Keith P. Gennuso

Sedentary behavior, physical activity, and markers of health in older adults.

INTRODUCTION The purpose of this study was to examine the association between sedentary behavior (SB), cardiometabolic risk factors, and self-reported physical function by level of moderate-vigorous physical activity (MVPA). METHODS Cross-sectional analysis was completed on 1914 older adults age ≥ 65 yr from the 2003-2006 U.S. National Health and Nutrition Examination Survey. MVPA and SB were derived from ActiGraph accelerometers worn for 1 wk. MVPA was categorized as sufficient to meet the current U.S. guidelines (≥ 150 min · wk(-1)) or not; SB was split into quartiles. Various biomarkers were examined in laboratory analyses and physical exams, and the number of functional limitations was self-reported. Statistical interaction between SB and MVPA on the biomarker associations was the primary analysis, followed by an examination of their independent associations with relevant covariate adjustment. RESULTS Average SB was 9.4 ± 2.3 h · d(-1) (mean ± SD), and approximately 35% were classified as sufficiently active. Overall, no significant meaningful statistical interactions were found between SB and MVPA for any of the outcomes; however, strong independent positive associations were found between SB and weight (P < 0.01), body mass index (P < 0.01), waist circumference (P < 0.01), C-reactive protein (P < 0.01), plasma glucose (P = 0.04), and number of functional limitations (P < 0.01) after adjustment for MVPA. Similarly, MVPA was negatively associated with weight (P = 0.01), body mass index (P < 0.01), waist circumference (P < 0.01), diastolic blood pressure (P = 0.04), C-reactive protein (P < 0.01), and number of functional limitations (P < 0.01) after adjustment for SB. CONCLUSIONS The results suggest that sufficient MVPA did not ameliorate the negative associations between SB and cardiometabolic risk factors or functional limitations in the current sample and that there was independence on a multiplicative scale in their associations with the outcomes examined. Thus, older adults may benefit from the joint prescription to accumulate adequate MVPA and avoid prolonged sitting.

County Health Rankings Relationships Between Determinant Factors and Health Outcomes

INTRODUCTION The County Health Rankings (CHR) provides data for nearly every county in the U.S. on four modifiable groups of health factors, including healthy behaviors, clinical care, physical environment, and socioeconomic conditions, and on health outcomes such as length and quality of life. The purpose of this study was to empirically estimate the strength of association between these health factors and health outcomes and to describe the performance of the CHR model factor weightings by state. METHODS Data for the current study were from the 2015 CHR. Thirty-five measures for 45 states were compiled into four health factors composite scores and one health outcomes composite score. The relative contributions of health factors to health outcomes were estimated using hierarchical linear regression modeling in March 2015. County population size; rural/urban status; and gender, race, and age distributions were included as control variables. RESULTS Overall, the relative contributions of socioeconomic factors, health behaviors, clinical care, and the physical environment to the health outcomes composite score were 47%, 34%, 16%, and 3%, respectively. Although the CHR model performed better in some states than others, these results provide broad empirical support for the CHR model and weightings. CONCLUSIONS This paper further provides a framework by which to prioritize health-related investments, and a call to action for healthcare providers and the schools that educate them. Realizing the greatest improvements in population health will require addressing the social and economic determinants of health.

The County Health Rankings: rationale and methods.

BackgroundAnnually since 2010, the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation have produced the County Health Rankings—a “population health checkup” for the nation’s over 3,000 counties. The purpose of this paper is to review the background and rationale for the Rankings, explain in detail the methods we use to create the health rankings in each state, and discuss the strengths and limitations associated with ranking the health of communities.MethodsWe base the Rankings on a conceptual model of population health that includes both health outcomes (mortality and morbidity) and health factors (health behaviors, clinical care, social and economic factors, and the physical environment). Data for over 30 measures available at the county level are assembled from a number of national sources. Z-scores are calculated for each measure, multiplied by their assigned weights, and summed to create composite measure scores. Composite scores are then ordered and counties are ranked from best to worst health within each state.ResultsHealth outcomes and related health factors vary significantly within states, with over two-fold differences between the least healthy counties versus the healthiest counties for measures such as premature mortality, teen birth rates, and percent of children living in poverty. Ranking within each state depicts disparities that are not apparent when counties are ranked across the entire nation.DiscussionThe County Health Rankings can be used to clearly demonstrate differences in health by place, raise awareness of the many factors that influence health, and stimulate community health improvement efforts. The Rankings draws upon the human instinct to compete by facilitating comparisons between neighboring or peer counties within states. Since no population health model, or rankings based off such models, will ever perfectly describe the health of its population, we encourage users to look to local sources of data to understand more about the health of their community.

