Laurson, KR, Saint-Maurice, PF, Welk, GJ, and Eisenmann, JC. Reference curves for field tests of musculoskeletal fitness in U.S. children and adolescents: The 2012 NHANES National Youth Fitness ...Survey. J Strength Cond Res 31(8): 2075-2082, 2017-The purpose of the study was to describe current levels of musculoskeletal fitness (MSF) in U.S. youth by creating nationally representative age-specific and sex-specific growth curves for handgrip strength (including relative and allometrically scaled handgrip), modified pull-ups, and the plank test. Participants in the National Youth Fitness Survey (n = 1,453) were tested on MSF, aerobic capacity (via submaximal treadmill test), and body composition (body mass index BMI, waist circumference, and skinfolds). Using LMS regression, age-specific and sex-specific smoothed percentile curves of MSF were created and existing percentiles were used to assign age-specific and sex-specific z-scores for aerobic capacity and body composition. Correlation matrices were created to assess the relationships between z-scores on MSF, aerobic capacity, and body composition. At younger ages (3-10 years), boys scored higher than girls for handgrip strength and modified pull-ups, but not for the plank. By ages 13-15, differences between the boys and girls curves were more pronounced, with boys scoring higher on all tests. Correlations between tests of MSF and aerobic capacity were positive and low-to-moderate in strength. Correlations between tests of MSF and body composition were negative, excluding absolute handgrip strength, which was inversely related to other MSF tests and aerobic capacity but positively associated with body composition. The growth curves herein can be used as normative reference values or a starting point for creating health-related criterion reference standards for these tests. Comparisons with prior national surveys of physical fitness indicate that some components of MSF have likely decreased in the United States over time.
Numerous studies have examined the validity of accelerometry-based activity monitors but few studies have systematically studied the reliability of different accelerometer units for assessing a ...standardized bout of physical activity. Improving understanding of error in these devices is an important research objective because they are increasingly being used in large surveillance studies and intervention trials that require the use of multiple units over time.
Four samples of college-aged participants were recruited to collect reliability data on four different accelerometer types (CSA/MTI, Biotrainer Pro, Tritrac-R3D, and Actical). The participants completed three trials of treadmill walking (3 mph) while wearing multiple units of a specific monitor type. For each trial, the participant completed a series of 5-min bouts of walking (one for each monitoring unit) with 1-min of standing rest between each bout. Generalizability (G) theory was used to quantify variance components associated with individual monitor units, trials, and subjects as well as interactions between these terms.
The overall G coefficients range from 0.43 to 0.64 for the four monitor types. Corresponding intraclass correlation coefficients (ICC) ranged from 0.62 to 0.80. The CSA/MTI was found to have the least variability across monitor units and trials and the highest overall reliability. The Actical was found to have the poorest reliability.
The CSA/MTI appeared to have acceptable reliability for most research applications (G values above 0.60 and ICC values above 0.80), but values with the other devices indicate some possible concerns with reliability. Additional work is needed to better understand factors contributing to variability in accelerometry data and to determine appropriate calibration protocols to improve reliability of these measures for different research applications.
The last few years have seen renewed interest in use-of-time recalls in epidemiological studies, driven by a focus on the 24-h day including sleep, sitting, and light physical activity (LPA) rather ...than just moderate-vigorous physical activity (MVPA). This paper describes four different computerised use-of-time instruments (ACT24, PAR, MARCA and cpar24) and presents population time-use data from a collective sample of 8286 adults from different population studies conducted in Australia/New Zealand, Germany and the United States.
The instruments were developed independently but showed a number of similarities: they were self-administered through the web or used computer-assisted telephone interviews; all captured energy expenditure using variants of the Ainsworth Compendium; each had been validated against criterion measures; and they used a domain structure whereby activities were aggregated under categories such as Personal Care and Work.
Estimates of physical activity level (average daily rate of energy expenditure in METs) ranged from 1.53 to 1.78 in the four studies, strikingly similar to population estimates derived from doubly labelled water. There was broad agreement in the amount of time spent in sleep (7.2-8.6 h), MVPA (1.6-3.1 h), personal care (1.6-2.4 h), and transportation (1.1-1.8 h). There were consistent sex differences, with women spending 28-81% more time on chores, 8-40% more time in LPA, and 3-39% less time in MVPA than men.
Although there were many similarities between instruments, differences in operationalizing definitions of sedentary behaviour and LPA resulted in substantive differences in the amounts of time reported in sedentary and physically active behaviours. Future research should focus on deriving a core set of basic activities and associated energy expenditure estimates, an agreed classificatory hierarchy for the major behavioural and activity domains, and systems to capture relevant social and environmental contexts.
