Biomedical subjects

A M Nevill

Publications and source records attributed to A M Nevill.

At least 55 records · Page 3Linked to original sources

PubMed1996

Scaling peak VO2 for differences in body size.

This paper examined the influence of different statistical modeling techniques on the interpretation of peak VO2 data in groups of prepubertal, circumpubertal, and adult males (group 1M, N = 29; group 2M, N = 26; group 3M, N = 8) and females (group 1F, N = 33; group 2F, N = 34; group 3F, N = 16). Conventional comparisons of the simple per-body-mass ratio (ml.kg-1.min-1) revealed no significant differences between the three male groups (P < 0.05). In females, a decline in VO2 between group 2F and 3F was observed (P < 0.05). Both linear and log-linear (allometric) models revealed significant increases across all three male groups for peak VO2 adjusted for body mass (P < 0.05). In females these scaling models identified a significantly lower peak VO2 in group 1F versus groups 2F and 3F (P < 0.05). Based upon the common mass exponent identified (b = 0.80, SE = 0.04), power function ratios (y.mass0.80) were generated and the logarithms of these compared. Again, results indicated a progressive increase in peak VO2 across groups 1M to 3M (P < 0.05) and an increase between groups 1F and 2F (P < 0.05). Incorporating stature into the allometric equation reduced the mass exponent to 0.71 (SE = 0.06) with the contribution of the stature exponent shown to be 0.44 (SE = 0.20). These results indicate that conventional ratio standards do not adequately account for body size differences when investigating functional changes in peak VO2.

Adolescent↗

PubMed1995

Recovery of power output and muscle metabolites following 30 s of maximal sprint cycling in man.

1. The recovery of power output and muscle metabolites was examined following maximal sprint cycling exercise. Fourteen male subjects performed two 30 s cycle ergometer sprints separated by 1.5, 3 and 6 min of recovery, on three separate occasions. On a fourth occasion eight of the subjects performed only one 30 s sprint and muscle biopsies were obtained during recovery. 2. At the end of the 30 s sprint phosphocreatine (PCr) and ATP contents were 19.7 +/- 1.2 and 70.5 +/- 6.5% of the resting values (rest), respectively, while muscle lactate was 119.0 +/- 4.6 mmol (kg dry wt)-1 and muscle pH was 6.72 +/- 0.06. During recovery, PCr increased rapidly to 65.0 +/- 2.8% of rest after 1.5 min, but reached only 85.5 +/- 3.5% of rest after 6 min of recovery. At the same time ATP and muscle pH remained low (19.5 +/- 0.9 mmol (kg dry wt)-1 and 6.79 +/- 0.02, respectively). Modelling of the individual PCr resynthesis using a power function curve gave an average half-time for PCr resynthesis of 56.6 +/- 7.3 s. 3. Recovery of peak power output (PPO), peak pedal speed (maxSp) and mean power during the initial 6 s (MPO6) of sprint 2 did not reach the control values after 6 min of rest, and occurred in parallel with the resynthesis of PCr, despite the low muscle pH. High correlations (r = 0.71-0.86; P < 0.05) were found between the percentage resynthesis of PCr and the percentage restoration of PPO, maxSp and MPO6 after 1.5 and 3 min of recovery. No relationship was observed between muscle pH recovery and power output restoration during sprint 2 (P > 0.05). 4. These data suggest that PCr resynthesis after 30 s of maximal sprint exercise is slower than previously observed after dynamic exercise of longer duration, and PCr resynthesis is important for the recovery of power during repeated bouts of sprint exercise.

Adenosine Triphosphate↗

PubMed1995

Body mass index: a measure of fatness or leanness?