Validity of Physical Activity Monitors in Assessing Energy Expenditure in Normal, Overweight, and Obese Adults

A comparison of the validity of downloadable motion sensors, which use either a glass-enclosed magnetic reed proximity switch technology, a piezo-electric sensor accelerometer with a horizontal beam technology, or an internal pendulum based mechanism to determine energy expenditure (EE), across different body sizes does not exist. Therefore, the purpose of this study was to determine the validity of three different downloadable motion sensors to estimate EE during walking activity in normal weight, overweight and obese volunteers. Forty-eight participants completed this study. Each participant had their body height and mass measured and completed a treadmill walking protocol. Body mass index (BMI) was calculated. The treadmill walking protocol included six 5-minute stages starting at 1.5 mph and increasing by 0.5 mph, up to 4.0 mph while grade was constant at 0% for the duration of the test. The Kenz Life-Corder EX (LC), the Omron HJ-700IT (OM) and the Sportbrain iStep X1 (SB) were worn during the treadmill walking protocol. Heart rate, oxygen consumption, carbon dioxide production and EE estimated from the motion sensors were monitored throughout the walking protocol. Results showed the OM overestimated net EE in normal, overweight and obese participants. The LC underestimated gross EE in all groups. The SB overestimated net EE in normal BMI participants, was not significantly different from the criterion measure of net EE in overweight participants and underestimated net EE in obese individuals. This study demonstrates that these devices do not offer the accuracy needed to provide precise feedback on EE for individuals with varying BMI levels.

The epidemic of despair among white americans: Trends in the leading causes of premature death, 1999-2015

Objectives To evaluate trends in premature death rates by cause of death, age, race, and urbanization level in the United States. Methods We calculated cause-specific death rates using the Compressed Mortality File, National Center for Health Statistics data for adults aged 25 to 64 years in 2 time periods: 1999 to 2001 and 2013 to 2015. We defined 48 subpopulations by 10-year age groups, race/ethnicity, and county urbanization level (large urban, suburban, small or medium metropolitan, and rural). Results The age-adjusted premature death rates for all adults declined by 8% between 1999 to 2001 and 2013 to 2015, with decreases in 39 of the 48 subpopulations. Most decreases in death rates were attributable to HIV, cardiovascular disease, and cancer. All 9 subpopulations with increased death rates were non-Hispanic Whites, largely outside large urban areas. Most increases in death rates were attributable to suicide, poisoning, and liver disease. Conclusions The unfavorable recent trends in premature death rate among non-Hispanic Whites outside large urban areas were primarily caused by self-destructive health behaviors likely related to underlying social and economic factors in these communities.

Reliability and Validity of 2 Self-Report Measures to Assess Sedentary Behavior in Older Adults

BACKGROUND The purpose of this study was to examine the reliability and validity of 2 currently available physical activity surveys for assessing time spent in sedentary behavior (SB) in older adults. METHODS Fifty-eight adults (≥65 years) completed the Yale Physical Activity Survey for Older Adults (YPAS) and Community Health Activities Model Program for Seniors (CHAMPS) before and after a 10-day period during which they wore an ActiGraph accelerometer (ACC). Intraclass correlation coefficients (ICC) examined test-retest reliability. Overall percent agreement and a kappa statistic examined YPAS validity. Lins concordance correlation, Pearson correlation, and Bland-Altman analysis examined CHAMPS validity. RESULTS Both surveys had moderate test-retest reliability (ICC: YPAS = 0.59 (P < .001), CHAMPS = 0.64 (P < .001)) and significantly underestimated SB time. Agreement between YPAS and ACC was low (κ = -0.0003); however, there was a linear increase (P < .01) in ACC-derived SB time across YPAS response categories. There was poor agreement between ACC-derived SB and CHAMPS (Lins r = .005; 95% CI, -0.010 to 0.020), and no linear trend across CHAMPS quartiles (P = .53). CONCLUSIONS Neither of the surveys should be used as the sole measure of SB in a study; though the YPAS has the ability to rank individuals, providing it with some merit for use in correlational SB research.

Research ArticleCounty Health Rankings: Relationships Between Determinant Factors and Health Outcomes

INTRODUCTION The County Health Rankings (CHR) provides data for nearly every county in the U.S. on four modifiable groups of health factors, including healthy behaviors, clinical care, physical environment, and socioeconomic conditions, and on health outcomes such as length and quality of life. The purpose of this study was to empirically estimate the strength of association between these health factors and health outcomes and to describe the performance of the CHR model factor weightings by state. METHODS Data for the current study were from the 2015 CHR. Thirty-five measures for 45 states were compiled into four health factors composite scores and one health outcomes composite score. The relative contributions of health factors to health outcomes were estimated using hierarchical linear regression modeling in March 2015. County population size; rural/urban status; and gender, race, and age distributions were included as control variables. RESULTS Overall, the relative contributions of socioeconomic factors, health behaviors, clinical care, and the physical environment to the health outcomes composite score were 47%, 34%, 16%, and 3%, respectively. Although the CHR model performed better in some states than others, these results provide broad empirical support for the CHR model and weightings. CONCLUSIONS This paper further provides a framework by which to prioritize health-related investments, and a call to action for healthcare providers and the schools that educate them. Realizing the greatest improvements in population health will require addressing the social and economic determinants of health.

