al., 2011 ; Wolpert & Flanagan, 2016 ), it remains to be determined whether the time course of fast learning differs between children, young adults, and the older adults. It is well known that compared with young adults, children have lower and more variable motor and cognitive performance capacities
Dalia Mickeviciene, Renata Rutkauskaite, Dovile Valanciene, Diana Karanauskiene, Marius Brazaitis and Albertas Skurvydas
Lisa Kakinami, Erin K. O’Loughlin, Erika N. Dugas, Catherine M. Sabiston, Gilles Paradis and Jennifer O’Loughlin
Compared with traditional nonactive video games, exergaming contributes significantly to overall daily physical activity (PA) in experimental studies, but the association in observational studies is not clear.
Data were available in the 2011 to 2012 wave of the Nicotine Dependence in Teens (NDIT) study (N = 829). Multivariable sex-stratified models assessed the association between exergaming (1–3 times per month in the past year) and minutes of moderate and vigorous physical activity in the previous week, and the association between exergaming and meeting PA recommendations.
Compared with male exergamers, female exergamers were more likely to believe exergames were a good way to integrate PA into their lives (89% vs 62%, P = .0001). After we adjusted for covariates, male exergamers were not significantly different from male nonexergamers in minutes of PA. Female exergamers reported 47 more minutes of moderate PA in the previous week compared with female nonexergamers (P = .03). There was no association between exergaming and meeting PA recommendations.
Exergaming contributes to moderate minutes of PA among women but not among men. Differences in attitudes toward exergaming should be further explored.
Phillip D. Tomporowski and Daniel M. Pendleton
activity on motor learning may depend on the temporal relation between the exercise bout and task training. Roig et al. ( 2012 ) observed that young adults who performed an intense 20-min cycling bout either prior to or following acquisition of a tracking task demonstrated better retention performance than
Barbara E. Ainsworth and Cheryl Der Ananian
There is a growing recognition of the need for the primary prevention of chronic illnesses across the lifespan. In recent years, diseases that were formerly associated with adulthood such as diabetes are being diagnosed in adolescents and young adults. While there have been many prevention efforts focusing on health in children and adolescents, there is a limited body of research examining prevention in young adults. This article examines the concept of wellness in the Millennial generation and describes how their life course experiences impact seven domains of wellness. Specifically, this article describes the period and cohort effects that influence the domains of wellness and how the Millennial generation differs from other generations in these aspects of wellness. Finally, this paper provides an overview of the technological and cultural influences on wellness in the Millennial generation.
Emma L. J. Eyre, Jason Tallis, Susie Wilson, Lee Wilde, Liam Akhurst, Rildo Wanderleys and Michael J. Duncan
. For these reasons, recent focus has been placed on the validity of estimating activity intensities in children ( Chinapaw et al., 2010 ; De Vries et al., 2009 ; Lubans et al., 2011 ), older adults ( Garatachea et al., 2010 ), and, to a lesser extent, young adults ( Watson et al., 2014 ). Young
Janet Robertson, Eric Emerson, Susannah Baines and Chris Hatton
reported low levels of physical activity in adolescents and young adults with mild to moderate intellectual disability, especially women 20 and children and young people with intellectual disability. 21 – 23 A large-scale study in Taiwan found that less than one-third of adolescents with intellectual
Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson and John R. Sirard
for video coders. Based on these initial agreement analyses and identification of problematic issues, the DO system was refined (Figure 1 , Phase II). In phase III, the final agreement analyses were conducted with data from the MOCA Study’s young adult subsample (Figure 1 , Phase III). Figure 1
John M. Jakicic, Wendy C. King, Bethany Barone Gibbs, Renee J. Rogers, Amy D. Rickman, Kelliann K. Davis, Abdus Wahed and Steven H. Belle
To compare moderate-to-vigorous intensity physical activity (MVPA) assessed via questionnaires to an objective measure of MVPA in overweight or obese young adults.
