Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration

INTRODUCTION

Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr.

METHODS

Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC).

RESULTS

Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95.

CONCLUSIONS

The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study

INTRODUCTION

This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry.

METHODS

Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph.

RESULTS

Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent.

CONCLUSIONS

Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Estimating Physical Activity and Sleep using the Combination of Movement and Heart Rate: A Systematic Review and Meta-Analysis

The purpose of this meta-analysis was to quantify the difference in physical activity and sleep estimates assessed via 1) movement, 2) heart rate (HR), or 3) the combination of movement and HR (MOVE+HR) compared to criterion indicators of the outcomes. Searches in four electronic databases were executed September 21-24 of 2021. Weighted mean was calculated from standardized group-level estimates of mean percent error (MPE) and mean absolute percent error (MAPE) of the proxy signal compared to the criterion measurement method for physical activity, HR, or sleep. Standardized mean difference (SMD) effect sizes between the proxy and criterion estimates were calculated for each study across all outcomes, and meta-regression analyses were conducted. Two-One-Sided-Tests method were conducted to metaanalytically evaluate the equivalence of the proxy and criterion. Thirty-nine studies (physical activity k = 29 and sleep k = 10) were identified for data extraction. Sample size weighted means for MPE were -38.0%, 7.8%, -1.4%, and -0.6% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Sample size weighted means for MAPE were 41.4%, 32.6%, 13.3%, and 10.8% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Few estimates were statistically equivalent at a SMD of 0.8. Estimates of physical activity from MOVE+HR were not statistically significantly different from estimates based on movement or HR only. For sleep, included studies based their estimates solely on the combination of MOVE+HR, so it was impossible to determine if the combination produced significantly different estimates than either method alone.

Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old)

BACKGROUND

Accurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children.

METHODS

We searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN).

RESULTS

Sixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes.

CONCLUSIONS

Up to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality.

PROSPERO REGISTRATION NUMBER

CRD42020184751.

Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers

Machine learning classification models for accelerometer data are potentially more accurate methods to measure physical activity in young children than traditional cut point methods. However, existing algorithms have been trained on laboratory-based activity trials, and their performance has not been investigated under free-living conditions.

PURPOSE

This study aimed to evaluate the accuracy of laboratory-trained hip and wrist random forest and support vector machine classifiers for the automatic recognition of five activity classes: sedentary (SED), light-intensity activities and games (LIGHT_AG), walking (WALK), running (RUN), and moderate to vigorous activities and games (MV_AG) in preschool-age children under free-living conditions.

METHODS

Thirty-one children (4.0 ± 0.9 yr) were video recorded during a 20-min free-living play session while wearing an ActiGraph GT3X+ on their right hip and nondominant wrist. Direct observation was used to continuously code ground truth activity class and specific activity types occurring within each class using a bespoke two-stage coding scheme. Performance was assessed by calculating overall classification accuracy and extended confusion matrices summarizing class-level accuracy and the frequency of specific activities observed within each class.

RESULTS

Accuracy values for the hip and wrist random forest algorithms were 69.4% and 59.1%, respectively. Accuracy values for hip and wrist support vector machine algorithms were 66.4% and 59.3%, respectively. Compared with the laboratory cross validation, accuracy decreased by 11%-15% for the hip classifiers and 19%-21% for the wrist classifiers. Classification accuracy values were 72%-78% for SED, 58%-79% for LIGHT_AG, 71%-84% for MV_AG, 9%-15% for WALK, and 66%-75% for RUN.

CONCLUSION

The accuracy of laboratory-based activity classifiers for preschool-age children was attenuated when tested on new data collected under free-living conditions. Future studies should train and test machine learning activity recognition algorithms using accelerometer data collected under free-living conditions.

The Relative Importance of Globalization and Public Expenditure on Life Expectancy in Europe: An Approach Based on MARS Methodology

BACKGROUND

There has been a widespread debate about the overall impact of globalization on population, not just economically, but also in terms of health status. Moreover, the current health crisis is going to force governments to review the structure of the public budget to most effectively alleviate the negative economic and health effects on the population.

OBJECTIVE

The aim of this paper is to analyze the relative importance of globalization and the public budget composition-specifically the participation of public expenditure on healthcare, social services and environment in gross domestic product (GDP)-on life expectancy at birth in European countries during the period 1995-2017.

