations. Taking similarities and differences between military and civilian populations into account, PDE studies can provide much more detailed insight into health-related questions of broad societal concern. Finding ways to make the rich repository of digitized information in the PDE available through military–civilian collaboration can help solve critical medical and behavioral issues affecting the health and well-being of our nations’ military and civilian populations