Stephen D. Herrmann and Karin A. Pfeiffer
Karin A. Pfeiffer and Michael J. Wierenga
Participation in a sport is widely considered a valuable form of physical activity, especially for children and adolescents. In addition, many think that sport participation translates to future physical activity. However, limited research has examined the ability of youth sport to significantly contribute to meeting daily physical activity guidelines (60 min/day of moderate to vigorous physical activity) and whether the physical activity behaviors of youth sport participants will translate into future, habitual activity in both the short and the long term. In this paper, available research on the role of youth sport in the promotion of physical activity is evaluated. Two major questions are used to frame the discussion: How much physical activity do youth sport participants attain during games and practices, and does sport participation during childhood and adolescence translate into habitual physical activity in adulthood? This is followed by ideas for future research and preliminary recommendations for best practices or policies.
Kimberly A. Clevenger, Karin A. Pfeiffer and Cheryl A. Howe
Portable metabolic units (PMUs) are used to assess energy expenditure, with the assumption that physical activity level and enjoyment are unaffected due to the light weight and small size. Purpose: To assess differences in physical activity level and enjoyment while wearing and not wearing a PMU. Method: Youth (8–17 y; N = 73) played children’s games or active video games while wearing and not wearing a PMU (crossover design). Participants wore an accelerometer and heart rate monitor and responded to questions about enjoyment on a facial affective scale. A repeated-measures analysis of variance was used to determine if accelerometer measures, heart rate, or enjoyment differed between conditions overall and by sex and weight status. Results: Steps per minute were lower while wearing the PMU than not wearing the PMU (40 vs 44, P = .03). There was an interaction between PMU condition and weight status for enjoyment (P = .01), with overweight participants reporting less enjoyment when wearing the PMU compared with not wearing the PMU (72 vs 75 out of 100). Heart rate, vector magnitude, and counts per minute were not different. Conclusion: There may be psychosocial effects of wearing the PMU, specifically in overweight participants. Activity level was minimally affected, but the practical significance for research is still unknown.
Felipe Lobelo, Marsha Dowda, Karin A. Pfeiffer and Russell R. Pate
Few investigations have assessed in adolescent girls the cross-sectional and longitudinal associations between elevated exposure to electronic media (EM) and activity-related outcomes such as compliance with physical activity (PA) standards or cardiorespiratory fitness (CRF).
Four-hundred thirty-seven white and African American girls were assessed at the 8th, 9th, and 12th grades. PA and EM (TV/video watching, electronic games, Internet use) were self-reported, and CRF was estimated using a cycle-ergometer test. Hi EM exposure was defined as ≥four 30-minute blocks/d.
8th-, 9th-, and 12th-grade girls in the Hi EM group showed lower compliance with PA standards and had lower CRF than the Low EM group (P ≤ .03). Girls reporting Hi EM exposure at 8th and 9th grades had lower vigorous PA and CRF levels at 12th grade than girls reporting less EM exposure (P ≤ .03).
Girls reporting exposure to EM for 2 or more hours per day are more likely to exhibit and maintain low PA and CRF levels throughout adolescence. These results enhance the scientific basis for current public health recommendations to limit adolescent girls’ daily exposure to television, electronic games, and Internet use to a combined maximum of 2 hours.
Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry and Karin A. Pfeiffer
Background: Machine learning may improve energy expenditure (EE) prediction from body-worn accelerometers. However, machine learning models are rarely cross-validated in an independent sample, and the use of machine learning raises additional questions including the effect of accelerometer placement and data type (count vs. raw) for optimal EE prediction. Purpose: To assess the accuracy of artificial neural network (ANN) models for EE prediction in youth using count-based or raw data from accelerometers worn on the hip, wrist, or in combination, and compare these to count-based, EE regression equations. Methods: Data were collected in two settings; one (n = 27) to calibrate the EE prediction models, and the other (n = 34) for model cross-validation. Participants wore a portable metabolic analyzer (EE criterion) and accelerometers on the left wrist and right hip while completing 30 minutes of exergames (calibration, cross-validation) and a maximal exercise test (calibration only). Six ANNs were created from the calibration data, separately by accelerometer placement (hip, wrist, combination) and data format (count-based, raw) to predict EE (15-second epochs). Three count-based linear regression equations were also developed for comparison to the ANNs. Results: The count-based, hip ANN demonstrated lower error (RMSE: 1.2 METs) than all other ANNs (RMSE: 1.7–3.6 METs) and EE regression equations (RMSE: 1.5–3.2 METs). However, all models showed bias toward the mean. Conclusion: An ANN developed for hip-worn accelerometers had higher accuracy for EE prediction during an exergame session than wrist or combination ANNs, and ANNs developed using count-based data had higher accuracy than ANNs developed using raw data.
Rebecca A. Schlaff, Claudia Holzman, Lanay M. Mudd, Karin A. Pfeiffer and James M. Pivarnik
Little is known about how leisure-time physical activity (LTPA) influences gestational weight gain (GWG) among body mass index (BMI) categories. The purpose of this study was to examine the relationship between pregnancy LTPA and the proportion of normal, overweight, and obese women who meet GWG recommendations.
Participants included 449 subcohort women from the Pregnancy Outcomes and Community Health (POUCH) study. LTPA was collapsed into 3 categories [(None, < 7.5 kcal/kg/wk (low), ≥ 7.5 kcal/kg/wk (recommended)]. GWG was categorized according to IOM recommendations (low, recommended, or excess). Chi-square and logistic regression analyses were used to evaluate relationships among LTPA, BMI, and GWG.
