There is evidence suggesting that active video gaming (AVG) has the potential to improve both health behaviors (eg, physical activity) and health-related fitness (eg, body composition, cardiorespiratory endurance) in children and adolescents. 1 – 3 Mechanisms to achieve these benefits include
Lee E.F. Graves, Nicola D. Ridgers, Greg Atkinson and Gareth Stratton
Active video game interventions typically provide children a single game that may become unappealing. A peripheral device (jOG) encourages step-powered gaming on multiple games. This trial evaluated the effect of jOG on children’s objectively measured PA, body fat and self-reported behaviors. 42 of 58 eligible children (8–10 y) randomly assigned to an intervention (jOG) or control (CON) completed the trial. Intervention children received two jOG devices for home use. Analyses of covariance compared the intervention effect at 6 and 12 weeks from baseline. No differences were found between groups for counts per minute (CPM; primary outcome) at 6 and 12 weeks (p > .05). Active video gaming increased (adjusted change 0.95 (95% CI 0.25, 1.65) h·d−1, p<.01) and sedentary video gaming decreased (-0.34 (-1.24, 0.56) h·d−1, p > .05) at 6 weeks relative to CON. No body fat changes were observed between groups. Targeted changes in video game use did not positively affect PA. Larger trials are needed to verify the impact of active video games on children’s PA and health.
Jungyun Hwang, I-Min Lee, Austin M. Fernandez, Charles H. Hillman and Amy Shirong Lu
. J Clin Med . 2018 ; 7 ( 9 ): 268 – 32 . doi: 10.3390/jcm7090268 31. Hwang J , Lu AS . Narrative and active video game in separate and additive effects of physical activity and cognitive function among young adults . Sci Rep . 2018 ; 8 ( 1 ): 11020 . PubMed ID: 30030456 doi: 10.1038/s41598
Jourdin Barkman, Karin Pfeiffer, Allie Diltz and Wei Peng
Replacing sedentary time with physical activity through new generation exergames (eg, XBOX Kinect) is a potential intervention strategy. The study’s purpose was to compare youth energy expenditure while playing different exergames in single- vs. multiplayer mode.
Participants (26 male, 14 female) were 10 to 13 years old. They wore a portable metabolic analyzer while playing 4 XBOX Kinect games for 15 minutes each (2 single-, 2 multiplayer). Repeated-measures ANOVA (with Bonferroni correction) was used to examine player mode differences, controlling for age group, sex, weight status, and game.
There was a significant difference in energy expenditure between single player (mean = 15.4 ml/kg/min, SD = 4.5) and multiplayer mode (mean = 16.8 ml/kg/min, SD = 4.7). Overweight and obese participants (mean = 13.7 ml/kg/min, SD = 4.2) expended less energy than normal weight (mean = 17.8 ml/kg/min, SD = 4.5) during multiplayer mode (d = 0.93).
Player mode, along with personal factors such as weight status, may be important to consider in energy expenditure during exergames.
Zan Gao and Ping Xiang
Exergaming has been considered a fun solution to promoting a physically active lifestyle. This study examined the impact of an exergaming-based program on urban children’s physical activity participation, body composition and perceptions of the program.
A sample of 185 children’s physical activity was measured in August 2009 (pretest), and percent body fat was used as index of body composition. Fourth graders were assigned to intervention group engaging in 30 minutes exergaming-based activities 3 times per week, while third and fifth graders were in comparison group. Measurements were repeated 9 months later (posttest). Interviews were conducted among 12 intervention children.
ANCOVA with repeated measures revealed a significant main effect for intervention, F(1, 179) = 10.69, P < .01. Specifically, intervention children had significantly greater increased physical activity levels than comparison children. Logistic regression for body composition indicated intervention children did not differ significantly in percent body fat change from comparison children, Chi square = 5.42, P = .14. Children interviewed reported positive attitudes toward the intervention.
The implementation of exergaming-based program could have a significantly positive effect on children’s physical activity participation and attitudes. Meanwhile, long-term effect of the program on children’s body composition deserves further investigation.
Soo Hyun Park, Eun Sun Yoon, Yong Hee Lee, Chul-Ho Kim, Kanokwan Bunsawat, Kevin S. Heffernan, Bo Fernhall and Sae Young Jae
We tested the hypothesis that an active video game following a high-fat meal would partially prevent the unfavorable effect of a high-fat meal on vascular function in overweight adolescents.
Twenty-four overweight adolescents were randomized to either a 60-minute active video game (AVG) group (n = 12) or seated rest (SR) as a control group (n = 12) after a high-fat meal. Blood parameters were measured, and vascular function was measured using brachial artery flow-mediated dilation (FMD) at baseline and 3 hours after a high-fat meal.