MVPA was assessed in 448 [median BMI = 31.2 (Interquartile Range: 28.5–34.3) kg/m2] young adults [median age: 30.9 (Interquartile Range: 27.8–33.7) years]. Measures included the SenseWear Armband (MVPAOBJ), the Paffenbarger Questionnaire (MVPAPAFF), and the Global Physical Activity Questionnaire (GPAQ). The GPAQ was used to compute total MVPA (MVPAGPAQ-TOTAL) and MVPA from transportation and recreation (MVPAGPAQ-REC).
The association between MVPAOBJ and MVPAPAFF was r s = 0.40 (P < .0001). Associations between MVPAOBJ and MVPAGPAQ-TOTAL and MVPAGPAQ-REC were r s = 0.19 and r s = 0.32, respectively (P < .0001). MVPAGPAQ-TOTAL was significantly greater than MVPAOBJ (P < .0001). Median differences in MET-min/week between MVPAOBJ and MVPAPAFF or MVPAGPAQ-REC were not significantly different from zero. There was proportional bias between each self-reported measure of MVPA and MVPAOBJ. There were significant associations between all measures of MVPA and fitness. MVPAOBJ was significantly associated with BMI and percent body fat.
Objective and self-reported measures of MVPA are weakly to moderately correlated, with substantial differences between measures. MVPAOBJ provided predictive validity with fitness, BMI, and percent body fat. Thus, an objective measure of MVPA may be preferred to self-report in young adults.
Babatunde O.A. Adegoke and Adewale L. Oyeyemi
This study assessed the prevalence of physical inactivity and the influence of sociodemographic variables on physical activity categories, highlighting the correlates of physical inactivity in Nigerian young adults.
A representative sample of young adults age 16 to 39 years (n = 1006) from a Nigerian University were categorized using the International Physical Activity Questionnaire as physically inactive, moderately active, and highly active. Prevalence rates were computed for the activity categories and the independent associations of sociodemographic correlates on each category were determined using the multinomial logistic regression.
Physical inactivity prevalence was 41%. More likely to be inactive were females (OR = 1.93; CI: 1.49−2.49), those of Hausa ethnicity (OR = 2.29; CI: 1.08−5.84), having BMI > 30 kg/m2 (OR = 2.88; CI: 1.16−7.17), and those whose parents’ annual income was < 180,000 NAIRA (OR = 1.69; CI: 1.04−2.95). Less likely to be moderately active were females (OR = 0.71; CI: 0.61−0.95), those with BMI between 25.0 to 29.9 kg/m2 (OR = 0.46; CI: 0.23−0.92), and those of Hausa ethnicity (OR = 0.17; CI: 0.04−0.74).
Important sociodemographic variables that can contribute to the preliminary analysis of correlates of physical inactivity among Nigerian young adults were identified.
Cheryl A. Howe, Marcus W. Barr, Brett C. Winner, Jenelynn R. Kimble and Jason B. White
Although promoted for weight loss, especially in young adults, it has yet to be determined if the physical activity energy expenditure (PAEE) and intensity of the newest active video games (AVGs) qualifies as moderate-to-vigorous physical activity (MVPA; > 3.0 METs). This study compared the PAEE and intensity of AVGs to traditional seated video games (SVGs).
Fifty-three young adults (18−35 y; 27 females) volunteered to play 6 video games (4 AVGs, 2 SVGs). Anthropometrics and resting metabolism were measured before testing. While playing the games (6−10 min) in random order against a playmate, the participants wore a portable metabolic analyzer for measuring PAEE (kcal/min) and intensity (METs). A repeated-measures ANOVA compared the PAEE and intensity across games with sex, BMI, and PA status as main effects.
The intensity of AVGs (6.1 ± 0.2 METs) was significantly greater than SVGs (1.8 ± 0.1 METs). AVGs elicited greater PAEE than SVGs in all participants (5.3 ± 0.2 vs 0.8 ± 0.0 kcal/min); PAEE during the AVGs was greater in males and overweight participants compared with females and healthy weight participants (p’s < .05).
The newest AVGs do qualify as MVPA and can contribute to the recommended dose of MVPA for weight management in young adults.