Mary T. Imboden

Comparison of four Fitbit and Jawbone activity monitors with a research-grade ActiGraph accelerometer for estimating physical activity and energy expenditure

Background/aim Consumer-based physical activity (PA) monitors have become popular tools to track PA behaviours. Currently, little is known about the validity of the measurements provided by consumer monitors. We aimed to compare measures of steps, energy expenditure (EE) and active minutes of four consumer monitors with one research-grade accelerometer within a semistructured protocol. Methods Thirty men and women (18–80 years old) wore Fitbit One (worn at the waist), Fitbit Zip (waist), Fitbit Flex (wrist), Jawbone UP24 (wrist) and one waist-worn research-grade accelerometer (ActiGraph) while participating in an 80 min protocol. A validated EE prediction equation and active minute cut-points were applied to ActiGraph data. Criterion measures were assessed using direct observation (step count) and portable metabolic analyser (EE, active minutes). A repeated measures analysis of variance (ANOVA) was used to compare differences between consumer monitors, ActiGraph, and criterion measures. Similarly, a repeated measures ANOVA was applied to a subgroup of subjects who didn’t cycle. Results Participants took 3321±571 steps, had 28±6 active min and expended 294±56 kcal based on criterion measures. Comparatively, all monitors underestimated steps and EE by 13%–32% (p<0.01); additionally the Fitbit Flex, UP24, and ActiGraph underestimated active minutes by 35%–65% (p<0.05). Underestimations of PA and EE variables were found to be similar in the subgroup analysis. Conclusion Consumer monitors had similar accuracy for PA assessment as the ActiGraph, which suggests that consumer monitors may serve to track personal PA behaviours and EE. However, due to discrepancies among monitors, individuals should be cautious when comparing relative and absolute differences in PA values obtained using different monitors.

Reference Standards for Cardiorespiratory Fitness Measured With Cardiopulmonary Exercise Testing Using Cycle Ergometry: Data From the Fitness Registry and the Importance of Exercise National Database (FRIEND) Registry

Abstract The importance of cardiorespiratory fitness (CRF) is well established. This report provides newly developed standards for CRF reference values derived from cardiopulmonary exercise testing (CPX) using cycle ergometry in the United States. Ten laboratories in the United States experienced in CPX administration with established quality control procedures contributed to the “Fitness Registry and the Importance of Exercise: A National Database” (FRIEND) Registry from April 2014 through May 2016. Data from 4494 maximal (respiratory exchange ratio, ≥1.1) cycle ergometer tests from men and women (20–79 years) from 27 states, without cardiovascular disease, were used to develop these references values. Percentiles of maximum oxygen consumption (VO2max) for men and women were determined for each decade from age 20 years through age 79 years. Comparisons of VO2max were made to reference data established with CPX data from treadmill data in the FRIEND Registry and previously published reports. As expected, there were significant differences between sex and age groups for VO2max (P<.01). For cycle tests within the FRIEND Registry, the 50th percentile VO2max of men and women aged 20 to 29 years declined from 41.9 and 31.0 mLO2/kg/min to 19.5 and 14.8 mLO2/kg/min for ages 70 to 79 years, respectively. The rate of decline in this cohort was approximately 10% per decade. The FRIEND Registry reference data will be useful in providing more accurate interpretations for the US population of CPX‐measured VO2max from exercise tests using cycle ergometry compared with previous approaches based on estimations of standard differences from treadmill testing reference values.

Reference standards for body fat measures using GE dual energy x-ray absorptiometry in Caucasian adults

Background Dual energy x-ray absorptiometry (DXA) is an established technique for the measurement of body composition. Reference values for these variables, particularly those related to fat mass, are necessary for interpretation and accurate classification of those at risk for obesity-related health complications and in need of lifestyle modifications (diet, physical activity, etc.). Currently, there are no reference values available for GE-Healthcare DXA systems and it is known that whole-body and regional fat mass measures differ by DXA manufacturer. Objective To develop reference values by age and sex for DXA-derived fat mass measurements with GE-Healthcare systems. Methods A de-identified sample of 3,327 participants (2,076 women, 1,251 men) was obtained from Ball State University’s Clinical Exercise Physiology Laboratory and University of Wisconsin-Milwaukee’s Physical Activity & Health Research Laboratory. All scans were completed using a GE Lunar Prodigy or iDXA and data reported included percent body fat (%BF), fat mass index (FMI), and ratios of android-to-gynoid (A/G), trunk/limb, and trunk/leg fat measurements. Percentiles were calculated and a factorial ANOVA was used to determine differences in the mean values for each variable between age and sex. Results Normative reference values for fat mass variables from DXA measurements obtained from GE-Healthcare DXA systems are presented as percentiles for both women and men in 10-year age groups. Women had higher (p<0.01) mean %BF and FMI than men, whereas men had higher (p<0.01) mean ratios of A/G, trunk/limb, and trunk/leg fat measurements than women. Conclusion These reference values provide clinicians and researchers with a resource for interpretation of DXA-derived fat mass measurements specific to use with GE-Healthcare DXA systems.

Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

To enable inter- and intrastudy comparisons it is important to ascertain comparability among accelerometer models. Purpose The purpose of this study was to compare raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. Methods Adults (n = 26 (n = 15 women); age, 49.1 ± 20.0 yr) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12–21 sedentary, household, and ambulatory/exercise activities lasting 2–15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities was derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent agreement was used to compare epoch-by-epoch activity intensity. Results For raw data, correlations for mean acceleration were 0.96 ± 0.05, 0.89 ± 0.16, 0.71 ± 0.33, and 0.80 ± 0.28, and those for variance were 0.98 ± 0.02, 0.98 ± 0.03, 0.91 ± 0.06, and 1.00 ± 0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00 ± 0.01, 0.98 ± 0.02, 0.96 ± 0.04, and 1.00 ± 0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher percent agreement for activity intensity (95.1% ± 5.6% and 95.5% ± 4.0%) compared with the Montoye 2015 raw data model (61.5% ± 27.6%; P < 0.001). Conclusions Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers.

Cardiorespiratory Fitness Is Inversely Associated With Clustering of Metabolic Syndrome Risk Factors: The Ball State Adult Fitness Program Longitudinal Lifestyle Study

Objective The focus of this study was the association between the metabolic syndrome (MetSyn) and cardiorespiratory fitness (CRF) defined as maximal oxygen uptake (VO2max). Although previous research has shown a relationship between MetSyn and CRF, most studies are based on less objective measures of CRF and different cardiometabolic risk factor thresholds from earlier guidelines. Participants and Methods The metabolic markers included in the present study were central obesity, elevated plasma triglycerides, elevated fasting high-density lipoprotein cholesterol, impaired fasting plasma glucose, hypertension, or pharmacologic treatment for diagnosed hypertension, hypertriglyceridemia, low high-density lipoprotein cholesterol, or diabetes. A cohort of 3636 adults (1629 women, 2007 men; mean ± SD age, 44.7±12.3 years) completed CRF and metabolic risk factor assessment between January 1, 1971, and November 1, 2016. The CRF was defined as a measured VO2max from a cardiopulmonary exercise test on a treadmill, with a respiratory exchange ratio value of 1.0 or more. Results Prevalence of MetSyn (≥3 factors) was 26% (n=953) in the cohort, with men having a greater likelihood for MetSyn compared with women (P<.001). The difference in VO2max between those individuals with MetSyn and those without was approximately 2.3 (2.0-2.5) metabolic equivalents. Logistic regression analyses showed a significant inverse and graded association between quartiles of CRF and MetSyn for the group overall (P<.001), with odds ratios (95% CI) using the lowest fitness group as the referent group of 0.67 (0.55-0.81), 0.41 (0.34-0.51), and 0.10 (0.07-0.14) for VO2max (P<.001). The sex-specific odds ratios were 0.25 (0.18-0.34), 0.05 (0.02-0.10), and 0.02 (0.01-0.09) for women and 0.43 (0.31-0.59), 0.19 (0.14-0.27), and 0.03 (0.02-0.05) for men (P<.001). Conclusion These results with current risk factor thresholds and a large number of women demonstrate that low VO2max is associated with MetSyn.

Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings

ABSTRACT This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square error (RMSE) for EE prediction (1.04–1.23 vs. 1.10–1.34 METs for V1/V2, V2/V2), and the ankle-worn accelerometer had the lowest RMSE of all accelerometers (1.04–1.18 vs. 1.17–1.34 METs for other placements). The ankle-worn accelerometer and associated EE prediction models developed using data from both structured and simulated free-living settings should be considered for optimal EE prediction accuracy.