Developmental Trends in the Energy Cost of Physical Activities Performed by Youth

Investigating Wrist-Based Acceleration Summary Measures across Different Sample Rates towards 24-Hour Physical Activity and Sleep Profile Assessment

Wrist-worn wearable sensors have attracted considerable research interest because of their potential in providing continuous, longitudinal, non-invasive measurements, leading to insights into Physical Activity (PA), sleep, and circadian variability. Three key practical considerations for research-grade wearables are as follows: (a) choosing an appropriate sample rate, (b) summarizing raw three-dimensional accelerometry data for further processing (accelerometry summary measures), and (c) accurately estimating PA levels and sleep towards understanding participants' 24-hour profiles. We used the CAPTURE-24 dataset, where 148 participants concurrently wore a wrist-worn three-dimensional accelerometer and a wearable camera over approximately 24 h to obtain minute-by-minute labels: sleep; and sedentary light, moderate, and vigorous PA. We propose a new acceleration summary measure, the Rate of Change Acceleration Movement (ROCAM), and compare its performance against three established approaches summarizing three-dimensional acceleration data towards replicating the minute-by-minute labels. Moreover, we compare findings where the acceleration data was sampled at 10, 25, 50, and 100 Hz. We demonstrate the competitive advantage of ROCAM towards estimating the five labels (80.2% accuracy) and building 24-hour profiles where the sample rate of 10 Hz is fully sufficient. Collectively, these findings provide insights facilitating the deployment of large-scale longitudinal actigraphy data processing towards 24-hour PA and sleep-profile assessment.

Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods

INTRODUCTION

Machine learning (ML) accelerometer data processing methods have potential to improve the accuracy of device-based assessments of physical activity (PA) in young children. Yet the uptake of ML methods by health researchers has been minimal and the use of cut-points (CP) continues to be the norm, despite evidence of significant misclassification error. The lack of studies demonstrating a relative advantage for ML approaches over CP methods maybe a key contributing factor.

PURPOSE

The current study compared the accuracy of PA intensity predictions provided by ML classification models and previously published CPs for preschool-aged children.

METHODS

In a free-living study, 31 preschool-aged children (mean age = 4.0 ± 0.9 y) wore wrist and hip ActiGraph GT3X+ accelerometers while completing a video recorded 20-minute free play session. Ground truth PA intensity was coded continuously using the Children's Activity Rating Scale (CARS). Accelerometer data was classified as sedentary (SED), light intensity (LPA), or moderate-to-vigorous intensity (MVPA) using ML random forest PA classifiers and published CPs for preschool-aged children. Performance differences were evaluated in a hold-out sample by comparing weighted kappa statistics, classification accuracy for each intensity band, and equivalence testing.

RESULTS

ML classification models (hip: κ = 0.76; wrist: κ = 0.72) exhibited significantly higher agreement with ground truth PA intensity than CP methods (hip: κ = 0.38-0.49; wrist: κ = 0.31-0.44). For the ML models, classification accuracy for SED and LPA ranged from 83% - 88%, while classification accuracy for MVPA ranged from 68% - 78%. For the CP's, classification accuracy ranged from 50% - 94% for SED, 19% - 75% for LPA, and 44% - 76.1% for MVPA. ML classification models showed equivalence (within ± 0.5 SD) with directly observed time in SED, LPA, and MVPA. None of the CP's exhibited evidence of equivalence.

CONCLUSIONS

Under free living conditions, ML classification models for hip or wrist accelerometer data provide more accurate assessments of PA intensity in young children than CP methods. The results demonstrate the relative advantage of ML methods over threshold-based approaches and adds to a growing evidence base supporting the feasibility and accuracy of ML accelerometer data processing methods.

Adolescents Are Less Physically Active Than Adults After Anterior Cruciate Ligament Reconstruction

Background:

Sources of physical activity (PA) and motivation for return to sport after anterior cruciate ligament reconstruction (ACLR) differ between adolescents and adults. It is unclear whether these differences influence participation in PA during the first year after ACLR when individuals are transitioning from rehabilitative care to unrestricted activity.

Purpose:

To compare device-assessed measures of PA between adolescents and adults at 6 to 12 months after ACLR.

Study Design:

Cross-sectional study; Level of evidence, 3.