This paper reports the primary outcomes of the Healthy Opportunities for Physical Activity and Nutrition (HOP'N) after-school project, which was an effectiveness trial designed to evaluate the ...prevention of childhood obesity through building the capacity of after-school staff to increase physical activity (PA) and fruit and vegetable (FV) opportunities.
We conducted a three-year, nested cross-sectional group randomized controlled effectiveness trial. After a baseline assessment year (2005-2006), schools and their after-school programs were randomized to the HOP'N after-school program (n = 4) or control (n = 3), and assessed for two subsequent years (intervention year 1, 2006-2007; intervention year 2, 2007-2008). Across the three years, 715 fourth grade students, and 246 third and fourth grade after-school program participants were included in the study. HOP'N included community government human service agency (Cooperative Extension) led community development efforts, a three-time yearly training of after-school staff, daily PA for 30 minutes following CATCH guidelines, a daily healthful snack, and a weekly nutrition and PA curriculum (HOP'N Club). Child outcomes included change in age- and gender-specific body mass index z-scores (BMIz) across the school year and PA during after-school time measured by accelerometers. The success of HOP'N in changing after-school program opportunities was evaluated by observations over the school year of after-school program physical activity sessions and snack FV offerings. Data were analyzed in 2009.
The intervention had no impact on changes in BMIz. Overweight/obese children attending HOP'N after-school programs performed 5.92 minutes more moderate-to-vigorous PA per day after intervention, which eliminated a baseline year deficit of 9.65 minutes per day (p < 0.05) compared to control site overweight/obese children. Active recreation program time at HOP'N sites was 23.40 minutes (intervention year 1, p = 0.01) and 14.20 minutes (intervention year 2, p = 0.10) greater than control sites. HOP'N sites and control sites did not differ in the number of FV offered as snacks.
The HOP'N program had a positive impact on overweight/obese children's PA and after-school active recreation time.
NCT01015599.
Summary
Disparities in physical activity and health outcomes exist between urban and rural youth. School settings can be utilized to promote physical activity in youth regardless of urban–rural ...status. This systematic review and meta‐analysis aimed to assess and compare the effect of rural and urban/suburban school‐based physical activity programs on total physical activity in youth. A search of five databases was conducted. A total of 33 studies remained after the exclusion process, 28 of which took place in urban/suburban schools and five of which took place in rural schools. The DerSimonian and Laird random effects model was employed with the estimates of heterogeneity taken from the inverse‐variance fixed‐effect model. For rural studies, the Hartung–Knapp–Sidak–Jonkman method was used to obtain error estimates. Results from the total sample indicated a significant but small pooled increase in daily physical activity (Hedge's g = 0.12, 95% confidence interval CI: 0.06–0.18), which held for interventions conducted in urban/suburban schools (Hedge's g = 0.12, 95% CI: 0.06–0.19). For rural school‐based interventions, there was no significant pooled effect (Hedge's g = 0.06, 95% CI: −0.50 to 0.61). This meta‐analysis provides evidence that school‐based interventions can be marginally effective for increasing daily physical activity in children and adolescents; however, no effect was observed for interventions implemented in rural settings.
Purpose
The study compares MET-defined cutpoints used to classify sedentary behaviors in children using a simulated free-living design.
Methods
A sample of 102 children (54 boys and 48 girls; ...7–13 years) completed a set of 12 activities (randomly selected from a pool of 24 activities) in a random order. Activities were predetermined and ranged from sedentary to vigorous intensities. Participant’s energy expenditure was measured using a portable indirect calorimetry system, Oxycon mobile. Measured minute-by-minute VO
2
values (i.e., ml/kg/min) were converted to an adult- or child-MET value using the standard 3.5 ml/kg/min or the estimated child resting metabolic rate, respectively. Classification agreement was examined for both the “standard” (1.5 adult-METs) and an “adjusted” (2.0 adult-METs) MET-derived threshold for classifying sedentary behavior. Alternatively, we also tested the classification accuracy of a 1.5 child-MET threshold. Classification accuracy of sedentary activities was evaluated relative to the predetermined intensity categorization using receiver operator characteristic curves.
Results
There were clear improvements in the classification accuracy for sedentary activities when a threshold of 2.0 adult-METs was used instead of 1.5 METs (Se
1.5 METs
= 4.7 %, Sp
1.5 METs
= 100.0 %; Se
2.0 METs
= 36.9 %, Sp
2.0 METs
= 100.0 %). The use of child-METs while maintaining the 1.5 threshold also resulted in improvements in classification (Se = 45.1 %, Sp = 100.0 %).