Overweight women were more likely to have high GWG vs. normal weight women (OR = 2.3, 95% CI 1.3–4.0). Obese women were more likely to experience low GWG (OR = 7.3, 95% CI 3.6–15.1; vs. normal and overweight women) or excess GWG (OR = 3.5, 95% CI 1.9–6.5; vs. normal weight women). LTPA did not vary by prepregnancy BMI category (P = .55) and was not related to GWG in any prepregnancy BMI category (P = .78).
Regardless of prepregnancy BMI, LTPA did not affect a woman’s GWG according to IOM recommendations. Results may be due to LTPA not differing among BMI categories.
Lanay M. Mudd, Jim M. Pivarnik, Karin A. Pfeiffer, Nigel Paneth, Hwan Chung and Claudia Holzman
We sought to evaluate the effects of maternal leisure-time physical activity (LTPA) during pregnancy and current child LTPA on child weight status.
Women with term pregnancies in the Pregnancy Outcomes and Community Health Study (1998–2004) were followed-up. A race-stratified subset of participants (cohort A) received extensive follow-up efforts leading to better response rates (592/926 = 64%) and diversity. The remainder (Cohort B) had a lower response rate (418/1629 = 26%). Women reported child height, weight and LTPA at 3 to 9 years (inactive vs. active), and recalled pregnancy LTPA (inactive vs. active). A 4-category maternal/child LTPA variable was created (reference: active pregnancy + active child). Children were classified as healthy weight, overweight, or obese using age- and sex-specific Body Mass Index percentiles. Logistic regression was used to assess the odds of child obesity (reference: healthy weight).
In unadjusted analyses, pregnancy inactivity increased odds for obesity when the child was active (1.6 [95% CI, 1.0−2.6] in Cohort A; 2.1 [95% CI, 1.1−4.0] in Cohort B), and more so when the child was inactive (2.4 [95% CI, 1.2−4.9] in Cohort A; 3.0 [95% CI, 1.0−8.8] in Cohort B). Adjustment for covariates attenuated results to statistical nonsignificance but the direction of relations remained.
Maternal inactivity during pregnancy may contribute to child obesity risk.
Jennifer R. O’Neill, Karin A. Pfeiffer, Marsha Dowda and Russell R. Pate
Little is known about the relationship between children’s physical activity (PA) in preschool (in-school) and outside of preschool (out-of-school). This study described this relationship.
Participants were 341 children (4.6 ± 0.3 years) in 16 preschools. Accelerometers measured moderate-to-vigorous physical activity (MVPA) and total physical activity (TPA) in-school and out-of-school. In the full sample, Pearson correlation was used to describe associations between in-school and out-of-school PA. In addition, children were categorized as meeting or not meeting a PA guideline during school. MVPA and TPA were compared between the 2 groups and in-school and out-of-school using 2-way repeated-measures analysis of variance.
In the full sample, in-school and out-of-school PA were positively correlated for MVPA (r = .13, P = .02) and TPA (r = .15, P = .01). Children who met the guideline in-school remained comparably active out-of-school. However, those who did not meet the guideline were more active out-of-school than in-school. The groups were active at comparable levels while out-of-school. Identical patterns were seen for MVPA and TPA.
Children’s in-school PA was positively associated with out-of-school PA. Children who did not meet the guideline in-school were more active out-of-school than in-school, suggesting preschool and classroom factors may reduce some children’s PA in-school.
John R. Sirard, Stewart G. Trost, Karin A. Pfeiffer, Marsha Dowda and Russell R. Pate
The purposes of this study were 1) to establish accelerometer count cutoffs to categorize activity intensity of 3 to 5-y old-children and 2) to evaluate the accelerometer as a measure of children’s physical activity in preschool settings.
While wearing an ActiGraph accelerometer, 16 preschool children performed five, 3-min structured activities. Receiver Operating Characteristic (ROC) curve analyses identified count cutoffs for four physical activity intensities. In 9 preschools, 281 children wore an ActiGraph during observations performed by three trained observers (interobserver reliability = 0.91 to 0.98).
Separate count cutoffs for 3, 4, and 5-y olds were established. Sensitivity and specificity for the count cutoffs ranged from 86.7% to 100.0% and 66.7% to 100.0%, respectively. ActiGraph counts/15 s were different among all activities (P < 0.05) except the two sitting activities. Correlations between observed and ActiGraph intensity categorizations at the preschools ranged from 0.46 to 0.70 (P < 0.001).
The ActiGraph count cutoffs established and validated in this study can be used to objectively categorize the time that preschool-age children spend in different physical activity intensity levels.
Rodrigo Antunes Lima, Karin A. Pfeiffer, Niels Christian Møller, Lars Bo Andersen and Anna Bugge
Background: To analyze the longitudinal association between academic performance and moderate to vigorous physical activity (MVPA), vigorous physical activity (VPA), and sedentary (SED) in a 3-year longitudinal study. A secondary aim was to determine whether MVPA and VPA were indirectly related with academic performance via waist circumference (WC). Methods: Physical activity (PA) and SED were measured by accelerometers. Academic performance was assessed by national tests in Danish and Math. Structural equation modeling was performed to evaluate whether MVPA, VPA, and SED were associated with academic performance and the potential PA–academic performance indirect relationship via WC. Results: MVPA and VPA were associated with academic performance, mediated via WC (β = 0.036; 95% confidence interval [CI], 0.002 to 0.070 and β = 0.096; 95% CI, 0.027 to 0.164, respectively). SED was directly associated with academic performance (β = 0.124; 95% CI, 0.030 to 0.217, MVPA model and β = 0.132; 95% CI, 0.044 to 0.221, VPA model). WC was negatively associated with academic performance. Conclusions: Both PA and SED time were positively associated with academic performance. Based on this, PA should be encouraged in children and youth not only to promote physical health but also to promote academic performance. Future studies should distinguish between school-related SED and other SED activities and their relationship with academic performance.