No significant interaction was found in any blood parameter. A high-fat meal significantly increased blood triglyceride and glucose concentrations in both groups in a similar manner. Brachial artery FMD significantly decreased in the SR group (13.8 ± 3.2% to 11.8 ± 2.5), but increased in the AVG group (11.4 ± 4.0% to 13.3 ± 3.5), with a significant interaction (P = .034).
These findings show that an active video game attenuated high-fat meal-induced endothelial dysfunction. This suggests that an active video game may have a cardioprotective effect on endothelial function in overweight adolescents when exposed to a high-fat meal.
Jennifer Sween, Sherrie Flynt Wallington, Vanessa Sheppard, Teletia Taylor, Adana A. Llanos and Lucile Lauren Adams-Campbell
The high prevalence of obesity in America can be attributed to inadequate energy expenditure as a result of high levels of physical inactivity. This review presents an overview of the current literature on physical activity, specifically through active videogame systems (exergaming) and how these systems can help to increase physical activity levels.
The search strategy for this review was to identify previous studies that investigated energy expenditure levels using a single active video game or a combination of active videogames.
Based on data from 27 studies, a strong correlation exists between exergaming and increased energy expenditure (up to 300% above resting levels). The majority of active videogames tested were found to achieve physical activity levels of moderate intensity, which meet American College of Sports Medicine guidelines for health and fitness.
Exergaming is a new and exciting strategy to potentially improve physical activity levels and reduce obesity among Americans.
Justin W.L. Keogh, Nicola Power, Leslie Wooller, Patricia Lucas and Chris Whatman
This mixed-methods, quasi-experimental pilot study examined whether the Nintendo Wii Sports (NWS) active video game (exergame) system could significantly improve the functional ability, physical activity levels, and quality of life of 34 older adults (4 men and 30 women, 83 ± 8 yr) living in 2 residential aged-care (RAC) centers. Change score analyses indicated the intervention group had significantly greater increases in bicep curl muscular endurance, physical activity levels, and psychological quality of life than the control group (p < .05). Analysis of the quotes underlying the 3 themes (feeling silly, feeling good; having fun; and something to look forward to) suggested that intervention group participants developed a sense of empowerment and achievement after some initial reluctance and anxiousness. They felt that the games were fun and provided an avenue for greater socialization. These results add some further support to the utilization of NWS exergames in the RAC context.
Ashleigh Thornton, Brendan Lay, Michael Rosenberg, Joanna Granich and Rebecca Braham
This study sought to explore the type of fundamental movement skills (FMS) performed during Active Video Game (AVG) play, as well as the frequency with which these FMS are performed. In addition, this study aimed to determine the relationship between FMS performance and energy expenditure during 15 min of AVG play across two Microsoft Xbox Kinect AVGs. Fundamental movement skills were observed via video by two raters and energy expenditure derived using Actiheart monitors in children aged 10–15 years. Six different FMS were observed during AVG play with differences in the number of FMS performed between the two AVGs. The overall energy expended (Joules/kg/minute), however, was similar between the AVGs, suggesting the frequency of FMS did not influence overall energy expended during play. The movements observed during AVG play that possibly accounted for the energy expenditure, were not of a quality that could be classified as FMS. This research demonstrates that children playing these two games have the opportunity to repeatedly perform mostly two FMS, namely jumping and dodging. The goal of the AVGs, however, could be achieved with generalized movements that did not always meet the criteria to be classified as a FMS.
B. Adar Emken, Ming Li, Gautam Thatte, Sangwon Lee, Murali Annavaram, Urbashi Mitra, Shrikanth Narayanan and Donna Spruijt-Metz
KNOWME Networks is a wireless body area network with 2 triaxial accelerometers, a heart rate monitor, and mobile phone that acts as the data collection hub. One function of KNOWME Networks is to detect physical activity (PA) in overweight Hispanic youth. The purpose of this study was to evaluate the in-laboratory recognition accuracy of KNOWME.
Twenty overweight Hispanic participants (10 males; age 14.6 ± 1.8 years), underwent 4 data collection sessions consisting of 9 activities/session: lying down, sitting, sitting fidgeting, standing, standing fidgeting, standing playing an active video game, slow walking, brisk walking, and running. Data were used to train activity recognition models. The accuracy of personalized and generalized models is reported.
Overall accuracy for personalized models was 84%. The most accurately detected activity was running (96%). The models had difficulty distinguishing between the static and fidgeting categories of sitting and standing. When static and fidgeting activity categories were collapsed, the overall accuracy improved to 94%. Personalized models demonstrated higher accuracy than generalized models.
KNOWME Networks can accurately detect a range of activities. KNOWME has the ability to collect and process data in real-time, building the foundation for tailored, real-time interventions to increase PA or decrease sedentary time.