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Official Journal of the American College of Sports Medicine METhOds: Fifty-three women wore the SenseWear® Mini armband for 7-days at week 18 and 35 of pregnancy. All MVPA completed in at least 10-, 20-, or 30-minute bouts were summed. Total MET-minutes for the week were summed to reflect total PA including sedentary and light activity. GWG was calculated as the difference of prepregnancy weight from measured weight at week 35. A one-way ANOVA evaluated the differences in GWG for women meeting prenatal PA recommendations (sufficient PA; >150 min MVPA per week), those with insufficient PA (1-149 min), and none (0 min) for all 3 bout lengths. Total MET-minutes per week were compared with independent t-tests for women gaining within or in excess of the IOM guidelines. rEsuLTs: Twenty-six (49%) women gained in excess of IOM guidelines. A trend for MVPA completed in ≥ 10 min bouts was identified: sufficient PA (11.3 ± 4.3 kg), insufficient PA (13.3 ± 5.1 kg), and none (18.9 ± 4.2 kg) (P = 0.056). GWG was not significantly different when PA was analyzed in at least 20 or 30 min bouts. Participants who gained within IOM guidelines had significantly higher MET-minutes at week 18 than women who gained in excess (11,068 vs. 10,414, respectively; P=0.032). PA at week 35 was not significantly related to weight gain. CONCLusION: Longer sustained activity (> 20 or 30 min bouts) did not significantly affect GWG, whereas moderate PA completed in ≥10-minute bouts may be a beneficial strategy to promote healthy GWG. Total activity (e.g. total MET-minutes per week) had a significant favorable relationship with GWG. Future work should address reducing sedentary time and increasing PA in at least 10-minute bouts. 1429 Board #21 May 30, 2:00 PM - 3:30 PM Objectively Measured Total accelerometer Counts and MVPa: Contrasting relationships with Biomarkers using 2003 - 2006 NhaNEs Dana L. Wolff1 , Eugene C. Fitzhugh1 , David R. Bassett, FACSM1 , James R. Churilla, FACSM2 . 1University of Tennessee, Knoxville, TN. 2University of North Florida, Jacksonville, FL. (No relationships reported) PurPOsE: To contrast the associations of objectively measured moderate-tovigorous physical activity (MVPA) and total accelerometer counts with biomarkers in a representative sample of U.S. adults. METhOds: Data from the 2003 – 2006 NHANES were used for this analysis. The sample included adults ≥ 20 y, not pregnant or lactating, who had self-reported PA and ≥ 4 d of accelerometer data with ≥ 10 h wear time (N = 5668). MVPA was defined as the mean minutes with counts ≥ 2020 using a 10-min bout criterion on valid days. Total accelerometer counts represented the mean total counts acquired on valid days. Biomarkers included: blood pressure, body mass index (BMI), waist circumference, triceps and subscapular skinfolds, cholesterol, triglyceride, glycohemoglobin, plasma glucose, C-peptide, insulin, C-reactive protein, and homocysteine. Simultaneous regressions were conducted in which each biomarker was regressed on MVPA and total accelerometer counts per day independently while adjusting for relevant covariates. rEsuLTs: When compared to MVPA, total accelerometer counts per day displayed stronger associations with the following biomarkers: BMI, waist circumference, triceps skinfolds, subscapular skinfolds, HDL, triglycerides, plasma glucose, C-peptide, insulin, C-reactive protein, and homocysteine (adj. Wald F = 9.04 – 97.41, P < 0.05 – 0.0001). Only one biomarker, glycohemoglobin, had stronger associations with MVPA (F = 6.67, P ≤ 0.05) than total accelerometer counts (F= 0.87, P > 0.05). After adjusting for BMI and other relevant covariates, total accelerometer counts remained more strongly associated with cardiometabolic biomarkers than MVPA, with glycohemoglobin found to have no relationship with either PA variable. CONCLusIONs: Total accelerometer counts per day were more robustly associated with various cardiobiomarkers than MVPA. Thus, using total accelerometer counts per day may provide a better estimate of the strength of the relationship between PA and biomarkers. 1430 Board #22 May 30, 2:00 PM - 3:30 PM reliability of Peak stepping Cadences using Generalizability Theory Minsoo Kang, FACSM1 , Youngdeok Kim1 , David A. Rowe, FACSM2 . 