Effects of a peer health coaching program on college student lifestyle behaviors.

Objective: Health coaching (HC) may promote healthy behaviors in incoming college students. Participants: 187 undergraduate students (76% female; age 18.6 ± 1.6) were recruited to participate in a peer HC program. Methods: Participants received four HC sessions in eight-weeks to work toward self-selected goals of physical activity (PA), diet, or stress management. PA was assessed using the International Physical Activity Questionnaire Short-Form, diet with the Eating Habits Confidence Survey, and stress with the Perceived Stress Scale. Results: Repeated measures ANOVAs investigated lifestyle behavior changes in 130 participants. Significant effects of time were reported, with a 39.6% gain in PA, 9.9% increase in dietary habit, and 16.3% decline in stress levels with moderate to large effect sizes (ηp2: 0.07-0.17; p< 0.01). Conclusion: The peer HC model promoted healthy lifestyles in college students. Additional research is needed to understand the effects of HC specifically for each goal, and student reactions to peer-led HC.

Feasibility of enhancing well-child visits with family nutrition and physical activity risk assessment on body mass index.

OBJECTIVE: Integration of behavioural risk assessment into well-child visits is recommended by clinical guidelines, but its feasibility and impact is unknown. METHODS: A quasi-experimental study evaluated the feasibility and effectiveness of risk assessment on body mass index (BMI) at 1-year follow-up. Children with assessments (intervention) were compared with those who did not complete assessments (non-respondent) and those who received standard care (non-exposed). RESULTS: Analyses included 10,647 children aged 2-9 years (2,724 intervention, 3,324 non-respondent and 4,599 non-exposed). Forty-five per cent of parents completed the assessments. Intervention and non-respondent groups differed in change in BMI z-score at 1 year by -0.05 (confidence interval [CI]: -0.08, -0.02; P = 0.0013); no difference was observed with non-exposed children. The intervention group had a smaller increase in BMI z-score (0.07 ± 0.63) than non-respondent group (0.13 ± 0.63). For children with normal weight at baseline, intervention versus non-respondent groups differed in BMI z-score change by -0.06 (CI: -0.10, -0.02; P = 0.0025). However, children with overweight at baseline in the intervention versus the non-exposed group differed in BMI z-score change (0.07 [CI: 0.02, 0.14]; P = 0.016). When analysed by age, results were similar for 2- to 5-year-olds, but no differences were found for 6- to 9-year-olds. CONCLUSION: Automating risk assessment in paediatric care is feasible and effective in promoting healthy weight among preschool but not older children.

Evidence for data missing at random in youth physical activity monitoring research.

This study examined whether or not activity monitor data collected as part of a typical 7-day physical activity (PA) measurement protocol can be expected to be missing at random. A total of 315 participants (9-18 years) each wore a SenseWear Armband monitor for 7 consecutive days. Participants were classified as "compliant" (86 boys and 124 girls) if they had recorded accelerometer data during 70% or more of the predefined awake time (7 AM-10 PM) on four different days; and "non-compliant" (44 boys and 51 girls) when not meeting these criteria. Linear mixed models were used to examine differences in energy expenditure (EE) levels by compliance across 10 different time periods. The results indicated that non-compliant girls were older (13.4 ± 2.9 vs. 12.2 ± 2.5) and taller (156.8 ± 10.3 vs. 152.8 ± 11.3) than their same gender compliant peers (P < .05). Comparisons of EE rates at segmented portions of the day revealed no differences between compliant and non-compliant groups (P ≥ .05). Differences in EE ranged from -0.32 kcal · kg-1 · h-1 (before school time) to 0.62 kcal · kg-1 · h-1 (physical education class) in boys and -0.39 kcal · kg-1 · h-1 (transportation from school) to 0.37 kcal · kg-1 · hour-1 (recess) in girls. The results showed that compliant and non-compliant individuals differed in a few demographic characteristics but exhibited similar activity patterns. This suggests that data were considered to be missing at random, but additional work is needed to confirm this observation in a representative sample of children using other types of activity monitors and protocols.

Assessing physical activity intensity by video analysis.

