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.
Karin A. Pfeiffer and Michael J. Wierenga
Stephen D. Herrmann and Karin A. Pfeiffer
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.
Kimberly A. Clevenger, Michael J. Wierenga, Cheryl A. Howe, and Karin A. Pfeiffer
The authors conducted a systematic review of children’s and adolescent’s physical activity by schoolyard location. PubMed and Web of Science were searched and articles were selected that included 3- to 17-year-olds and specifically examined and reported physical activity by schoolyard location. The primary outcomes of interest were the percentage of total time or observation intervals spent in each location and percentage of time or observation intervals in each location being sedentary or participating in moderate to vigorous physical activity. Included studies (N = 24) focused on preschoolers (n = 6), children (n = 11), adolescents (n = 2), or children and adolescents (n = 5) and primarily used direct observation (n = 17). Fields, fixed equipment, and blacktop were all important locations for physical activity participation, but there were differences by age group and sex. More research is needed that uses consistent methodology and accounts for other factors such as time of year, provided equipment, and differences in schoolyard designs.
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.
Jeanette M. Ricci, Katharine D. Currie, Todd A. Astorino, and Karin A. Pfeiffer
Girls’ acute responses to group-based high-intensity interval exercise (HIIE) are not well characterized.
Purpose: To compare acute responses to treadmill-based HIIE (TM) and body-weight resistance exercise circuit (CIRC) and to CIRC performed in a small-group setting (group CIRC).
Method: Nineteen girls (9.1 [1.1] y) completed exercise testing on a TM to determine peak oxygen uptake, peak heart rate (HRpeak), and maximal aerobic speed. The TM involved eight 30-second sprints at 100% maximal aerobic speed. The CIRC consisted of 8 exercises of maximal repetitions performed for 30 seconds. Each exercise bout was followed by 30 seconds of active recovery. The blood lactate concentration was assessed preexercise and postexercise. The ratings of perceived exertion, affective valence, and enjoyment were recorded at preexercise, Intervals 3 and 6, and postexercise.
Results: The mean heart rate was higher during group CIRC (92% [7%] HRpeak) than CIRC (86% [7%] HRpeak) and TM (85% [4%] HRpeak) (
Jeanette M. Ricci, Todd A. Astorino, Katharine D. Currie, and Karin A. Pfeiffer
The majority of studies examining children’s responses to high-intensity interval exercise primarily utilized running; however, this modality does not require/include other important aspects of physical activity including muscular fitness. Purpose: To compare acute responses between a body weight resistance exercise circuit (CIRC) and treadmill-based (TM) high-intensity interval exercise. Method: A total of 17 boys (age = 9.7 [1.3] y) completed a graded exercise test to determine peak heart rate, peak oxygen uptake (VO2peak), and maximal aerobic speed. Sessions were randomized and counterbalanced. CIRC required 2 sets of 30-second maximal repetitions of 4 exercises. TM included eight 30-second bouts of running at 100% maximal aerobic speed. Both included 30-second active recovery between bouts. Blood lactate concentration was measured preexercise and postexercise. Rating of perceived exertion, affective valence, and enjoyment were recorded preexercise, after intervals 3 and 6, and postexercise. Results: Participants attained 88% (5%) peak heart rate and 74% (9%) VO2peak for CIRC and 89% (4%) peak heart rate and 81% (6%) VO2peak for TM, with a significant difference in percentage of VO2peak (P = .003) between protocols. Postexercise blood lactate concentration was higher following CIRC (5.0 [0.7] mM) versus TM (2.0 [0.3] mM) (P < .001). Rating of perceived exertion, affective valence, and enjoyment responses did not differ between protocols (P > .05). Conclusion: HR responses were near maximal during CIRC, supporting that this body-weight circuit is representative of high-intensity interval exercise.
Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, Karin A. Pfeiffer, Alexander H.K. Montoye, and Jan Christian Brønd
ActiGraph counts are commonly used for characterizing physical activity intensity and energy expenditure and are among the most well-studied accelerometer metrics. Researchers have recently replicated the counts processing method using a mechanical setup, now allowing users to generate counts from raw acceleration data. Purpose: The purpose of this study was to compare ActiGraph-generated counts to open-source counts and assess the impact on free-living physical activity levels derived from cut points, machine learning, and two-regression models. Methods: Children (n = 488, 13.0 ± 1.1 years of age) wore an ActiGraph wGT3X-BT on their right hip for 7 days during waking hours. ActiGraph counts and counts generated from raw acceleration data were compared at the epoch-level and as overall means. Seven methods were used to classify overall and epoch-level activity intensity. Outcomes were compared using weighted kappa, correlations, mean absolute deviation, and two one-sided equivalence testing. Results: All outcomes were statistically equivalent between ActiGraph and open-source counts; weighted kappa was ≥.971 and epoch-level correlations were ≥.992, indicating very high agreement. Bland–Altman plots indicated differences increased with activity intensity, but overall differences between ActiGraph and open-source counts were minimal (e.g., epoch-level mean absolute difference of 23.9 vector magnitude counts per minute). Regardless of classification model, average differences translated to 1.4–2.6 min/day for moderate- to vigorous-intensity physical activity. Conclusion: Open-source counts may be used to enhance comparability of future studies, streamline data analysis, and enable researchers to use existing developed models with alternative accelerometer brands. Future studies should verify the performance of open-source counts for other outcomes, like sleep.