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.

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.

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.

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.

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.

Cardiorespiratory Fitness and Mortality in Healthy Men and Women

BACKGROUND

There is a well-established inverse relationship between cardiorespiratory fitness (CRF) and mortality. However, this relationship has almost exclusively been studied using estimated CRF.

OBJECTIVES

This study aimed to assess the association of directly measured CRF, obtained using cardiopulmonary exercise (CPX) testing with all-cause, cardiovascular disease (CVD), and cancer mortality in apparently healthy men and women.

METHODS

Participants included 4,137 self-referred apparently healthy adults (2,326 men, 1,811 women; mean age: 42.8 ± 12.2 years) who underwent CPX testing to determine baseline CRF. Participants were followed for 24.2 ± 11.7 years (1.1 to 49.3 years) for mortality. Cox-proportional hazard models were performed to determine the relationship of CRF (ml·kg^-1·min^-1) and CRF level (low, moderate, and high) with mortality outcomes.

RESULTS

During follow-up, 727 participants died (524 men, 203 women). CPX-derived CRF was inversely related to all-cause, CVD, and cancer mortality. Low CRF was associated with higher risk for all-cause (hazard ratio [HR]: 1.73; 95% confidence interval [CI]: 1.20 to 3.50), CVD (HR: 2.27; 95% CI: 1.20 to 3.49), and cancer (HR: 2.07; 95% CI: 1.18 to 3.36) mortality compared with high CRF. Further, each metabolic equivalent increment increase in CRF was associated with a 11.6%, 16.1%, and 14.0% reductions in all-cause, CVD, and cancer mortality, respectively.

CONCLUSIONS

Given the prognostic ability of CPX-derived CRF for all-cause and disease-specific mortality outcomes, its use should be highly considered for apparently healthy populations as it may help to improve the efficacy of the individualized patient risk assessment and guide clinical decisions.

The Influence of Change in Cardiorespiratory Fitness With Short-Term Exercise Training on Mortality Risk From The Ball State Adult Fitness Longitudinal Lifestyle Study

OBJECTIVE

To assess the influence of changes in cardiorespiratory fitness (CRF) after exercise training on mortality risk in a cohort of self-referred, apparently healthy adults.

PATIENTS AND METHODS

A total of 683 participants (404 men, 279 women; mean age: 42.7±11.0 y) underwent two maximal cardiopulmonary exercise tests (CPX) between March 20, 1970, and December 11, 2012, to assess CRF at baseline (CPX1) and post-exercise training (CPX2). Participants were followed for an average of 29.8±10.7 years after their CPX2. Cox proportional hazards models were performed to determine the relationship of CRF change with mortality, with change in CRF as a continuous variable, as well as a categorical variable. A Wald chi-square test was used to compare the coefficients estimating the relationship of peak oxygen consumption (VO_2peak) at CPX1 with VO_2peak measured at CPX2 with time until death for all-cause mortality.

RESULTS

During the follow-up period there were 180 deaths. When assessed independently, there were 20% (95% CI, 10-49%) and 38% (95% CI, 7-66%) lower mortality risks per 1 metabolic equivalent improvement in CRF (P<.01) in men and women, respectively, after multivariable adjustment. Those that remained unfit had ∼2-fold higher risk for all-cause mortality compared with those that remained fit and CRF at CPX2 was a stronger predictor of all-cause mortality than at CPX1 (P=.02).

CONCLUSION

Improving CRF through exercise training lowers mortality risk. Clinicians should encourage individuals to participate in exercise training to improve CRF to lower risk of mortality.

Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

To enable inter- and intrastudy comparisons it is important to ascertain comparability among accelerometer models.

PURPOSE

The purpose of this study was to compare raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers.

METHODS

Adults (n = 26 (n = 15 women); age, 49.1 ± 20.0 yr) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12-21 sedentary, household, and ambulatory/exercise activities lasting 2-15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities was derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent agreement was used to compare epoch-by-epoch activity intensity.

RESULTS

For raw data, correlations for mean acceleration were 0.96 ± 0.05, 0.89 ± 0.16, 0.71 ± 0.33, and 0.80 ± 0.28, and those for variance were 0.98 ± 0.02, 0.98 ± 0.03, 0.91 ± 0.06, and 1.00 ± 0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00 ± 0.01, 0.98 ± 0.02, 0.96 ± 0.04, and 1.00 ± 0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher percent agreement for activity intensity (95.1% ± 5.6% and 95.5% ± 4.0%) compared with the Montoye 2015 raw data model (61.5% ± 27.6%; P < 0.001).

CONCLUSIONS

Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers.

Cardiorespiratory Fitness Is Inversely Associated With Clustering of Metabolic Syndrome Risk Factors: The Ball State Adult Fitness Program Longitudinal Lifestyle Study

Objective

The focus of this study was the association between the metabolic syndrome (MetSyn) and cardiorespiratory fitness (CRF) defined as maximal oxygen uptake (VO_2max). Although previous research has shown a relationship between MetSyn and CRF, most studies are based on less objective measures of CRF and different cardiometabolic risk factor thresholds from earlier guidelines.

Participants and Methods

The metabolic markers included in the present study were central obesity, elevated plasma triglycerides, elevated fasting high-density lipoprotein cholesterol, impaired fasting plasma glucose, hypertension, or pharmacologic treatment for diagnosed hypertension, hypertriglyceridemia, low high-density lipoprotein cholesterol, or diabetes. A cohort of 3636 adults (1629 women, 2007 men; mean ± SD age, 44.7±12.3 years) completed CRF and metabolic risk factor assessment between January 1, 1971, and November 1, 2016. The CRF was defined as a measured VO_2max from a cardiopulmonary exercise test on a treadmill, with a respiratory exchange ratio value of 1.0 or more.

Results

Prevalence of MetSyn (≥3 factors) was 26% (n=953) in the cohort, with men having a greater likelihood for MetSyn compared with women (P<.001). The difference in VO_2max between those individuals with MetSyn and those without was approximately 2.3 (2.0-2.5) metabolic equivalents. Logistic regression analyses showed a significant inverse and graded association between quartiles of CRF and MetSyn for the group overall (P<.001), with odds ratios (95% CI) using the lowest fitness group as the referent group of 0.67 (0.55-0.81), 0.41 (0.34-0.51), and 0.10 (0.07-0.14) for VO_2max (P<.001). The sex-specific odds ratios were 0.25 (0.18-0.34), 0.05 (0.02-0.10), and 0.02 (0.01-0.09) for women and 0.43 (0.31-0.59), 0.19 (0.14-0.27), and 0.03 (0.02-0.05) for men (P<.001).

Conclusion

These results with current risk factor thresholds and a large number of women demonstrate that low VO_2max is associated with MetSyn.

Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure

BACKGROUND/AIM

Consumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol.

METHODS

Thirty men and women (18-80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80 min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn't cycle.

RESULTS

Participants took 3321±571 steps, had 28±6 active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%-32% (p<0.01); additionally the Fitbit Flex, UP24, and ActiGraph underestimated active minutes by 35%-65% (p<0.05). Underestimations of PA and EE variables were found to be similar in the subgroup analysis.

CONCLUSION

Consumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors.

Reference standards for lean mass measures using GE dual energy x-ray absorptiometry in Caucasian adults

OBJECTIVE

To develop reference values by age and sex for LM measures using GE-Healthcare DXA systems.

METHODS

A de-identified sample was obtained from Ball State University's Clinical Exercise Physiology Laboratory and University of Wisconsin-Milwaukee's Physical Activity & Health Research Laboratory. DXA scans of 2,076 women and 1,251 men were completed using a GE Lunar Prodigy or iDXA. Percentiles (%ile) were calculated for all variables of interest (LM, LMI, %LM, and ALMI) and a factorial ANOVA was used to assess differences for each variable between 10-year age groups and sex, as well as the interaction between age and sex.

