Dataset Information

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events.

ABSTRACT:

Purpose

(i) To develop an automated measurement technique for the assessment of both the form and intensity of physical activity undertaken by children during play. (ii) To profile the varying activity across a cohort of children using a multivariate analysis of their movement patterns.

Methods

Ankle-worn accelerometers were used to record 40 min of activity during a school recess, for 24 children over five consecutive days. Activity events of 1.1 s duration were identified within the acceleration time trace and compared with a reference motif, consisting of a single walking stride acceleration trace, obtained on a treadmill operating at a speed of 4 km h. Dynamic time warping of motif and activity events provided metrics of comparative movement duration and intensity, which formed the data set for multivariate mapping of the cohort activity using a principal component analysis (PCA).

Results

The two-dimensional PCA plot provided clear differentiation of children displaying diverse activity profiles and clustering of those with similar movement patterns. The first component of the PCA correlated to the integrated intensity of movement over the 40-min period, whereas the second component informed on the temporal phasing of activity.

Conclusions

By defining movement events and then quantifying them by reference to a motion-standard, meaningful assessment of highly varied activity within free play can be obtained. This allows detailed profiling of individual children's activity and provides an insight on social aspects of play through identification of matched activity time profiles for children participating in conjoined play.

SUBMITTER: Rocha JN

PROVIDER: S-EPMC7028522 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events.

Rocha Joana N JN Barnes Claire M CM Rees Paul P Clark Cain T CT Stratton Gareth G Summers Huw D HD

Medicine and science in sports and exercise 20200101 1

<h4>Purpose</h4>(i) To develop an automated measurement technique for the assessment of both the form and intensity of physical activity undertaken by children during play. (ii) To profile the varying activity across a cohort of children using a multivariate analysis of their movement patterns.<h4>Methods</h4>Ankle-worn accelerometers were used to record 40 min of activity during a school recess, for 24 children over five consecutive days. Activity events of 1.1 s duration were identified within ...[more]

PMID: 31436733

Similar Datasets

Project description:BACKGROUND:Children's physical activity levels are low and efforts to improve their physical activity levels have proven difficult. Freely chosen and unstructured physical activity (active play) has the potential to be promoted in a variety of settings and potentially every day of the year in contrast to other physical activity domains, but active play interventions are an under-researched area. Therefore, the primary aim of this systematic review was to determine the effect of active play interventions on children's physical activity levels, particularly moderate-to-vigorous intensity physical activity (MVPA), and fundamental movement skills (FMS). METHODS:Studies were included if they were solely or predominantly active play randomised, or cluster randomised controlled trials that targeted children aged 3-12 years. They had to report on at least one of the following outcomes: objectively measured physical activity, FMS, cognition and weight status. During December 2016, four databases (PE Index, SPORTDiscus, Medline and ERIC) were searched for relevant titles. Duplicates and irrelevant titles and abstracts were removed. The included studies had their quality assessed using the Effective Public Health Practice Project (EPHPP) tool. Suitable studies were combined in a meta-analysis using a random-effect model. A narrative synthesis was conducted for all outcomes. RESULTS:Of the 4033 records, 91 studies were eligible for full text screening, of which 87 were removed, leaving four studies (representing five papers). The meta-analysis of two studies highlighted there was no significant effect of active play interventions on MVPA. However, the narrative synthesis suggested that active play interventions may increase total volume of physical activity. Only two studies examined the effect of active play interventions on children's FMS, one study examined effects on weight status and none examined effects on cognition. CONCLUSIONS:Due to the small number of eligible studies and their heterogeneity, the review could not draw firm conclusions on the effect of active play interventions on children's physical activity levels. High-quality active play interventions, targeting different times of the day (school and after school) in different populations and settings, and with a wider range of outcomes, are required to determine the potential of active play.

Project description:BACKGROUND:Physical activity is a cornerstone for promoting good metabolic health in children, but it is heavily debated which intensities (including sedentary time) are most influential. A fundamental limitation to current evidence for this relationship is the reliance on analytic approaches that cannot handle collinear variables. The aim of the present study was to determine the physical activity signature related to metabolic health in children, by investigating the association pattern for the whole spectrum of physical activity intensities using multivariate pattern analysis. METHODS:We used a sample of 841 children (age 10.2?±?0.3 years; BMI 18.0?±?3.0; 50% boys) from the Active Smarter Kids study, who provided valid data on accelerometry (ActiGraph GT3X+) and several indices of metabolic health (aerobic fitness, abdominal fatness, insulin sensitivity, lipid metabolism, blood pressure) that were used to create a composite metabolic health score. We created 16 physical activity variables covering the whole intensity spectrum (from 0-100 to ? 8000 counts per minute) and used multivariate pattern analysis to analyze the data. RESULTS:Physical activity intensities in the vigorous range (5000-7000 counts per minute) were most strongly associated with metabolic health. Moderate intensity physical activity was weakly related to health, and sedentary time and light physical activity were not related to health. CONCLUSIONS:This study is the first to determine the multivariate physical activity signature related to metabolic health in children across the whole intensity spectrum. This novel approach shows that vigorous physical activity is strongest related to metabolic health. We recommend future studies adapt a multivariate analytic approach to further develop the field of physical activity epidemiology. TRIAL REGISTRATION:The study was registered in Clinicaltrials.gov (www.clinicaltrials.gov) 7th of April 2014 with identification number NCT02132494 .

Project description:Quantitative susceptibility mapping employs regularization to reduce artifacts, yet many recent denoisers are unavailable for reconstruction. We developed a plug-and-play approach to QSM reconstruction (PnP QSM) and show its flexibility using several patch-based denoisers. We developed PnP QSM using alternating direction method of multiplier framework and applied collaborative filtering denoisers. We apply the technique to the 2016 QSM Challenge and in 10 glioblastoma multiforme datasets. We compared its performance with four published QSM techniques and a multi-orientation QSM method. We analyzed magnetic susceptibility accuracy using brain region-of-interest measurements, and image quality using global error metrics. Reconstructions on glioblastoma data were analyzed using ranked and semiquantitative image grading by three neuroradiologist observers to assess image quality (IQ) and sharpness (IS). PnP-BM4D QSM showed good correlation (β = 0.84, R2 = 0.98, p < 0.05) with COSMOS and no significant bias (bias = 0.007 ± 0.012). PnP-BM4D QSM achieved excellent quality when assessed using structural similarity index metric (SSIM = 0.860), high frequency error norm (HFEN = 58.5), cross correlation (CC = 0.804), and mutual information (MI = 0.475) and also maintained good conspicuity of fine features. In glioblastoma datasets, PnP-BM4D QSM showed higher performance (IQGrade = 2.4 ± 0.4, ISGrade = 2.7 ± 0.3, IQRank = 3.7 ± 0.3, ISRank = 3.9 ± 0.3) compared to MEDI (IQGrade = 2.1 ± 0.5, ISGrade = 2.1 ± 0.6, IQRank = 2.4 ± 0.6, ISRank = 2.9 ± 0.2) and FANSI-TGV (IQGrade = 2.2 ± 0.6, ISGrade = 2.1 ± 0.6, IQRank = 2.7 ± 0.3, ISRank = 2.2 ± 0.2). We illustrated the modularity of PnP QSM by interchanging two additional patch-based denoisers. PnP QSM reconstruction was feasible, and its flexibility was shown using several patch-based denoisers. This technique may allow rapid prototyping and validation of new denoisers for QSM reconstruction for an array of useful clinical applications.

Dataset Information

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events.

Purpose

Methods

Results

Conclusions

Publications

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets