Dataset Information

Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents.

ABSTRACT: Background: The objective and accurate assessment of children's sedentary and physical behavior is important for investigating their relation to health. The purpose of this study is to validate a simple and robust method for the identification of sitting, standing, walking, running and biking performed by preschool children, children and adolescents in the age from 3 to 16 years from a single thigh-worn accelerometer. Method: A total of 96 children were included in the study and all subjects followed a structured activity protocol performed in the subject's normal kindergarten or school environment. Thigh acceleration was measured using the Axivity AX3 (Axivity, Newcastle, UK) device. Method development and accuracy was evaluated by equally dividing the subjects into a development and test group. Results: The sensitivity and specificity for identifying sitting and standing was above 99.3% and for walking and running above 82.6% for all age groups. The sensitivity and specificity for identifying biking was above 85.8% for children and adolescents and above 64.8% for the preschool group using running bikes. Conclusion: The accurate assessment of sitting, standing, walking, running and biking from thigh acceleration and with children in the age range of 3 to 16 is valid, although not with preschool children using running bikes.

SUBMITTER: Brond JC

PROVIDER: S-EPMC7401882 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents.

Brønd Jan Christian JC Grøntved Anders A Andersen Lars Bo LB Arvidsson Daniel D Olesen Line Grønholt LG

Children (Basel, Switzerland) 20200702 7

<b>Background:</b> The objective and accurate assessment of children's sedentary and physical behavior is important for investigating their relation to health. The purpose of this study is to validate a simple and robust method for the identification of sitting, standing, walking, running and biking performed by preschool children, children and adolescents in the age from 3 to 16 years from a single thigh-worn accelerometer. <b>Method:</b> A total of 96 children were included in the study and al ...[more]

PMID: 32630836

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Project description: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.

Project description:BACKGROUND:Despite physical activity (PA) being recognized as a critically important factor for good physical and mental health already early in life and throughout the life course, prospective data on activity behavior during the preschool years remains scarce. This study examined trajectories and determinants of levels and change in total PA (TPA), moderate-to-vigorous PA (MVPA) and sedentary behavior (SB) in a representative sample of Swiss preschoolers. METHODS:Data were drawn from the Swiss Preschoolers' Health Study (SPLASHY), a multi-site prospective cohort study including 555 children (53% boys) aged 2-to-6 years at baseline. A follow-up was conducted after 12 months. Activity behavior was measured using accelerometers. Information on 35 potential determinants from different socio-ecological domains was either directly measured or parent-reported. Trajectories of TPA, MVPA and SB over time were described for boys and girls. Linear mixed models were used to investigate factors that predicted levels and change in TPA, MVPA and SB. RESULTS:All children were sufficiently physically active according to published recommendations for preschoolers. Trajectory profiles revealed a marked increase in TPA and MVPA in boys and girls whereas SB remained fairly stable over time. Mixed modeling demonstrated that variables most relevant to determining PA levels were sex, age and activity temperament (all positively associated). Together with gross motor skills, birth weight, family structure (only for TPA) and season (only for MVPA), these factors accounted for 26 and 32% of total variance explained in TPA and MVPA, respectively. Activity temperament emerged as the strongest determinant of SB (negative association) and explained with sex, season and family structure 20% of total variance in SB. The presence of older siblings was the only factor that predicted change in PA over time. CONCLUSIONS:In this healthy physically active cohort of preschoolers, non-modifiable individual-level factors had the greatest influence on PA. The limited success of this and previous studies to identify modifiable determinants and the finding that most preschoolers were sufficiently active suggest that future attempts should provide insights into how preschoolers' activity levels can be maintained and fostered to prevent subsequent harmful declines attributable, amongst others, to educational transitions. Thus, good-quality longitudinal studies are needed. TRIAL REGISTRATION:Current Controlled Trials ISRCTN41045021 (date of registration: 21.03.14).

Dataset Information

Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents.

Publications

Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents.

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