RESULTS

Men had higher mean total LM, %LM, LMI, and ALMI than women (p<0.01), across all age groups. All LM variables decreased significantly over the 5 decades in men, however in women only total LM, %LM, and ALMI decreased from the youngest to oldest age groups (p<0.01).

CONCLUSION

These reference values provide for a more accurate interpretation of GE-Healthcare DXA-derived LM measurements offering clinicians and researchers with an initial resource to aid in the early detection and assessment of LM deficits.

Reference standards for body fat measures using GE dual energy x-ray absorptiometry in Caucasian adults

Background

Dual energy x-ray absorptiometry (DXA) is an established technique for the measurement of body composition. Reference values for these variables, particularly those related to fat mass, are necessary for interpretation and accurate classification of those at risk for obesity-related health complications and in need of lifestyle modifications (diet, physical activity, etc.). Currently, there are no reference values available for GE-Healthcare DXA systems and it is known that whole-body and regional fat mass measures differ by DXA manufacturer.

Objective

To develop reference values by age and sex for DXA-derived fat mass measurements with GE-Healthcare systems.

Methods

A de-identified sample of 3,327 participants (2,076 women, 1,251 men) was obtained from Ball State University’s Clinical Exercise Physiology Laboratory and University of Wisconsin-Milwaukee’s Physical Activity & Health Research Laboratory. All scans were completed using a GE Lunar Prodigy or iDXA and data reported included percent body fat (%BF), fat mass index (FMI), and ratios of android-to-gynoid (A/G), trunk/limb, and trunk/leg fat measurements. Percentiles were calculated and a factorial ANOVA was used to determine differences in the mean values for each variable between age and sex.

Results

Normative reference values for fat mass variables from DXA measurements obtained from GE-Healthcare DXA systems are presented as percentiles for both women and men in 10-year age groups. Women had higher (p<0.01) mean %BF and FMI than men, whereas men had higher (p<0.01) mean ratios of A/G, trunk/limb, and trunk/leg fat measurements than women.

Conclusion

These reference values provide clinicians and researchers with a resource for interpretation of DXA-derived fat mass measurements specific to use with GE-Healthcare DXA systems.

Reference Standards for Cardiorespiratory Fitness Measured With Cardiopulmonary Exercise Testing Using Cycle Ergometry: Data From the Fitness Registry and the Importance of Exercise National Database (FRIEND) Registry

The importance of cardiorespiratory fitness (CRF) is well established. This report provides newly developed standards for CRF reference values derived from cardiopulmonary exercise testing (CPX) using cycle ergometry in the United States. Ten laboratories in the United States experienced in CPX administration with established quality control procedures contributed to the "Fitness Registry and the Importance of Exercise: A National Database" (FRIEND) Registry from April 2014 through May 2016. Data from 4494 maximal (respiratory exchange ratio, ≥1.1) cycle ergometer tests from men and women (20-79 years) from 27 states, without cardiovascular disease, were used to develop these references values. Percentiles of maximum oxygen consumption (VO_2max) for men and women were determined for each decade from age 20 years through age 79 years. Comparisons of VO_2max were made to reference data established with CPX data from treadmill data in the FRIEND Registry and previously published reports. As expected, there were significant differences between sex and age groups for VO_2max (P<.01). For cycle tests within the FRIEND Registry, the 50th percentile VO_2max of men and women aged 20 to 29 years declined from 41.9 and 31.0 mLO₂/kg/min to 19.5 and 14.8 mLO₂/kg/min for ages 70 to 79 years, respectively. The rate of decline in this cohort was approximately 10% per decade. The FRIEND Registry reference data will be useful in providing more accurate interpretations for the US population of CPX-measured VO_2max from exercise tests using cycle ergometry compared with previous approaches based on estimations of standard differences from treadmill testing reference values.