Assessing physical activity (PA) is a challenging task and many different approaches have been proposed. Direct observation (DO) techniques can objectively code both the behavior and the context in which it occurred, however, they have significant limitations such as the cost and burden associated with collecting and processing data. Therefore, this study evaluated the utility of an automated video analysis system (CAM) designed to record and discriminate the intensity of PA using a subject tracking methodology. The relative utility of the CAM system and DO were compared with criterion data from an objective accelerometry-based device (Actigraph GT3X+). Eight 10 year old children (three girls and five boys) wore the GT3X+ during a standard basketball session. PA was analyzed by two observers using the SOPLAY instrument and by the CAM system. The GT3X+ and the CAM were both set up to collect data at 30 Hz while the DO was performed every two minutes, with 10 s of observation for each gender. The GT3X+ was processed using cut points by Evanson and the outcome measure was the percentage of time spent in different intensities of PA. The CAM data were processed similarly using the same speed thresholds as were used in establishing the Evenson cut-off points (light: <2 mph; walking: 2-4 mph; very active: >4 mph). Similar outcomes were computed from the SOPLAY default analyses. A chi-square test was used to test differences in the percentage of time at the three intensity zones (light, walking and very active). The Yates' correction was used to prevent overestimation of statistical significance for small data. When compared with GT3X+, the CAM had better results than the SOPLAY. The chi-square test yielded the following pairwise comparisons: CAM versus GT3x+ was χ(2) (5) = 24.18, p < .001; SOPLAY2 versus GT3x+ was χ(2) (5) = 144.44, p < .001; SOPLAY1 versus GT3x+ was χ(2) (5) = 119.55, p < .001. The differences were smaller between CAM and GT3x+, suggesting that the video tracking system provided better agreement than DO. The small sample size precludes a definitive evaluation but the results show that the CAM video system may have promise for automated coding of physical activity behavior.

Objective and subjective measurement of energy expenditure in older adults: a doubly labeled water study.

BACKGROUND/OBJECTIVES: Objective and subjective measurement instruments have been used to estimate energy expenditure (EE) as alternatives to the doubly labeled water (DLW) methodology, but their relative validity for older adults remains uncertain. The purpose of this study was to validate an objective monitor (SenseWear Mini Armband) and a self-report instrument (7-Day Physical Activity Recall, 7D-PAR) relative to the DLW under free-living conditions in older adults. SUBJECTS/METHODS: Twenty-nine older adults (60-78 years) each wore the Mini for 14 consecutive days and completed two 7D-PARs after each week. For each measurement method, activity EE (AEE) was calculated as total EE (TEE)­measured resting metabolic rate (RMR)­diet induced thermogenesis (10% of TEE). TEE and AEE from the Mini and 7D-PAR were each compared with values from the DLW. RESULTS: Equivalence testing indicated that estimates of TEE from the Mini and the 7D-PAR were statistically equivalent to those measured with DLW; however, differences were evident for estimates of AEE. The Mini had smaller mean absolute percent error for TEE (8.0%) and AEE (28.4%) compared with the 7D-PAR (13.8 and 84.5%, respectively) and less systematic bias in the estimates. CONCLUSIONS: The Mini and 7D-PAR provided reasonably valid estimates of TEE but large errors in estimating AEE. The Mini and 7D-PAR have the potential to accurately estimate TEE for older adults.

The criterion-referenced validity of the FITNESSGRAM Trunk-Extension test.

AIM: The purpose of this study was to test utility of different fitness field tests to predict self-reported low-back pain (LBP) in youth. METHODS: A sample of 376 school aged (4th through 10th grade students) first completed a survey instrument designed to assess past events (and degree) of LBP. On subsequent days, participants completed a battery of different muscular fitness tests including the current FITNESSGRAM trunk extension test. Receiver operator characteristic curves (ROC) were used to test the utility of single and combined scores from various tests to predict self-reported LBP. RESULTS: The results show that the trunk extension test used in FITNESSGRAM has poor utility for predicting self-reported LBP. In girls, the sit-and-reach, the static curl-up test, and a composite score using static and dynamic curl-up scores had the highest Area Under the Curve (AUC=0.80, 0.71, and 0.79, respectively). Sensitivity (Se) values ranged from 75.0-85.7 while Specificity (Sp) ranged from 59.1-81.4. In boys, the dynamic curl-up test alone was the best predictor of LBP in high-school boys (AUC=0.75, Sensitivity=75.0 and Specificity=74.1). CONCLUSION: The reasonable Sensitivity values from the ROC analyses indicate that individual and aggregate indicators of musculoskeletal fitness can potentially identify girls that had LBP in the past. However, the low values for Specificity indicates that girls with "No LBP" can be misclassified as being at risk for LBP. The current FITNESSGRAM trunk extension test was not able to discriminate between students with and without LBP therefore, the utility of this test for fitness assessment in youth should be revisited.

