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The disruption, trauma, and stressors brought by COVID-19 have increased recognition and normalization of workforce mental health needs. 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. [ABSTRACT FROM AUTHOR]

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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−1·min−1) 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−1·min−1 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. [ABSTRACT FROM AUTHOR]

Objective: To explore the factor structure of the HERO Health and Wellbeing 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 (β = 0.56), followed by incentives (β = 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Body composition assessments commonly focus predominantly on fat mass, however lean mass (LM) measurements also provide useful information regarding clinical and nutritional status. LM measurements help predict health outcomes and diagnose sarcopenia, which has been associated with frailty. Dual energy x-ray absorptiometry (DXA) is an established technique used in clinical and research settings to assess body composition including total and regional LM. Currently, there are no reference values available that were derived from GE-Healthcare DXA systems directly for US adults for LM, LM index (LMI), percent LM (%LM), and appendicular lean mass index (ALMI) and it is known that whole-body and regional LM measures differ by DXA manufacturer. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article discusses the benefits of physical activity on brain health and emphasizes the importance of integrating physical activity promotion into workplace health promotion efforts. Regular physical activity has positive effects on mental health and cognitive function, and it can reduce the risk of cognitive decline. The article highlights the relationship between physical activity and brain health, including its impact on anxiety, depression, cognition, dementia, and other neurological conditions. It also suggests strategies for integrating physical activity into worksite health promotion, such as policy and systems changes, benchmarking, and scorecards. The article concludes by calling for further research to provide specific physical activity prescriptions for improving cognition and reducing the risk of neurological conditions. [Extracted from the article]

Introduction: Over the past 30 years, cross-sectional studies have established an inverse relationship between cardiorespiratory fitness (CRF) and mortality. Exercise training typically leads to improvements in CRF. Given the strong association between CRF and mortality, the change in CRF post-exercise training could alter one's mortality risk. However, longitudinal analyses assessing the influence of short-term changes in CRF on mortality have not been assessed in apparently healthy adults. The primary purpose of this study was to assess the influence of post-exercise training (≤8 months) changes in directly measured CRF on all-cause mortality risk in a cohort of self-referred, apparently healthy men and women. Methods: Participants included 689 adults (410 men, 279 women; mean age: 42.7± 11.0 years) who underwent two maximal cardiopulmonary exercise tests (CPX) between 1970 and 2012 to determine CRF at baseline and post-exercise training. Participants were followed for 29.8 ± 10.7 years after their post-exercise CPX for mortality outcomes. Cox-proportional hazard models were performed to determine the relationship of CRF change with all-cause mortality outcomes. Results: During follow-up, 186 participants died (27%). Overall, the change in CPX-derived CRF was inversely related to all-cause mortality, with a 7% lower risk reduction per 1 ml·kg-1·min-1 improvement. Remaining unfit was associated with approximately 2-fold higher risk for all-cause mortality (hazard ratio 1.99, 95% CI 1.18-3.35), compared with those that stayed fit. The relationship remained significant when men and women were examined independently. Following multivariable adjustment an approximately 6 and 13% lower mortality risk was found per 1 ml·kg-1·min-1 improvement in men and women respectively (p<0.01). Conclusions: Improving CRF through exercise training can lower the risk for premature mortality. Therefore, clinicians should encourage individuals, especially those identified as unfit, to participate in an exercise training program to improve CRF, as it may provide survival benefit. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. 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. [ABSTRACT FROM AUTHOR]

