Chronic use of Antihypertensive Medications and Peak Exercise Blood Pressure in Adult Men and Women from the BALL ST Cohort.

PURPOSE: To determine if individuals chronically (>1 year) prescribed antihypertensive medications have a normal BP response to peak exercise compared to unmedicated individuals. METHODS: Participants included 2,555 adults from the Ball State Adult Fitness Longitudinal Lifestyle STudy cohort who performed a peak treadmill exercise test. Participants were divided into groups by sex and antihypertensive medication status. Individuals prescribed antihypertensive medications for >1 year were included. Exaggerated and blunted SBP within each group was categorized using the Fitness Registry and the Importance of Exercise: A National Database (FRIEND) and absolute criteria as noted by the Amercian Heart Association. RESULTS: The unmedicated group had a greater prevalence (p < 0.05) of blunted SBP responses, whereas the medicated group had a higher prevalence (p < 0.05) of exaggerated SBP responses using both the FRIEND and absolute criteria. Peak SBP was higher (p < 0.01) in medicated compared to unmedicated participants in the overall cohort when controlling for age and sex, but not after controlling for resting SBP (p = 0.613), risk factors (p = 0.104), or cardiorespiratory fitness (p = 0.191). When men and women were assessed independently, peak SBP remained higher in the medicated women after controlling for age and resting SBP (p = 0.039), but not for men (p = 0.311). Individuals on beta-blockers had a higher peak SBP even after controlling for age, sex, risk factors and cardiorespiratory fitness (p = 0.022). CONCLUSIONS: Individuals on antihypertensive medications have a higher peak SBP response to exercise. Given the prognostic value of exaggerated peak SBP, control of exercise BP should be considered in routine BP assessment and in the treatment of hypertension.

Evaluating Workforce Mental Health and Well-Being: A Review of Assessments.

OBJECTIVE: Given the importance of mental health and well-being assessments to employers' efforts to optimize employee health and well-being, this paper reviews mental health assessments that have utility in the workplace. DATA SOURCE: A review of publicly available mental health and well-being assessments was conducted with a primary focus on burnout, general mental health and well-being, loneliness, psychological safety, resilience, and stress. INCLUSION CRITERIA: Assessments had to be validated for adult populations; available in English as a stand-alone tool; have utility in an employer setting; and not have a primary purpose of diagnosing a mental health condition. DATA EXTRACTION: All assessments were reviewed by a minimum of two expert reviewers to document number of questions, subscales, fee structure, international use, translations available, scoring/reporting, respondent (ie, employee or organization), and the target of the assessment (ie, mental health domain and organizational or individual level assessments. DATA SYNTHESIS & RESULTS: Sixty-six assessments across the six focus areas met inclusion criteria, enabling employers to select assessments that meet their self-identified measurement needs. CONCLUSION: This review provides employers with resources that can help them understand their workforce's mental health and well-being status across multiple domains, which can serve as a needs assessment, facilitate strategic planning of mental health and well-being initiatives, and optimize evaluation efforts.

A consensus method for estimating physical activity levels in adults using accelerometry.

Identifying the best analytical approach for capturing moderate-to-vigorous physical activity (MVPA) using accelerometry is complex but inconsistent approaches employed in research and surveillance limits comparability. We illustrate the use of a consensus method that pools estimates from multiple approaches for characterising MVPA using accelerometry. Participants (n = 30) wore an accelerometer on their right hip during two laboratory visits. Ten individual classification methods estimated minutes of MVPA, including cut-point, two-regression, and machine learning approaches, using open-source count and raw inputs and several epoch lengths. Results were averaged to derive the consensus estimate. Mean MVPA ranged from 33.9-50.4 min across individual methods, but only one (38.9 min) was statistically equivalent to the criterion of direct observation (38.2 min). The consensus estimate (39.2 min) was equivalent to the criterion (even after removal of the one individual method that was equivalent to the criterion), had a smaller mean absolute error (4.2 min) compared to individual methods (4.9-12.3 min), and enabled the estimation of participant-level variance (mean standard deviation: 7.7 min). The consensus method allows for addition/removal of methods depending on data availability or field progression and may improve accuracy and comparability of device-based MVPA estimates while limiting variability due to convergence between estimates.

Change in Metabolic Syndrome and Cardiorespiratory Fitness Following Exercise Training - The Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST).

