To investigate the effects of exercise intensity on growth hormone (GH) release, 10 male subjects were tested on 6 randomly ordered occasions [1 control condition (C), 5 exercise conditions (Ex)]. Serum GH concentrations were measured in samples obtained at 10-min intervals between 0700 and 0900 (baseline) and 0900 and 1300 (exercise+ recovery). Integrated GH concentrations (IGHC) were calculated by trapezoidal reconstruction. During Ex subjects exercised for 30 min (0900-0930) at one of the following intensities [normalized to the lactate threshold (LT)]: 25 and 75% of the difference between LT and rest (0.25LT and 0.75LT, respectively), at LT, and at 25 and 75% of the difference between LT and peak (1.25LT and 1.75LT, respectively). No differences were observed among conditions for baseline IGHC. Exercise+recovery IGHC (mean +/- SE: C = 250 +/- 60; 0.25LT = 203 +/- 69; 0.75LT = 448 +/- 125; LT = 452 +/- 119; 1.25LT = 512 +/- 121; 1.75LT = 713 +/- 115 microg x l(-1) x min(-1)) increased linearly with increasing exercise intensity (P < 0.05). Deconvolution analysis revealed that increasing exercise intensity resulted in a linear increase in the mass of GH secreted per pulse and GH production rate [production rate increased from 16. 5 +/- 4.5 (C) to 32.1 +/- 5.2 microg x distribution volume(-1) x min(-1) (1.75LT), P < 0.05], with no changes in GH pulse frequency or half-life of elimination. We conclude that the GH secretory response to exercise is related to exercise intensity in a linear dose-response pattern in young men.
We examined the validity of percent body fat (%Fat) estimation by two-compartment (2-Comp) hydrostatic weighing (Siri 2-Comp), 3-Comp dual-energy X-ray absorptiometry (DEXA 3-Comp), 3-Comp hydrostatic weighing corrected for the total body water (Siri 3-Comp), and anthropometric methods in young and older individuals (n = 78). A 4-Comp model of body composition served as the criterion measure of %Fat (Heymsfield 4-Comp; S. B. Heymsfield, S. Lichtman, R. N. Baumgartner, J. Wang, Y. Kamen, A. Aliprantis, and R. N. Pierson Jr., Am. J. Clin. Nutr. 52: 52-58, 1990.). Comparison of the Siri 3-Comp with the Heymsfield 4-Comp model revealed mean differences of =0.4 %Fat, r values >/= r = 0.997, total error values = 0.85 %Fat, and 95% confidence intervals (Bland-Altman analysis) of =1.7 %Fat. Comparison of Siri 2-Comp, DEXA, and anthropometric models with the Heymsfield 4-Comp revealed that total error scores ranged from +/-4. 0 to +/-10.7 %Fat, and 95% confidence intervals associated with the Bland-Altman analysis ranged from +/-5.1 to +/-15.0 %Fat. We conclude that the Siri 3-Comp model provides valid and accurate body composition data when compared with a 4-Comp criterion model. However, the individual variability associated with the Siri 2-Comp, DEXA 3-Comp, and anthropometric models may limit their use in research settings. The use of anthropometric estimation methods resulted in large mean differences and a considerable amount of interindividual variability. These data suggest that the use of these techniques should be viewed with caution.
We previously reported that in young adult males growth hormone (GH) release is related to exercise intensity in a linear dose-response manner (Pritzlaff et al. J Appl Physiol 87: 498-504, 1999). To investigate the effects of gender and exercise intensity on GH release, eight women (24.3 Ϯ 1.3 yr, 171 Ϯ 3.2 cm height, 63.6 Ϯ 8.7 kg weight) were each tested on six randomly ordered occasions [1 control condition (C), 5 exercise conditions (Ex)]. Serum GH concentrations were measured in samples obtained at 10-min intervals between 0700 and 0900 (baseline) and 0900 and 1300 (Ex ϩ recovery or C). Integrated GH concentrations (IGHC) were calculated by trapezoidal reconstruction. During Ex, subjects exercised for 30 min (0900-0930) at one of the following intensities [normalized to the lactate threshold (LT)]: 25 and 75% of the difference between LT and rest, at LT, and at 25 and 75% of the difference between LT and peak O2 uptake. No differences were observed among conditions for baseline IGHC. To determine whether total (Ex ϩ recovery) IGHC changed with increasing exercise intensity, slopes associated with individual linear regression models were subjected to a Wilcoxon signed-rank test. To test for gender differences, data in women were compared with the previously published data in men. A Wilcoxon ranked-sums two-tailed test was used to analyze the slopes and intercepts from the regression models. Total IGHC increased linearly with increasing exercise intensity. The slope and intercept values for the relationship between total IGHC and exercise intensity were greater in women than in men. Deconvolution analysis (0700-1300 h) revealed that, regardless of gender, increasing exercise intensity resulted in a linear increase in the mass of GH secreted per pulse and summed GH production rate, with no changes in GH secretory pulse frequency or apparent half-life of elimination. Exercise reduced the half-duration of GH secretory burst in men but not in women. Gender comparisons revealed that women had greater basal (nonpulsatile) GH secretion across all conditions, more frequent GH secretory pulses, a greater GH secretory pulse amplitude, a greater production rate, and a trend for a greater mass of GH secreted per pulse than men. We conclude that, in young adults, the GH secretory response to exercise is related to exercise intensity in a linear dose-response pattern. For each incremental increase in exercise intensity, the fractional stimulation of GH secretion is greater in women than in men. male; female; lactate threshold; endocrinology; pituitary; somatotropin ACUTE EXERCISE IS A POWERFUL stimulus to growth hormone (GH) release (2-5, 15, 17, 20, 32, 33). Previous research has suggested that exercise intensity plays a key role, wherein a particular threshold of exercise intensity must be exceeded to elicit GH release (3-5). However, recent data from our laboratory indicate that, in young men, the magnitude of GH release rises with increasing exercise intensity in a linear doseresponse relationship (as opposed to a thre...
Objectives After completing this article, readers should be able to:1. Describe outside influences on adolescent psychological development. 2. Explain why cognitive development advances during adolescence should be assessed before initiating counseling. 3. Describe the relationship between risk-taking behaviors of adolescents and cognitive maturity. 4. Explain how visible and nonvisible health conditions affect the adolescent's view of self. 5. Discuss how physicians can help improve compliance in an adolescent.
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