Age-related effects of neuromuscular fatigue and acute recovery responses on maximal and rapid torque measures of the leg extensors and flexors.

PURPOSE: To examine the effects of neuromuscular fatigue and recovery on maximal and rapid torque characteristics in young and old men for the leg extensors and flexors. METHODS: Twenty-one young (age = 24.8 years) and 19 old (72.1 years) men performed maximal voluntary contractions (MVCs) before and at 0, 7, 15, and 30 min following an intermittent submaximal fatigue task. Outcome measures included endurance time, maximal (peak torque; PT) and rapid (absolute and normalized rate of torque development; RTD and nRTD) torque characteristics. RESULTS: The old men had greater endurance times than the young men. Differential recovery patterns were observed for PT, and early and late RTD phases between the leg extensor and flexor muscle groups such that the early rapid torque variables and the flexors demonstrated slower recovery compared to later rapid torque variables and the extensors. The normalized RTD variables were reduced less after the fatigue task and differential muscle and age effects were observed where the flexors were reduced more at the early phase (nRTD1/6) compared to the extensors, however, for the later phase (nRTD2/3) the young men exhibited a greater reduction compared to the old men. CONCLUSIONS: Dissimilar fatigue recovery patterns across different phases of RTD, lower limb muscles, and age groups may have important fatigue-related performance and injury risk implications across the adult lifespan.

Quantifying time spent outdoors: A versatile method using any type of global positioning system (GPS) and accelerometer devices.

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices.

Accelerometer techniques for capturing human movement validated against direct observation: a scoping review.

INTRODUCTION: Accelerometers are devices commonly used to measure human physical activity and sedentary time. Accelerometer capabilities and analytical techniques have evolved rapidly, making it difficult for researchers to keep track of advances and best practices for data processing and analysis. OBJECTIVE: The objective of this scoping review is to determine the existing methods for analyzing accelerometer data for capturing human movement which have been validated against the criterion measure of direct observation. METHODS: This scoping review searched 14 academic and 5 grey databases. Two independent raters screened by title and abstract, then full text. Data were extracted using Microsoft Excel and checked by an independent reviewer. RESULTS: The search yielded 1039 papers and the final analysis included 115 papers. 71 unique accelerometer models were used across a total of 4217 participants. While all studies underwent validation from direct observation, most direct observation occurred live (55%) or using recordings (42%). Analysis techniques included machine learning approaches (22%), the use of existing cut-points (18%), ROC curves to determine cut-points (14%), and other strategies including regressions and non-machine learning algorithms (8%). DISCUSSION: Machine learning techniques are becoming more prevalent and are often used for activity identification. Cut-point methods are still frequently used. Activity intensity is the most assessed activity outcome; however, both the analyses and outcomes assessed vary by wear location. CONCLUSIONS: This scoping review provides a comprehensive overview of accelerometer analysis and validation techniques using direct observation and is a useful tool for researchers using accelerometers.

Association of Recess Provision With Accelerometer-Measured Physical Activity and Sedentary Time in a Representative Sample of 6- to 11-Year-Old Children in the United States.

PURPOSE: To assess the association between the amount of recess provision and children's accelerometer-measured physical activity (PA) levels. METHODS: Parents/guardians of 6- to 11-year-olds (n = 451) in the 2012 National Youth Fitness Survey reported recess provision, categorized as low (10-15 min; 31.9%), medium (16-30 min; 48.0%), or high (>30 min; 20.1%). Children wore a wrist-worn accelerometer for 7 days to estimate time spent sedentary, in light PA, and in moderate to vigorous PA using 2 different cut points for either activity counts or raw acceleration. Outcomes were compared between levels of recess provision while adjusting for covariates and the survey's multistage, probability sampling design. RESULTS: Children with high recess provision spent less time sedentary, irrespective of type of day (week vs weekend) and engaged in more light or moderate to vigorous PA on weekdays than those with low recess provision. The magnitude and statistical significance of effects differed based on the cut points used to classify PA (eg, 4.7 vs 11.9 additional min·d-1 of moderate to vigorous PA). CONCLUSIONS: Providing children with >30 minutes of daily recess, which exceeds current recommendations of ≥20 minutes, is associated with more favorable PA levels and not just on school days. Identifying the optimal method for analyzing wrist-worn accelerometer data could clarify the magnitude of this effect.