The relationship between body fat and stature-adjusted weight indices was explored. Assuming the term height2 is a valid indicator of a subject's lean body mass, height2/weight was shown to be an accurate measure of percentage lean body mass and, as such, a better predictor of percentage body fat than the traditional body mass index (BMI; weight/height2). The name, lean body mass index (LBMI), is proposed for the index height2/weight. These assumptions were confirmed empirically using the results from the Allied Dunbar National Fitness Survey (ADNFS). Using simple allometric modelling, the term heightp explained 74% of the variance in lean body mass compared with less than 40% in body weight. For the majority of ADNFS subjects the fitted exponent from both analyses was approximately p = 2, the only exception being the female subjects aged 55 years and over, where the exponent was found to be significantly less than 2. Using estimates of percentage body fat as the dependent variable, regression analysis was able to confirm that LBMI was empirically, as well as theoretically, superior to the traditional BMI. Finally, when the distributional properties of the two indices were compared, BMI was positively skewed and hence deviated considerably from a normal distribution. In contrast, LBMI was found to be both symmetric and normally distributed. When height and weight are recorded in centimetres and kilograms respectively, the suggested working normal range for LBMI is 300-500 with the median at 400.

Actuarial Analysis↗

PubMed1995

Scaling, normalizing, and per ratio standards: an allometric modeling approach.

The practice of scaling or normalizing physiological variables (Y) by dividing the variable by an appropriate body size variable (X) to produce what is known as a "per ratio standard" (Y/ X), has come under strong criticism from various authors. These authors propose an alternative regression standard based on the linear regression of (Y) on (X) as the predictor variable. However, if linear regression is to be used to adjust such physiological measurements (Y), the residual errors should have a constant variance and, in order to carry out parametric tests of significance, be normally distributed. Unfortunately, since neither of these assumptions appear to be satisfied for many physiological variables, e.g., maximum oxygen uptake, peak and mean power, an alternative approach is proposed of using allometric modeling where the concept of a ratio is an integral part of the model form. These allometric models naturally help to overcome the heteroscedasticity and skewness observed with per ratio variables. Furthermore, if per ratio standards are to be incorporated in regression models to predict other dependent variables, the allometric or log-linear model form is shown to be more appropriate than linear models. By using multiple regression, simply by taking logarithms of the dependent variable and entering the logarithmic transformed per ratio variables as separate independent variables, the resulting estimated log-linear multiple-regression model will automatically provide the most appropriate per ratio standard to reflect the dependent variable, based on the proposed allometric model.

Exercise↗

PubMed1994

Menstrual-cycle effects on mood and perceptual-motor performance.

Fifty-four participants took part in a study to evaluate the effects of the menstrual cycle on mood and the performance of a perceptual-motor task. The task involved tracking a randomly-moving circle on a computer screen with a joystick-controlled dot. Women were tested on three occasions, during the premenstrual, menstrual and ovulatory phases. Each testing session involved completing the Menstrual Distress Questionnaire, the monopolar Profile of Mood States questionnaire and performing five, timed trials on the task. A control group of male participants completed the Profile of Mood States questionnaire on three separate occasions over 28 days according to a pseudo-menstrual cycle experimental protocol. Results revealed that females experienced low energy and impaired cognitive function, both premenstrually and during menstruation. Task performance did not vary with menstrual-cycle phase, suggesting that they either tried to compensate for a lack of well being, or that negative mood was of insufficient magnitude to manifest a performance change. The findings suggest that when the purpose of an assessment is disguised, typically-reported menstrual-cycle and mood-related effects on performance are not observed reliably.

Adult↗

PubMed1994

Accumulated oxygen deficit and short-distance running performance.

Recent studies have suggested that determining the accumulated oxygen deficit (AOD), in units of oxygen equivalents per kilogram body mass (ml O2 Eq.kg-1), during a short exhaustive run, may represent a non-invasive measure of anaerobic metabolism. However, there is little information either on the reproducibility of the laboratory determination or its relationship with human performance. The purpose of the present investigation was to determine the reproducibility of AOD during inclined treadmill running (study 1), and to examine its relationship with short-distance running performance (study 2). Twelve volunteers (11 males, 1 female) took part in study 1 and AOD was determined (relative exercise intensity approximately 120% VO2 max) on two separate occasions at a treadmill inclination of 10.5%; the laboratory-measured AOD values were 65.2 +/- 10.9 vs 66.3 +/- 12.5 ml O2 Eq.kg-1, respectively (r = 0.94, P < 0.01). A second subject group (10 males, 4 females) undertook study 2, which investigated AOD values and track times over 100, 400 and 800 m. The mean calculated AOD value was 66.1 +/- 12.0 ml O2 Eq.kg-1, and the average track times were 13.6 +/- 1.3, 60.9 +/- 6.8 and 138.8 +/- 18.5 s for the 100, 400 and 800 m, respectively. The r-values for the relationship between AOD and track time were -0.88, -0.82 (P < 0.01) and -0.61 (P < 0.05) for the 100, 400 and 800 m, respectively. The results of the present study suggest that the AOD (ml O2 Eq.kg-1) is a unique and reproducible physiological characteristic which is strongly correlated with sprint capacity.

