Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique.

BACKGROUND: Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device. METHODS: Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland-Altman analysis. RESULTS: Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323-362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353-464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland-Altman analysis. CONCLUSIONS: Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults.

BACKGROUND: The challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%-20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. OBJECTIVES: The purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. METHODS: Twenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. RESULTS: There was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. CONCLUSIONS: Commercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

Comparing enhancements to well-child visits in the prevention of obesity: ENCIRCLE cluster-randomized controlled trial.

BACKGROUND: Obesity disproportionally impacts rural, lower-income children in the United States. Primary care providers are well-positioned to engage parents in early obesity prevention, yet there is a lack of evidence regarding the most effective care delivery models. The ENCIRCLE study, a pragmatic cluster-randomized controlled trial, will respond to this gap by testing the comparative effectiveness of standard care well-child visits (WCV) versus two enhancements: adding a patient-reported outcome (PRO) measure (PRO WCV) and PRO WCV plus Food Care (telehealth coaching and a grocery store tour). METHODS: A total of 2,025 parents and their preschool-aged children (20-60 months of age) will be recruited from 24 Geisinger primary care clinics, where providers are randomized to the standard WCV, PRO WCV, or PRO WCV plus Food Care intervention arms. The PRO WCV includes the standard WCV plus collection of the PRO-the Family Nutrition and Physical Activity (FNPA) risk assessment-from parents. Parents complete the PRO in the patient-portal or in the clinic (own device, tablet, or kiosk), receive real-time feedback, and select priority topics to discuss with the provider. These results are integrated into the child's electronic health record to inform personalized preventive counseling by providers. PRO WCV plus Food Care includes referrals to community health professionals who deliver evidence-based obesity prevention and food resource management interventions via telehealth following the WCV. The primary study outcome is change in child body mass index z-score (BMIz), based on the World Health Organization growth standards, 12 months post-baseline WCV. Additional outcomes include percent of children with overweight and obesity, raw BMI, BMI50, BMIz extended, parent involvement in counseling, health behaviors, food resource management, and implementation process measures. DISCUSSION: Study findings will inform health care systems' choices about effective care delivery models to prevent childhood obesity among a high-risk population. Additionally, dissemination will be informed by an evaluation of mediating, moderating, and implementation factors. TRIAL REGISTRATION: ClinicalTrials.gov identifier (NCT04406441); Registered May 28, 2020.

School Wellness Environments: Perceptions Versus Realities.

The U.S. Department of Agriculture Final Rule on School Wellness Policy requires schools to self-evaluate wellness policies and environments. To understand the utility of this information, this study evaluates the validity of school-reported wellness information against directly observed data. Wellness leaders at 10 Midwestern elementary schools completed a questionnaire spanning nine school wellness settings. School-reported information was compared against a direct observation protocol. Percent agreement and κ statistics were used to assess agreement between school reporters and direct observation. Overall percent agreement between reporters and direct observation was 77.1%. Agreement ranged from 67.3% (Lunchroom Environment) to 92.0% (School Wellness Policies) across the nine categories. κ results showed that 65.7% of the items demonstrated fair or better reporter agreement. The results provide preliminary support for the utility of schools' self-reported wellness information. Facilitation of independent reporting on wellness environments by school leaders will contribute to broader applications for school wellness programming.

Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis.