Purpose: To evaluate how the changes in directly measured cardiorespiratory fitness (CRF) relate to the changes in metabolic syndrome (MetS) status following 4-6 months of exercise training. Methods: Maximal cardiopulmonary exercise (CPX) tests and MetS risk factors were analyzed prospectively from 336 adults (46% women) aged 45.8 ± 10.9 years. MetS was defined according to the National Cholesterol Education Program-Adult Treatment Panel III criteria, as updated by the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI). Pearson correlations, chi-squares, and dependent 2-tail t-tests were used to assess the relationship between the change in CRF and the change in MetS risk factors, overall number of MetS risk factors, and a MetS severity score following 4-6 months of participation in a self-referred, community-based exercise program. Results: Overall prevalence of MetS decreased from 23% to 14% following the exercise program (P < 0.05), while CRF improved 15% (4.7 ± 8.4 mL/kg/min, P < 0.05). Following exercise training, the number of positive risk factors declined from 1.4 ± 1.3 to 1.2 ± 1.2 in the overall cohort (P < 0.05). The change in CRF was inversely related to the change in the overall number of MetS risk factors (r = -0.22; P < 0.05) and the MetS severity score (r = -0.28; p < 0.05). Conclusion: This observational cohort study indicates an inverse relationship between the change in CRF and the change in MetS severity following exercise training. These results suggest that participation in a community-based exercise program yields significant improvements in CRF, MetS risk factors, the prevalence of the binary MetS, and the MetS severity score. Improvement in CRF through exercise training should be a primary prevention strategy for MetS.

Physical Activity Surveillance in the United States for Work and Commuting: Understanding the Impact on Population Health and Well-being.

OBJECTIVE: To summarize and describe the current US surveillance systems that assess physical activity (PA) for work and commuting. METHODS: An expert group conducted an environmental scan, generating a list (n = 18) which was ultimately reduced to 12, based on the inclusion of PA and/or sedentary behavior data. RESULTS: The 12 surveys or surveillance systems summarized provide nationally representative data on occupational-level PA or individual-level PA at work, data on active commuting, some are scorecards that summarize workplace health best practices and allow benchmarking, and one is a comprehensive nationally representative survey of employers assessing programs and practices in different worksites. CONCLUSIONS: The various surveillance systems and surveys/scorecards are disparate and need to be better analyzed and summarized to understand the impact of occupational-level PA and commuting on population health and well-being, life expectancy, and workforce productivity.

Comparison of non-exercise cardiorespiratory fitness prediction equations in apparently healthy adults.

AIMS: A recent scientific statement suggests clinicians should routinely assess cardiorespiratory fitness using at least non-exercise prediction equations. However, no study has comprehensively compared the many non-exercise cardiorespiratory fitness prediction equations to directly-measured cardiorespiratory fitness using data from a single cohort. Our purpose was to compare the accuracy of non-exercise prediction equations to directly-measured cardiorespiratory fitness and evaluate their ability to classify an individual's cardiorespiratory fitness. METHODS: The sample included 2529 tests from apparently healthy adults (42% female, aged 45.4 ± 13.1 years (mean±standard deviation). Estimated cardiorespiratory fitness from 28 distinct non-exercise prediction equations was compared with directly-measured cardiorespiratory fitness, determined from a cardiopulmonary exercise test. Analysis included the Benjamini-Hochberg procedure to compare estimated cardiorespiratory fitness with directly-measured cardiorespiratory fitness, Pearson product moment correlations, standard error of estimate values, and the percentage of participants correctly placed into three fitness categories. RESULTS: All of the estimated cardiorespiratory fitness values from the equations were correlated to directly measured cardiorespiratory fitness (p < 0.001) although the R2 values ranged from 0.25-0.70 and the estimated cardiorespiratory fitness values from 27 out of 28 equations were statistically different compared with directly-measured cardiorespiratory fitness. The range of standard error of estimate values was 4.1-6.2 ml·kg-1·min-1. On average, only 52% of participants were correctly classified into the three fitness categories when using estimated cardiorespiratory fitness. CONCLUSION: Differences exist between non-exercise prediction equations, which influences the accuracy of estimated cardiorespiratory fitness. The present analysis can assist researchers and clinicians with choosing a non-exercise prediction equation appropriate for epidemiological or population research. However, the error and misclassification associated with estimated cardiorespiratory fitness suggests future research is needed on the clinical utility of estimated cardiorespiratory fitness.

Accuracy of Exercise-based Equations for Estimating Cardiorespiratory Fitness.