Methods:

Included were 22 adolescents (age, 15.9 ± 1.2 years; time since surgery = 8.0 ± 2.1 months) and 23 adults (age, 22.5 ± 5.0 years; time since surgery = 8.2 ± 2.1 months) who were cleared for unrestricted PA after primary unilateral ACLR. Participants were considered physically active if they met their age-specific United States Department of Health and Human Services PA guidelines. Participants wore an accelerometer-based PA monitor for at least 7 days. Daily minutes of moderate to vigorous–PA (MVPA) and daily step counts were reported and compared between age groups using analysis of covariance, with monitor wear time and sex included as covariates. The association between age group and meeting age-specific PA guidelines was assessed using binary logistic regression and reported as an odds ratio.

Results:

Adults with ACLR participated in 16 minutes more MVPA per day (49 ± 22 vs 33 ± 16 minutes per day; P < .001) and took 2212 more steps per day (8365 ± 2294 vs 6153 ± 1765 steps per day; P < .001) when compared with adolescent participants. In addition, 83% of adults were physically active, compared with 9% of adolescents (odds ratio = 60.2; 95% CI, 7.6-493.4).

Conclusion:

Adolescents with ACLR were less physically active than adults with ACLR, and only 9% of adolescents met aerobic PA guidelines. This is concerning because PA patterns adopted early in life are predictive of PA patterns in adulthood. Our findings indicate a need to better understand underlying causes of reduced PA among adolescents with ACLR and to develop intervention strategies that promote engagement in adequate PA after rehabilitation.

Application of the trans-contextual model to predict change in leisure time physical activity

OBJECTIVE

This study tested effects of changes in the psychological constructs of the trans-contextual model (TCM) on changes in adolescents' outside of school moderate-to-vigorous physical activity (PA) measured using self-report and accelerometer-based device.

DESIGN

A three-wave longitudinal design was used. High school students (N = 331) completed measures of all the TCM constructs at Time1 and at Time2, five weeks apart. Self-reported PA behaviour was measured also at Time3, five weeks after Time2. PA was measured using accelerometer-based devices for seven days following Time1 and Time3 for a census week.

RESULTS

A structural equation model using residual change scores revealed that perceived autonomy support from physical education (PE) teachers positively predicted autonomous motivation in PE. Autonomous motivation in PE positively predicted autonomous motivation in leisure time. Leisure-time autonomous motivation was positively and indirectly related to intention, mediated by attitude and perceived behavioural control. Intention positively predicted self-reported PA, and mediated the effect of autonomous motivation on self-reported PA. There were no effects on outside of school PA measured by accelerometer-based device.

CONCLUSIONS

Results provide qualified support for the TCM in the prediction of change in adolescents' leisure-time autonomous motivation, intention, and self-reported PA, but not change in PA measured by accelerometer-based device.

Effects of dancing on physical activity levels of children and adolescents: a systematic review

BACKGROUND

Dancing has been suggested to increase the levels of physical activity of the youth. However, it is not clear what are the physiological characteristics of the dance classes for young people, mainly regarding the levels of moderate to vigorous physical activity (MVPA) during classes. It is also unclear if regular engagement in dance practices can contribute with increases in the amounts of daily/weekly MVPA, recommended by health organizations.

OBJECTIVES

To conduct a systematic review verifying the amount of time spent at MVPA (primary outcome), by children and adolescents in the following situations: i) During dance classes, and ii) Before and after dance interventions. Secondary outcomes included: markers of exercise intensity during class, such as oxygen consumption (VO₂) and heart rate (HR); VO₂peak and lipid profile before and after dance interventions.

METHODS

Six data sources were accessed (MEDLINE, EMBASE, Cochrane Wiley, PEDRO and SCOPUS). Study selection included different designs (acute, cohort, randomized controlled trials and others). Participants were from 6 to 19 years old, regularly engaged in dance practices. Methodological quality was assessed using the Downs and Black checklist. Two independent reviewers extracted characteristics and results of each study.

RESULTS

3216 articles were retrieved, and 37 included. Studies indicated that dance classes do not achieve 50% of total class time at MVPA. However, there are peaks of HR and VO₂ during dance classes, which reach moderate and vigorous intensities. MVPA/daily/weekly did not improve before and after dance interventions for most of the studies, also VO₂peak did not. The few results on lipid profile showed improvements only in overweight and obese participants.

LIMITATIONS

Lack of meta-analysis, because there were not enough articles to be analyzed on any given outcome of interest, neither under the same study design.

CONCLUSIONS

Results of individual studies indicated that dance classes did not active 50% of the total time at MVPA levels. This may be related to the absence of improvements in daily/weekly MVPA before and after dance interventions. VO₂ and HR attained peaks of moderateto vigorous intensity during dance classes, suggesting that the structure of the classes may be manipulated to maintain longer periods at MVPA levels. Lack of data on cardiorespiratory fitness and metabolic outcomes limit conclusions on these parameters.