Associations of physical activity with muscular fitness in adolescents.

The aim of this study was to examine the associations between objectively assessed physical activity (PA) and muscular fitness (MF) in adolescents, and to determine whether only resistance training exercise is associated with the increase of MF. A sample of 211 Spanish adolescents (105 girls) wore the ActiGraph monitor for 7 days. Participants also completed a fitness evaluation including three MF tests (handgrip strength, a 60-s abdominal test and a standing broad jump), a cardiorespiratory fitness assessment and anthropometric measures. A standardized MF score was computed using the three muscular tests. Adolescent participation in resistance training was self-reported. Linear regression showed that only vigorous PA was significantly (P=0.041) and positively (ß=0.133) associated with MF after adjustment by gender, age, pubertal status, BMI, and cardiorespiratory fitness. Significant differences in MF were found between youth in the lowest and upper tertiles of vigorous PA. Moreover, adolescents who were involved in resistance training had significantly higher MF scores compared with youth in low and medium tertiles of vigorous PA, but no significant differences were found when compared with non-lifters from the highest tertile of vigorous PA (P=0.356). These findings suggest that vigorous activity may be associated with indicators of MF in adolescents.

Preliminary evidence of physical activity levels measured by accelerometer in Spanish adolescents: the AFINOS Study.

BACKGROUND: It is necessary to know the levels of physical activity (PA) for a better understanding of the development of chronic diseases in youth. The aim of this study was to assess levels of total PA and time spent in different PAintensities in Spanish adolescents by accelerometer. METHODS: A sub-sample of 214 healthy Spanish adolescents (107 females), aged 13-16 years, enrolled in the AFINOS Study was selected for this study. Participants wore the ActiGraph GT1M accelerometer for 7 consecutive days. Total PA and time spent in sedentary, light, moderate, vigorous, and moderate to vigorous physical activity (MVPA) was estimated using the accelerometer. Adiposity was measured in the sample using sum of 6 skinfoldthickness, waist circumference and BMI. RESULTS: Adolescent boys were engaged in higher levels of total PA, moderate PA, vigorous PA and MVPA than adolescent girls, whereas girls were engaged in higher levels of light PA. Differences between age groups showed that the 15-16 years group did more total PA (P = 0.008) than the 13-14 years group. Adolescents with highest levels of body fat were less active and spent less time in vigorous PA and MVPA than adolescents with less body fat. Among the current sample, 71.1% of the adolescents (82.2% adolescent boys and 60.7% adolescent girls) reached the recommendation of > or = 60 min in MVPA. CONCLUSIONS: Although these findings suggest that Spanish adolescents have similar PA levels than other European adolescents, further cross-sectional and longitudinal studies must assess PA levels in free-living conditions in Spanish children and adolescents using objective methods such as accelerometers, heart rate monitors and pedometers.

Comparison of the ActiGraph accelerometer and Bouchard diary to estimate energy expenditure in Spanish adolescents.

INTRODUCTION: The aim of this study was to evaluate the agreement between the ActiGraph accelerometer and the Bouchard diary to estimate energy expenditure (EE) in a Spanish adolescent population. METHODS: Sixty-one Spanish adolescents, aged 12-16 years, were recruited for this study. The Bouchard diary and the ActiGraph were administered for 3 consecutive days. EE estimated by the Bouchard diary was calculated using 2 different MET category values (BD-Bouchard and BD-Bratteby). EE estimated by the ActiGraph was calculated using 3 predictive equations (AC-Trost, AC-Freedson, and AC-Ekelund). Participants with complete 24 h data were also analyzed to control the possible loss of accuracy. Agreement was examined by Pearson and concordance correlations, paired t-test, and Bland-Altman method. RESULTS: Thirty-seven adolescents were included in the analyses with complete 72 h. Relationships between EE calculated by the Bouchard diary and the ActiGraph were high (ranged: r = 0.61-0.78). Concordance correlations were moderate (r c = 0.60) by BD-Bratteby and AC-Trost, and low using BD-Bratteby and AC-Ekelund (r c = 0.15). EE estimated by BD-Bratteby and AC-Trost also showed no significant differences (P> 0.05) and absolutely agree 0.0 +/- 5.0 MJ/d (95% confidence interval: +/- 0.90 MJ/d), but with wide limits of agreement (+/- 9.80 MJ/d). Relationships were higher and differences were smaller in the 72 h sample than in the 24 h sample. CONCLUSIONS: The Bouchard diary and the ActiGraph showed high relationships, moderate concordance, and large differences to estimate EE in Spanish adolescents. Advantages, disadvantages, and agreements between both instruments must be taking into consideration for health-related research.