BACKGROUND/OBJECTIVES: Evidence on the associations between lifestyle movement behaviors and obesity has been established without taking into account the time-constrained nature of categorized, time-based lifestyle behaviors. We examined the associations of sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous PA (MVPA) with body mass index (BMI) using Compositional Data Analysis (CoDA), and compared the associations between a report-based method (24-h Physical Activity Recall; 24PAR) and a monitor-based method (SenseWear Armband; SWA). SUBJECTS/METHODS: Replicate data from a representative sample of 1247 adults from the Physical Activity Measurement Survey (PAMS) were used in the study. Participants completed activity monitoring on two randomly selected days, each of which required wearing a SWA for a full day, and then completing a telephone-administered 24PAR the following day. Relationships among behavioral compositional parts and BMI were analyzed using CoDA via multiple linear regression models with both 24PAR and SWA data. RESULTS: Using 24PAR, time spent in sleep (γ = -3.58, p = 0.011), SED (γ = 3.70, p = 0.002), and MVPA (γ = -0.53, p = 0.018) was associated with BMI. Using SWA, time spent in sleep (γ = -5.10, p < 0.001), SED (γ = 8.93, p < 0.001), LPA (γ = -3.12, p < 0.001), and MVPA (γ = -1.43, p < 0.001) was associated with BMI. The SWA models explained more variance in BMI (R2 = 0.28) compared with the 24PAR models (R2 = 0.07). The compositional isotemporal substitution models revealed reductions in BMI when replacing SED by MVPA, LPA (not with 24PAR) or sleep for both 24PAR and SWA, but the effect estimates were larger with SWA. CONCLUSIONS: Favorable levels of relative time spent in lifestyle movement behaviors were, in general, associated with decreased BMI. The observed associations were stronger using the monitor-based SWA method compared with the report-based 24PAR method.

Feasibility and reliability of the Spanish version of the Youth Activity Profile questionnaire (YAP-Spain) in children and adolescents.

Considerable public health efforts across the globe have focused on promoting physical activity (PA) and minimizing sedentary behaviour (SB) in youths. However, it is important to have valid, reliable and feasible methods to assess these behaviours in youths. The purpose of this study was to analyse the feasibility and reliability of the Spanish version of the previously validated Youth Activity Profile questionnaire (YAP) in children and adolescents. The YAP-S is a 15-item self-report instrument designed to capture PA and SB in youths. A total of 604 children (5-12 years old) and 346 adolescents (12-17 years old) filled out the questionnaire twice (14 days apart). Feasibility was evaluated through required time and number of misunderstood questions by participants. The test-retest reliability was examined using the weighted kappa coefficient (κ) and intraclass correlation coefficient. The average time to complete the questionnaire was 28.85 ± 14.28 and 12.24 ± 9.84 minutes in children and adolescents, respectively. No misunderstanding of questions was reported. The questionnaire showed an adequate reliability for activity at school, out-of-school and sedentary behaviours (k = 0.61-0.77; ICC = 0.77-0.89) in children and adolescents. The YAP-S might be considered a feasible and reliable questionnaire for assessing PA and SB in Spanish children and adolescents.

Association Between Comprehensive School Physical Activity Program Implementation and Principal Support.

The use of Comprehensive School Physical Activity Program (CSPAP) has been recommended to help students achieve 60-minutes of physical activity each day. Implementing a CSPAP requires planning, coordination, and ongoing oversight, but an understudied factor is how principal support influences CSPAP implementation. The purpose of this study was to evaluate the impact of principal support on CSPAP implementation. Method. Schools in the Iowa FitnessGram Initiative (n = 84), a participatory network of schools committed to supporting physical education and wellness efforts, were invited to participate in the study. Physical education teachers from 42 schools completed a survey assessing CSPAP implementation and principal support for school wellness. Descriptive statistics and correlation analyses were used to report associations between the variables. A regression analysis was conducted to evaluate the impact of principal support on CSPAP implementation. Results. Almost half of the schools were reported to be fully implementing just one CSPAP component and no school was reported to be fully implementing all five. The CSPAP component with the highest reported level of implementation was quality physical education, while the lowest level of implementation was reported for family and community engagement and staff involvement. The regression analysis identified that principal support was a significant predictor of CSPAP implementation, b = 0.55, t(37) = 3.10, p < .004. Conclusions. Principal support is associated with implementation of CSPAP initiatives. Strategies that focus on how to attain principal support for CSPAP initiatives are needed and could have a significant impact on student physical activity and health.

Evaluating the implementation of the SWITCH® school wellness intervention and capacity-building process through multiple methods.