Equations are often used to predict cardiorespiratory fitness (CRF) from submaximal or maximal exercise tests. However, no study has comprehensively compared these exercise-based equations with directly measured CRF using data from a single, large cohort. PURPOSE: This study aimed to compare the accuracy of exercise-based prediction equations with directly measured CRF and evaluate their ability to classify an individual's CRF. METHODS: The sample included 4871 tests from apparently healthy adults (38% female, age 44.4 ± 12.3 yr (mean ± SD)). Estimated CRF (eCRF) was determined from 2 nonexercise equations, 3 submaximal exercise equations, and 10 maximal exercise equations; all eCRF calculations were then compared with directly measured CRF, determined from a cardiopulmonary exercise test. Analysis included Pearson product-moment correlations, standard error of estimate values, intraclass correlation coefficients, Cohen κ coefficients, and the Benjamini-Hochberg procedure to compare eCRF with directly measured CRF. RESULTS: All eCRF values from the prediction equations were associated with directly measured CRF (P < 0.01), with intraclass correlation coefficient estimates ranging from 0.07 to 0.89. Although significant agreement was found when using eCRF to categorize participants into fitness tertiles, submaximal exercise equations correctly classified an average of only 51% (range, 37%-58%) and maximal exercise equations correctly classified an average of only 59% (range, 43%-76%). CONCLUSIONS: Despite significant associations between exercise-based prediction equations and directly measured CRF, the equations had a low degree of accuracy in categorizing participants into fitness tertiles, a key requirement when stratifying risk within a clinical setting. The present analysis highlights the limited accuracy of exercise-based determinations of eCRF and suggests the need to include cardiopulmonary measures with maximal exercise to accurately assess CRF within a clinical setting.

Influence of Incentive Design and Organizational Characteristics on Wellness Participation and Health Outcomes.

OBJECTIVE: To explore how changing incentive designs influence wellness participation and health outcomes. METHODS: Aggregated retrospective data were evaluated using cluster analysis to group 174 companies into incentive design types. Numerous statistical models assessed between-group differences in wellness participation, earning incentives, and over-time differences in health outcomes. RESULTS: Four incentive design groups based on requirements for earning incentives were identified. The groups varied in support for and participation in wellness initiatives within each company. All four design types were associated with improved low density lipoprotein (LDL) (P < 0.01), three with improved blood pressure (P < 0.001), and two with improved fasting glucose (P < 0.03). No incentive plan types were associated with improved body mass index (BMI), but designs predominantly focused on health outcomes (eg, Outcomes-Focused) exhibited a significant increase over time in BMI risk. CONCLUSION: Incentive design and organizational characteristics impact population-level participation and health outcomes.

Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were: <2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Accuracy of Nonexercise Prediction Equations for Assessing Longitudinal Changes to Cardiorespiratory Fitness in Apparently Healthy Adults: BALL ST Cohort.

Background Repeated assessment of cardiorespiratory fitness (CRF) improves mortality risk predictions in apparently healthy adults. Accordingly, the American Heart Association suggests routine clinical assessment of CRF using, at a minimum, nonexercise prediction equations. However, the accuracy of nonexercise prediction equations over time is unknown. Therefore, we compared the ability of nonexercise prediction equations to detect changes in directly measured CRF. Methods and Results The sample included 987 apparently healthy adults from the BALL ST (Ball State Adult Fitness Longitudinal Lifestyle Study) cohort (33% women; average age, 43.1±10.4 years) who completed 2 cardiopulmonary exercise tests ≥3 months apart (3.2±5.4 years of follow-up). The change in estimated CRF (eCRF) from 27 distinct nonexercise prediction equations was compared with the change in directly measured CRF. Analysis included Pearson product moment correlations, SEE values, intraclass correlation coefficient values, Cohen's κ coefficients, γ coefficients, and the Benjamini-Hochberg procedure to compare eCRF with directly measured CRF. The change in eCRF from 26 of 27 equations was significantly associated to the change in directly measured CRF (P<0.001), with intraclass correlation coefficient values ranging from 0.06 to 0.63. For 16 of the 27 equations, the change in eCRF was significantly different from the change in directly measured CRF. The median percentage of participants correctly classified as having increased, decreased, or no change in CRF was 56% (range, 39%-61%). Conclusions Variability was observed in the accuracy between nonexercise prediction equations and the ability of equations to detect changes in CRF. Considering the appreciable error that prediction equations had with detecting even directional changes in CRF, these results suggest eCRF may have limited clinical utility.

