276 research outputs found
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
Glucose tolerance and physical fitness: An epidemiologic study in an entire community
A modification of the Harvard Step Test was administered to approximately 4700 males and females, age 10–69 in Tecumseh, Michigan. Heart rate response to this standardized exercise test is an estimate of capacity for muscular work. A blood sample was drawn 1 h after a glucose challenge on the same day the exercise test was given. Four skinfolds were measured as an index of body fatness. It was the purpose of this analysis to study the relationship of glucose tolerance to heart rate response to exercise.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47419/1/421_2004_Article_BF00421779.pd
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
Ischemic Preconditioning Does Not Improve Time Trial Performance in Recreational Runners
International Journal of Exercise Science 13(6): 1402-1417, 2020. Some evidence indicates that ischemic preconditioning (IPC) may positively affect endurance exercise performance, but IPC’s effect on running performance is unclear. This study’s purpose was to examine the effect of IPC on running performance in recreational runners. Participants (n=12) completed IPC, a sham (SH) condition, and a leg elevation without blood restriction (LE) control condition on separate days (order randomized). For IPC, blood was restricted using blood pressure cuffs inflated to 220 mmHg at the thigh. For SH, the cuffs were inflated to only 20 mmHg. For LE, participants positioned their legs at 90 degrees against a wall while laying supine. The duration of each protocol was 30 minutes (three 5-minute bouts with 5-minute breaks). Following each protocol, participants ran 2.4 kilometers as fast as possible on a motorized treadmill. Run time, heart rate, and perceived exertion were measured and statistically compared, using repeated-measures ANOVA, each 0.8 kilometers. There were no differences in heart rate or time trial performance across protocols (p\u3e0.05; IPC, 612.5±61.2 sec; SH, 608.1±57.9 sec; LE, 612.7±59.1 sec). Rating of perceived exertion at 0.8 kilometers was significantly lower for the IPC protocol than SH in females only (~5.7%, or ~0.8 points on a 6-20 scale; p\u3c0.05). Our IPC protocol did not improve running performance or physiological parameters during a time trial run in recreational runners. The performance benefit seen in this study’s most fit individuals suggests that fitness level may influence IPC’s efficacy for improving endurance running performance
Performance and Power Evaluation of an In-line Accelerator
ABSTRACT In this paper we evaluate the performance and power of a processor-attached in-line accelerator. The accelerator provides high-performance SIMD computing and power efficiency by means of a very large register file and a set of vector multimedia extensions based on IBM's PowerPC VMX. Our experiments show significant performance improvements and power reduction, compared to a baseline vector execution unit, mainly due to the drastic decrease of memory accesses caused by the software-managed locality of the very large register file. Total execution time is, on average, reduced by 61%, while consuming 55% less energy
Investigating optimal accelerometer placement for energy expenditure prediction in children using a machine learning approach
Accurate measurement of energy expenditure (EE) is imperative for identifying and targeting health-associated implications. Whilst numerous accelerometer-based regression equations to predict EE have been developed, there remains little consensus regarding optimal accelerometer placement. Therefore, the purpose of the present study was to validate and compare artificial neural networks (ANNs) developed from accelerometers worn on various anatomical positions, and combinations thereof, to predict EE.Twenty-seven children (15 boys; 10.8  ±  1.1 years) participated in an incremental treadmill test and 30 min exergaming session wearing a portable gas analyser and nine ActiGraph GT3X+  accelerometers (chest and left and right wrists, hips, knees, and ankles). Age and sex-specific resting EE equations (Schofield) were used to estimate METs from the oxygen uptake measures. Using all the data from both exergames, incremental treadmill test and the transition period in between, ANNs were created and tested separately for each accelerometer and for combinations of two or more using a leave-one-out approach to predict EE compared to measured EE. Six features (mean and variance of the three accelerometer axes) were extracted within each 15 s window as inputs in the ANN. Correlations and root mean square error (RMSE) were calculated to evaluate prediction accuracy of each ANN, and repeated measures ANOVA was used to statistically compare accuracy of the ANNs.All single-accelerometer ANNs and combinations of two-, three-, and four-accelerometers performed equally (r  =  0.77–0.82), demonstrating higher correlations than the 9-accelerometer ANN (r  =  0.69) or the Freedson linear regression equation (r  =  0.75). RMSE did not differ between single-accelerometer ANNs or combinations of two, three, or four accelerometers (1.21–1.31 METs), demonstrating lower RMSEs than the 9-accelerometer ANN (1.46 METs) or Freedson equation (1.74 METs).These findings provide preliminary evidence that ANNs developed from single accelerometers mounted on various anatomical positions demonstrate equivalency in the accuracy to predict EE in a semi-structured setting, supporting the use of ANNs in improving EE prediction accuracy compared with linear regression
Effectiveness of quality improvement initiative (Guidelines Applied to Practice [GAP] project) in improving the care of medicare patients and women with acute myocardial infarction
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
Relationship between psychological and biological factors and physical activity and exercise behaviour in Filipino students
The aim of the present study was threefold. Firstly, it investigated whether a general measure or specific measure of motivational orientation was better in describing the relationship between motivation and exercise behaviour. Secondly, it examined the relationship between the four most popular indirect methods of body composition assessment and physical activity and exercise patterns. Thirdly, the interaction between motivation and body composition on physical activity and exercise behaviour was explored in a sample of 275 Filipino male and female students. Males were found to have higher levels of exercise whereas females had higher levels of physical activity. Furthermore, general self-motivation together with body weight and percentage body fat were found to be the best predictor of exercise behaviour whereas the tension/pressure subscale of the ‘Intrinsic Motivation Inventory’ (IMI) was the best predictor of levels of physical activity. However, significant gender differences were observed. That is, for the males only self-motivation and for the females only body weight and BMI predicted exercise behaviour. Also, tension/pressure predicted physical activity levels for the females but not the males. No inverse relationship was found between the four body composition measures and exercise and physical activity behaviour. The results support the notion that the psychobiological approach might be particularly relevant for high intensity exercise situations but also highlights some important gender differences. Finally, the results of this study emphasise the need for more cross-cultural research
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