BACKGROUND: School wellness programming is important for promoting healthy lifestyles and academic achievement in youth; however, research is needed on methods that can help schools implement and sustain such programs on their own. The purpose of this study was to investigate factors within and outside the school environment that influenced school capacity for implementation and potential sustainability of wellness programming. METHODS: As part of the School Wellness Integration Targeting Child Health (SWITCH®) intervention, elementary school wellness teams (N = 30) were guided through a capacity-building process focused on promoting the adoption of healthy lifestyle behaviors in students. Data on implementation were collected through three standardized surveys and interviews (pre-mid-post) and a post-implementation interview. Indicators of organizational capacity were assessed using the School Wellness Readiness Assessment (SWRA). Paired t-tests were run to assess changes in implementation (classroom, physical education, and lunchroom settings), capacity, and stakeholder engagement over time. One-way analysis of variance (ANOVA) tests were run to examine how implementation of best practices (low, moderate, high) explained differences in capacity gains. Qualitative data were analyzed through inductive and deductive analysis, following the Consolidated Framework for Implementation Research (CFIR). RESULTS: Paired t-tests showed non-significant increases in school and setting-specific capacity and implementation of SWITCH best practices over time, in addition to a consistent level of engagement from key stakeholders. ANOVA results revealed non-significant associations between implementation group and gains in school capacity (F [2, 24] = 1.63; p = .21), class capacity (F [2, 24]=0.20 p = .82), lunchroom capacity (F [2, 24]=0.29; p = .78), and physical education (F [2, 24]=1.45; p = .25). Qualitative data demonstrated that factors within the outer setting (i.e., engaging community partners) facilitated programming. Inner-setting factors (i.e., relationships with administration and staff) influenced implementation. Implementation process themes (e.g., planning, adaptation of resources to meet school capacity/needs, and engaging students as leaders) were cited as key facilitators. Schools discussed factors affecting sustainability, such as school culture and knowledge of school wellness policy. CONCLUSIONS: The results from this implementation study document the importance of allowing schools to adapt programming to meet their local needs, and highlight the strengths of measuring multiple implementation outcomes. Increased support is needed for schools regarding the formation and improvement of wellness policies as a means to enhance sustainability over time.

Use of previous-day recalls of physical activity and sedentary behavior in epidemiologic studies: results from four instruments.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Accelerometer and self-reported measures of sedentary behaviour and associations with adiposity in UK youth.

This study used accelerometer and self-report measures of overall sedentary time (ST) and screen time behaviours to examine their respective associations with adiposity among UK youth. Participants (Year groups 5, 8, and 10; n=292, 148 girls) wore the SenseWear Armband Mini accelerometer for eight days and completed the Youth Activity Profile, an online report tool designed to estimate physical activity and ST.Stature, body mass and waist circumference were measured to classify adiposity outcomes (overweight/obese and central obesity). One-way between groups ANOVA and adjusted linear, logistic and multinomial logistic regression analyses were conducted. There was a significant main effect of age on total ST across the whole week (F(2, 289)=41.64, p≤0.001). ST increased monotonically across Year 5 (581.09±107.81 min·d-¹), 8 (671.96±112.59 min·d-¹) and 10 (725.80±115.20 min·d-¹), and all pairwise comparisons were significant at p≤0.001. A steep age-related gradient to mobile phone use was present (p≤0.001). ST was positively associated with adiposity outcomes independent of moderate-to-vigorous intensity physical activity (MVPA; p≤0.001). Engaging in >3 hours of video gaming daily was positively associated with central obesity (OR=2.12, p≤0.05) but not after adjustment for MVPA. Results further demonstrate the importance of reducing overall ST to maintain healthy weight status among UK youth.

Energy Intake Derived from an Energy Balance Equation, Validated Activity Monitors, and Dual X-Ray Absorptiometry Can Provide Acceptable Caloric Intake Data among Young Adults.

