Pedro F. Saint-Maurice

Parenting styles and home obesogenic environments.

Parenting behaviors are known to have a major impact on childhood obesity but it has proven difficult to isolate the specific mechanism of influence. The present study uses Baumrind’s parenting typologies (authoritative, authoritarian, and permissive) to examine associations between parenting styles and parenting practices associated with childhood obesity. Data were collected from a diverse sample of children (n = 182, ages 7–10) in an urban school district in the United States. Parenting behaviors were assessed with the Parenting Styles and Dimension Questionnaire (PSDQ), a 58-item survey that categorizes parenting practices into three styles: authoritative, authoritarian, and permissive. Parent perceptions of the home obesogenic environment were assessed with the Family Nutrition and Physical Activity (FNPA) instrument, a simple 10 item instrument that has been shown in previous research to predict risk for overweight. Cluster analyses were used to identify patterns in the PSDQ data and these clusters were related to FNPA scores and measured BMI values in children (using ANCOVA analyses that controlled for parent income and education) to examine the impact of parenting styles on risk of overweight/obesity. The FNPA score was positively (and significantly) associated with scores on the authoritative parenting scale (r = 0.29) but negatively (and significantly) associated with scores on the authoritarian scale (r = −0.22) and permissive scale (r = −0.20). Permissive parenting was significantly associated with BMIz score but this is the only dimension that exhibited a relationship with BMI. A three-cluster solution explained 40.5% of the total variance and clusters were distinguishable by low and high z-scores on different PSDQ sub-dimensions. A cluster characterized as Permissive/Authoritarian (Cluster 2) had significantly lower FNPA scores (more obesogenic) than clusters characterized as Authoritative (Cluster 1) or Authoritarian/Authoritative (Cluster 3) after controlling for family income and parent education. No direct effects of cluster were evident on the BMI outcomes but the patterns were consistent with the FNPA outcomes. The results suggest that a permissive parenting style is associated with more obesogenic environments while an authoritative parenting style is associated with less obesogenic environments.

Validity of physical activity monitors for assessing lower intensity activity in adults.

BackgroundAccelerometers can provide accurate estimates of moderate-to-vigorous physical activity (MVPA). However, one of the limitations of these instruments is the inability to capture light activity within an acceptable range of error. The purpose of the present study was to determine the validity of different activity monitors for estimating energy expenditure (EE) of light intensity, semi-structured activities.MethodsForty healthy participants wore a SenseWear Pro3 Armband (SWA, v.6.1), the SenseWear Mini, the Actiheart, ActiGraph, and ActivPAL monitors, while being monitored with a portable indirect calorimetry (IC). Participants engaged in a variety of low intensity activities but no formalized scripts or protocols were used during these periods.ResultsThe Mini and SWA overestimated total EE on average by 1.0% and 4.0%, respectively, while the AH, the GT3X, and the AP underestimated total EE on average by 7.8%, 25.5%, and 22.2%, respectively. The pattern-recognition monitors yielded non-significant differences in EE estimates during the semi-structured period (p = 0.66, p = 0.27, and p = 0.21 for the Mini, SWA, and AH, respectively).ConclusionsThe SenseWear Mini provided more accurate estimates of EE during light to moderate intensity semi-structured activities compared to other activity monitors. This monitor should be considered when there is interest in tracking low intensity activities in groups of individuals.

Prevalence of Youth Fitness in the United States: Baseline Results from the NFL PLAY 60 FITNESSGRAM Partnership Project

OBJECTIVE To assess age- and sex-specific patterns of 6 health-related fitness components in youth, baseline data from the NFL PLAY 60 FITNESSGRAM Partnership Project were analyzed. STUDY DESIGN A total of 192,848 students from 1st through 12th grade in 725 schools completed the standard FITNESSGRAM testing in 2010-2014, including assessments of aerobic capacity (AC), body mass index (BMI), upper body strength and endurance, trunk extensor strength and flexibility, abdominal strength and endurance, and flexibility. Individual data were aggregated by grade and sex. Age- and sex-specific health-related criterion-referenced standards were used to classify fitness results into the healthy fitness zone (HFZ), needs improvement zone, or needs improvement health risk. RESULTS The proportion of youth meeting the HFZ for AC varied considerably by grade for both boys (62.1%-37.6%) and girls (49.1%-26.1%) among 1st-12th grade. There was less variability by age and sex for achievement of the BMI HFZ (ranged from 52.7%-65.0%). The prevalence of achievement was similar for the remaining fitness components. Significantly lower achievement was found in the middle school years for BMI HFZ in both sexes and for AC HFZ achievement in boys. Continuous age-related lower HFZ achievement was evident in girls for AC. CONCLUSIONS The results provide updated health-related fitness profiles for US youth and identify the critical ages when youth fitness levels start to decline.

The Associations of Youth Physical Activity and Screen Time with Fatness and Fitness: The 2012 NHANES National Youth Fitness Survey

The purpose of the study is to examine the associations of youth physical activity and screen time with weight status and cardiorespiratory fitness in children and adolescents, separately, utilizing a nationally representative sample. A total of 1,113 participants (692 children aged 6–11 yrs; 422 adolescents aged 12–15 yrs) from the 2012 NHANES National Youth Fitness Survey. Participants completed physical activity and screen time questionnaires, and their body mass index and cardiorespiratory fitness (adolescents only) were assessed. Adolescents completed additional physical activity questions to estimate daily MET minutes. Children not meeting the screen time guideline had 1.69 times the odds of being overweight/obese compared to those meeting the screen time guideline, after adjusting for physical activity and other control variables. Among adolescent, screen time was significantly associated with being overweight/obese (odds ratio = 1.82, 95% confidence interval: 1.06–3.15), but the association attenuated toward the borderline of being significant after controlling for physical activity. Being physically active was positively associated with cardiorespiratory fitness, independent of screen time among adolescents. In joint association analysis, children who did not meet physical activity nor screen time guidelines had 2.52 times higher odds of being overweight/obese than children who met both guidelines. Adolescents who did not meet the screen time guideline had significantly higher odds ratio of being overweight/obese regardless of meeting the physical activity guideline. Meeting the physical activity guideline was also associated with cardiorespiratory fitness regardless of meeting the screen time guideline in adolescents. Screen time is a stronger factor than physical activity in predicting weight status in both children and adolescents, and only physical activity is strongly associated with cardiorespiratory fitness in adolescents.

Validity and Calibration of the Youth Activity Profile

Purpose The purpose of this study was to calibrate and cross-validate the Youth Activity Profile (YAP), a self-report tool designed to capture physical activity (PA) and sedentary behaviors (SB) in youth. Methods Eight schools in the Midwest part of the U.S. were involved and a total of 291 participants from grades 4–12 agreed to wear an accelerometer (SWA Armband) and complete the YAP in two separate weeks (5–7 days apart). Individual YAP items capture PA behavior during specific segments of the week and these items were combined with temporally matched estimates of moderate-to-vigorous PA (MVPA) and sedentary time from the SWA to enable calibration. Quantile regression procedures yielded YAP prediction algorithms that estimated MVPA at School, MVPA at Out-of-School, MVPA on Weekend, as well as time spent in SB. The YAP estimates of time spent in MVPA and SB were cross-validated using Pearson product correlations and limits of agreement, as indicative of individual error and, equivalence testing techniques as indicative of group-level error. Result Following calibration, the correlations between YAP and SWA estimates of MVPA were low to moderate (rrange = .19 to .58) and individual-level YAP estimates of MVPA ranged from -134.9% to +110.0% of SWA MVPA values. Differences between aggregated YAP and SWA MVPA ranged from -3.4 to 21.7 minutes of MVPA at the group-level and predicted YAP MVPA estimates were within 15%, 20%, and 30%, of values from the SWA for the School, Out-of-School, and Weekend time periods, respectively. Estimates of time spent in SB were highly correlated with each other (r = .75). The individual estimates of SB ranged from -54.0% to +44.0% of SWA sedentary time, and the aggregated group-level estimates differed by 49.7 minutes (within 10% of the SWA aggregated estimates). Conclusions This study provides preliminary evidence that the calibration procedures enabled the YAP to provide estimates of MVPA and SB that approximated values from an objective monitor. The YAP provides a simple, low-cost and educationally sound method to accurately estimate children’s MVPA and SB at the group level.

Web-Based Assessments of Physical Activity in Youth: Considerations for Design and Scale Calibration

This paper describes the design and methods involved in calibrating a Web-based self-report instrument to estimate physical activity behavior. The limitations of self-report measures are well known, but calibration methods enable the reported information to be equated to estimates obtained from objective data. This paper summarizes design considerations for effective development and calibration of physical activity self-report measures. Each of the design considerations is put into context and followed by a practical application based on our ongoing calibration research with a promising online self-report tool called the Youth Activity Profile (YAP). We first describe the overall concept of calibration and how this influences the selection of appropriate self-report tools for this population. We point out the advantages and disadvantages of different monitoring devices since the choice of the criterion measure and the strategies used to minimize error in the measure can dramatically improve the quality of the data. We summarize strategies to ensure quality control in data collection and discuss analytical considerations involved in group- vs individual-level inference. For cross-validation procedures, we describe the advantages of equivalence testing procedures that directly test and quantify agreement. Lastly, we introduce the unique challenges encountered when transitioning from paper to a Web-based tool. The Web offers considerable potential for broad adoption but an iterative calibration approach focused on continued refinement is needed to ensure that estimates are generalizable across individuals, regions, seasons and countries.

Measurement Agreement between Estimates of Aerobic Fitness in Youth: The Impact of Body Mass Index.