Adult↗

PubMed1994

The need to scale for differences in body size and mass: an explanation of Kleiber's 0.75 mass exponent.

When modeling intraspecific relationships between selected measurements (Y) for differences in body mass (m) using the allometric equation Y = amb (where a is a constant and b is the exponent parameter), various studies have reported exponents greater than the anticipated 2/3, often closer to the exponent 0.75 identified by Kleiber. A possible explanation for these exponents is proposed based on the findings of Alexander et al. (J. Zool. Lond. 194: 539-552, 1981), who observed that, within a variety of species, larger mammals have a greater proportion of proximal leg muscle mass in relation to their body mass, m1.1. If subjects that are used to record Y exhibit a similar disproportionate increase in muscle mass with body size, then the allometric equation is likely to identify both a contribution proportional to the subject's body mass and a contribution from the disproportionate increase in muscle mass within the group. These confounding influences in Y can be identified separately by incorporating a body size parameter as well as a mass component in the allometric equation. The factor "body size" can be introduced either by partitioning the sample into discrete subgroups according to body size or, in studies involving human subjects, by introducing height as a continuous covariate. In both studies reported involving human maximal exercise, these methods were able to identify a systematic increase in Y with body size, leaving the subject's body mass component, found to be proportional to m2/3, independent of body size.

Animals↗

PubMed1993

Effect of diet on performance during recovery from intermittent sprint exercise.

Eighteen games players (9 males, 9 females) performed 30 maximum 6-sprints on a non-motorized treadmill. Each sprint was preceded by a 60-s jog at 40% maximum speed and was followed by a 54-s walk at 20% maximum speed. Thus, the entire test was of 60-min duration. The subjects were then randomly assigned to three groups and repeated the 1-h test 24 h later after consuming either a high, normal or low carbohydrate diet (79 +/- 3, 47 +/- 8, 12 +/- 1% CHO, respectively). During trial 1, mean power output declined from 653 +/- 131 to 600 +/- 158 W during the 30 sprints (P < 0.01) and power output was lower during trial 2 than during trial 1 (n = 18, P < 0.01). During trial 2, there were no differences in sprint performance between the dietary groups for the exercise test as a whole (trial 2 mean power lower than trial 1 by 0.2, 0.5 and 5.0% for the high, normal and low CHO groups, respectively; N.S.), but if only the first nine sprints are considered, then the high CHO group performed better than the low CHO group (P < 0.05). Blood lactate and glucose concentrations were lower during trial 2 than trial 1 by 4.5, 13.8 and 29.0% (lactate) and 14.9, 11.3 and 35.8% (glucose) for the high, normal and low CHO groups, respectively (both P < 0.01). Thus, both the metabolic responses to, and the performance of, maximum intermittent exercise were reduced when the test was repeated after 24 h recovery.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗

PubMed1992

Modelling bivariate relationships when repeated measurements are recorded on more than one subject.

