991 resultados para Incremental Training


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Introduction. Respiratory difficulties in athletes are common, especially in adolescents, even in the absence of exercise-induced bronchoconstriction. Immaturity of the respiratory muscles coupling at high respiratory rates could be a potential mechanism. Whether respiratory muscle training (RMT) can positively influence it is yet unknown. Goal. We investigate the effects of RMT on ventilation and performance parameters in adolescent athletes and hypothesize that RMT will enhance respiratory capacity. Methods. 12 healthy subjects (8 male, 4 female, 17±0.5 years) from a sports/study high school class, competitively involved in various sports (minimum of 10 hours per week) underwent respiratory function testing, maximal minute ventilation (MMV) measurements and a maximal treadmill incremental test with VO2max and ventilatory thresholds (VT1 and VT2) determination. They then underwent one month of RMT (4 times/week) using a eucapnic hyperventilation device, with an incremental training program. The same tests were repeated after RMT. Results. Subjects completed 14.8 sessions of RMT, with an increase in total ventilation per session of 211±29% during training. Borg scale evaluation of the RMT session was unchanged or reduced in all subjects, despite an increase in total respiratory work. No changes (p>0.05) were observed pre/post RMT in VO2max (53.4±7.5 vs 51.6±7.7 ml/kg/min), VT2 (14.4±1.4 vs 14.0±1.1 km/h) or Speed max at end of test (16.1±1.7 vs 15.8±1.7 km/h). MVV increased by 9.2% (176.7±36.9 vs 192.9±32.6 l/min, p<0.001) and FVC by 3.3% (6.70±0.75 vs 4.85±0.76 litres, p<0.05). Subjective evaluation of respiratory sensations during exercise and daily living were also improved. Conclusions. RMT improves MMV and FVC in adolescent athletes, along with important subjective respiratory benefits, although no changes are seen in treadmill maximal performance tests and VO2max measurements. RMT can be easily performed in adolescent without side effects, with a potential for improvement in training capacity and overall well-being.

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Two jamming cancellation algorithms are developed based on a stable solution of least squares problem (LSP) provided by regularization. They are based on filtered singular value decomposition (SVD) and modifications of the Greville formula. Both algorithms allow an efficient hardware implementation. Testing results on artificial data modeling difficult real-world situations are also provided.

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The Plaut, McClelland, Seidenberg and Patterson (1996) connectionist model of reading was evaluated at two points early in its training against reading data collected from British children on two occasions during their first year of literacy instruction. First, the network’s non-word reading was poor relative to word reading when compared with the children. Second, the network made more non-lexical than lexical errors, the opposite pattern to the children. Three adaptations were made to the training of the network to bring it closer to the learning environment of a child: an incremental training regime was adopted; the network was trained on grapheme– phoneme correspondences; and a training corpus based on words found in children’s early reading materials was used. The modifications caused a sharp improvement in non-word reading, relative to word reading, resulting in a near perfect match to the children’s data on this measure. The modified network, however, continued to make predominantly non-lexical errors, although evidence from a small-scale implementation of the full triangle framework suggests that this limitation stems from the lack of a semantic pathway. Taken together, these results suggest that, when properly trained, connectionist models of word reading can offer insights into key aspects of reading development in children.

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This study aimed to compare maximal fat oxidation rate parameters between moderate-and low-performance runners. Eighteen runners performed an incremental treadmill test to estimate individual maximal fat oxidation rate (Fat(max)) based on gases measures and a 10,000-m run on a track. The subjects were then divided into a low and moderate performance group using two different criteria: 10,000-m time and VO(2)max values. When groups were divided using 10,000-m time, there was no significant difference in Fat(max) (0.41 +/- 0.16 and 0.27 +/- 0.12 g.min(-1), p = 0.07) or in the exercise intensity that elicited Fat(max) (59.9 +/- 16.5 and 68.7 +/- 10.3 % (V) over dotO(2max), p = 0.23) between the moderate and low performance groups, respectively (p > 0.05). When groups were divided using VO(2max) values, Fat(max) was significantly lower in the low VO(2max) group than in the high VO(2max) group (0.29 +/- 0.10 and 0.47 +/- 0.17 g.min(-1), respectively, p < 0.05) but the intensity that elicited Fat(max) did not differ between groups (64.4 +/- 14.9 and 61.6 +/- 15.4 % VO(2max)). Fat(max) or % VO(2max) that elicited Fat(max) was not associated with 10,000 m time. The only variable associated with 10,000-m running performance was % VO(2max) used during the run (p < 0.01). In conclusion, the criteria used for the division of groups according to training status might influence the identification of differences in Fat(max) or in the intensity that elicits Fat(max).