METHODS

The Multivariate Adaptive Regression Splines (MARS) methodology was applied to analyze the socioeconomic determinants of life expectancy at birth.

RESULTS

Our findings show that globalization has no relative importance as an explanatory variable of life expectancy in European countries, while government expenditure on social protection is the most relevant followed by public expenditure on health, gross national income per capita, education level of the population and public expenditure on environmental protection.

CONCLUSION

European strategies intended to impact on health outcome should spend more attention to the composition of public budget.

Active design of built environments for increasing levels of physical activity in adults: the ENABLE London natural experiment study

Background

Low physical activity is widespread and poses a serious public health challenge both globally and in the UK. The need to increase population levels of physical activity is recognised in current health policy recommendations. There is considerable interest in whether or not the built environment influences health behaviours, particularly physical activity levels, but longitudinal evidence is limited.

Objectives

The effect of moving into East Village (the former London 2012 Olympic and Paralympic Games Athletes’ Village, repurposed on active design principles) on the levels of physical activity and adiposity, as well as other health-related and well-being outcomes among adults, was examined.

Design

The Examining Neighbourhood Activities in Built Environments in London (ENABLE London) study was a longitudinal cohort study based on a natural experiment.

Setting

East Village, London, UK.

Participants

A cohort of 1278 adults (aged ≥ 16 years) and 219 children seeking to move into social, intermediate and market-rent East Village accommodation were recruited in 2013–15 and followed up after 2 years.

Intervention

The East Village neighbourhood, the former London 2012 Olympic and Paralympic Games Athletes’ Village, is a purpose-built, mixed-use residential development specifically designed to encourage healthy active living by improving walkability and access to public transport.

Main outcome measure

Change in objectively measured daily steps from baseline to follow-up.

Methods

Change in environmental exposures associated with physical activity was assessed using Geographic Information System-derived measures. Individual objective measures of physical activity using accelerometry, body mass index and bioelectrical impedance (per cent of fat mass) were obtained, as were perceptions of change in crime and quality of the built environment. We examined changes in levels of physical activity and adiposity using multilevel models adjusting for sex, age group, ethnic group, housing sector (fixed effects) and baseline household (random effect), comparing the change in those who moved to East Village (intervention group) with the change in those who did not move to East Village (control group). Effects of housing sector (i.e. social, intermediate/affordable, market-rent) as an effect modifier were also examined. Qualitative work was carried out to provide contextual information about the perceived effects of moving to East Village.

Results

A total of 877 adults (69%) were followed up after 2 years (mean 24 months, range 19–34 months, postponed from 1 year owing to the delayed opening of East Village), of whom 50% had moved to East Village; insufficient numbers of children moved to East Village to be considered further. In adults, moving to East Village was associated with only a small, non-significant, increase in mean daily steps (154 steps, 95% confidence interval –231 to 539 steps), more so in the intermediate sector (433 steps, 95% confidence interval –175 to 1042 steps) than in the social and market-rent sectors (although differences between housing sectors were not statistically significant), despite sizeable improvements in walkability, access to public transport and neighbourhood perceptions of crime and quality of the built environment. There were no appreciable effects on time spent in moderate to vigorous physical activity or sedentary time, body mass index or percentage fat mass, either overall or by housing sector. Qualitative findings indicated that, although participants enjoyed their new homes, certain design features might actually serve to reduce levels of activity.

Conclusions

Despite strong evidence of large positive changes in neighbourhood perceptions and walkability, there was only weak evidence that moving to East Village was associated with increased physical activity. There was no evidence of an effect on markers of adiposity. Hence, improving the physical activity environment on its own may not be sufficient to increase population physical activity or other health behaviours.

Funding

This project was funded by the National Institute for Health Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 8, No. 12. See the NIHR Journals Library website for further project information. This research was also supported by project grants from the Medical Research Council National Prevention Research Initiative (MR/J000345/1).

Calibrating Wrist-Worn Accelerometers for Physical Activity Assessment in Preschoolers: Machine Learning Approaches

BACKGROUND

Physical activity (PA) level is associated with multiple health benefits during early childhood. However, inconsistency in the methods for quantification of PA levels among preschoolers remains a problem.