1Middle Tennessee State University, Murfreesboro, TN. 2University of Strathclyde, Glasgow, United Kingdom. (No relationships reported) Recent studies have introduced the concept of using peak stepping cadences to quantify the intensity of ambulatory physical activity using step-count data. Although this is a novel approach and has demonstrated evidence of validity (Tudor-Locke et al., 2011), the reliability of peak stepping cadences associated with measurement conditions (e.g., a number of monitoring days) has not been addressed. PurPOsE: The purpose of this study was to establish the minimum number of days required for reliably estimating peak stepping cadence among adults. METhOds: Data from the 2005-2006 National Health and Nutrition Examination Survey were analyzed. A total of 1,300 adults (> 17 yr) with valid accelerometer step-count data (defined as 7 days with ≥ 10 hr wear time) were included. One-min, 30-min, and 60-min peak cadences were obtained across 7 days. Generalizability (G) theory with unbalanced two-facet nested design [i.e., person x (days: type of days)] was applied for each peak stepping cadence, with days nested within weekend or week Vol. 45 No. 5 Supplement S273 days. G-studies were performed to quantify the percentage of variance associated with each facet and interaction in the model. Follow-up D-studies were performed to determine the optimal combinations of number of days for weekend and week days to achieve a desirable reliability coefficient (G ≥ .80). rEsuLTs: The G-study showed that the relative magnitudes of variances attributed to differences between weekend and week days were relatively low (i.e., < 10%) for all peak stepping cadence measures; however, the D-study indicated that at least 1 weekend day should be included in the monitoring period in addition to 4 week days for 1-min peak cadence. For 30-min and 60-min peak cadences, at least 5 week days, or the combinations of 1 weekend day and 3 week days, or 2 weekend days and 2 week days would be necessary for reliable measures of peak stepping cadences. CONCLusIONs: This study provides empirical evidence of the number of required monitoring days to obtain reliable measures of peak stepping cadences based on a large, nationally-representative sample of US adults. The guidelines suggested in this study could be applied in future research as a reference for monitoring periods to obtain a reliable measure of peak stepping cadences. 1431 Board #23 May 30, 2:00 PM - 3:30 PM Physical activity and Energy Expenditure Measurements using sensewear armband, actiheart and actigraph in adults Dharini M. Bhammar, Brandon J. Sawyer, Wesley J. Tucker, Julian C. Baez, Glenn A. Gaesser, FACSM. Arizona State University, Phoenix, AZ. (No relationships reported) PurPOsE: Our aim was to estimate validity and reliability of the Sensewear Armband (SWA), Actiheart (AH) and Actigraph GT3X+ (AG) monitors for assessing physical activity (PA) levels and energy expenditure (EE) in adults. METhOds: Twenty-four adults (ages 29 ± 9 years; BMI: 23.9 ± 3.7 kg/m 2 ) underwent two identical 96-min exercise sessions, at least 24 h apart, including walking, cycling, arm cranking, and simulated activities of daily living. Each activity lasted 8 min with a 4-min seated rest period between activities. EE and PA levels were measured by indirect calorimetry using the Oxycon Mobile TM (OM) and estimated by four activity monitors: SWA (version 7.0), AH software versions 2.0 (AH 2.0) and 4.0 (AH 4.0) and AG. Linear mixed models and intraclass correlation coefficients (ICC) were used for statistical analysis. rEsuLTs: Total EE for 96-min was not significantly different between SWA and OM (P = 0.084). Total EE for AH 4.0, AH 2.0 and AG was significantly lower than OM by 62 ± 63 kcal (P = 0.015), 110 ± 47 kcal (P < 0.001) and 129 ± 60 kcal (P < 0.001) respectively. During steady-state periods (min 4 to 7 of each activity), only AH 4.0 accurately predicted walking EE (P = 1.000) and only AH 2.0 accurately predicted jogging EE (P = 0.704). AH 2.0 and AH 4.0 accurately estimated time spent in light and moderate PA, while only AH 2.0 accurately tracked time spent in vigorous PA. ICC analysis for OM and the four activity monitors showed high test-retest reliability for total EE (0.847 to 0.911) and for time spent in moderate to vigorous PA (MVPA; 0.888 to 0.999). ICC analysis for individual activities showed high test-retest reliability for walking at 3 mph (0.824 to 0.943), moderate-to-high reliability for walking at 4 mph, jogging at 5 mph, sweeping, and loading and unloading boxes (0.506 to 0.989), and low to moderate reliability for cycling, arm cranking and seated rest (0.005 to 0.738). CONCLusIONs: The SWA provides valid estimates for total EE, while the AH provides valid estimates for walking and jogging EE. All devices were unreliable for estimating EE for activities with minimal or variable accelerometer input such as cycling, arm cranking and activities of daily living. Additionally, only AH 2.0 accurately tracked time spent in MVPA suggesting that it may be the best device for tracking PA levels. supported by NIh grant r01 hL091006 1432 Board #24 May 30, 2:00 PM - 3:30 PM The Context of Physical activity in a representative sample of adults: Physical activity Measurement survey Bradley P. Peters, Youngwon Kim, Gregory J. Welk, FACSM, Sarah M. Nusser, Alicia L. Carriquiry, Miguel A. Calabro, Jungmin Lee. Iowa State University, Ames, IA. (No relationships reported) Numerous studies have sought to assess and evaluate levels of participation in physical activity (PA) in the population but few studies have provided contextual information about PA behaviors. This information is needed to better understand and promote PA in the population. PURPOSE: The purpose of this study was to provide insights on the purpose and location of accumulating PA in a representative sample of adults ages 21 to 70. METhOds: Data were obtained from the Physical Activity Measurement Survey (PAMS), a large field-based study of PA behavior conducted in Iowa. As part of the study, a representative sample of 1459 Iowa adults from four counties completed a telephone-administered 24hour PA recall (PAR). Participants were asked to report participation in different types of PA and to categorize each activity by both purpose (work, home/family, leisure, sports, education/volunteering) and location (work, home/ Abstracts were prepared by the authors and printed as submitted. Thursday, May 30, 2013
Thursday, May 30, 2013 S274 Vol. 45 No. 5 Supplement indoor, home/outdoor, transportation, and community). The proportion of time spent in moderate PA (MPA) and vigorous PA (VPA) was segmented by both purpose and location to understand the context of PA behavior. rEsuLTs: Approximately 83% of participants reported participating in some MPA (3-6 METS) on the previous day (mean = 156.4 +/- 32.6 min) while 21% reported participating in some VPA (> 6 METS) on the previous day (mean = 67.1 +/- 9.1 min). Participants reported that they performed 42% of their MPA while working, 37% for home needs, 14% for leisure, 5% for sports, and about 2% for volunteer / education tasks. Corresponding allocations for VPA were 33%, 27%, 14%, 25%, and 1%, respectively. The majority of MPA was performed at work (41%) followed by home indoor (26%), community settings (18%), home outdoor (13%), and in transportation (2%). The majority of VPA was also performed at work (33%), followed by community settings (32%), home indoor (21%), home outdoor (13%) and transportation (1%). Differences in allocations for purpose and location were noted when the sample was stratified by gender, age, and education CONCLusION: The results demonstrate the diverse nature of PA behavior and reveal that the majority of MPA and VPA is accumulated as part of daily living rather than for leisure or recreation. These results provide new insights about the settings and context of PA behavior in adults. 1433 Board #25 May 30, 2:00 PM - 3:30 PM Pedometer accuracy in Lower-Limb amputees John D. Smith 1 , Gary Guerra 2 . 1 Texas A&M University-San Antonio, San Antonio, TX. 2 Loma Linda University, Loma Linda, CA. (No relationships reported) The accuracy of the pedometer on lower limb amputees has yet to be explored. PurPOsE: To assess the accuracy of pedometers in lower limb amputees. METhOds: Thirty-nine participants (age = 48.0±14.1 yrs, ht = 172.7±9.3 cm, wt = 76.2±14.8 kg) had their height and weight measured and were then fitted with a pedometer belt around the waistline at the hip. They were fitted with three pedometers: a SW-701 spring-levered pedometer on the anterior mid-line of the right hip, an NL-800 pedometer just laterally to the SW-701, and an Omron HJ-112 on the outside of the NL- 800. They then engaged in one over-ground level walk for 200m at a self-selected pace at an indoor facility. At the end of each trial pedometer counts and actual step counts from a hand tally (AC) were recorded. A repeated measures ANOVA was used to determine significant differences between the counts. Single measure intraclass correlation (ICC) from a two-way random effects ANOVA was used to assess the agreement between AC and pedometer counts. Bland-Altman plots of AC vs. pedometer counts were also used to provide an indication of agreement between the measures. Pedometer error was calculated as [(pedometer steps-actual steps)/actual steps] * 100. rEsuLTs: Counts from the SW were significantly lower than AC (255.6±12.6 and 314.7±6.0 steps, p = .001) and counts from the NL and Omron were not significantly different from AC (p > .05). Agreement according to ICC was very low (0.26) for the SW, low for the NL (0.71) and high for the Omron (.98). Bland-Altman plots indicated agreement was strongest with the Omron and weakest with the SW. Percent error was greatest with SW at 18.0%, 2.1% for the NL, and least in the Omron (0.7%). CONCLusION: The results indicate that of the three pedometers tested, the Omron HJ-112 is the best pedometer for a person with a lower limb amputation wearing a prosthesis. 