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.
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.
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.
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.
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.
Physical activity (PA) behavior tends to decline as youth get older, especially in female adolescents. The purpose of this study was to develop an understanding of female adolescent ...moderate-to-vigorous physical activity (MVPA) behavior. Baseline MVPA data was collected during year one of a female-specific PA related program. The Youth Activity Profile was administered to contextualize current middle school female PA levels. Data were collected on over 600 6th-8th grade youths with even distributions by grade. No significant differences between grade, race/ethnicity, and MVPA minutes were found. The average estimated value for daily MVPA across all grades was 43.93 (+/-12.97) min, which is considerably lower than the public health recommendation of 60 min per days. Similar amounts were observed for weekend days 45.03 (+/-19.98) and weekdays 45.50 (+/-13.14); however, allocations were smaller during school (9.45 +/- 5.13 min) than at home (34.04 +/- 11.15). The findings from this study highlight the need for further investigation in developing sustainable and innovative PA interventions that target adolescent females.
Public health research on sedentary behavior (SB) in youth has heavily relied on accelerometers. However, it has been limited by the lack of consensus on the most accurate accelerometer cut-points as ...well as by unknown effects caused by accelerometer position (wrist vs. hip) and output (single axis vs. multiple axes). The present study systematically evaluates classification accuracy of different Actigraph cut-points for classifying SB using hip and wrist-worn monitors and establishes new cut-points to enable use of the 3-dimensional vector magnitude data (for both hip and wrist placement).
A total of 125 children ages 7-13 yrs performed 12 randomly selected activities (from a set of 24 different activities) for 5 min each while wearing tri-axial Actigraph accelerometers on both the hip and wrist. The accelerometer data were categorized as either sedentary or non-sedentary minutes using six previously studied cut-points: 100 counts-per-minute (CPM), 200 CPM, 300 CPM, 500 CPM, 800 CPM and 1100 CPM. Classification accuracy was evaluated with Cohen's Kappa (κ) and new cut-points were identified from Receiver Operating Characteristic (ROC).
Of the six cut-points, the 100 CPM value yielded the highest classification accuracy (κ = 0.81) for hip placement. For wrist placement, all of the cut-points produced low classification accuracy (ranges of κ from 0.44 to 0.67). Optimal sedentary cut-points derived from ROC were 554.3 CPM (ROC-AUC of 0.99) for vector magnitude for hip, 1756 CPM (ROC-AUC of 0.94) for vertical axis for wrist, and 3958.3 CPM (ROC-AUC of 0.93) for vector magnitude for wrist placement.
The 100 CPM was supported for use with vertical axis for hip placement, but not for wrist placement. The ROC-derived cut-points can be used to classify youth SB with the wrist and with vector magnitude data.
The purpose of the study was to explore the utility of school- and county-level variables in explaining variability in children and adolescent body mass index (BMI).
BMI data from nearly 2.5 million ...of children and adolescents were aggregated at the school level from more than 5000 schools in Texas. School-level predictors included enrollment and the percentage of students qualifying for free and reduced lunch. Seven county-level variables were obtained from the County Health Rankings website, including adult obesity, food environment index, adult physical inactivity, access to exercise, college completion, childhood poverty, and income inequality. Multilevel modeling was used to examine school- and county-level predictors that may explain the variability in group level youth BMI.
School-level socioeconomic status, school enrollment, and age-group were identified as significant predictors in youth BMI for both boy and girls. In girls, county-level adult obesity, food environment index, college completion, and income inequality were also significantly associated with youth BMI. In boys, the significant county-level predictors were food environment index and income inequality. Approximately 11%-16% of the variations in BMI Healthy Fitness Zone achievement were attributable to the differences between counties. The predictors included in the present study collectively explained approximately 50%-60% of between-county variation and 24%-47% of within-county variation.
The results of the current study advance research on the correlates that are associated with youth obesity at both school and county levels. These factors should be taken into account by policy makers and researchers interested in childhood obesity research.