Combined influence of physical activity and television viewing on the risk of overweight in US youth.

OBJECTIVE: The purpose of this study was to examine the combined influence of physical activity (PA) and television viewing (TV) on the risk of overweight in US youth ages 14-18 years. RESEARCH DESIGN AND METHODS: Cross-sectional data from a nationally representative sample of approximately 13,600 US high school students participating in the 2001 Centers for Disease Control and Prevention Youth Risk Behavior Survey (YRBS) were examined. Participants were cross-tabulated into nine PA-TV groups according to the level of moderate (MPA) or vigorous PA (VPA) (low: < or = 2 days per week; moderate: 3-5 days per week; high: 6-7 days per week) and TV (low: < or = 1 h per day; moderate: 2-3 h per day; high: > or = 4 h per day). The referent group was the low TV/high PA group. The body mass index was used to determine overweight and obesity based on the International Obesity Task Force reference values. Logistic regression was used to examine the influence of TV and PA on the odds of overweight in boys and girls, while controlling for age and ethnicity. RESULTS: Boys and girls watching low levels of TV did not have increased odds of overweight regardless of PA level with the exception of girls with low TV/low VPA (odds ratio (OR)=1.48). Girls who watched moderate and high levels of TV had increased odds of overweight at any level of MPA or VPA (OR 1.24-3.11). In girls, the odds of overweight increased in a graded manner across PA levels for both the moderate and high levels of TV. Girls with high TV/low VPA had the highest odds of overweight (OR=3.11). In general, most of the associations were stronger in girls compared to boys. CONCLUSION: The results highlight the importance of considering both PA and TV as risk factors for overweight in adolescents.

Physical activity staging distribution: establishing a heuristic using multiple studies.

The purpose of this study was to identify the population prevalence across the stages of change (SoC) for regular physical activity and to establish the prevalence of people at risk. With support from the National Institutes of Health, the American Heart Association, and the Robert Wood Johnson Foundation, nine Behavior Change Consortium studies with a common physical activity SoC measure agreed to collaborate and share data. The distribution pattern identified in these predominantly reactively recruited studies was Precontemplation (PC) = 5% (+/- 10), Contemplation (C) = 10% (+/- 10), Preparation (P) = 40% (+/- 10), Action = 10% (+/- 10), and Maintenance = 35% (+/- 10). With reactively recruited studies, it can be anticipated that there will be a higher percentage of the sample that is ready to change and a greater percentage of currently active people compared to random representative samples. The at-risk stage distribution (i.e., those not at criteria or PC, C, and P) was approximately 10% PC, 20% C, and 70% P in specific samples and approximately 20% PC, 10% C, and 70% P in the clinical samples. Knowing SoC heuristics can inform public health practitioners and policymakers about the population's motivation for physical activity, help track changes over time, and assist in the allocation of resources.

The utility of the Digi-walker step counter to assess daily physical activity patterns.

PURPOSE: The Digi-Walker step counter is a promising and cost-effective tool to measure physical activity under free-living conditions. Two specific studies were conducted to evaluate the number of steps required to meet current physical activity guidelines. METHODS: Thirty-one adults (17 men, 14 women) served as participants. In study 1, we determined the number of steps to complete a mile under two different conditions and three paces. In study 2, we conducted a field trial to examine the relationship between daily step counts and other indices of physical activity. Participants in this study wore a Digi-Walker for 2 consecutive weeks and completed the 7-d physical activity recall (PAR) after each week. RESULTS: In study 1, there were no differences in step counts by site, but steps were inversely related to pace, with values ranging from 1330 to 1996. Individual step counts at a specific pace were negatively correlated with height, weight, leg length, and stride length and were positively correlated with body fatness. In study 2, participants had average daily step counts of 11,603 when structured vigorous activity was included and 8265 when only light and moderate activity were measured. Modest correlations were found between step counts and estimated energy expenditure. Similar correlations were observed when step counts were related to minutes of activity per day and minutes of sitting per day. CONCLUSIONS: Pedometers provide a useful indicator of daily step counts but variability in activity patterns make it difficult to establish step count guidelines that correspond with other public health guidelines.