1434 Board #26 May 30, 2:00 PM - 3:30 PM association Between Cardiorespiratory Fitness and Metabolic risk Factors: an application of Quantile regression Wonwoo Byun1 , Jonathan A. Mitchell2 , Mitchell H. Whaley, FACSM1 , Leonard A. Kaminsky, FACSM1 . 1Ball State University, Muncie, IN. 2University of Pennsylvania, Philadelphia, PA. (No relationships reported) Higher cardiorespiratory fitness (CRF) has been associated with improved metabolic risk factors (MRFs). However, the evidence that exists largely assessed the mean values of MRFs and it is not known if the association between CRF and MRFs is uniform across the distributions of individual MRFs. PurPOsE: To determine the association between CRF and MRFs, using quantile regression, in a cross-sectional sample of men and women. METhOds: Participants were 429 adults (Female: 51%, Age: 49 ± 13 yrs), who enrolled in Adult Physical Fitness Program at Ball State University. CRF was assessed via maximal exercise testing with measured VO max (ml/kg/min). Participants were 2 also screened for fasting glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), mean arterial blood pressure (MAP), and waist circumference (WC). Quantile regression was used to examine the associations between CRF and MRFs at the 10th , 25th , 50th , 75th , and 90th percentiles of each MRF. The models were adjusted for age, gender, education level, smoking status, and medications for hypertension, diabetes, and dyslipidemia. rEsuLTs: CRF was inversely associated with GLU at the 50th (β= -0.34, 95% CI: MEDICINE & SCIENCE IN SPORTS & EXERCISE ® -0.52, -0.17), 75th (β= -0.53, 95% CI: -0.74, -0.32), and 90th (β= -0.70, 95% CI: -0.99, -0.40) percentiles; and inversely associated with MAP at the 50th (β= -0.21, 95% CI: -0.35, -0.07), 75th (β= -0.25, 95% CI: -0.44, -0.06), and 90th (β= -0.29, 95% CI: -0.49, -0.09) percentiles. Significant inverse associations between CRF and TG and WC were found at the 10th, 25th, 50th, 75th, and 90th percentiles; the magnitude of associations were gradually stronger at upper tails of TG distribution (from 10th percentile β= -1.14, 95% CI: -1.62, -0.66 to 90th percentile β= -3.74, 95% CI: -6.75, -0.72) and WC distribution (from 10th percentile β= -0.72, 95% CI: -1.10, -0.33 to 90th percentile β= -1.25, 95% CI: -1.48, -1.02). CRF was positively associated with HDL-C at 25th (β= 0.33, 95% CI: 0.21, 0.45), 50th (β= 0.28, 95% CI: 0.11, 0.45), 75th (β= 0.39, 95% CI: 0.07, 0.71), and 90th (β= 0.43, 95% CI: 0.09, 0.85) percentiles. CONCLusION: CRF was significantly associated with better MRFs profiles. The associations were stronger at upper tails of MRFs distributions indicating that improving CRF is especially important for those with poor MRF profiles. 1435 Board #27 May 30, 2:00 PM - 3:30 PM Type I and Type II Errors in Correlation analyses of Various sample sizes Duane Knudson, FACSM1 , Crawford Lindsey2 . 1Texas State University, San Marcos, TX. 2Tennis Warehouse, San Louis Obispo, CA. (No relationships reported) Studies are designed to balance pragmatic and theoretical issues. A common problem in sport and exercise science research is small sample sizes, which was recently noted in ACSM abstracts (Pyne & Hopkins, 2012). PurPOsE: This study documented the likely rates of type I and type II errors in correlation analyses in sample sizes common in sport and exercise science. METhOds: Data from mechanical testing of most all polyester tennis strings commercially available in 2012 using the protocol by Cross et al. (2000) were analyzed in this study. Variables measured were gauge, impact duration, string deflection, peak force, string stiffness, and energy return for 198 different strings tested at 276 N of tension. Three additional sample sizes were created by randomly down sampling to 99, 50, and 25. Correlations and partial correlations were calculated and compared to those observed for the complete sample (N=198). rEsuLTs: For the whole sample, 14 of 15 zero-order correlations were significant (p
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