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Introduction Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75... more
Introduction Life expectancy is a public health metric used to assess mortality. We describe life expectancy calculations for US counties and present methodologic considerations compared with years of potential life lost before age 75 (YPLL-75) and premature age-adjusted mortality (PAAM), 2 commonly used length-of-life metrics. Methods We used death data from the National Center for Health Statistics for 2015-2017 and other health measures from the 2019 County Health Rankings & Roadmaps. We calculated life expectancy from birth at the county level using an abridged life table and the Chiang method of variance. Studentized residuals identified counties with discordant life expectancy and YPLL-75 or PAAM values. Correlations tested associations of life expectancy with key health measures (eg, smoking, child poverty, uninsured). Results Among 3073 US counties, life expectancy ranged from 62.4 to 98.0 years, with a mean of 77.4 years. Life expectancy was strongly and negatively correlat...
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Accurate measurement of free-living physical activity is challenging in population-based research, whether using device-based or reported methods. Our purpose was to identify demographic predictors of discordance between physical activity... more
Accurate measurement of free-living physical activity is challenging in population-based research, whether using device-based or reported methods. Our purpose was to identify demographic predictors of discordance between physical activity assessment methods and to determine how these predictors modify the discordance between device-based and reported physical activity measurement methods. Three hundred forty-seven adults from the Survey of the Health of Wisconsin wore the ActiGraph accelerometer for 7 days and completed the Global Physical Activity Questionnaire. Multivariate linear regression was conducted to assess predictors of discordance including gender, education, body mass index, marital status, and other individual level characteristics in physical activity reporting. Seventy-seven percent of men and 72% of women self-reported meeting the U.S. Centers for Disease Control and Prevention guidelines for aerobic activity but when measured by accelerometer, only 21% of men and 1...
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Research Interests: Anthropometry, Independent Living, Humans, Walking, Female, and 14 moreMale, Acceleration, Physical Fitness, Aged, Middle Aged, Analysis of Variance, Time Factors, Geriatric Assessment, Older Adult, Medicine and Science In Sports and Exercise, Cross Sectional Studies, Physical Function, Exercise Test, and Exercise Tolerance
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The aim of this study was to examine the dose-response relationship between walking activity and physical function (PF) in community-dwelling older adults. Physical activity (PA, pedometry) and PF (self-report [SF-36] and 6-minute walk... more
The aim of this study was to examine the dose-response relationship between walking activity and physical function (PF) in community-dwelling older adults. Physical activity (PA, pedometry) and PF (self-report [SF-36] and 6-minute walk test [6MWT]) were assessed in 836 individuals. Accumulated PA was categorized into four groups (1 = ≤ 2,500; 2 = 2,501–5,000; 3 = 5,001–7,500; and 4 = ≥ 7,501 steps/day). Across individual groups 1–4, SF-36 scores increased from 66.9 ± 25.0% to 73.5 ± 23.2% to 78.8 ± 19.7% to 81.3 ± 20.6%, and 6MWT increased from 941.7 ± 265.4 ft to 1,154.1 ± 248.2 ft to 1,260.1 ± 226.3 ft to 1,294.0 ± 257.9 ft. Both SF-36 and 6MWT scores were statistically different across all groups, apart from groups 3 and 4. PA and ranks of groups were highly significant predictors (p < .0001) for both SF-36 and 6MWT. There was a positive dose-response relationship evident for both SF-36 and 6MWT with increasing levels of PA. Low levels of PA appear to be an important indicator...
Research Interests: Anthropometry, Independent Living, Humans, Walking, Female, and 14 moreMale, Acceleration, Physical Fitness, Aged, Middle Aged, Analysis of Variance, Time Factors, Geriatric Assessment, Older Adult, Medicine and Science In Sports and Exercise, Cross Sectional Studies, Physical Function, Exercise Test, and Exercise Tolerance
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Research Interests: Anthropometry, Independent Living, Humans, Walking, Female, and 14 moreMale, Acceleration, Physical Fitness, Aged, Middle Aged, Analysis of Variance, Time Factors, Geriatric Assessment, Older Adult, Medicine and Science In Sports and Exercise, Cross Sectional Studies, Physical Function, Exercise Test, and Exercise Tolerance
Although many researchers agree that multiple determinants impact health, there is no consensus regarding the magnitude of the relative contributions of individual health factors to health outcomes. This study presents a method to... more
Although many researchers agree that multiple determinants impact health, there is no consensus regarding the magnitude of the relative contributions of individual health factors to health outcomes. This study presents a method to empirically estimate the relative contributions of health behaviors, clinical care, social and economic factors, and the physical environment to health outcomes using nationally representative county-level data and statistical approaches that account for potential sources of bias. The analyses for this study were conducted in 2014. Data were from the 2010-2013 County Health Rankings & Roadmaps. Data covered 2,996 of 3,141 U.S. counties. Ordinary least squares modeling was used as a baseline model. Multilevel latent growth curve modeling was used to estimate the relative contributions of health factors to health outcomes while accounting for measurement errors and state-specific characteristics. Almost half of the variance of health outcomes was due to stat...