Development of a Nationally Representative Built Environment Measure of Access to Exercise Opportunities

We sought to develop a county-level measure to evaluate residents’ access to exercise opportunities. Data were acquired from Esri, DeLorme World Vector (MapMart), and OneSource Global Business Browser (Avention). Using ArcGIS (Esri), we considered census blocks to have access to exercise opportunities if the census block fell within a buffer area around at least 1 park or recreational facility. The percentage of county residents with access to exercise opportunities was reported. Measure validity was examined through correlations with other County Health Rankings & Roadmaps’ measures. Included were 3,114 of 3,141 US counties. The average population with access to exercise opportunities was 52% (range, 0%–100%) with large regional variation. Access to exercise opportunities was most notably associated with no leisure-time physical activity (r = −0.47), premature death (r = −0.38), and obesity (r = −0.36). The measure uses multiple sources to create a valid county-level measure of exercise access. We highlight geographic disparities in access to exercise opportunities and call for improved data.

Accelerometer-derived physical activity and sedentary time by cancer type in the United States

The 2003–2004 and 2005–2006 cycles of the National Health and Nutrition Examination Survey (NHANES) were among the first population-level studies to incorporate objectively measured physical activity and sedentary behavior, allowing for greater understanding of these behaviors. However, there has yet to be a comprehensive examination of these data in cancer survivors, including short- and long-term survivors of all cancer types. Therefore, the purpose of this analysis was to use these data to describe activity behaviors in short- and long-term cancer survivors of various types. A secondary aim was to compare activity patterns of cancer survivors to that of the general population. Cancer survivors (n = 508) and age-matched individuals not diagnosed with cancer (n = 1,016) were identified from a subsample of adults with activity measured by accelerometer. Physical activity and sedentary behavior were summarized across cancer type and demographics; multivariate regression was used to evaluate differences between survivors and those not diagnosed with cancer. On average, cancer survivors were 61.4 (95% CI: 59.6, 63.2) years of age; 57% were female. Physical activity and sedentary behavior patterns varied by cancer diagnosis, demographic variables, and time since diagnosis. Survivors performed 307 min/day of light-intensity physical activity (95% CI: 295, 319), 16 min/day of moderate-vigorous intensity activity (95% CI: 14, 17); only 8% met physical activity recommendations. These individuals also reported 519 (CI: 506, 532) minutes of sedentary time, with 86 (CI: 84, 88) breaks in sedentary behavior per day. Compared to non-cancer survivors, after adjustment for potential confounders, survivors performed less light-intensity activity (P = 0.01), were more sedentary (P = 0.01), and took fewer breaks in sedentary time (P = 0.04), though there were no differences in any other activity variables. These results suggest that cancer survivors are insufficiently active. Relative to adults of similar age not diagnosed with cancer, they engage in more sedentary time with fewer breaks. As such, sedentary behavior and light-intensity activity may be important intervention targets, particularly for those for whom moderate-to-vigorous activity is not well accepted.

Assessment of Factors Contributing to Health Outcomes in the Eight States of the Mississippi Delta Region.

Introduction The objective of this observational study was to examine the key contributors to health outcomes and to better understand the health disparities between Delta and non-Delta counties in 8 states in the Mississippi River Delta Region. We hypothesized that a unique set of contributors to health outcomes in the Delta counties could explain the disparities between Delta and non-Delta counties. Methods Data were from the 2014 County Health Rankings for counties in 8 states (Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee). We used the Delta Regional Authority definition to identify the 252 Delta counties and 468 non-Delta counties or county equivalents. Information on health factors (eg, health behaviors, clinical care) and outcomes (eg, mortality) were derived from 38 measures from the 2014 County Health Rankings. The contributions of health factors to health outcomes in Delta and non-Delta counties were examined using path analysis. Results We found similarities between Delta counties and non-Delta counties in the health factors (eg, tobacco use, diet and exercise) that significantly predicted the health outcomes of self-rated health and low birthweight. The most variation was seen in predictors of mortality; however, Delta counties shared 2 of the 3 significant predictors (ie, community safety and income) of mortality with non-Delta counties. On average across all measures, values in the Delta were 16% worse than in the non-Delta and 22% worse than in the rest of the United States. Conclusion The health status of Delta counties is poorer than that of non-Delta counties because the health factors that contribute to health outcomes in the entire region are worse in the Delta counties, not because of a unique set of health predictors.