Conclusion
Adult-MET thresholds are not appropriate for children when classifying sedentary activities. Classification accuracy for identifying sedentary activities was improved when either an adult-MET of 2.0 or a child-MET of 1.5 was used.
Purpose: To develop models to estimate aerobic fitness (VO
2
max) from PACER performance in 10- to 18-year-old youth, with and without body mass index (BMI) as a predictor. Method: Youth (N = 280) ...completed the PACER and a maximal treadmill test to assess VO
2
max. Validation and cross-validation groups were randomly formed to develop and examine accuracy of models. Participants were classified into FitnessGram® Healthy Fitness Zone categories based on measured and estimated VO
2
max and criterion-referenced validity was evaluated. Results: Multiple correlations between measured and estimated VO
2
max ranged from .70 to .73, with standard errors of estimate between 6.43 and 6.68 mL·kg
−1
·min
−1
. Accuracy with and without BMI was nearly identical. Overall, criterion-referenced validity evidence was moderate. Conclusion: Moderately accurate and feasible models were developed. Minimal improvement in accuracy was noted when BMI was added as a predictor. The model with PACER and age as predictors has a high level of utility for youth fitness testing.
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.
Harmonization of assessment methods represents an ongoing challenge in physical activity research. Previous research has demonstrated the utility of calibration approaches to enhance agreement ...between measures of physical activity. The present study utilizes a calibration methodology to add behavioral context from the Global Physical Activity Questionnaire (GPAQ), an established report-based measure, to enhance interpretations of monitor-based data scored using the novel Monitor Independent Movement Summary (MIMS) methodology.
Matching data from the GPAQ and MIMS were obtained from adults (20-80 yr of age) assessed in the 2011-2014 National Health and Nutrition Examination Survey. After developing percentile curves for self-reported activity, a zero-inflated quantile regression model was developed to link MIMS to estimates of moderate to vigorous physical activity (MVPA) from the GPAQ.
Cross-validation of the model showed that it closely approximated the probability of reporting MVPA across age and activity-level segments, supporting the accuracy of the zero-inflated model component. Validation of the quantile regression component directly corresponded to the 25%, 50%, and 75% values for both men and women, further supporting the model fit.
This study offers a method of improving activity surveillance by translating accelerometer signals into interpretable behavioral measures using nationally representative data. The model provides accurate estimates of minutes of MVPA at a population level but, because of the bias and error inherent in report-based measures of physical activity, is not suitable for converting or interpreting individual-level data. This study provides an important preliminary step in utilizing information from both device- and report-based methods to triangulate activity related outcomes; however additional measurement error modeling is needed to improve precision.
While widely used and endorsed, there is limited evidence supporting the benefits of activity trackers for increasing physical activity; these devices may be more effective when combined with ...additional strategies that promote sustained behavior change like motivational interviewing (MI) and habit development.
This study aims to determine the utility of wearable activity trackers alone or in combination with these behavior change strategies for promoting improvements in active and sedentary behaviors.
A sample of 91 adults (48/91 female, 53%) was randomized to receive a Fitbit Charge alone or in combination with MI and habit education for 12 weeks. Active and sedentary behaviors were assessed pre and post using research-grade activity monitors (ActiGraph and activPAL), and the development of habits surrounding the use of the trackers was assessed postintervention with the Self-Reported Habit Index. During the intervention, Fitbit wear time and activity levels were monitored with the activity trackers. Linear regression analyses were used to determine the influence of the trial on outcomes of physical activity and sedentary time. The influence of habits was examined using correlation coefficients relating habits of tracker use (wearing the tracker and checking data on the tracker and associated app) to Fitbit wear time and activity levels during the intervention and at follow-up.
Regression analyses revealed no significant differences by group in any of the primary outcomes (all P>.05). However, personal characteristics, including lower baseline activity levels (beta=-.49, P=.01) and lack of previous experience with pedometers (beta=-.23, P=.03) were predictive of greater improvements in moderate and vigorous physical activity. Furthermore, for individuals with higher activity levels at the baseline, MI and habit education were more effective for maintaining these activity levels when compared with receiving a Fitbit alone (eg, small increase of ~48 steps/day, d=0.01, vs large decrease of ~1830 steps/day, d=0.95). Finally, habit development was significantly related to steps/day during (r=.30, P=.004) and following the intervention (r=.27, P=.03).
This study suggests that activity trackers may have beneficial effects on physical activity in healthy adults, but benefits vary based on individual factors. Furthermore, this study highlights the importance of habit development surrounding the wear and use of activity trackers and the associated software to promote increases in physical activity.
ClinicalTrials.gov NCT03837366; https://clinicaltrials.gov/ct2/show/NCT03837366.
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