Adherence to Recess Guidelines in the United States Using Nationally Representative Data: Implications for Future Surveillance Efforts.

BACKGROUND: National adherence to the recess recommendations of the Centers for Disease Control and Prevention (CDC) has not been comprehensively studied in the United States. METHODS: Data from 6 nationally representative data sets over the last decade (Classification of Laws Associated with School Students, Early Childhood Longitudinal Study, National Health and Nutrition Examination Survey, National Youth Fitness Survey, School Health Policies and Practices Survey, and the School Nutrition and Meal Cost Study) provided estimates for adherence to CDC recess guidelines. RESULTS: While approximately 65-80% of elementary school-children receive the recommended 20+ minutes of daily recess according to parent-, principal-, and school-report, adherence declines by sixth grade, and little information is available for middle/high school students. Adherence to playground safety was high (90%), but adherence to recommendations about recess before lunch (<50%), withholding recess as punishment (∼50%), and training recess staff (<50%) were lower. CONCLUSIONS: School policy and practice should align with CDC recommendations, with the aim of providing sufficient quality recess to all youth, K-12th grade. Comprehensive, on-going national surveillance of multiple recess domains is needed to inform policy and ensure equitable provision of recess.

Elementary Classroom Views of Nature Are Associated with Lower Child Externalizing Behavior Problems.

Exposure to nature views has been associated with diverse mental health and cognitive capacity benefits. Yet, much of this evidence was derived in adult samples and typically only involves residential views of nature. Findings from studies with children suggest that when more greenness is available at home or school, children have higher academic performance and have expedited attention restoration, although most studies utilize coarse or subjective assessments of exposure to nature and largely neglect investigation among young children. Here, we investigated associations between objectively measured visible nature at school and children's behavior problems (attention and externalizing behaviors using the Brief Problem Monitor Parent Form) in a sample of 86 children aged seven to nine years old from 15 classrooms across three schools. Images of classroom windows were used to quantify overall nature views and views of specific nature types (sky, grass, tree, shrub). We fitted separate Tobit regression models to test associations between classroom nature views and attention and externalizing behaviors, accounting for age, sex, race/ethnicity, residential deprivation score, and residential nature views (using Google Street View imagery). We found that higher levels of visible nature from classroom windows were associated with lower externalizing behavior problem scores, after confounder adjustment. This relationship was consistent for visible trees, but not other nature types. No significant associations were detected for attention problems. This initial study suggests that classroom-based exposure to visible nature, particularly trees, could benefit children's mental health, with implications for landscape and school design.

Relationship between neighborhood walkability and the prevalence, type, timing, and temporal characteristics of walking.

We examined associations of neighborhood walkability with the prevalence, type, timing, and temporal characteristics of walking in a representative sample of United States adults. Adults (N = 2649) completed the ACT24 previous-day recall. Home address was linked to block-group National Walkability Index. Survey-adjusted Poisson and logistic regression examined the association of walkability with outcomes. Those who lived in more walkable neighborhoods were more likely to walk overall, for transport, or in the evening. In those who walked, higher walkability was associated with less morning but more evening walking. There were no associations of walkability with the frequency or duration of walking episodes.

Association of Recess Provision With Elementary School-Aged Children's Physical Activity, Adiposity, and Cardiorespiratory and Muscular Fitness.

PURPOSE: To identify associations between amount of school recess provision and children's physical activity (PA), weight status, adiposity, cardiorespiratory endurance, muscular strength, and muscular endurance. METHOD: Data from 6- to 11-year-old participants (n = 499) in the 2012 National Youth Fitness Survey were analyzed. Parents/guardians reported children's PA levels and recess provision, categorized as no/minimal (9.0%), low (26.1%), medium (46.0%), or high (18.9%). Children wore a wrist-worn accelerometer for 7 days and completed anthropometric measurements. Fitness was assessed using grip strength and treadmill, pull-up, and plank tests. Cross-sectional linear and logistic regression compared outcomes across levels of recess provision adjusting for the survey's complex sampling design. RESULTS: Children with high provision of recess were 2.31 times more likely to meet PA guidelines according to parent report than those with no/minimal recess. Accelerometer-measured PA followed a more U-shaped pattern, wherein PA was higher in children with high, compared to low, recess provision but comparable to those with no/minimal recess provision. There were no associations with weight status, adiposity, or fitness. CONCLUSION: Current recess recommendations (20 min·d-1) may be insufficient as 30 minutes per day of recess was associated with a 2-fold greater likelihood of achieving recommended PA levels. Additional research on recess quantity and quality is needed.

Using linked accelerometer and GPS data for characterizing children's schoolyard physical activity: An overview of hot spot analytic decisions with reporting guidelines.

Hot spot analysis of linked accelerometer and Global Positioning System data is often used to identify areas of high/low activity in the schoolyard. We illustrate the potential impact of a suite of methodological decisions (i) accelerometer metric; (ii) monitor epoch; (iii) number of recess periods/days and level of aggregation; (iv) sample size; (v) distance band; (vi) spatial versus spatiotemporal weighting scheme; and (vii) time band. Accelerometer metrics resulted in different clustering patterns. Longer epochs resulted in a less detailed picture of schoolyard behavior. Level of data aggregation impacted cluster patterns due to inter-period and inter-day differences, but clusters were consistent with increasing sample size. Use of spatiotemporal weight matrices resulted in better separation of hot and cold spots and revealed potentially important temporal clustering patterns. Increasing distance or time band resulted in reallocation of small clusters to larger clusters. Hot spot analysis decisions should be clearly reported in future studies.

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.

Associations between Amount of Recess, Physical Activity, and Cardiometabolic Traits in U.S. Children.

Introduction/purpose: In the United States, it is recommended that schools provide at least 20 minutes of daily recess, but the optimal amount for health benefits is unknown. We examined associations between amount of recess and health indicators using National Health and Nutrition Examination Survey data (NHANES; 2013-2016). Methods: For this cross-sectional analysis, parents/guardians of 6-11 year olds (n=738) reported recess provision which was classified as low (22.8%; approximately 10-15 min, 5 days per week), medium (54.9%; approximately 16-30 min, 5 days per week), or high (22.3%; approximately >30 min, 5 days per week). Outcomes measured included parent/guardian-reported and accelerometer-measured physical activity (PA), blood pressure, cholesterol, grip strength, bone mineral content, weight status, percent body fat, vitamin D level, and C-reactive protein level. Linear and logistic regression compared outcomes by level of recess provision accounting for the NHANES complex survey design. Results: The odds of meeting PA guidelines according to parent/guardian reports were 1.70 and 2.05 times higher in those with medium and high (respectively) versus low recess provision. Accelerometer-measured weekday activity was highest in those with high recess provision while weekend activity was highest in those with low recess provision (Cohen's d = 0.40-0.45). There were no other significant associations. Conclusion: At least 30 minutes of daily recess is associated with two-fold greater odds of achieving recommended PA levels according to parent/guardian reports; accelerometer data suggest this is through increased weekday activity. This finding suggests current national recess recommendations are insufficient for PA promotion. More detailed data on the frequency and duration of recess are needed to quantify optimal provision more precisely.

Impact of ActiGraph sampling rate on free-living physical activity measurement in youth.

ActiGraph sampling frequencies of more than 30 Hz may result in overestimation of activity counts in both children and adults, but research on free-living individuals has not included the range of sampling frequencies used by researchers.Objective.We compared count- and raw-acceleration-based metrics from free-living children and adolescents across a range of sampling frequencies.Approach.Participants (n = 445; 10-15 years of age) wore an ActiGraph accelerometer for at least one 10 h day. Vector magnitude counts, mean amplitude deviation, monitor-independent movement summary units, and activity intensity classified using six methods (four cut-points, two-regression model, and artificial neural network) were compared between 30 Hz and 60, 80, 90, and 100 Hz sampling frequencies using mean absolute differences, correlations, and equivalence testing.Main results.All outcomes were statistically equivalent, and correlation coefficients were ≥0.970. Absolute differences were largest for the 30 versus 80 and 30 versus 100 Hz count comparisons. For comparisons of 30 with 60, 80, 90, or 100 Hz, mean (and maximum) absolute differences in minutes of moderate-to-vigorous physical activity per day ranged from 0.1 to 0.3 (0.4 to 1.5), 0.3 to 1.3 (1.6 to 8.6), 0.1 to 0.3 (1.1 to 2.5), and 0.3 to 2.5 (1.6 to 14.3) across the six classification methods.Significance.Acceleration-based outcomes are comparable across the full range of sampling rates and therefore recommended for future research. If using counts, we recommend a multiple of 30 Hz because using a 100 Hz sampling rate resulted in large maximum individual differences and epoch-level differences, and increasing differences with activity level.

Methods for estimating physical activity and energy expenditure using raw accelerometry data or novel analytical approaches: a repository, framework, and reporting guidelines.

The proliferation of approaches for analyzing accelerometer data using raw acceleration or novel analytic approaches like machine learning ('novel methods') outpaces their implementation in practice. This may be due to lack of accessibility, either because authors do not provide their developed models or because these models are difficult to find when included as supplementary material. Additionally, when access to a model is provided, authors may not include example data or instructions on how to use the model. This further hinders use by other researchers, particularly those who are not experts in statistics or writing computer code.Objective: We created a repository of novel methods of analyzing accelerometer data for the estimation of energy expenditure and/or physical activity intensity and a framework and reporting guidelines to guide future work.Approach: Methods were identified from a recent scoping review. Available code, models, sample data, and instructions were compiled or created.Main Results: Sixty-three methods are hosted in the repository, in preschoolers (n = 6), children/adolescents (n = 20), and adults (n = 42), using hip (n = 45), wrist (n = 25), thigh (n = 4), chest (n = 4), ankle (n = 6), other (n = 4), or a combination of monitor wear locations (n = 9). Fifteen models are implemented in R, while 48 are provided as cut-points, equations, or decision trees.Significance: The developed tools should facilitate the use and development of novel methods for analyzing accelerometer data, thus improving data harmonization and consistency across studies. Future advances may involve including models that authors did not link to the original published article or those which identify activity type.

Accessibility and use of novel methods for predicting physical activity and energy expenditure using accelerometry: a scoping review.

Use of raw acceleration data and/or 'novel' analytic approaches like machine learning for physical activity measurement will not be widely implemented if methods are not accessible to researchers.Objective: This scoping review characterizes the validation approach, accessibility and use of novel analytic techniques for classifying energy expenditure and/or physical activity intensity using raw or count-based accelerometer data.Approach: Three databases were searched for articles published between January 2000 and February 2021. Use of each method was coded from a list of citing articles compiled from Google Scholar. Authors' provision of access to the model (e.g., by request, sample code) was recorded.Main Results: Studies (N = 168) included adults (n = 143), and/or children (n = 38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n = 107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n = 48) and/or by providing access to code (n = 13).Significance: The proliferation of approaches for analyzing accelerometer data outpaces the use of these models in practice. As less than half of the developed models are made accessible, it is unsurprising that so many models are not used by other researchers. We encourage researchers to make their models available and accessible for better harmonization of methods and improved capabilities for device-based physical activity measurement.

Cross-Sectional Association of State Recess Laws With District-Level Policy and School Recess Provision in the United States.

BACKGROUND: State recess laws are recommended to encourage adequate and equitable access to recess and its benefits, but the downstream effects of state recess laws are unknown. We examined the association of state recess laws with district-level policy and school recess provision. METHODS: This is cross-sectional analysis of the School Health Policies and Practices Survey, a US nationally representative sample of school districts (2016) and schools (2014). State-level recess laws were coded as none, recommend, or require recess. Logistic and linear regression were used to examine the association between state law with district policies and school recess provision, respectively. Data from 2000 are presented to highlight changes in recess policy and provision over time. RESULTS: The odds of a district policy requiring recess were 2.22 and 2.34 times greater when state recess law recommended or required recess, respectively, compared to states with no recess policy. There were no significant differences in school-level recess provision by state recess law but point estimates from 2000 indicated states without a law had the largest declines in recess provision over time. CONCLUSIONS: State recess laws are positively associated with district-level policy. Effects at the school level are unclear and continued surveillance is needed.

Associations Between State Laws Governing Recess Policy with Children's Physical Activity and Health.

BACKGROUND: State-level laws governing recess policies vary widely across the United States. We characterize the presence of such laws and assess their associations with child-level outcomes. METHODS: The presence of a state recess law was determined using the Classification of Laws Associated with School Students (CLASS) database. Parents of 6- to 11-year-old children reported physical activity, overall health, school absences, school-related problems, and ability to make/keep friends as part of the National Survey of Children's Health (NSCH). Logistic regression was used to compare outcomes in states with and without recess laws cross-sectionally in 2018 and between 2003 and 2011/2012 using a difference-in-differences analysis. RESULTS: In 2018, 20 states had a law recommending or requiring recess. Cross-sectionally, the odds of being physically active every day (odds ratio, 95% confidence interval: 2.8, 1.2-6.5) and having no difficulty making or keeping friends (2.9, 1.2-7.2) were significantly higher for children residing in states with versus without a recess law. There were no significant associations in the difference-in-differences model. CONCLUSIONS: Significant cross-sectional associations in 2018 were not confirmed by a difference-in-differences analysis of two waves of the NSCH. Short follow-up time and the apparent weakness of existing state laws warrant further assessment of state-level recess law.

Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers.

PURPOSE: We sought to determine if individually calibrated machine learning models yielded higher accuracy than a group calibration approach for physical activity intensity assessment. METHODS: Participants (n = 48) wore accelerometers on the right hip and nondominant wrist while performing activities of daily living in a semistructured laboratory and/or free-living setting. Criterion measures of activity intensity (sedentary, light, moderate, vigorous) were determined using direct observation. Data were reintegrated into 30-s epochs, and eight random forest models were created to determine physical activity intensity by using all possible conditions of training data (individual vs group), protocol (laboratory vs free-living), and placement (hip vs wrist). A 2 × 2 × 2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [κ]) of the models when used to determine activity intensity in an independent sample of free-living participants. RESULTS: Main effects were significant for the type of training data (group: accuracy = 80%, κ = 0.59; individual: accuracy = 74% [P = 0.02], κ = 0.50 [P = 0.01]) and protocol (free-living: accuracy = 81%, κ = 0.63; laboratory: accuracy = 74% [P = 0.04], κ = 0.47 [P < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, κ = 0.58; wrist: accuracy = 75% [P = 0.18]; κ = 0.52 [P = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement. CONCLUSIONS: Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. In addition, models should be developed in free-living settings when possible to optimize predictive accuracy.

Feelings of safety during daytime walking: associations with mental health, physical activity and cardiometabolic health in high vacancy, low-income neighborhoods in Detroit, Michigan.