Background: Assessments of energy intake (EI) are frequently affected by measurement error. Recently, a simple equation was developed and validated to estimate EI on the basis of the energy balance equation [EI = changed body energy stores + energy expenditure (EE)]. Objective: The purpose of this study was to compare multiple estimates of EI, including 2 calculated from the energy balance equation by using doubly labeled water (DLW) or activity monitors, in free-living adults. Methods: The body composition of participants (n = 195; mean age: 27.9 y; 46% women) was measured at the beginning and end of a 2-wk assessment period with the use of dual-energy X-ray absorptiometry. Resting metabolic rate (RMR) was calculated through indirect calorimetry. EE was assessed with the use of the DLW technique and an arm-based activity monitor [Sensewear Mini Armband (SWA); BodyMedia, Inc.]. Self-reported EI was calculated by using dietitian-administered 24-h dietary recalls. Two estimates of EI were calculated with the use of a validated equation: quantity of energy stores estimated from the changes in fat mass and fat-free mass occurring over the assessment period plus EE from either DLW or the SWA. To compare estimates of EI, reporting bias (estimated EI/EE from DLW × 100) and Goldberg ratios (estimated EI/RMR) were calculated. Results: Mean ± SD EEs from DLW and SWA were 2731 ± 494 and 2729 ± 559 kcal/d, respectively. Self-reported EI was 2113 ± 638 kcal/d, EI derived from DLW was 2723 ± 469 kcal/d, and EI derived from the SWA was 2720 ± 730 kcal/d. Reporting biases for self-reported EI, DLW-derived EI, and SWA-derived EI are as follows: -21.5% ± 22.2%, -0.7% ± 18.5%, and 0.2% ± 20.8%, respectively. Goldberg cutoffs for self-reported EI, DLW EI, and SWA EI are as follows: 1.39 ± 0.39, 1.77 ± 0.38, and 1.77 ± 0.38 kcal/d, respectively. Conclusions: These results indicate that estimates of EI based on the energy balance equation can provide reasonable estimates of group mean EI in young adults. The findings suggest that, when EE derived from DLW is not feasible, an activity monitor that provides a valid estimate of EE can be substituted for EE from DLW.

Feasibility study of the SWITCH implementation process for enhancing school wellness.

BACKGROUND: There is a need to identify strategies that enhance the implementation of evidence-based school wellness intervention programs in real-world settings. The present study evaluates the feasibility of empowering school wellness leaders to deliver an evidence-based, childhood obesity-prevention program called Switch ™. We specifically evaluated the feasibility of a new implementation framework, based on the robust Healthy Youth Places framework, to increase capacity of school leaders to lead school wellness programming. METHODS: The SWITCH (School Wellness Integration Targeting Child Health) implementation process was evaluated in a convenience sample of eight Iowa elementary schools. Teams of three leaders from each school attended an in-person school wellness conference followed by five online webinar sessions delivered by two SWITCH team members. The capacity-building and quality improvement process was designed to empower schools to lead wellness change using methods and concepts from the original 16-week Switch ™ program. School wellness leaders completed checklists on two occasions to assess overall school-level implementation as well as setting-level changes in physical education, classrooms, and the lunchroom. Student acceptability of SWITCH was evaluated by the degree of behavior tracking using an online SWITCH Tracker system that promoted self-monitoring. School acceptability and practicality were assessed through an exit survey completed by school leaders. RESULTS: All school staff reported satisfaction with the SWITCH implementation process. Reports of school- and setting-level implementation were relatively high (2.0 to 2.8 on a 3-point scale) but student engagement, based on use of the online tracking system, varied greatly over time and across schools. Three high implementation schools had average tracking rates exceeding 70% (range: 72-90%) while three low implementation schools had rates lower than 30% (range = 0-23%). CONCLUSIONS: This feasibility study supports the utility of the new implementation framework for promoting school and student engagement with SWITCH. Further testing regarding effectiveness and scale-up of this evidence-based school wellness intervention program is warranted.

Evaluation of Youth Enjoyment Toward Physical Activity and Sedentary Behavior.

PURPOSE: The consensus is that physical activity (PA) and sedentary behavior (SB) are independent behaviors, but past findings suggest that they may be influenced by common underlying factors. To clarify this issue, we examined associations between enjoyment of PA and participation in both PA and SB in a large sample of 4th- to 12th-grade US youth. METHODS: A total of 18,930 students from 187 schools completed the youth activity profile, a self-report 15-item survey that assesses time spent in PA and SB in school and home settings. Two additional items captured enjoyment of PA and physical education. Two-way (gender × enjoyment and grade × enjoyment) mixed analysis of variances were conducted. RESULTS: Pearson correlation results revealed a positive relationship between enjoyment and PA (r = .38, P < .05) and an inverse correlation between enjoyment and SB (r = -.23, P < .05). Statistically significant main effects of enjoyment were found in the 2-way analysis of variance for both PA and SB. The simple main effect from analysis of variance indicated students with high enjoyment of PA reported higher levels of PA and lower levels of SB compared with students reporting moderate or low levels of enjoyment. CONCLUSION: The results provide new insights related to the relevance of enjoyment as a common underlying variable influencing both PA and SB across gender and grade levels.

Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR.

The purpose of this investigation was to examine the validity of energy expenditure (EE), steps, and heart rate measured with the Apple Watch 1 and Fitbit Charge HR. Thirty-nine healthy adults wore the two monitors while completing a semi-structured activity protocol consisting of 20 minutes of sedentary activity, 25 minutes of aerobic exercise, and 25 minutes of light intensity physical activity. Criterion measures were obtained from an Oxycon Mobile for EE, a pedometer for steps, and a Polar heart rate strap worn on the chest for heart rate. For estimating whole-trial EE, the mean absolute percent error (MAPE) from Fitbit Charge HR (32.9%) was more than twice that of Apple Watch 1 (15.2%). This trend was consistent for the individual conditions. Both monitors accurately assessed steps during aerobic activity (MAPEApple: 6.2%; MAPEFitbit: 9.4%) but overestimated steps in light physical activity. For heart rate, Fitbit Charge HR produced its smallest MAPE in sedentary behaviors (7.2%), followed by aerobic exercise (8.4%), and light activity (10.1%). The Apple Watch 1 had stronger validity than the Fitbit Charge HR for assessing overall EE and steps during aerobic exercise. The Fitbit Charge HR provided heart rate estimates that were statistically equivalent to Polar monitor.

Comparison of In-Person and Online Motivational Interviewing-Based Health Coaching.

Health coaching is a common approach for promoting lifestyle changes, but little is known about the effectiveness of different delivery methods. The purpose of this study was to evaluate the effectiveness of in-person versus online health coaching when used as part of a facilitated behavior change program. To increase translatability, the study used a naturalistic design that enabled participants to self-select the coaching delivery method (Group) as well as the target behavior (diet, physical activity, or weight management). Regardless of group, participants were provided with a behavior-based monitoring device and guided to use it by the health coach. A sample of 92 adults participated and 86 completed pre-post evaluations to assess behavior change strategies and posttest outcome measurements for their specific goal. Two-way (Group × Time) analyses of variance were used to evaluate changes in behavioral strategies. Intent-to-treat regression analyses were used to compare postintervention outcomes for groups. The in-person group had significantly higher Healthy Eating Index scores than the online group ( p < .05), but nonsignificant group differences were observed for those targeting physical activity or weight change ( p > .05). The results support the use of health coaching for promoting behavior change and suggest that online coaching may be equally effective as in-person methods.

The accuracy of the 24-h activity recall method for assessing sedentary behaviour: the physical activity measurement survey (PAMS) project.

Sedentary behaviour (SB) has emerged as a modifiable risk factor, but little is known about measurement errors of SB. The purpose of this study was to determine the validity of 24-h Physical Activity Recall (24PAR) relative to SenseWear Armband (SWA) for assessing SB. Each participant (n = 1485) undertook a series of data collection procedures on two randomly selected days: wearing a SWA for full 24-h, and then completing the telephone-administered 24PAR the following day to recall the past 24-h activities. Estimates of total sedentary time (TST) were computed without the inclusion of reported or recorded sleep time. Equivalence testing was used to compare estimates of TST. Analyses from equivalence testing showed no significant equivalence of 24PAR for TST (90% CI: 443.0 and 457.6 min · day-1) relative to SWA (equivalence zone: 580.7 and 709.8 min · day-1). Bland-Altman plots indicated individuals that were extremely or minimally sedentary provided relatively comparable sedentary time between 24PAR and SWA. Overweight/obese and/or older individuals were more likely to under-estimate sedentary time than normal weight and/or younger individuals. Measurement errors of 24PAR varied by the level of sedentary time and demographic indicators. This evidence informs future work to develop measurement error models to correct for errors of self-reports.

