Dataset Information

Home-Based Exergaming on Preschoolers' Energy Expenditure, Cardiovascular Fitness, Body Mass Index and Cognitive Flexibility: A Randomized Controlled Trial.

ABSTRACT: PURPOSE:The effects of exergaming-based physical activity (PA) interventions on preschoolers' health outcomes and cognition remain largely unexplored. Therefore, we conducted a randomized controlled trial to discern the effectiveness of a home-based educational exergaming intervention on preschoolers' energy expenditure, fitness, body mass index, and cognition. METHODS:Participants were 32 preschoolers (16 girls; 59.4% Asian; Mage = 4.72, SD = ±0.73) recruited from the Twin Cities area in Minnesota. During baseline testing, we measured preschooler's daily energy expenditure (EE), cardiovascular fitness, body mass index, and cognitive flexibility using validated instruments. Participants were then randomly assigned to one of two conditions: (1) the exergaming intervention condition; or (2) control condition. The intervention program requested children participate in home-based educational exergaming using the LeapTV gaming console for at least 30 min/session 5 times/week. The control condition asked children to maintain regular PA patterns without any exergaming gameplay. Each condition lasted 12 weeks. We conducted identical outcome assessments for all children at baseline and post-intervention. RESULTS:Analysis of covariance with repeated measures yielded significant time x group interaction effects for cognitive flexibility, Wilks' Lambda = 0.81, F(1, 29) = 6.98, p = 0.01, ?2 = 0.19, suggesting children in the exergaming group demonstrated significantly greater increases in cognitive flexibility as compared to those in the control group over time. There were no significant differences for time x group changes between the two groups for EE, Wilks' Lambda = 0.92, F(1, 29) = 2.29, p = 0.14, ?2 = 0.08; cardiovascular fitness, Wilks' Lambda = 0.96, F(1, 29) = 1.07, p = 0.31, ?2 = 0.04; and BMI, Wilks' Lambda = 0.99, F(1, 29) = 0.05, p = 0.82, ?2 = 0.01. However, our data did suggest a marginal effect of time for EE, Wilks' Lambda = 0.89, F(1, 29) = 3.26, p = 0.08, ?2 = 0.08, indicating that children's daily EE increased from baseline to post-intervention. CONCLUSIONS:Home-based educational exergaming may positively impact cognitive flexibility in preschoolers. Studies with larger sample sizes in multiple geographic locations are needed, with our study suggesting a longer intervention period might also be warranted.

SUBMITTER: Gao Z

PROVIDER: S-EPMC6832462 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Home-Based Exergaming on Preschoolers' Energy Expenditure, Cardiovascular Fitness, Body Mass Index and Cognitive Flexibility: A Randomized Controlled Trial.

Gao Zan Z Lee Jung Eun JE Zeng Nan N Pope Zachary C ZC Zhang Ying Y Li Xianxiong X

Journal of clinical medicine 20191021 10

<h4>Purpose</h4>The effects of exergaming-based physical activity (PA) interventions on preschoolers' health outcomes and cognition remain largely unexplored. Therefore, we conducted a randomized controlled trial to discern the effectiveness of a home-based educational exergaming intervention on preschoolers' energy expenditure, fitness, body mass index, and cognition.<h4>Methods</h4>Participants were 32 preschoolers (16 girls; 59.4% Asian; M<sub>age</sub> = 4.72, SD = ±0.73) recruited from the ...[more]

PMID: 31640158

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Project description: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.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.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.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.

Project description:Active video games that require physical exertion during game play have been shown to confer health benefits. Typically, energy expended during game play is measured using devices attached to players, such as accelerometers, or portable gas analyzers. Since 2010, active video gaming technology incorporates marker-less motion capture devices to simulate human movement into game play. Using the Kinect Sensor and Microsoft SDK this research aimed to estimate the mechanical work performed by the human body and estimate subsequent metabolic energy using predictive algorithmic models. Nineteen University students participated in a repeated measures experiment performing four fundamental movements (arm swings, standing jumps, body-weight squats, and jumping jacks). Metabolic energy was captured using a Cortex Metamax 3B automated gas analysis system with mechanical movement captured by the combined motion data from two Kinect cameras. Estimations of the body segment properties, such as segment mass, length, centre of mass position, and radius of gyration, were calculated from the Zatsiorsky-Seluyanov's equations of de Leva, with adjustment made for posture cost. GPML toolbox implementation of the Gaussian Process Regression, a locally weighted k-Nearest Neighbour Regression, and a linear regression technique were evaluated for their performance on predicting the metabolic cost from new feature vectors. The experimental results show that Gaussian Process Regression outperformed the other two techniques by a small margin. This study demonstrated that physical activity energy expenditure during exercise, using the Kinect camera as a motion capture system, can be estimated from segmental mechanical work. Estimates for high-energy activities, such as standing jumps and jumping jacks, can be made accurately, but for low-energy activities, such as squatting, the posture of static poses should be considered as a contributing factor. When translated into the active video gaming environment, the results could be incorporated into game play to more accurately control the energy expenditure requirements.

Dataset Information

Home-Based Exergaming on Preschoolers' Energy Expenditure, Cardiovascular Fitness, Body Mass Index and Cognitive Flexibility: A Randomized Controlled Trial.

Publications

Home-Based Exergaming on Preschoolers' Energy Expenditure, Cardiovascular Fitness, Body Mass Index and Cognitive Flexibility: A Randomized Controlled Trial.

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