Wearable activity trackers have become popular for tracking individual's daily physical activity, but little information is available to substantiate the validity of these devices in step counts. ...Thirty-five healthy individuals completed three conditions of activity tracker measurement: walking/jogging on a treadmill, walking over-ground on an indoor track, and a 24-hour free-living condition. Participants wore 10 activity trackers at the same time for both treadmill and over-ground protocol. Of these 10 activity trackers three were randomly given for 24-hour free-living condition. Correlations of steps measured to steps observed were r = 0.84 and r = 0.67 on a treadmill and over-ground protocol, respectively. The mean MAPE (mean absolute percentage error) score for all devices and speeds on a treadmill was 8.2% against manually counted steps. The MAPE value was higher for over-ground walking (9.9%) and even higher for the 24-hour free-living period (18.48%) on step counts. Equivalence testing for step count measurement resulted in a significant level within ±5% for the Fitbit Zip, Withings Pulse, and Jawbone UP24 and within ±10% for the Basis B1 band, Garmin VivoFit, and SenseWear Armband Mini. The results show that the Fitbit Zip and Withings Pulse provided the most accurate measures of step count under all three different conditions (i.e. treadmill, over-ground, and 24-hour condition), and considerable variability in accuracy across monitors and also by speeds and conditions.
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).
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.
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 (R
= 0.28) compared with the 24PAR models (R
= 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.
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.
Objectives To examine the combined influence of physical activity and screen time (television and video games) on the odds of being overweight and to evaluate the utility of current public policy ...recommendations. Study design Physical activity was assessed by a pedometer and screen time was assessed by survey in a sample of 709 children age 7 to 12 years. The percentage of subjects meeting current physical activity and screen time recommendations was calculated. Cross-tabulated physical activity–screen time groups were formed depending on whether or not the children were meeting current recommendations. Logistic regression was used to examine the influence of physical activity and screen time on the odds of being overweight. Results Children meeting physical activity and screen time recommendations were the least likely to be overweight. Approximately 10% of the boys and 20% of the girls meeting both recommendations were overweight, compared with 35% to 40% of those who did not meet either recommendation. Screen time and physical activity appeared to be equivalent risk factors for boys, even though physical activity in girls was more strongly associated with body mass index. Conclusions Children not meeting the physical activity or screen time recommendations were 3 to 4 times more likely to be overweight than those complying with both recommendations.
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.
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).
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.
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.
The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a ...mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.
Physical activity is an effective method of reducing fall risk among older adults. Previous evaluations of the six-week Walk with Ease (WWE) program have documented benefits to functional outcomes, ...but the potential effects on reducing fall risk have not been evaluated. This pilot study evaluates outcomes of a community delivered WWE program for potential suitability as a fall risk reduction program.
A total of 59 older adults (age > 60) enrolled in a group version of WWE delivered by trained community-based leaders. Complete data (pre- and post-program) from functional fitness tests and behavioral instruments were obtained from 41 participants (aged 74.4 ± 6.6 years, 70% female). Functional outcomes included the 10-foot timed up and go (TUG), 30-second chair stand (CST) and 4-stage balance test (BT) included as part of STEADI, as well as a two-minute step test (ST) and normal gait speed test (GST). Survey assessments included STEADI fall risk screening, self-reported physical activity, and fear of falling measures. Analyses focused on reporting pre-post effect sizes, but paired t-tests were used to test statistical significance of differences.
Improvements in functional performance approached significance for both CST (d = 0.31, p = 0.06) and ST (d = 0.26, p = 0.12), but all other tests were nonsignificant. Survey results demonstrated significant increases in self-reported walking (d = 0.54, p = 0.02) and moderate-to-vigorous physical activity (MVPA; d = 0.56, p = 0.004), but perceived fear of falling and overall fall risk scores had smaller, non-significant, effects (d ranging from 0.01 to 0.31). Stratified analysis suggested that participants screened at an elevated risk for falls at baseline consistently had larger effects on all functional and survey assessments, though the analysis was underpowered to test significance.
Walk with Ease participation significantly increased self-reported physical activity but did not significantly improve physical function or reduce fall risk. However, consistently larger effect sizes among participants screened as at-risk for falls suggest that the program may be beneficial for those with elevated risk for falls or functional limitations. Further research is needed to document the consistency of these effects among participants with elevated fall risk status.
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