This paper examines the problems of modelling bivariate relationships when repeated observations are recorded for each subject. The statistical methods required to test for a common group model were introduced using an example from exercise physiology, where the oxygen cost of running at four different speeds was recorded for a group of 30 recreationally active males. When data for each subject were studied individually, both the plots and correlations suggested the relationship to be linear. Hence, the homogeneity of the subjects' regression lines was compared using the appropriate ANOVA test. The analysis revealed a significant difference in the slopes and intercepts of the lines, thus precluding the use of a single linear model to represent the group. If the subjects were divided into two groups according to the median maximum oxygen consumption (VO2max), a multivariate analysis of variance of the slope and intercept parameters helped to explain some of this heterogeneity (P less than 0.05). However, for physiological rather than statistical reasons, it was necessary to re-analyse the data without the fourth running speed. The revised analysis suggested that the subjects' lines would be better modelled with a common slope but separate intercepts. As before, by dividing the subjects into two groups according to the median VO2max score, a simple t-test indicated that differences in the subjects' intercept parameters were not significant (P = 0.08). Notwithstanding the relatively homogeneous nature of the 30 subjects in terms of VO2max, the statistical methods showed that differences in running economy are, to some extent, dependent on VO2max.

Adolescent↗

PubMed1992

Scaling physiological measurements for individuals of different body size.

This paper examines how selected physiological performance variables, such as maximal oxygen uptake, strength and power, might best be scaled for subject differences in body size. The apparent dilemma between using either ratio standards or a linear adjustment method to scale was investigated by considering how maximal oxygen uptake (l.min-1), peak and mean power output (W) might best be adjusted for differences in body mass (kg). A curvilinear power function model was shown to be theoretically, physiologically and empirically superior to the linear models. Based on the fitted power functions, the best method of scaling maximum oxygen uptake, peak and mean power output, required these variables to be divided by body mass, recorded in the units kg 2/3. Hence, the power function ratio standards (ml.kg-2/3.min-1) and (W.kg-2/3) were best able to describe a wide range of subjects in terms of their physiological capacity, i.e. their ability to utilise oxygen or record power maximally, independent of body size. The simple ratio standards (ml.kg-1.min-1) and (W.kg-1) were found to best describe the same subjects according to their performance capacities or ability to run which are highly dependent on body size. The appropriate model to explain the experimental design effects on such ratio standards was shown to be log-normal rather than normal. Simply by taking logarithms of the power function ratio standard, identical solutions for the design effects are obtained using either ANOVA or, by taking the unscaled physiological variable as the dependent variable and the body size variable as the covariate, ANCOVA methods.

Analysis of Variance↗

PubMed1992

Mood, mileage and the menstrual cycle.

Forty women took part in a study to determine the effects of high-intensity training and the menstrual cycle on mood states. Half of the sample were competitive distance runners following a training load of between 50 km and 130 km running per week. Seven athletes were amenorrhoeic and 13 either eumenorrhoeic or oligomenorrhoeic. The remaining 20 subjects were inactive women who menstruated regularly. The mean age of all 40 subjects was 29 years. Each subject completed two identical Profile of Mood States (POMS) questionnaires. The 33 menstruating subjects completed both a premenstrual and a midcycle form and the amenorrhoeic athletes completed the questionnaires at a 3-week interval, which acted as a control for the potential effects of premenstrual syndrome (PMS) among the menstruating females. Results showed highly significant differences in mood profiles among amenorrhoeic athletes, non-amenorrhoeic athletes and inactive women. The greatest difference was between premenstrual and midcycle measures for the inactive group. PMS appears to cause marked negative mood swings among menstruating women which the POMS inventory is sensitive in detecting. While the lower-intensity-training runners appeared to benefit psychologically from a training distance of approximately 50 km week-1, high-intensity training had an adverse effect on mood.

Adult↗

PubMed1991

Using generalized linear models (GLMs) to model errors in motor performance.