OBJECTIVE

This study aimed to develop PA intensity cut points for wrist-worn accelerometers by using machine learning (ML) approaches to assess PA in preschoolers.

METHODS

Wrist- and hip-derived acceleration data were collected simultaneously from 34 preschoolers on 3 consecutive preschool days. Two supervised ML models, receiver operating characteristic curve (ROC) and ordinal logistic regression (OLR), and one unsupervised ML model, k-means cluster analysis, were applied to establish wrist-worn accelerometer vector magnitude (VM) cut points to classify accelerometer counts into sedentary behavior, light PA (LPA), moderate PA (MPA), and vigorous PA (VPA). Physical activity intensity levels identified by hip-worn accelerometer VM cut points were used as reference to train the supervised ML models. Vector magnitude counts were classified by intensity based on three newly established wrist methods and the hip reference to examine classification accuracy. Daily estimates of PA were compared to the hip-reference criterion.

RESULTS

In total, 3600 epochs with matched hip- and wrist-worn accelerometer VM counts were analyzed. All ML approaches performed differently on developing PA intensity cut points for wrist-worn accelerometers. Among the three ML models, k-means cluster analysis derived the following cut points: ≤2556 counts per minute (cpm) for sedentary behavior, 2557-7064 cpm for LPA, 7065-14532 cpm for MPA, and ≥14533 cpm for VPA; in addition, k-means cluster analysis had the highest classification accuracy, with more than 70% of the total epochs being classified into the correct PA categories, as examined by the hip reference. Additionally, k-means cut points exhibited the most accurate estimates on sedentary behavior, LPA, and VPA as the hip reference. None of the three wrist methods were able to accurately assess MPA.

CONCLUSIONS

This study demonstrates the potential of ML approaches in establishing cut points for wrist-worn accelerometers to assess PA in preschoolers. However, the findings from this study warrant additional validation studies.

Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

BACKGROUND

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing the way the heart responds to PTSD has shown promise in detecting and understanding the onset of symptoms.

OBJECTIVE

This study aimed to extract statistical and mathematical approaches that researchers can use to analyze heart rate (HR) data to understand PTSD.

METHODS

A scoping literature review was conducted to extract HR models. A total of 5 databases including Medical Literature Analysis and Retrieval System Online (Medline) OVID, Medline EBSCO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO, Excerpta Medica Database (Embase) Ovid, and Google Scholar were searched. Non-English language studies, as well as studies that did not analyze human data, were excluded. A total of 54 studies that met the inclusion criteria were included in this review.

RESULTS

We identified 4 categories of models: descriptive time-independent output, descriptive and time-dependent output, predictive and time-independent output, and predictive and time-dependent output. Descriptive and time-independent output models include analysis of variance and first-order exponential; the descriptive time-dependent output model includes a classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analysis of the HR-based fluctuation-dissipation method. Finally, predictive time-dependent output models include the time-variant method and nonlinear dynamic modeling.

CONCLUSIONS

All of the identified modeling categories have relevance in PTSD, although the modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for the inference of PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models and other factors (eg, activity level) that have not been analyzed with descriptive models.

Validity of Wrist-Worn photoplethysmography devices to measure heart rate: A systematic review and meta-analysis

Heart rate (HR), when combined with accelerometry, can dramatically improve estimates of energy expenditure and sleep. Advancements in technology, via the development and introduction of small, low-cost photoplethysmography devices embedded within wrist-worn consumer wearables, have made the collection of heart rate (HR) under free-living conditions more feasible. This systematic review and meta-analysis compared the validity of wrist-worn HR estimates to a criterion measure of HR (electrocardiography ECG or chest strap). Searches of PubMed/Medline, Web of Science, EBSCOhost, PsycINFO, and EMBASE resulted in a total of 44 articles representing 738 effect sizes across 15 different brands. Multi-level random effects meta-analyses resulted in a small mean difference (beats per min, bpm) of -0.40 bpm (95 confidence interval (CI) -1.64 to 0.83) during sleep, -0.01 bpm (-0.02 to 0.00) during rest, -0.51 bpm (-1.60 to 0.58) during treadmill activities (walking to running), while the mean difference was larger during resistance training (-7.26 bpm, -10.46 to -4.07) and cycling (-4.55 bpm, -7.24 to -1.87). Mean difference increased by 3 bpm (2.5 to 3.5) per 10 bpm increase of HR for resistance training. Wrist-worn devices that measure HR demonstrate acceptable validity compared to a criterion measure of HR for most common activities.