A comparative evaluation of three accelerometry-based physical activity monitors.

PURPOSE: Accelerometry-based activity monitors offer promise for the assessment of free-living physical activity. They provide an objective record of frequency, intensity, and duration of physical activity with minimal burden on participants. The purpose of this study was to evaluate the absolute and relative validity of three contemporary activity monitors (Computer Science and Applications, Inc. [CSA], Tritrac, and Biotrainer) under both laboratory and field conditions. METHODS: Fifty-two participants completed two 30-min choreographed routines designed to simulate a variety of lifestyle physical activities. Three different treadmill paces were completed in both routines to evaluate reliability and validity under laboratory conditions. Six different lifestyle activities were also examined to evaluate the validity of the monitors under field conditions. During each routine, the activity levels of participants were monitored with the three activity monitors as well as by indirect calorimetry systems. RESULTS: The correlations between the monitors and measured VO2 were higher for treadmill activity (mean r = 0.86) compared with lifestyle activity (mean r = 0.55). Correlations among the different monitors were high for both treadmill (r = 0.86) and lifestyle activities (r = 0.70), suggesting that the monitors provide similar information under both conditions. Under laboratory conditions, the CSA yielded accurate predictions of energy expenditure (EE), whereas the Tritrac and Biotrainer tended to overestimate the EE (101-136% of measured value). The Tritrac, however, was found to have less error in individual estimates of EE. Under field conditions, all of the monitors underestimated EE (range: 42-67% of measured value). CONCLUSION: The observed differences among the monitors were attributed primarily to differences in the accuracy of the calibration equations rather than to the monitors themselves. Further research is needed to better understand how to use these devices for field-based assessments of physical activity.

Measurement issues in the assessment of physical activity in children.

This paper reviewed the nature of children's physical activity patterns and how the unique nature of children can impact the assessment of physical activity. To accurately assess children's activity patterns, an instrument must be sensitive enough to detect, code, or record sporadic and intermittent activity. Care also must be used to select criterion measures that reflect appropriate physical activity guidelines for children. A number of different measurement approaches have been described for assessing children's activity, but no specific method can be identified as the best option for all studies. Selection of an appropriate instrument depends on the specific research question being addressed as well as the relative importance of accuracy and practicality (Baranowski & Simons-Morton, 1991). For example, accurate measures of energy expenditure using doubly-labeled water, indirect calorimetry, or heart rate calibration equations may be needed for certain clinical studies, but the cost and inconvenience would make them impractical for field-based assessments on larger samples. The "accuracy-practicality" trade-off presents a more challenging predicament with children than for adults. In adults, a number of self-report instruments have been found useful for large epidemiological studies or interventions where less precision is needed. Because of developmental differences, especially in ability to think abstractly and perform detailed recall (Going et al., 1999; Sallis, 1991), children are less likely to make accurate self-report assessment than adults. Though self-report methods are still likely to be a principal source of information for many studies, other approaches (or the use of combined measures) may be needed to better characterize children's activity levels. While objective instruments (e.g., direct observation or activity monitoring) require more time and resources than self-report, there are options available to simplify data collection. One approach may be to focus assessments on key times or places that allow children to be active. The time after school, for example, appears to be a critical period that defines their propensity for physical activity (Hager, 1999). Monitoring of entire groups for discrete periods of time (e.g., recess or physical education) may also be useful to understand variability in activity patterns since children would all be exposed to the same stimulus or opportunity to be active. Proxy measures may also be useful in studying activity in children. For example, several studies (Baranowski, Thompson, DuRant, Baranowski, & Puhl, 1993; Sallis et al., 1993) have demonstrated that time spent outside is strongly predictive of activity in children. Involvement in community sports programs may also be a useful proxy measure as sports programs have been found to account for approximately 55-65% of children's moderate to vigorous activity (Katzmarzyk & Malina, 1999). Another option for improving assessments in children is to employ multiple measures of physical activity. A number of studies (Coleman, Saelens, Wiedrich-Smith, Finn, & Epstein, 1997; McMurray et al., 1998; Sallis et al., 1998; Simons-Morton et al., 1994) have reported differences in levels of activity when activity monitors were compared with self-report data. The method of measurement has also been shown to influence the results of studies on the determinants of physical activity in children (Epstein, Paluch, Coleman, Vito, & Anderson, 1996). While we do not currently know which measure is most accurate, reporting the results with different instruments provides a more complete description of children's activity and permit a triangulation of outcomes. In summary, there remains no single way of obtaining a highly accurate account of physical activity or energy expenditure in children. The nature of children's movement patterns, the various types of activities engaged in, and the inherent limitations of each assessment tool limit the ultima

Effects of physical activity interventions in youth. Review and synthesis.

