OBJECTIVE—To determine the prevalence of a metabolic syndrome phenotype among U.S. adolescents using the most recent national data and to examine trends in metabolic syndrome prevalence. RESEARCH DESIGN AND METHODS—Analysis of data on 991 adolescents (aged 12–19 years) who had fasted for at least 6 h, from the National Health and Nutrition Examination Survey (NHANES 1999–2000). The metabolic syndrome was determined using the National Cholesterol Education Program (Adult Treatment Panel III) definition modified for age. RESULTS—The overall prevalence of a metabolic syndrome phenotype among U.S. adolescents increased from 4.2% in NHANES III (1988–1992) to 6.4% in NHANES 1999–2000 (P < 0.001). The syndrome was more prevalent (P < 0.01) in male than female adolescents (9.1 vs. 3.7%) and was found in 32.1% of overweight adolescents (BMI ≥95th percentile for age and sex), compared with 7.1% of adolescents at risk for overweight (BMI between 85th and 95th percentiles) (P < 0.001). Based on population-weighted estimates, >2 million U.S. adolescents currently have a metabolic syndrome phenotype. CONCLUSIONS—The prevalence of a metabolic syndrome phenotype has increased significantly over the past decade among U.S. adolescents and is particularly prevalent (>30%) in overweight adolescents. These findings have important implications for public health because of the well-known health risks associated with the metabolic syndrome in adults.

Smart cities use information and communication technologies (ICTs) to scale services include utilities and transportation to a growing population. In this paper, we discuss how smart city ICTs can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.

The health benefits of engaging in regular physical activity have been well established.^1-5 However, most US adults are not sufficiently active regularly, and 26% are not active at all.⁶ Guidelines recommended by the American Heart Association,⁷ the Centers for Disease Control and Prevention/American College of Sports Medicine,⁸ and the US Surgeon General⁹ suggest that the accumulation of 30 minutes of moderate-intensity (ModI) physical activity on most days of the week will produce significant health benefits. The reports supporting these guidelines also describe a dose-response relationship between physical activity and health outcomes. Moderate-intensity (45%-60% of heart rate reserve [HRres]) activities completed on as few as 3 d/wk may produce observable changes in disease risk factors, and greater benefits generally accrue from engaging in exercise of greater intensity or volume.^7-9 Indeed, recent analyses^10,11 of the dose-response issue affirm an inverse and generally linear relationship between physical activity and cardiovascular disease (CVD)–related morbidity and mortality. However, because of the large variability in adherence that occurs when individuals are advised to perform unsupervised exercise in their natural environments, the exercise prescription required to produce beneficial changes in CVD risk factors among free-living sedentary adults requires further research.¹²

Objective To determine the current prevalence of self-reported diabetes and impaired fasting glucose levels among US adolescents. Design and Setting Cross-sectional, National Health and Nutrition Examination Survey (1999-2002). Participants A sample of 4370 adolescents (aged 12-19 years) with self-reported diabetes and a subsample of 1496 adolescents without self-reported diabetes who had fasted for at least 8 hours. Subjects who reported having diabetes and using insulin were further categorized as having type 1 diabetes, those reporting having diabetes and not using insulin as having type 2 diabetes, and those with a fasting plasma glucose level greater than or equal to 5.6 mmol/L (100 mg/dL) as having impaired fasting glucose levels. Main Outcome Measures Prevalence of self-reported diabetes, type 1 and type 2 diabetes, and impaired fasting glucose levels among US adolescents. Results In the full sample, 0.5% (95% confidence interval [CI], 0.24%-0.76%) of adolescents reported having diabetes. Of those reporting diabetes, roughly 71% (95% CI, 56%-86%) were categorized as having type 1 and 29% (95% CI, 14%-44%) as having type 2 diabetes. In the subsample, roughly 11% (95% CI, 8%-14%) had impaired fasting glucose levels. Using population-based sample weights, these proportions were equivalent to 39 005 US adolescents with type 2 diabetes and 2 769 736 with impaired fasting glucose levels. Conclusions The prevalence of type 2 diabetes and impaired fasting glucose levels is substantial among US adolescents. These estimates have important implications for public health because of the high rate of conversion from impaired fasting glucose level to type 2 diabetes in adults and the increased risk of cardiovascular disease in individuals with type 2 diabetes.

Physical distancing and other COVID-19 pandemic mitigation strategies may have unintended consequences on a number of health behaviors and health outcomes. The purpose of this study was to investigate the association between perceived change in physical activity or exercise and mental health outcomes over the short-term in response to COVID-19 mitigation strategies in a sample of adult twins. This was a cross-sectional study of 3,971 identical and same-sex fraternal adult twins (909 pairs, 77% identical) from the community-based Washington State Twin Registry. Participants in this study completed an online survey examining the impact of COVID-19 mitigation on a number of health-related behaviors and outcomes, administered between March 26 and April 5, 2020. In the present study, the exposure was perceived change in physical activity or exercise. The outcomes were levels of perceived anxiety and stress. More twin pairs reported a decrease in physical activity levels (42%) than those reporting no change (31%) or increased physical activity levels (27%). A perceived decrease in physical activity or exercise was associated with higher stress and anxiety levels. However, the physical activity–stress relationship was confounded by genetic and shared environmental factors. On the other hand, the physical activity–anxiety relationship held after controlling for genetic and shared environmental factors, although it was no longer significant after further controlling for age and sex, with older twins more likely to report lower levels of anxiety and females more likely to report higher levels of anxiety. Strategies to mitigate the COVID-19 pandemic may be impacting physical activity and mental health, with those experiencing a decrease in physical activity also having higher levels of stress and anxiety. These relationships are confounded by genetic and shared environmental factors, in the case of stress, and age and sex, in the case of anxiety.

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Next, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially-oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors.