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.

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.

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.

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.

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.

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.

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.

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.

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 y) 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 vs. 80 and 30 vs. 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.

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

Modeling approaches for translating accelerometer data into physical activity metrics are often developed using a group calibration approach. However, it is unknown if models developed for specific individuals will improve measurement accuracy.

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 non-dominant wrist while performing activities of daily living in a semi-structured 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-second 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 2x2x2 repeated-measures analysis of variance was used to compare epoch-level accuracy statistics (% accuracy, kappa [k]) 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%, k = 0.59; individual: accuracy = 74% [p = 0.02], k = 0.50 [p = 0.01]) and protocol (free-living: accuracy = 81%, k = 0.63; laboratory: accuracy = 74% [p = 0.04], k = 0.47 [p < 0.01]). Main effects were not significant for placement (hip: accuracy = 79%, k = 0.58; wrist: accuracy = 75% [p = 0.18]; k = 0.52 [p = 0.18]). Point estimates for mean absolute error were generally lowest for the group training, free-living protocol, and hip placement.

CONCLUSION

Contrary to expectations, individually calibrated machine learning models yielded poorer accuracy than a traditional group approach. Additionally, 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 (β = - 2.34, p = 0.017), depression scores (β = - 4.22, p = 0.006), and BMI (β = - 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.

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.

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.

Accelerometer-based assessment of physical activity within the Fun For Wellness online behavioral intervention: protocol for a feasibility study

BACKGROUND

Fun For Wellness (FFW) is an online behavioral intervention designed to promote growth in well-being and physical activity by providing capability-enhancing learning opportunities to participants. The conceptual framework for the FFW intervention is guided by self-efficacy theory. Evidence has been provided for the efficacy of FFW to promote self-reported free-living physical well-being actions in adults who comply with the intervention. The objective of this manuscript is to describe the protocol for a feasibility study designed to address uncertainties regarding the inclusion of accelerometer-based assessment of free-living physical activity within the FFW online intervention among adults with obesity in the United States of America (USA).

METHOD

The study design is a prospective, double-blind, parallel group randomized pilot trial. Thirty participants will be randomly assigned to the FFW or usual care (UC) group to achieve a 1:1 group (i.e., FFW:UC) assignment. Recruitment of participants is scheduled to begin on 29 April 2019 at a local bariatric services center within a major healthcare organization in the Midwest of the USA. There are five eligibility criteria for participation in this study: (1) between 18 and 64 years old, (2) a body mass index ≥ 25.00 kg/m2, (3) ability to access the online intervention, (4) the absence of simultaneous enrollment in another intervention program promoting physical activity, and (5) willingness to comply with instructions for physical activity monitoring. Eligibility verification and data collection will be conducted online. Three waves of data will be collected over a 13-week period. Instruments designed to measure demographic information, anthropometric characteristics, acceptability and feasibility of accelerometer-based assessment of physical activity, self-efficacy, and well-being will be included in the study. Data will be analyzed using descriptive statistics (e.g., recruitment rates), Pearson's correlation coefficient, Bland-Altman analyses, and inferential statistical models under both an intent to treat approach and a complier average causal effect approach.

DISCUSSION

Results are intended to inform the preparation of a future definitive randomized controlled trial.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03906942, registered 8 April 2019.

TRIAL FUNDING

The Erwin and Barbara Mautner Charitable Foundation and the Michigan State University College of Education.

Comparison of Accelerometer-Based Cut-Points for Children's Physical Activity: Counts vs. Steps

Background: Accelerometers measure complex movements of children’s free play moderate-vigorous physical activity (MVPA), including step and non-step movements. Current accelerometer technology has introduced algorithms to measure steps, along with counts. Precise interpretation of accelerometer-based cadence (steps/min) cut-points is necessary for accurately measuring and tracking children’s MVPA. The purpose of this study was to assess the relationships and agreement between accelerometer-based cut-points (cadence and counts/min) to estimate children’s MVPA compared to measured values. Methods: Forty children (8–12 years; 25 boys) played 6–10 games while wearing a portable metabolic analyzer and GT3X⁺ to measure and estimate MVPA, respectively. Correlation, kappa, sensitivity, and specificity assessed the relationships and agreement between measured and estimated MVPA. Results: Games elicited, on average, 6.3 ± 1.6 METs, 64.5 ± 24.7 steps/min, and 3318 ± 1262 vertical (V) and 5350 ± 1547 vector-magnitude (VM) counts/min. The relationship between measured and estimated MVPA intensity was higher for cadence (r = 0.50) than V and VM counts/min (r = 0.38 for both). Agreement using V and VM counts/min for measuring PA intensity varied by cut-points (range: 6.8% (κ = −0.02) to 97.6% (κ = 0.49)), while agreement was low using cadence cut-points (range: 4.0% (κ = 0.0009) to 11.3% (κ = 0.001)). Conclusion: While measured and estimated values were well correlated, using cadence tended to misclassify children’s free-play MVPA.

Effect of Prior Game Experience on Energy Expenditure During Xbox Kinect in Children and Teens

OBJECTIVE

To examine the effect of game-specific and overall Kinect experience and overall gaming habits on energy expenditure (EE) and enjoyment of children and adolescents (8-17 years) while playing Xbox^® Kinect exergames.

MATERIALS AND METHODS

Participants (N = 55) played four active videogames for 6-10 minutes. Height, weight, and resting metabolic rate were measured and participants completed a survey on gaming habits and previous experience. Habit (none, low, moderate, or high) was based on the number of game systems at home, frequency, and duration of game play. Game-specific experience was classified as either inexperienced or experienced. A composite score was created for how much experience they had with each game, classified as none, low, or high. The participant wore a portable metabolic analyzer (total and physical activity energy expenditure [PAEE]), heart rate (HR) monitor, and accelerometer (waist, counts/min). Enjoyment was measured after each game using a three-item face scale. Bonferroni-adjusted three-way ANOVA assessed PAEE, intensity, and enjoyment across overall and game-specific experience and habits (P < 0.05).

RESULTS

Intensity, PAEE, and HR were greater in experienced versus inexperienced players (5.1 ± 0.2 vs. 4.4 ± 0.2 metabolic equivalents [METs]; 4.1 ± 0.2 vs. 3.3 ± 0.2 kcal/min; 138 ± 2.5 vs. 130 ± 1.9 bpm). Higher game-specific experience levels elicited greater counts/min compared with no experience. Moderate gaming habits elicited greater PAEE and METs than low gaming habits. Enjoyment was equal in all groups.

CONCLUSION

Participants with more game-specific Kinect experience or overall gaming habits elicited greater PA energy and intensity. This study supports that children and adolescents can play Xbox Kinect without decrements in PAEE or enjoyment.