INTRODUCTION: Physical inactivity has been identified as an important public health concern for youth. School and community settings can be important infrastructures for promoting physical activity (PA). This paper reviews studies of physical activity in school and community settings among preschool through college-aged persons to determine characteristics and effects of interventions. Studies in progress are included. METHODS: Studies from 1980 to 1997 testing physical activity interventions in schools and community settings were identified by computerized search methods and reference lists of published reviews. Studies needed to have used a quantitative assessment of PA, used a comparison or control group, included participants who were preschool through college age, and be conducted in the United States or foreign school or community settings. Significance of effects was examined overall and for various types of interventions. RESULTS: Twenty-two school-based studies were reviewed, 14 completed and 8 in progress. Three studies were in countries other than the United States. The 8 studies in progress were all in the United States. Only 7 community studies were reviewed, all in the United States. Four studies were in progress. Several community studies involved a high percentage of African-American or Hispanic youth and their families. Studies showing the best results used randomized designs, valid and reliable measurements, and more extensive interventions. Some follow-up results showed PA was sustained after interventions ended. CONCLUSIONS: The collection of school and community studies is limited for several age groups with none below third grade and only three at college age. There are few community studies. The most is known about upper-elementary-age-students, including the first multicenter randomized trial to report significant results for increasing moderate to vigorous physical activity (MVPA) in physical education (PE) and increase vigorous PA outside of school. A number of older study designs were weak and assessments less than optimal, but studies in progress are stronger. Special attention is needed for girls, middle schools, and community settings for all youth. More objective assessments are needed for measuring PA outside of school and in younger children, since they cannot provide reliable self-report.

The validity of the Tritrac-R3D Activity Monitor for the assessment of physical activity in children.

The purpose of this study was to evaluate the validity of the Tritrac-R3D Activity Monitor, a new instrument designed to improve assessments of physical activity. Comparisons were made with a heart rate monitor and with a Caltrac Activity Monitor. Thirty-five children (ages 9-11 years) were monitored on 3 different school days with all 3 instruments. The Tritrac was moderately correlated with the heart rate monitor (r = .58) and highly correlated with the Caltrac monitor (r = .88). By taking advantage of the minute-by-minute timing capability of the Tritrac and the heart rate monitors, it was discovered that the correlations between these instruments were highest during free play situations (lunch/recess, recess, after school) and were lower when activity was more limited (class time) or structured (physical education). The ability of the Tritrac to assess activity on a minute-by-minute basis may greatly enhance its overall utility.

Anaerobic and aerobic power in arms and legs of elite senior wrestlers.

UNLABELLED: The purpose of this study was to characterize anaerobic power and aerobic power (power at peak oxygen uptake, or peak VO2) of elite senior (post-collegiate) wrestlers. Subjects (n = 14) from the U.S. national senior freestyle and Greco Roman wrestling teams were evaluated at separate tests using cycle ergometry for peak VO2 of the arms, peak VO2 of the legs, arm anaerobic peak power, and leg anaerobic peak power. Power output at peak VO2 was recorded for each test and compared to the anaerobic power of the appropriate body segment. The results (mean +/- SD) showed that wrestlers produced 2.3 +/- 0.4 W.kg body weight-1 at arm peak VO2 (43.7 +/- 4.6 ml.kg body weight-1. min-1), 4.2 +/- 0.5 W.kg body weight-1 at leg peak VO2 (50.9 +/- 5.1 ml.kg body weight-1.min-1), arm PP of 7.7 +/- 1.0 W.kg body weight-1, and leg PP of 10.5 +/- 1.7 W.kg body weight-1. Comparing power output during anaerobic and aerobic tests, the subjects performed at 3.4 +/- 0.6 times their peak oxygen uptake during arm anaerobic ergometry, whereas leg anaerobic peak power was 2.7 +/- 0.4 times the power at peak oxygen uptake for the legs (p < 0.05 for difference between ratios). CONCLUSION: relative to aerobic power, elite senior wrestlers may produce power anaerobically in the upper body at significantly higher levels than in the lower body.