Because of differences in design factors, experiments in human motor performance sometimes produce a wide range in variability or consistency in a subject's individual errors. These differences in variation often lead to heterogeneity in the variance-covariance matrices between group factors, which prohibits the use of repeated-measures (RM) ANOVA or MANOVA techniques to analyze the error data. Provided certain conditions are met, however, each subject's individual errors can be collapsed into the summary error measures, constant error (CE) and variable error (VE), which can still provide a more than adequate description of the subjects' performance. This article proposes the appropriate conditions and the corresponding generalized linear models (GLMs) with which a subject's individual errors, recorded in short-term motor memory research, can be combined into the summary measures, CE and VE, which can be analyzed subsequently as the dependent variables in the experimental design. The CE scores can be modeled using GLMs without requiring the assumption of homogeneity of variances. Similarly, the VE scores can be modeled as a GLM, using a log-linear regression model that assumes a gamma distribution, rather than using a traditional analysis of variance (ANOV A) model that assumes an inappropriate normal error distribution for these scores. An example reveals that the analysis of VE scores, unlike the analysis of CE scores, is able to differentiate between group practice methods. These differences tend to be underestimated by traditional ANOVA methods, however. Differences between the ANOV A and GLM analyses of the VE scores are further clarified by simulation. Based on differences like those observed in the example, when simulated VE scores were analyzed, assuming both a normal and a gamma distribution, the power of the gamma tests was found to be superior to the normal analyses in all but a small range of cases, in which such differences were found to be negligible. Hence, it is only by declaring the VE scores to have a GLM with a gamma distribution that the anticipated group practice differences can be properly identified.

Journal Article↗

PubMed1991

Modelling mood states in athletic performance.

Because moods are transitory emotional states that can be influenced by a range of personality and environmental factors, the notion that elite athletes will always tend to produce a so-called iceberg profile of mood, and that less successful performers will not, is open to question. Evidence for such a claim is based principally upon descriptive studies. The present experiment used the POMS inventory as a predictor of cross-country running performance among a group of experienced male athletes. Race times from two competitive events were plotted against each of six mood factors. Using data from race 1, a multiple-regression model--incorporating the interdependence of tension, anger and depression--was able to predict rank order of finishing positions for race 2 with acceptable accuracy (rs = 0.74, P less than 0.01). The present approach differs from the traditional model of mood research in sport in that it provides a prescriptive, rather than a descriptive, focus. Although the model that has been developed appears promising, it is likely that in sports where demands on athletes are very different from those made upon cross-country runners, an alternative model may be required.

Affect↗

PubMed1991

Effects of load on oxygen intake in trained boys and men during treadmill running.

This investigation examines the effects of vertical and horizontal loading on the O2 intake (VO2) response of children (n = 8) and adults (n = 8) to treadmill running. In unloaded running, the children required a significantly greater VO2 (P less than 0.001) than the adults [mean difference 7 ml.kg-1.min-1 (18.5%)]. There was no significant difference in the VO2 response of the children and the adults to either vertical or horizontal loading. Vertical loading with 5 and 10% of body mass did not produce a significant increase in the VO2 response of either group. In contrast, horizontal loading produced a significant increase (P less than 0.001) in both groups. The consistent response to the two forms of loading suggests that there is no difference between children and adults in the apparent efficiency of running with an external load. Stride frequency showed a significant increase with vertical loading (P less than 0.001) and a significant decrease with horizontal loading (P less than 0.001) in both groups.

Adolescent↗

PubMed1988

The analysis of errors in short-term motor memory research using trial profiles.

In the past, researchers have debated the problem of selecting the most appropriate error measure (e.g., CE, VE, AE, or E) for use as the dependent variable when analyzing the results of their experiments in short-term motor memory research (Gessaroli & Schutz, 1982; Henry, 1974; Safrit, Spray, & Diewert, 1980; Schutz, 1977; Schutz & Roy, 1973). This paper suggests that the subjects' error scores, recorded over a series of trials, are analyzed individually, using repeated measures (RM) ANOVA. This analysis divides the total error sum of squares into recognizable components that, when identified, adequately explain the subjects' performance. The between-subjects sources of variation will indicate any differences in CE bias between the levels of each factor in the experiment. Similarly, any VE differences between the levels of each factor will be identified by significant trial-by-factor interactions. However, not all significant trial-by-factor interactions will necessarily indicate differences in VE performances. Nevertheless by plotting the group's perceived mean trial profiles for any significant trial-by-factor interactions, valuable insight can be gained into difference performance responses in trial adaptation for each level of the factors in the experimental design.

