Variability of Objectively Measured Sedentary Behavior

The primary purpose of this study was to evaluate variability of sedentary behavior (SB) throughout a 7-d measurement period and to determine if G7 d of SB measurement would be comparable with the typical 7-d measurement period. Methods: Retrospective data from Ball State University_s Clinical Exercise Physiology Laboratory on 293 participants (99 men, 55 T 14 yr, body mass index = 29 T 5 kgImj2; 194 women, 51 T 12 yr, body mass index = 27 T 7 kgImj2) with seven consecutive days of data collected with ActiGraph accelerometers were analyzed (ActiGraph, Fort Walton Beach, FL). Time spent in SB (either G100 counts per minute or G150 counts per minute) and breaks in SB were compared between days and by sex using a two-way repeated-measures ANOVA. Stepwise regression was performed to determine if G7 d of SB measurement were comparable with the 7-d method, using an adjusted R2 of Q0.9 as a criterion for equivalence. Results: There were no differences in daily time spent in SB between the 7 d for all participants. However, there was a significant interaction between sex and days, with women spending less time in SB on both Saturdays and Sundays than men when using the 100 counts per minute cut-point. Stepwise regression showed using any 4 d would be comparable with a 7-d measurement (R2 9 0.90). Conclusions: When assessed over a 7-d measurement period, SB appears to be very stable from day to day, although there may be some small differences in time spent in SB and breaks in SB between men and women, particularly on weekend days. The stepwise regression analysis suggests that a measurement period as short as 4 d could provide comparable data (91% of variance) with a 1-wk assessment. Shorter assessment periods would reduce both researcher and subject burden in data collection

Reference Standards for Body Fat Measure 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 obesityrelated 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\u27s Clinical Exercise Physiology Laboratory and University of Wisconsin- Milwaukee\u27s 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\u3c0.01) mean %BF and FMI than men, whereas men had higher (p\u3c0.01) mean ratios of A/G, trunk/limb, and trunk/leg fat measurements than women

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 T 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 predictMETs. 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 T 0.05, 0.89 T 0.16, 0.71 T 0.33, and 0.80 T 0.28, and those for variance were 0.98 T 0.02, 0.98 T 0.03, 0.91 T 0.06, and 1.00 T 0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00 T 0.01, 0.98 T 0.02, 0.96 T 0.04, and 1.00 T 0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher percent agreement for activity intensity (95.1% T 5.6% and 95.5% T 4.0%) compared with theMontoye 2015 raw data model (61.5% T 27.6%; P G 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

Effect of sampling rate on acceleration and counts of hip- and wrist-worn ActiGraph accelerometers in children

Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mg's), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15-sec and mean acceleration and to compare differences due to sampling rate between accelerometer wear locations and across different types of activities. Approach: Children (n=29) wore a hip- and wrist-worn accelerometer (sampled at 100 Hz, downsampled in MATLAB to 30 Hz) during rest/transition periods, active video games, and a treadmill test to volitional exhaustion. Mean acceleration and counts/15-sec were computed for each axis and as vector magnitude. Main Results: There were mostly no significant differences in mean acceleration. However, 100 Hz data resulted in significantly more total counts/15-sec (mean bias 4-43 counts/15-sec across axes) for both the hip- and wrist-worn monitor when compared to 30 Hz data. Absolute differences increased with activity intensity (hip: r=0.46-0.63; wrist: r=0.26-0.55) and were greater for hip- versus wrist-worn monitors. Percent agreement between 100 and 30 Hz data was high (97.4-99.7%) when cut-points or machine learning algorithms were used to classify activity intensity. Significance: Our findings support that sampling rate affects the generation of counts but adds that differences increase with intensity and when using hip-worn monitors. We recommend researchers be consistent and vigilantly report the sampling rate used, but note that classifying data into activity intensities resulted in agreement despite differences in sampling rate

Validation of the SmartPlate for detecting food weight and type

This study determined accuracy (comparing to criterion), inter-plate reliability (comparing measures between two plates), and intra-plate reliability (comparing successive measures on one plate) of the SmartPlate for food weight and type. Food weight validation included comparing SmartPlate weights to criterion [reference] scale weights (1,980 measures) and weights of 188 foods (2,256 measures). Food type validation included assessing SmartPlate accuracy for 188 foods. For weight, mean absolute percent errors for accuracy, inter-plate reliability, and intra-plate reliability were 6.2, 7.4, and 4.9%, respectively. For food type, foods were correctly identified/listed or searchable 67.0 or 98.9% of the time, respectively, with 76.0% inter-plate reliability and 86.3% intra-plate reliability. The SmartPlate had acceptable accuracy and reliability for assessing food weight and type and may be appealing for monitoring dietary surveillance or intervention. Due to high intra-plate reliability, the SmartPlate may be especially useful for one-on-one interventions and assessing change over time.</p

Determining the Reliability of Several Consumer-Based Physical Activity Monitors

Limited research exists on the reliability of consumer-based physical activity monitors (CPAMs) despite numerous studies on their validity. Consumers often purchase CPAMs to assess their physical activity (PA) habits over time, emphasizing CPAM reliability more so than their validity; therefore, the purpose of this study was to investigate the reliability of several CPAMs. In this study, 30 participants wore a pair of four CPAM models (Fitbit One, Zip, Flex, and Jawbone Up24) for a total of eight monitors, while completing seven activities in the laboratory. Activities were completed in two consecutive five-minute bouts. Participants then wore either all wrist- or hip-mounted CPAMs in a free-living setting for the remainder of the day. Intra-monitor reliability for steps (0.88–0.99) was higher than kcals (0.77–0.94), and was higher for hip-worn CPAMs than for wrist-worn CPAMs (p &lt; 0.001 for both). Inter-monitor reliability in the laboratory for steps (0.81–0.99) was higher than kcals (0.64–0.91) and higher for hip-worn CPAMs than for wrist-worn CPAMs (p &lt; 0.001 for both). Free-living correlations were 0.61–0.98, 0.35–0.96, and 0.97–0.98 for steps, kcals, and active minutes, respectively. These findings illustrate that all CPAMs assessed yield reliable estimations of PA. Additionally, all CPAMs tested can provide reliable estimations of physical activity within the laboratory but appear less reliable in a free-living setting

Assessing physical activity as a core component in cardiac rehabilitation: a position statement of the American association of cardiovascular and pulmonary rehabilitation

Physical inactivity is a well-established major risk factor for cardiovascular disease. As such, physical activity counseling is 1 of the 10 core components of cardiac rehabilitation/secondary prevention programs recommended by the American Heart Association and the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR). In addition, the ability to perform a physical activity assessment and report outcomes is 1 of the 10 core competencies of cardiac rehabilitation/secondary prevention professionals published by the AACVPR. Unfortunately, standardized procedures for physical activity assessment of cardiac rehabilitation patients have not been developed and published. Thus, the objective of this AACVPR statement is to provide an overview of physical activity assessment concepts and procedures and to provide a recommended approach for performing a standardized assessment of physical activity in all comprehensive cardiac rehabilitation programs following the core components recommendations

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

<div><p>Background</p><p>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.</p><p>Objective</p><p>To develop reference values by age and sex for DXA-derived fat mass measurements with GE-Healthcare systems.</p><p>Methods</p><p>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 (¿), 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.</p><p>Results</p><p>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 ¿ 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.</p><p>Conclusion</p><p>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.</p></div