Search Results

You are looking at 71 - 80 of 395 items for :

  • "sedentary time" x
Clear All
Restricted access

Sarah Kozey Keadle, Shirley Bluethmann, Charles E. Matthews, Barry I. Graubard and Frank M. Perna

Background:

This paper tested whether a physical activity index (PAI) that integrates PA-related behaviors (ie, moderate-to-vigorous physical activity [MVPA] and TV viewing) and performance measures (ie, cardiorespiratory fitness and muscle strength) improves prediction of health status.

Methods:

Participants were a nationally representative sample of US adults from 2011 to 2012 NHANES. Dependent variables (self-reported health status, multimorbidity, functional limitations, and metabolic syndrome) were dichotomized. Wald-F tests tested whether the model with all PAI components had statistically significantly higher area under the curve (AUC) values than the models with behavior or performance scores alone, adjusting for covariates and complex survey design.

Results:

The AUC (95% CI) for PAI in relation to health status was 0.72 (0.68, 0.76), and PAI-AUC for multimorbidity was 0.72 (0.69, 0.75), which were significantly higher than the behavior or performance scores alone. For functional limitations, the PAI AUC was 0.71 (0.67, 0.74), significantly higher than performance, but not behavior scores, while the PAI AUC for metabolic syndrome was 0.69 (0.66, 0.73), higher than behavior but not performance scores.

Conclusions:

These results provide empirical support that an integrated PAI may improve prediction of health and disease. Future research should examine the clinical utility of a PAI and verify these findings in prospective studies.

Restricted access

Nirjhar Dutta and Mark A. Pereira

Background:

The objective of this study was to estimate the mean difference in energy expenditure (EE) in healthy adults between playing active video games (AVGs) compared with traditional video games (TVGs) or rest.

Methods:

A systematic search was conducted on Ovid MEDLINE, Web of Knowledge, and Academic Search Premier between 1998 and April 2012 for relevant keywords, yielding 15 studies. EE and heart rate (HR) data were extracted, and random effects meta-analysis was performed.

Results:

EE during AVG play was 1.81 (95% CI, 1.29–2.34; I 2 = 94.2%) kcal/kg/hr higher, or about 108 kcal higher per hour for a 60-kg person, compared with TVG play. Mean HR was 21 (95% CI, 13.7–28.3; I 2 = 93.4%) beats higher per minute during AVG play compared with TVG play. There was wide variation in the EE and HR estimates across studies because different games were evaluated. Overall metabolic equivalent associated with AVG play was 2.62 (95% CI, 2.25–3.00; I 2 = 99.2%), equivalent to a light activity level. Most studies had low risk of bias due to proper study design and use of indirect calorimetry to measure EE.

Conclusion:

AVGs may be used to replace sedentary screen time (eg, television watching or TVG play) with light activity in healthy adults.

Restricted access

Jennifer L. Copeland and Dale W. Esliger

Despite widespread use of accelerometers to objectively monitor physical activity among adults and youth, little attention has been given to older populations. The purpose of this study was to define an accelerometer-count cut point for a group of older adults and to then assess the group’s physical activity for 7 days. Participants (N = 38, age 69.7 ± 3.5 yr) completed a laboratory-based calibration with an Actigraph 7164 accelerometer. The cut point defining moderate to vigorous physical activity (MVPA) was 1,041 counts/min. On average, participants obtained 68 min of MVPA per day, although more than 65% of this occurred as sporadic activity. Longer bouts of activity occurred in the morning (6 a.m. to 12 p.m.) more frequently than other times of the day. Almost 14 hr/day were spent in light-intensity activity. This study demonstrates the rich information that accelerometers provide about older adult activity patterns—information that might further our understanding of the relationship between physical activity and healthy aging.

Restricted access

Kelly R. Laurson, Joey A. Lee and Joey C. Eisenmann

Background:

Physical activity (PA), television time (TV), and sleep duration (SLP) are considered individual risk factors for adolescent obesity. Our aim was to investigate the concurrent influence of meeting PA, SLP, and TV recommendations on adolescent obesity utilizing 2011 Youth Risk Behavior Surveillance Survey (YRBSS) data.

Methods:

Subjects included 9589 (4874 females) high school students. PA, SLP, and TV were categorized utilizing established national recommendations and youth were cross-tabulated into 1 of 8 groups based on meeting or not meeting each recommendation. Logistic models were used to examine the odds of obesity for each group. Results: Youth meeting the PA recommendation were not at increased odds of obesity, regardless of SLP or TV status. However, not meeting any single recommendation, in general, led to increased odds of not meeting the other two. In boys, 11.8% met all recommendations while 14.1% met 0 recommendations. In girls, only 5.0% met all recommendations while 17.8% met none.

Conclusions:

Boys and girls not meeting any of the recommendations were 4.0 and 3.8 times more likely to be obese compared with their respective referent groups. Further research considering the simultaneous influence these risk factors may have on obesity and on one another is warranted.

Restricted access

Evelin Lätt, Jarek Mäestu and Jaak Jürimäe

adults have shown that not only the total amount of daily sedentary time may be unhealthy, but also the pattern of sedentary time accumulation might have adverse health effects. 3 – 5 Therefore, a more detailed analysis of sedentary behavior accumulation and examining associations for cardiometabolic

Restricted access

Jade L. Morris, Andy Daly-Smith, Margaret A. Defeyter, Jim McKenna, Steve Zwolinsky, Scott Lloyd, Melissa Fothergill and Pamela L. Graham

effects on sedentary time, LPA, and MVPA. Covariates entered in the model were maturity offset, BMI SD scores, gender, and the appropriate preactivity category. Two subgroups were created using initial accelerometry data on MVPA levels; (1) Low Active (eg, achieved <45 min/d MVPA) and (2) High Active (eg

Restricted access

Anna Pulakka, Eric J. Shiroma, Tamara B. Harris, Jaana Pentti, Jussi Vahtera and Sari Stenholm

sedentary time, but mainly from accelerometers worn on the hip during wake time only ( Evenson & Terry, 2009 ; Keadle et al., 2014 ; Masse et al., 2005 ; Peeters, van Gellecum, Ryde, Farías, & Brown, 2013 ; Winkler et al., 2012 ). In addition, some studies have assessed impact of sleep algorithms on

Restricted access

Manon L. Dontje, Calum F. Leask, Juliet Harvey, Dawn A. Skelton and Sebastien F.M. Chastin

daily physical activity ( Chau et al., 2013 ; de Rezende, Rey-López, Matsudo, & do Carmo Luiz, 2014 ; Dogra & Stathokostas, 2012 ). Several national and international health guidelines explicitly recommend that older adults should reduce their sedentary time and break prolonged periods of sitting to

Restricted access

Peng Zhang, Jung Eun Lee, David F. Stodden and Zan Gao

noted PA and sedentary data generally represent cross-sectional data with longitudinal tracking of individual trajectories of PA and sedentary time receiving limited attention. Lai et al 7 conducted a meta-analysis on the impact of PA interventions and reported only 4 out of 14 studies investigated the

Restricted access

Ronit Aviram, Netta Harries, Anat Shkedy Rabani, Akram Amro, Ibtisam Nammourah, Muhammed Al-Jarrah, Yoav Raanan, Yeshayahu Hutzler and Simona Bar-Haim

encouraged to increase habitual physical activity (HPA) and reduce sedentary behavior ( 29 ). It is also clear that physical activity declines in typically developing (TD) individuals through adolescence with a concurrent increase in sedentary time in many populations that have been sampled ( 5 , 11 , 23