Journal Article↗

PubMed1986

Health, fitness, physical activity, and morbidity of middle aged male factory workers. I.

A description of the fitness, physical activity of lifestyle, and some aspects of health status and attitudes in a population of male factory workers aged 35-60 is presented as the first part of a report on a study of morbidity in this population. A total of 1394 subjects were included, undergoing medical examination, fitness testing by bicycle ergometry, assessment of body fat, and interview questionnaire. The inter-relation of fitness, body composition, habitual exertion, health risk factors, and attitudes to exercise are discussed. Fitness levels are compared with those reported in other studies and discussed in terms of capacity for walking and running and in relation to criteria for health benefit. In these two latter respects fitness appears to be inadequate among the great majority of those tested, although it is comparable with that reported by several other recent studies. Fitness is associated with physical activity of leisure but not that of work. Only relatively strenuous physical activity in leisure time appears to be related to fitness, and is only participated in by some 28% of the sample. Cycling has the strongest association with fitness of all the physical activity variables. Blood pressure and percentage body fat are also associated, inversely, with fitness, the latter not unexpectedly because of the weight related measure of fitness.

Adult↗

PubMedDate not supplied

Modelling health-related performance indices.

PRIMARY OBJECTIVE: The purpose of the present review is to critically examine the various models used to describe health-related performance indices (e.g. grip-strength, lung function, arterial blood pressure, anaerobic and aerobic power, etc.). MAIN OUTCOMES AND RESULTS: The majority of health-related performance indices are known to increase proportionally with body size and decline with age, although some, for example arterial blood pressure and cholesterol, increase with both age and excessive body weight. Historically, the confounding effects of body size and age have been controlled using multiple linear or polynomial regression models assuming a constant additive error variance. Based on maximum likelihood goodness-of-fit criterion, an alternative class of allometric models with a multiplicative error term arc shown to describe such proportional relationships more adequately in both cross-sectional and longitudinal studies. CONCLUSIONS: When modelling health-related performance indices, multiplicative, allometric models incorporate the concept of a proportional association as an integral part of its model form. Such models guarantee realistic values of health-related response variables both within and beyond the range of observations, ensuring biologically sound estimates of the response variables for subjects of all ages. After a logarithmic transformation, the models can be fitted using ordinary linear regression that will naturally help to overcome the heteroscedasticity observed in such data. However, if the logarithmic transformation fails to provide a constant error variance, an alternative approach is to model the error variance itself using log-linear regression. For longitudinal data, the use of multilevel modelling provides an alternative method of modelling error variation observed in health-related performance indices.

Adolescent↗

PubMedDate not supplied

Can greater muscularity in larger individuals resolve the 3/4 power-law controversy when modelling maximum oxygen uptake?

BACKGROUND: The power function relationship, MR = a.m(b), between metabolic rate (MR) and body mass m has been the source of much controversy amongst biologists for many years. Various studies have reported mass exponents (b) greater than the anticipated 'surface-area' exponent 0.67, often closer to 0.75 originally identified by Kleiber. AIM: The study aimed to provide a biological explanation for these 'inflated' exponents when modelling maximum oxygen uptake (max), based on the observations from this and previous studies that larger individuals develop disproportionately more muscle mass in the arms and legs. RESEARCH DESIGN AND SUBJECTS: A cross-sectional study of 119 professional soccer players from Croatia aged 18-34 was carried out. RESULTS: Here we confirm that the power function relationship between max and body mass of the professional soccer players results in an 'inflated' mass exponent of 0.75 (95% confidence interval from 0.56 to 0.93), but also the larger soccer players have disproportionately greater leg muscle girths. When the analysis was repeated incorporating the calf and thigh muscle girths rather than body mass as predictor variables, the analysis not only explained significantly more of the variance in max, but the sum of the exponents confirmed a surface-area law. CONCLUSIONS: These findings confirm the pitfalls of fitting body-mass power laws and suggest using muscle-girth methodology as a more appropriate way to scale or normalize metabolic variables such as max for individuals of different body sizes.

Adolescent↗