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Exploring Energy Expenditure and Body Movement of Exergaming in Children of Different Weight Status.

ABSTRACT: PURPOSE:This study examined differences in energy expenditure and bodily movement among children of different weight status during exergames that varied in mode and intensity. METHODS:Fifty-seven 8- to 12-year-old children including overweight/obesity (n = 28) and normal weight (n = 29) played three 10-minute interval Xbox One exergames (Fruit Ninja, Kung-Fu, and Shape Up) categorized based on predominantly upper-, whole-, or lower-limb movement, respectively. The authors measured bodily movement through accelerometry and obtained energy expenditure and metabolic equivalent (MET) via indirect calorimetry. RESULTS:Energy expended during gameplay was the highest in Shape Up (P < .01) and higher in Kung-Fu than Fruit Ninja (P < .01). Absolute energy expenditure was significantly higher in overweight/obese children (P < .01), but not when controlling for body mass across 3 exergames (P > .05). Based on the MET cut-points, overweight/obese children spent more time at light intensity (<3 METs) for Fruit Ninja (P < .05) and Shape Up (P < .01), but less time at vigorous intensity (?6 METs) for Kung-Fu (P < .01) and Shape Up (P < .01). Lower-limb movements during Shape Up were less in overweight/obese children (P = .03). CONCLUSION:Although children in both groups expended similar energy relative to their body mass during gameplay, overweight/obese children spent more time at light intensity but less time at vigorous intensity with fewer movements especially while playing a lower limb-controlled exergame.

SUBMITTER: Hwang J

PROVIDER: S-EPMC7030900 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Exploring Energy Expenditure and Body Movement of Exergaming in Children of Different Weight Status.

Hwang Jungyun J Lee I-Min IM Fernandez Austin M AM Hillman Charles H CH Lu Amy Shirong AS

Pediatric exercise science 20190808 4

<h4>Purpose</h4>This study examined differences in energy expenditure and bodily movement among children of different weight status during exergames that varied in mode and intensity.<h4>Methods</h4>Fifty-seven 8- to 12-year-old children including overweight/obesity (n = 28) and normal weight (n = 29) played three 10-minute interval Xbox One exergames (Fruit Ninja, Kung-Fu, and Shape Up) categorized based on predominantly upper-, whole-, or lower-limb movement, respectively. The authors measured ...[more]

PMID: 31394495

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Project description: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.

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

Exploring Energy Expenditure and Body Movement of Exergaming in Children of Different Weight Status.

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Exploring Energy Expenditure and Body Movement of Exergaming in Children of Different Weight Status.

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