Biomedical subjects

T S Olds

Publications and source records attributed to T S Olds.

6 recordsLinked to original sources

PubMed1996

Infrared thermometry in the diagnosis and treatment of heat exhaustion.

Infrared (IR) thermometers (FirstTemp 2000A, Intelligent Medical Systems, California) were used to monitor tympanic temperature (Tty) in 12 collapsed fun-runners suspected of suffering exertion-induced heat exhaustion (EIHE). Rectal temperature (Tre) was monitored via digital clinical thermometers. Conditions during the fun-run and in the field treatment centre were cool (air temperature 16-18 degrees C, relative humidity 60-65%). On admission, Tty was (mean +/- SEM) 1.2 +/- 0.3 degrees C lower than Tre. For admission plus subsequent monitoring data pooled, although Tty correlated significantly with Tre (r = 0.86, p < 0.001), mean Tty (37.4 +/- 0.2 degrees C) was significantly lower (p < 0.01) than mean Tre (38.4 +/- 0.4 degrees C). Cotton wool ear pads, applied to 10 of the runners on admission to minimise environmental effects on Tty, did not significantly improve the IR monitoring. A Tty > or = 37.1 degrees C predicted a Tre > or = 38 degrees C (an established diagnostic criterion for EIHE) with a sensitivity of 0.93 and a specificity of 0.63. These data indicate that IR tympanic thermometry, when utilised in cool environments, can result in misdiagnosis of heat exhaustion. Although IR thermometry shows some promise as a rapid, non-invasive means of monitoring core temperature, it should not be used in the diagnosis and treatment of heat exhaustion unless further research validates the method.

Adult↗

PubMed1995

Exercise stimulus increases ventilation from maximal to supramaximal intensity.

This study investigated the influence of an exercise stimulus on pulmonary ventilation (VE) during severe levels of exercise in a group of ten athletes. The altered ventilation was assessed in relation to its effect on blood gas status, in particular to the incidence and severity of exercise induced hypoxaemia. Direct measurements of arterial blood were made at rest and during the last 15 s of two intense periods of cycling; once at an intensity found to elicit maximal oxygen uptake (VO2max; MAX) and once at an intensity established to require 115% of VO2max (SMAX). Oxygen uptake (VO2) and ventilatory markers were continually recorded during the exercise and respiratory flow-volume loops were measured at rest and during the final 30 s of each minute for both exercise intensities. When compared to MAX exercise, the subjects had higher ventilation and partial pressure of arterial oxygen (PaO2) during the SMAX intensity. Regression analysis for both conditions indicated the levels of PaO2 and oxygen saturation of arterial blood (SaO2) were positively correlated with relative levels of ventilation during exercise. It was apparent that mechanical constraints to ventilate further were not present during the MAX test since the subjects were able to elevate VE during SMAX and attenuate the level of hypoxaemia. This was also confirmed by analysis of the flow volume recordings. These data support the conclusions firstly, that overwhelming mechanical constraints on VE were not present during the MAX exercise, secondly, the subjects exhibiting the most severe hypoxaemia had no consistent relationship with any measure of expiratory flow limitation, and thirdly, ventilatory patterns during intense exercise are strong predictors of blood gas status.

Adolescent↗

PubMed1995

The effects of altered exercise distribution on lymphocyte subpopulations.

The effects of exercise distribution on lymphocyte count, lymphocyte subpopulations and plasma cortisol concentration in peripheral blood were assessed in 19 healthy subjects. The subjects were randomly divided into group A (n = 10) or group B (n = 9) according to exercise distribution. Both groups underwent a 10-week programme involving 5 x 2-week blocks: baseline (B), training period 1 (TP1), stabilisation 1 (S1), training period 2 (TP2), and stabilisation 2 (S2). During B, S1 and S2 normal training was undertaken. During TP1 and TP2 the subjects increased the amount of training by 50% in week 1 and by 100% in week 2. During TP1 subjects in group A exercised 6 days.week-1, while during TP2 these subjects exercised on 3 alternate days.week-1, but doubled the duration of each training session. The subjects in group B reversed this training order. Blood was collected 36-42 h following exercise period B, and at the end of periods TP1, S1, TP2 and S2, and also 12-18 h following completion of exercise at the end of TP1 and TP2. There were no significant differences (P > 0.05) between the 6 day.week-1 programme and the 3 alternate day.week-1 programme in total lymphocyte count, CD3+, CD4+, CD8+, CD16+, or CD19+ cells, the CD4:CD8 ratio, HLA-DR+ (activated) T cells or plasma cortisol concentrations. Following both TP1 and TP2 there was a nonsignificant decrease in lymphocyte subpopulations. However following both S1 and S2 (baseline training) there was a significant increase in total lymphocyte count, CD3+, CD4+ and CD8+ lymphocytes. The S2 variables statistically significant from B were: total lymphocyte count (P < 0.01), CD3+ T-cells and percentage of circulating lymphocytes (P < 0.01), CD4+ cells (P < or = 0.0001), CD8+ cells (P < 0.05), and HLA-DR+ (activated) T-cells (P < 0.05). The results indicated that provided the amount of exercise is constant for a given period, then exercise distribution is not a critical variable in the alteration of lymphocyte subpopulations that may occur in response to overload training. However 2 weeks of overload training followed by 2 weeks of active recovery (baseline) training may induce an increase in the lymphocyte count.