Calibration of context-specific survey items to assess youth physical activity behaviour.

This study tests calibration models to re-scale context-specific physical activity (PA) items to accelerometer-derived PA. A total of 195 4th-12th grades children wore an Actigraph monitor and completed the Physical Activity Questionnaire (PAQ) one week later. The relative time spent in moderate-to-vigorous PA (MVPA%) obtained from the Actigraph at recess, PE, lunch, after-school, evening and weekend was matched with a respective item score obtained from the PAQ's. Item scores from 145 participants were calibrated against objective MVPA% using multiple linear regression with age, and sex as additional predictors. Predicted minutes of MVPA for school, out-of-school and total week were tested in the remaining sample (n = 50) using equivalence testing. The results showed that PAQ ß-weights ranged from 0.06 (lunch) to 4.94 (PE) MVPA% (P < 0.05) and models root mean square error ranged from 4.2% (evening) to 20.2% (recess). When applied to an independent sample, differences between PAQ and accelerometer MVPA at school and out-of-school ranged from -15.6 to +3.8 min and the PAQ was within 10-15% of accelerometer measured activity. This study demonstrated that context-specific items can be calibrated to predict minutes of MVPA in groups of youth during in- and out-of-school periods.

Reference Curves for Field Tests of Musculoskeletal Fitness in U.S. Children and Adolescents: The 2012 NHANES National Youth Fitness Survey.

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.

Impact of activity outcome and measurement instrument on estimates of youth compliance with physical activity guidelines: a cross-sectional study.

BACKGROUND: The national physical activity guidelines (PAG) in many countries recommend that youth accumulate 60 min or more of moderate-to-vigorous physical activity (MVPA) daily (PAG-MVPA). A daily target of ≥ 11,500 steps/day has been proposed as a step count alternative to this guideline (PAG-Steps). Contemporary activity monitors are capable of estimating both MVPA and steps, but it is not clear how these units compare when used to evaluate compliance with the national PAG. The purpose of this study was to compare prevalence estimates of meeting the PAG-MVPA and PAG-Steps using two commonly used monitors, the ActiGraph (AG) and SenseWear Armband (SWA). METHODS: A sample of 69 children (25 girls and 44 boys) aged 9-16 years each wore a wrist-mounted AG and a SWA over a one-week period. Days with ≥10 h of wear time for both monitors were included in the analysis. Estimates of time spent in MVPA were obtained using the Crouter equation for the AG and from proprietary algorithms for the SWA. Step counts for the AG and SWA were directly obtained from the respective software. The prevalence of meeting the PAG-MVPA and PAG-Steps was compared within each monitor, using Cohen's kappa (κ) statistic. Agreement was similarly assessed between monitors using each guideline individually. RESULTS: When assessed with the AG, the prevalence of meeting PAG was substantially higher for the PAG-MVPA (87.2 %) than for the PAG-Steps (54.2 %), with fair classification agreement (κ = 0.30) between the two guidelines. Higher prevalence rates were also observed for the PAG-MVPA (83.6 %) than for the PAG-Steps (33.8 %) when assessed using the SWA, but the prevalence rates and classification agreement (κ = 0.18) were lower than the values from the AG. Classification agreement between AG and SWA was lower for the PAG-MVPA (κ = 0.42) than for the PAG-Steps (κ = 0.55). CONCLUSIONS: The results show differential patterns of compliance with the PAG-MVPA and PAG-Steps, as assessed by the AG and SWA. Additional research is needed to directly evaluate and compare findings from public health research based on different guidelines and measurement methods.

Kids are not little adults: what MET threshold captures sedentary behavior in children?

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 VO2 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 (Se1.5 METs = 4.7%, Sp1.5 METs = 100.0%; Se2.0 METs = 36.9%, Sp2.0 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.