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.

Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

BACKGROUND

Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus.

OBJECTIVES

The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified.

METHODS

Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015.

RESULTS

The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific.

CONCLUSION

This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data.

PROSPERO REGISTRATION NUMBER

CRD42016039991.

An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data

Background

Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data.

Methods

The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability.

Results

Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals’ travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%).

Conclusion

We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.

Electronic supplementary material

The online version of this article (10.1186/s12966-018-0724-y) contains supplementary material, which is available to authorized users.

Role of physical activity and sleep duration in growth and body composition of preschool-aged children

OBJECTIVE

The impact of physical activity patterns and sleep duration on growth and body composition of preschool-aged children remains unresolved. Aims were (1) to delineate cross-sectional associations among physical activity components, sleep, total energy expenditure (TEE), and body size and composition; and (2) to determine whether physical activity components, sleep, and TEE predict 1-year changes in body size and composition in healthy preschool-aged children.

METHODS

Anthropometry, body composition, accelerometry, and TEE by doubly labeled water were measured at baseline; anthropometry and body composition were repeated 1 year later (n = 111).

RESULTS

Cross-sectionally, positive associations between sedentary activity and weight and fat-free mass (FFM) (P = 0.009-0.047), and a negative association between moderate-vigorous physical activity (MVPA) and percent fat mass (FM) (P = 0.015) were observed. TEE and activity energy expenditure (AEE) were positively associated with weight, body mass index (BMI), FFM, and FM (P = 0.0001-0.046). Prospectively, TEE, AEE, physical activity level, and MVPA, but not sedentary activity, were positively associated with changes in BMI (P = 0.0001-0.051) and FFM (P = 0.0001-0.037), but not percent FM. Sleep duration inversely predicted changes in FM (P = 0.005) and percent FM (P = 0.006).

CONCLUSIONS

Prospectively, MVPA, TEE, AEE, and physical activity level promote normal growth and accretion of FFM, whereas sleep duration inversely predicts changes in adiposity in preschool-aged children.

Prediction of energy expenditure and physical activity in preschoolers

PURPOSE

Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) for the prediction of EE using room calorimetry and doubly labeled water (DLW) and established accelerometry cut points for PA levels.

METHODS

Fifty preschoolers, mean ± SD age of 4.5 ± 0.8 yr, participated in room calorimetry for minute-by-minute measurements of EE, accelerometer counts (AC) (Actiheart and ActiGraph GT3X+), and HR (Actiheart). Free-living 105 children, ages 4.6 ± 0.9 yr, completed the 7-d DLW procedure while wearing the devices. AC cut points for PA levels were established using smoothing splines and receiver operating characteristic curves.

RESULTS

On the basis of calorimetry, mean percent errors for EE were -2.9% ± 10.8% and -1.1% ± 7.4% for CSTS models and -1.9% ± 9.6% and 1.3% ± 8.1% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. On the basis of DLW, mean percent errors were -0.5% ± 9.7% and 4.1% ± 8.5% for CSTS models and 3.2% ± 10.1% and 7.5% ± 10.0% for MARS models using the Actiheart and ActiGraph+HR devices, respectively. Applying activity EE thresholds, final accelerometer cut points were determined: 41, 449, and 1297 cpm for Actiheart x-axis; 820, 3908, and 6112 cpm for ActiGraph vector magnitude; and 240, 2120, and 4450 cpm for ActiGraph x-axis for sedentary/light, light/moderate, and moderate/vigorous PA (MVPA), respectively. On the basis of confusion matrices, correctly classified rates were 81%-83% for sedentary PA, 58%-64% for light PA, and 62%-73% for MVPA.

CONCLUSIONS

The lack of bias and acceptable limits of agreement affirms the validity of the CSTS and MARS models for the prediction of EE in preschool-aged children. Accelerometer cut points are satisfactory for the classification of sedentary, light, and moderate/vigorous levels of PA in preschoolers.