Purpose: Cardiopulmonary exercise testing (CPX) provides valuable clinical information, including peak ventilation (V ˙Epeak), which has been shown to have diagnostic and prognostic value in the assessment of patients with underlying pulmonary disease. This report provides reference standards for V ˙Epeak derived from CPX on treadmills in apparently healthy individuals. Methods: Nine laboratories in the United States experienced in CPX administration with established quality control procedures contributed to the Fitness Registry and the Importance of Exercise National Database from 2014 to 2017. Data from 5232 maximal exercise tests from men and women without cardiovascular or pulmonary disease were used to create percentiles of V ˙Epeak for both men and women by decade between 20 and 79 yr. Additionally, prediction equations were developed for V ˙Epeak using descriptive information. R esults : V ˙Epeak was found to be significantly different between men and women and across age groups (P < 0.05). The rate of decline in V ˙Epeak was 8.0% per decade for both men and women. A stepwise regression model of 70% of the sample revealed that sex, age, and height were significant predictors of V ˙Epeak. The equation was cross-validated with data from the remaining 30% of the sample with a final equation developed from the full sample (r = 0.73). Additionally, a linear regression model revealed forced expiratory volume in 1 s significantly predicted V ˙Epeak (r = 0.73). Conclusions: Reference standards were developed for V ˙Epeak for the United States population. Cardiopulmonary exercise testing laboratories will be able to provide interpretation of V ˙Epeak from these age and sex-specific percentile reference values or alternatively can use these nonexercise prediction equations incorporating sex, age, and height or with a single predictor of forced expiratory volume in 1 s. [ABSTRACT FROM AUTHOR]

One large employer surveyed their employees; with greater than 90% response, employees most frequently describe returning to an office as i ' I pointless'. Employers are concerned with employees' well-being, work culture, employee burnout, and retention. In response to COVID-19, many employers have offered increased flexibility for where employees work, including remote (an employee's workstation is at home) and hybrid work (an employee works both at the employer worksite and remotely, on pre-determined schedules). [Extracted from the article]

Hybrid environments and remote work are shifting how employees in more sedentary positions work during the day and how employers can offer opportunities for employees to be physically active. Additionally, organizations with a higher proportion of employees working from home reported their physical work environment to be less likely to support physical activity by providing amenities such as onsite fitness centers, walking trails, and standing desks.[20] A person's employment industry determines how and when they work. Keywords: physical activity; exercise; workplace wellness; workplace physical activity; remote work; technology; activity tracking; exercise is medicine; community partnerships EN physical activity exercise workplace wellness workplace physical activity remote work technology activity tracking exercise is medicine community partnerships 723 730 8 06/06/23 20230601 NES 230601 Introduction How and where we do our work is changing in the United States across industry, government, and non-profit sectors. These employees may get more of their physical activity at work (ie, occupational physical activity) vs in leisure time, so workplace health promotion programs should prioritize the safety of their physical activity and adjust for other health and well-being priorities like work-life balance. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recent industry surveys indicate that a majority of employers are offering health and well-being (HWB) programs to their employees,[1],[2] but the reasons for offering them have changed over time. Metrics which are directly related can be positioned most proximal to the central key metric while indirectly related metrics might be positioned in the outer periphery and linked to the more proximal metrics to indicate how their influence is realized. Executive leaders are likely to be interested in business performance and financial impact metrics, but it is important to demonstrate how leading process indicators such as program utilization and satisfaction rates as well as health outcomes influence these metrics. The Value Demonstration Dashboard organizes the meaningful metrics into a narrative about how the metrics are related to one another and link the HWB initiative to broader organizational goals. [Extracted from the article]

Most employers understand the importance of measuring and evaluating their health and well-being initiatives. As consultants, researchers, and practitioners working in the workplace wellness field for decades, we've often observed organizations that are benefits and data rich but information poor. In a concise manner, the dashboard data should be able to identify and support key messages about the value the organization is realizing as a result of its investment in workforce HWB. Consider the data you will need, identify the ease of obtaining the data, the level of detail that will be available, and associated data use rights. The data included in the Well-Being Dashboard is not dynamic, but rather an annual snapshot representing HRA data for the University of Iowa. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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% T 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. [ABSTRACT FROM AUTHOR]

This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square error (RMSE) for EE prediction (1.04–1.23 vs. 1.10–1.34 METs for V1/V2, V2/V2), and the ankle-worn accelerometer had the lowest RMSE of all accelerometers (1.04–1.18 vs. 1.17–1.34 METs for other placements). The ankle-worn accelerometer and associated EE prediction models developed using data from both structured and simulated free-living settings should be considered for optimal EE prediction accuracy. [ABSTRACT FROM PUBLISHER]