Cardiorespiratory Fitness Normalized to Fat-Free Mass and Mortality Risk.

PURPOSE: Cardiorespiratory fitness (CRF) is known to be directly related to fat-free mass (FFM), therefore, it has been suggested that normalizing CRF to FFM (V˙O2peakFFM) may be the most accurate expression of CRF as related to exercise performance and cardiorespiratory function. However, the influence of V˙O2peakFFM (mL·kg FFM·min) on predicting mortality has been largely unexplored. This study aimed to primarily assess the relationship between V˙O2peakFFM and all-cause and disease-specific mortality risk in apparently healthy adults. Further, this study sought to compare the predictive ability of V˙O2peakFFM to V˙O2peak normalized to total body weight (V˙O2peakTBW) for mortality outcomes. METHODS: Participants included 2905 adults (1555 men, 1350 women) who completed a cardiopulmonary exercise test between 1970 and 2016 to determine CRF. Body composition was assessed using the skinfold method to estimate FFM. Cardiorespiratory fitness was expressed as V˙O2peakTBW and V˙O2peakFFM. Participants were followed for 19.0 ± 11.7 yr after their cardiopulmonary exercise test for mortality outcomes. Cox-proportional hazard models were performed to determine the relationship of V˙O2peakFFM with mortality outcomes. Parameter estimates were assessed to compare the predictive ability of CRF expressed as V˙O2peakTBW and V˙O2peakFFM. RESULTS: Overall, V˙O2peakFFM was inversely related to all-cause, cardiovascular disease, and cancer mortality, with a 16.2%, 8.4%, and 8.0% lower risk per 1 mL·kg FFM·min improvement, respectively (P < 0.01). Further, assessment of the parameter estimates showed V˙O2peakFFM to be a significantly stronger predictor of all-cause mortality than V˙O2peakTBW (parameter estimates, -0.49 vs -0.16). CONCLUSIONS: Body composition is an important factor when considering the relationship between CRF and mortality risk. Clinicians should consider normalizing CRF to FFM when feasible, because it will strengthen the predictive power of the measure.

Workplace Well-Being Factors That Predict Employee Participation, Health and Medical Cost Impact, and Perceived Support.

PURPOSE: This study tested relationships between health and well-being best practices and 3 types of outcomes. DESIGN: A cross-sectional design used data from the HERO Scorecard Benchmark Database. SETTING: Data were voluntarily provided by employers who submitted web-based survey responses. SAMPLE: Analyses were limited to 812 organizations that completed the HERO Scorecard between January 12, 2015 and October 2, 2017. MEASURES: Independent variables included organizational and leadership support, program comprehensiveness, program integration, and incentives. Dependent variables included participation rates, health and medical cost impact, and perceptions of organizational support. ANALYSIS: Three structural equation models were developed to investigate the relationships among study variables. RESULTS: Model sample size varied based on organizationally reported outcomes. All models fit the data well (comparative fit index > 0.96). Organizational and leadership support was the strongest predictor (P < .05) of participation (n = 276 organizations), impact (n = 160 organizations), and perceived organizational support (n = 143 organizations). Incentives predicted participation in health assessment and biometric screening (P < .05). Program comprehensiveness and program integration were not significant predictors (P > .05) in any of the models. CONCLUSION: Organizational and leadership support practices are essential to produce participation, health and medical cost impact, and perceptions of organizational support. While incentives influence participation, they are likely insufficient to yield downstream outcomes. The overall study design limits the ability to make causal inferences from the data.

Development and Validity of a Workplace Health Promotion Best Practices Assessment.

OBJECTIVE: To explore the factor structure of the HERO Health and Well-being Best Practices Scorecard in Collaboration with Mercer (HERO Scorecard) to develop a reduced version and examine the reliability and validity of that version. METHODS: A reduced version of the HERO Scorecard was developed through formal statistical analyses on data collected from 845 organizations that completed the original HERO Scorecard. RESULTS: The final factors in the reduced Scorecard represented content pertaining to organizational and leadership support, program comprehensiveness, program integration, and incentives. All four implemented practices were found to have a strong, statistically significant effect on perceived effectiveness. Organizational and leadership support had the strongest effect (ß = 0.56), followed by incentives (ß = 0.23). CONCLUSION: The condensed version of the HERO Scorecard has the potential to be a promising tool for future research on the extent to which employers are adopting best practices in their health and well-being (HWB) initiatives.