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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... more
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. 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. 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. 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.
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Background: To examine the effectiveness of the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) resistance training (RT) guidelines to improve physical function and functional classification in older... more
Background: To examine the effectiveness of the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) resistance training (RT) guidelines to improve physical function and functional classification in older adults with reduced physical abilities. Methods: Twenty-five at-risk older adults were randomized to a control (CON = 13) or 8-week resistance training intervention arm (RT = 12). Progressive RT included 8 exercises for 1 set of 10 repetitions at a perceived exertion of 5-6 performed twice a week. Individuals were assessed for physical function and functional classification change (low, moderate or high) by the short physical performance battery (SPPB) and muscle strength measures. Results: Postintervention, significant differences were found between groups for SPPB-Chair Stand [F(1,22) = 9.14, P < .01, η= .29] and SPPB-Total Score [F(1,22) = 7.40, P < .05, η = .25]. Functional classification was improved as a result of the intervention with 83...
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To examine the effectiveness of the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) resistance training (RT) guidelines to improve physical function and functional classification in older adults with... more
To examine the effectiveness of the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) resistance training (RT) guidelines to improve physical function and functional classification in older adults with reduced physical abilities. Twenty-five at-risk older adults were randomized to a control (CON = 13) or 8-week resistance training intervention arm (RT = 12). Progressive RT included 8 exercises for 1 set of 10 repetitions at a perceived exertion of 5-6 performed twice a week. Individuals were assessed for physical function and functional classification change (low, moderate or high) by the short physical performance battery (SPPB) and muscle strength measures. Postintervention, significant differences were found between groups for SPPB-Chair Stand [F(1,22) = 9.14, P < .01, η = .29] and SPPB-Total Score [F(1,22) = 7.40, P < .05, η = .25]. Functional classification was improved as a result of the intervention with 83% of participants in the RT gr...
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The aim of this study was to examine the relationship among sedentary behaviour (SB) and the metabolic syndrome and its components by age, moderate-to-vigorous physical activity (MVPA) and sex. A cross-sectional analysis was performed on... more
The aim of this study was to examine the relationship among sedentary behaviour (SB) and the metabolic syndrome and its components by age, moderate-to-vigorous physical activity (MVPA) and sex. A cross-sectional analysis was performed on 2003-2006 National Health and Nutrition Examination Survey data from 5,076 adults aged ≥18 years (mean ± SD = 43.8 ± 19.5). SB was measured using ActiGraph accelerometers worn for 1 week and defined as &amp;amp;amp;amp;amp;amp;lt;100 counts/min. Metabolic syndrome was defined using the Adult Treatment Panel III criteria. Natural cubic spline logistic regression models were used to estimate the odds of meeting criteria for the metabolic syndrome and its components by total daily SB time and breaks in SB. Statistical interactions between SB and age, sex and MVPA were explored. The prevalence of the metabolic syndrome was 19% and the average daily SB time was 8.1 ± 2.8 h, with 90 ± 25 breaks/day. The relationship between daily SB time and the metabolic syndrome was linear and characterised by an OR of 1.09 (95% CI 1.01, 1.18) for each hour of SB. Total SB was associated with the following components: high triacylglycerol, low HDL-cholesterol and high fasting glucose. All three associations were modified by MVPA level. No relationship between breaks in SB and the metabolic syndrome was found. There appears to be no SB threshold at which the risk of the metabolic syndrome is elevated. Therefore, an effort should be made to maintain low levels of total time spent in SB and so lessen the risk of the metabolic syndrome.
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The present study assessed the psychometric properties and construct validity of two self-report measures of psychopathy in a male-college sample: the Levenson Psychopathy scales (LPS; Levenson, Kiehl,... more
The present study assessed the psychometric properties and construct validity of two self-report measures of psychopathy in a male-college sample: the Levenson Psychopathy scales (LPS; Levenson, Kiehl, &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; Fitzpatrick, 1995) and the Psychopathic Personality Inventory (PPI; Lilienfeld &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; Andrews, 1996). Both the LPS and the PPI demonstrated good internal consistency, although selected items from the PPI correlated weakly with their respective factor scores, suggesting the need for further investigation of the factors&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39; item content. The PPI showed stronger validity than the LPS in terms of convergent and discriminant validity of its factor scores and factor associations with two criterion variables, aggression, and anxiety. Overall, the current study provides greater support for the use of the PPI over the LPS in studies investigating psychopathic traits in nonclinical and nonforensic samples.