IMPLICATIONS OF KEYS FINDS

Considering there are peaks of HR and VO₂ during dance classes, we suggest that the structure of a dance class can be manipulate in order to induce cardiorespiratory and metabolic adaptations. Thus, dancing is a potential strategy to contribute with a healthy life style since the earliest ages. Prospero registration: CRD42020144609.

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions.

Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation

Machine learning models to predict energy expenditure (EE) from accelerometer data have traditionally been trained on data from laboratory-based activity trials. However, accuracy is typically attenuated when implemented in free-living scenarios. Currently, no studies involving preschool children have evaluated the accuracy of EE prediction models trained on laboratory (LAB) under free-living conditions.

Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth

PURPOSE

This study aimed to examine the validity of wrist acceleration cut points for classifying moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity.

METHODS

Fifty-seven children (5-12 yr) completed 15 semistructured activities. Three sets of wrist cut points (>192 mg, >250 mg, and >314 mg), previously developed using Euclidian norm minus one (ENMO192+), GENEActiv software (GENEA250+), and band-pass filter followed by Euclidian norm (BFEN314+), were evaluated against indirect calorimetry. Analyses included classification accuracy, equivalence testing, and Bland-Altman procedures.

RESULTS

All cut points classified MPA, VPA, and MVPA with substantial accuracy (ENMO192+: κ = 0.72 [95% confidence interval = 0.72-0.73], MVPA: area under the receiver operating characteristic curve (ROC-AUC) = 0.85 [0.85-0.86]; GENEA250+: κ = 0.75 [0.74-0.76], MVPA: ROC-AUC = 0.85 [0.85-0.86]; BFEN314+: κ = 0.73 [0.72-0.74], MVPA: ROC-AUC = 0.86 [0.86-0.87]). BFEN314+ misclassified 19.7% non-MVPA epochs as MPA, whereas ENMO192+ and GENEA250+ misclassified 32.6% and 26.5% of MPA epochs as non-MVPA, respectively. Group estimates of MPA time were equivalent (P < 0.01) to indirect calorimetry for the BFEN314+ MPA cut point (mean bias = -1.5%, limits of agreement [LoA] = -57.5% to 60.6%), whereas estimates of MVPA time were equivalent (P < 0.01) to indirect calorimetry for the ENMO192+ (mean bias = -1.1%, LoA = -53.7% to 55.9%) and GENEA250+ (mean bias = 2.2%, LoA = -56.5% to 52.2%) cut points. Individual variability (LoA) was large for MPA (min: BFEN314+, -60.6% to 57.5%; max: GENEA250+, -42.0% to 104.1%), VPA (min: BFEN314+, -238.9% to 54.6%; max: ENMO192+, -244.5% to 127.4%), and MVPA (min: ENMO192+, -53.7% to 55.0%; max: BFEN314+, -83.9% to 25.3%).

CONCLUSION

Wrist acceleration cut points misclassified a considerable proportion of non-MVPA and MVPA. Group-level estimates of MVPA were acceptable; however, error for individual-level prediction was larger.

A Youth Compendium of Physical Activities: Activity Codes and Metabolic Intensities

PURPOSE

A Youth Compendium of Physical Activities (Youth Compendium) was developed to estimate the energy costs of physical activities using data on youth only.

METHODS

On the basis of a literature search and pooled data of energy expenditure measurements in youth, the energy costs of 196 activities were compiled in 16 activity categories to form a Youth Compendium of Physical Activities. To estimate the intensity of each activity, measured oxygen consumption (V˙O2) was divided by basal metabolic rate (Schofield age-, sex-, and mass-specific equations) to produce a youth MET (METy). A mixed linear model was developed for each activity category to impute missing values for age ranges with no observations for a specific activity.

RESULTS

This Youth Compendium consists of METy values for 196 specific activities classified into 16 major categories for four age-groups, 6-9, 10-12, 13-15, and 16-18 yr. METy values in this Youth Compendium were measured (51%) or imputed (49%) from youth data.

CONCLUSION

This Youth Compendium of Physical Activities uses pediatric data exclusively, addresses the age dependency of METy, and imputes missing METy values and thus represents advancement in physical activity research and practice. This Youth Compendium will be a valuable resource for stakeholders interested in evaluating interventions, programs, and policies designed to assess and encourage physical activity in youth.