Adult↗

PubMed1995

Modeling road-cycling performance.

This paper presents a complete set of equations for a "first principles" mathematical model of road-cycling performance, including corrections for the effect of winds, tire pressure and wheel radius, altitude, relative humidity, rotational kinetic energy, drafting, and changed drag. The relevant physiological, biophysical, and environmental variables were measured in 41 experienced cyclists completing a 26-km road time trial. The correlation between actual and predicted times was 0.89 (P < or = 0.0001), with a mean difference of 0.74 min (1.73% of mean performance time) and a mean absolute difference of 1.65 min (3.87%). Multiple simulations were performed where model inputs were randomly varied using a normal distribution about the measured values with a SD equivalent to the estimated day-to-day variability or technical error of measurement in each of the inputs. This analysis yielded 95% confidence limits for the predicted times. The model suggests that the main physiological factors contributing to road-cycling performance are maximal O2 consumption, fractional utilization of maximal O2 consumption, mechanical efficiency, and projected frontal area. The model is then applied to some practical problems in road cycling: the effect of drafting, the advantage of using smaller front wheels, the effects of added mass, the importance of rotational kinetic energy, the effect of changes in drag due to changes in bicycle configuration, the normalization of performances under different conditions, and the limits of human performance.

Adolescent↗

PubMed1993

Aerobic and anaerobic indices contributing to track endurance cycling performance.

A group of 18 male high performance track endurance and sprint cyclists were assessed to provide a descriptive training season specific physiological profile, to examine the relationship between selected physiological and anthropometric variables and cycling performance in a 4000-m individual pursuit (IP4000) and to propose a functional model for predicting success in the IP4000. Anthropometric characteristics, absolute and relative measurements of maximal oxygen uptake (VO2max), blood lactate transition thresholds (Thla- and Th(an),i), VO2 kinetics, cycling economy and maximal accumulated oxygen deficit (MAOD) were assessed, with cyclists also performing a IP4000 under competition conditions. Peak post-competition blood lactate concentrations and acid-base values were measured. Although all corresponding indices of Thla- and Th(an),i occurred at significantly different intensities there were high intercorrelations between them (0.51-0.85). There was no significant difference in MAOD when assessed using a 2 or 5 min protocol (61.4 vs 60.2 ml.kg-1, respectively). The highest significant correlations were found among IP4000 and the following: VO2max (ml.kg-2/3.min-1; r = -0.79), power output at lactate threshold (Wthla) (W; r = -0.86), half time of VO2 response whilst cycling at 115% VO2max (s; r = 0.48) and MAOD when assessed using the 5 min protocol (ml.kg-1; r = -0.50). A stepwise multiple regression yielded the following equation, which had an r of 0.86 and a standard error of estimate of 5.7 s: IP4000 (s) = 462.9 - 0.366 x (Wthla) - 0.306 x (MAOD) - 0.438 x (VO2max) where Wthla is in W, MAOD is in ml.kg-1 and VO2max is in ml.kg-1 x min-1.(ABSTRACT TRUNCATED AT 250 WORDS)

Acid-Base Equilibrium↗

PubMed1993

Mathematical model of cycling performance.

A model of cycling performance is presented. The model is based on equating two expressions for the total amount of work performed. One expression is deduced from biomechanical principles deriving energy requirements from total resistance. The other models the energy available from aerobic and anaerobic energy systems, including the effect of oxygen uptake kinetics at the onset of exercise. The equation can then be solved for any of the variables. Empirically derived field and laboratory data were used to assess the accuracy of the model. Model estimates of 4,000-m individual pursuit performance times showed a correlation of 0.803 (P < or = 0.0001) with times measured in 18 high-performance track cyclists, with a mean difference (predicted--measured) of 4.6 s (1.3% of mean performance time). The model enables estimates of the performance impact of alterations in physiological, biomechanical, anthropometric, and environmental parameters.

Aerobiosis↗