INTRODUCTION: Individuals living in low-income neighborhoods have disproportionately high rates of obesity, Type-2 diabetes, and cardiometabolic conditions. Perceived safety in one's neighborhood may influence stress and physical activity, with cascading effects on cardiometabolic health. METHODS: In this study, we examined relationships among feelings of safety while walking during the day and mental health [perceived stress (PSS), depression score], moderate-to-vigorous physical activity (PA), Body Mass Index (BMI), and hemoglobin A1C (A1C) in low-income, high-vacancy neighborhoods in Detroit, Michigan. We recruited 69 adults who wore accelerometers for one week and completed a survey on demographics, mental health, and neighborhood perceptions. Anthropometrics were collected and A1C was measured using A1CNow test strips. We compiled spatial data on vacant buildings and lots across the city. We fitted conventional and multilevel regression models to predict each outcome, using perceived safety during daytime walking as the independent variable of interest and individual or both individual and neighborhood-level covariates (e.g., number of vacant lots). Last, we examined trends in neighborhood features according to perceived safety. RESULTS: In this predominantly African American sample (91%), 47% felt unsafe during daytime walking. Feelings of perceived safety significantly predicted PSS (ß = - 2.34, p = 0.017), depression scores (ß = - 4.22, p = 0.006), and BMI (ß = - 2.87, p = 0.01), after full adjustment. For PA, we detected a significant association for sex only. For A1C we detected significant associations with blighted lots near the home. Those feeling unsafe lived in neighborhoods with higher park area and number of blighted lots. CONCLUSION: Future research is needed to assess a critical pathway through which neighborhood features, including vacant or poor-quality green spaces, may affect obesity-via stress reduction and concomitant effects on cardiometabolic health.

Use of a spatiotemporal approach for understanding preschoolers' playground activity.

This study used spatiotemporal hot-spot analysis to characterize physical activity on the childcare center playground. Preschool-aged children (N = 34) wore a GPS and accelerometer during 2-3 outdoor periods on one day. A spatiotemporal weights matrix was generated so that points within a specified distance in meters (space) and 3 min (time) were considered neighbors. The Getis-Ord G* statistic was calculated to detect locations of significant hot/cold spots in vector magnitude counts/15­sec. Hot/cold spots changed within a single outdoor period and between outdoor periods, highlighting the importance of time. This approach can be used to identify points of intervention during provided outdoor time.

Cross-generational comparability of hip- and wrist-worn ActiGraph GT3X+, wGT3X-BT, and GT9X accelerometers during free-living in adults.

ActiGraph accelerometers are frequently used to characterize physical activity, but free-living cross-generational comparability of newer models has not been verified. Participants (N = 70) wore GT9X and wGT3X-BT accelerometers at the hip and a sub-sample (n = 54) wore GT9X and either wGT3X-BT or GT3X+ monitors at each wrist for 4 days. Vector magnitude (VM) counts, VM acceleration, Mean Amplitude Deviation (MAD), and Euclidean Norm Minus One (ENMO) were calculated (60-s epoch), and cut-points were used to determine percent of time spent in each intensity (sedentary/light, moderate, vigorous). Epoch-level correlation coefficients (r) were ≥0.73, and weighted kappa for intensity classifications ranged from 0.71 (ENMO, hip) to 0.98 (VM counts, non-dominant wrist). Monitors were equivalent for all outcomes, except ENMO (all locations/monitors), percent of time spent in sedentary/light (hip) and moderate (hip and non-dominant wrist) activity as classified by ENMO-based cut-points, and vigorous activity as classified by VM count cut-points (non-dominant wrist; p > 0.05). While epoch-level data were not identical, most outcomes were strongly related between models (e.g., MAD, VM) and equivalent once reduced to percent of time spent in each intensity. However, monitor output was not equivalent for the acceleration-based metric ENMO, suggesting that caution should be exercised when comparing this outcome among ActiGraph models.