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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]

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]

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. [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]

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]

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]

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]

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]