Purpose The purpose of this study was to examine the impact of body mass index (BMI) on the agreement between aerobic capacity estimates from different Progressive Aerobic Cardiorespiratory Endurance Run (PACER) equations and the Mile Run Test. Method The agreement between 2 different tests of aerobic capacity was examined on a large data set from 2 suburban school districts (n = 1,686 youth in Grades 3–10). Difference estimates between the Mile Run Test and several PACER equations were computed, and residuals were examined using cluster analysis. The implication of the discrepancy between these tests was also examined using FITNESSGRAM® health-related standards for BMI. Comparisons were made against corresponding estimates of peak oxygen consumption from the Mile run because this equation is more established. Results Results supported a 2-cluster solution. The discrepancy between tests was higher in participants with higher BMI scores (Z scores for residuals in this group ranged from − 0.07 to 1.57). BMI was able to explain 30% to 34% of the disagreement between the Mile and different PACER equations of aerobic fitness. Classification analyses revealed that kappa scores were lower among PACER equations that do not include a BMI term (kappa = .12–.34 vs. .59–.81). Overall, the test-equating approach used in the Fitnessgram program to process PACER data had better agreement than alternative PACER equations that included BMI. Conclusion The results support the inclusion of BMI in prediction equations used to estimate aerobic capacity from the PACER.

Cross-Validation of Aerobic Capacity Prediction Models in Adolescents

Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74-0.78), and prediction error (RMSE) ranged from 5.95 ml·kg⁻¹,min⁻¹ to 8.27 ml·kg⁻¹.min⁻¹. Criterion-referenced agreement into FITNESSGRAMs Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31-0.62; Agreement = 75.5-89.9%; F = 0.08-0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAMs Healthy Fitness Zones.

Calibration and Validation of the Youth Activity Profile: The FLASHE Study

INTRODUCTION This study describes the calibration and validity of the Youth Activity Profile (YAP) for use in the National Cancer Institutes Family Life, Activity, Sun, Health, and Eating (FLASHE) study. The calibrated YAP was designed to estimate minutes of moderate to vigorous physical activity (MVPA) and sedentary behavior (SB). METHODS The YAP was calibrated/validated in adolescents (aged 12-17 years) using cross-sectional data from the FLASHE study. Participants wore a GT3X+ ActiGraph on the dominant wrist for 7 days and then completed the YAP. Calibration was conducted for school (n=118); out of school (n=119); weekend (n=61); and SB (n=116) subsections of the YAP and by regressing percentage time in MVPA/SB (%MVPA/%SB) on each respective YAP subsection score, age, and the interaction between these two. The final algorithms were applied to independent samples (n=39-51) to examine validity (median absolute percentage error, equivalence testing). RESULTS The final algorithms explained 15% (school); 16% (out of school); and 12% (weekend) of the variability in GT3X+ %MVPA and 7% of the variability in GT3X+ %SB. The calibrated algorithms were applied to independent samples and predicted GT3X+ minutes of MVPA/SB, with median absolute percentage error values ranging from 12.5% (SB section) to 32.5% (weekend section). Predicted values obtained from the YAP were within 10%-20% of those produced by the GT3X+. CONCLUSIONS The YAP-predicted minutes of MVPA/SB resulted in similar group estimates obtained from an objective measure. The YAP offers good utility for large-scale research projects to characterize PA/SB levels among groups of youth.

Surveillance of Youth Physical Activity and Sedentary Behavior With Wrist Accelerometry

INTRODUCTION Accurate tracking of physical activity (PA) and sedentary behavior (SB) is important to advance public health, but little is known about how to interpret wrist-worn accelerometer data. This study compares youth estimates of SB and moderate to vigorous PA (MVPA) obtained using raw and count-based processing methods. METHODS Data were collected between April and October 2014 for the National Cancer Institutes Family Life, Activity, Sun, Health, and Eating Study: a cross-sectional Internet-based study of youth/family cancer prevention behaviors. A subsample of 628 adolescents (aged 12-17 years) wore the ActiGraph GT3X+ on the wrist for 7 days. In 2015-2016, SB and MVPA time were calculated from raw data using R-package GGIR and from activity counts data using published cutpoints (Crouter and Chandler). Estimates were compared across age, sex, and weight status to examine the impact of processing methods on behavioral outcomes. RESULTS ActiGraph data were available for 408 participants. Large differences in SB and MVPA time were observed between processing methods, but age and gender patterns were similar. Younger children (aged 12-14 years) had lower sedentary time and greater MVPA time (p-values <0.05) than older children (aged 15-17 years), consistent across methods. The proportion of youth with ≥60 minutes of MVPA/day was highest with the Crouter methods (~50%) and lowest with GGIR (~0%). CONCLUSIONS Conclusions about youth PA and SB are influenced by the wrist-worn accelerometer data processing method. Efforts to harmonize processing methods are needed to promote standardization and facilitate reporting of monitor-based PA data.