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This study aimed to describe and compare the ventilation behavior during an incremental test utilizing three mathematical models and to compare the feature of ventilation curve fitted by the best mathematical model between aerobically trained (TR) and untrained ( UT) men. Thirty five subjects underwent a treadmill test with 1 km.h(-1) increases every minute until exhaustion. Ventilation averages of 20 seconds were plotted against time and fitted by: bi-segmental regression model (2SRM); three-segmental regression model (3SRM); and growth exponential model (GEM). Residual sum of squares (RSS) and mean square error (MSE) were calculated for each model. The correlations between peak VO2 (VO2PEAK), peak speed (Speed(PEAK)), ventilatory threshold identified by the best model (VT2SRM) and the first derivative calculated for workloads below (moderate intensity) and above (heavy intensity) VT2SRM were calculated. The RSS and MSE for GEM were significantly higher (p < 0.01) than for 2SRM and 3SRM in pooled data and in UT, but no significant difference was observed among the mathematical models in TR. In the pooled data, the first derivative of moderate intensities showed significant negative correlations with VT2SRM (r = -0.58; p < 0.01) and Speed(PEAK) (r = -0.46; p < 0.05) while the first derivative of heavy intensities showed significant negative correlation with VT2SRM (r = -0.43; p < 0.05). In UT group the first derivative of moderate intensities showed significant negative correlations with VT2SRM (r = -0.65; p < 0.05) and Speed(PEAK) (r = -0.61; p < 0.05), while the first derivative of heavy intensities showed significant negative correlation with VT2SRM (r= -0.73; p < 0.01), Speed(PEAK) (r = -0.73; p < 0.01) and VO2PEAK (r = -0.61; p < 0.05) in TR group. The ventilation behavior during incremental treadmill test tends to show only one threshold. UT subjects showed a slower ventilation increase during moderate intensities while TR subjects showed a slower ventilation increase during heavy intensities.

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Pires, FO, Hammond, J, Lima-Silva, AE, Bertuzzi, RCM, and Kiss, MAPDM. Ventilation behavior during upper-body incremental exercise. J Strength Cond Res 25(1): 225-230, 2011-This study tested the ventilation (V(E)) behavior during upper-body incremental exercise by mathematical models that calculate 1 or 2 thresholds and compared the thresholds identified by mathematical models with V-slope, ventilatory equivalent for oxygen uptake (V(E)/(V) over dotO(2)), and ventilatory equivalent for carbon dioxide uptake (V(E)/(V) over dotCO(2)). Fourteen rock climbers underwent an upper-body incremental test on a cycle ergometer with increases of approximately 20 W.min(-1) until exhaustion at a cranking frequency of approximately 90 rpm. The V(E) data were smoothed to 10-second averages for V(E) time plotting. The bisegmental and the 3-segmental linear regression models were calculated from 1 or 2 intercepts that best shared the V(E) curve in 2 or 3 linear segments. The ventilatory threshold(s) was determined mathematically by the intercept(s) obtained by bisegmental and 3-segmental models, by V-slope model, or visually by V(E)/(V) over dotO(2) and V(E)/(V) over dotCO(2). There was no difference between bisegmental (mean square error [MSE] = 35.3 +/- 32.7 l.min(-1)) and 3-segmental (MSE = 44.9 +/- 47.8 l.min(-1)) models in fitted data. There was no difference between ventilatory threshold identified by the bisegmental (28.2 +/- 6.8 ml.kg(-1).min(-1)) and second ventilatory threshold identified by the 3-segmental (30.0 +/- 5.1 ml.kg(-1).min(-1)), V(E)/(V) over dotO(2) (28.8 +/- 5.5 ml.kg(-1).min(-1)), or V-slope (28.5 +/- 5.6 ml.kg(-1).min(-1)). However, the first ventilatory threshold identified by 3-segmental (23.1 +/- 4.9 ml.kg(-1).min(-1)) or by VE/(V) over dotO(2) (24.9 +/- 4.4 ml.kg(-1).min(-1)) was different from these 4. The V(E) behavior during upper-body exercise tends to show only 1 ventilatory threshold. These findings have practical implications because this point is frequently used for aerobic training prescription in healthy subjects, athletes, and in elderly or diseased populations. The ventilatory threshold identified by V(E) curve should be used for aerobic training prescription in healthy subjects and athletes.

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The aim of the present study was to compare and correlate training impulse (TRIMP) estimates proposed by Banister (TRIMP(Banister)), Stagno (TRIMP(Stagno)) and Manzi (TRIMP(Manzi)). The subjects were submitted to an incremental test on cycle ergometer with heart rate and blood lactate concentration measurements. In the second occasion, they performed 30 min. of exercise at the intensity corresponding to maximal lactate steady state, and TRIMP(Banister), TRIMP(Stagno) and TRIMP(Manzi) were calculated. The mean values of TRIMP(Banister) (56.5 +/- 8.2 u.a.) and TRIMP(Stagno) (51.2 +/- 12.4 u.a.) were not different (P > 0.05) and were highly correlated (r = 0.90). Besides this, they presented a good agreement level, which means low bias and relatively narrow limits of agreement. On the other hand, despite highly correlated (r = 0.93), TRIMP(Stagno) and TRIMP(Manzi) (73.4 +/- 17.6 u.a.) were different (P < 0.05), with low agreement level. The TRIMP(Banister) e TRIMP(Manzi) estimates were not different (P = 0.06) and were highly correlated (r = 0.82), but showed low agreement level. Thus, we concluded that the investigated TRIMP methods are not equivalent. In practical terms, it seems prudent monitor the training process assuming only one of the estimates.

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In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

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Background: Sexually transmitted diseases (STD) are important co-factors in HIV transmission. We studied the impact of health worker training and STD syndrome packets (containing recommended drugs, condoms, partner notification cards and information leaflets) on the quality of STD case management in primary care clinics in rural South Africa. Methods: A randomized controlled trial of five matched pairs of clinics compared the intervention with routine syndromic management. Outcomes were measured by simulated patients using standardized scripts, and included the proportion given recommended drugs; correctly case managed (given recommended drugs plus condoms and partner cards); adequately counselled; reporting good staff attitude; and consulted in privacy. Results: At baseline, the quality of STD case management was similarly poor in both groups. Only 36 and 46% of simulated patients visiting intervention and control clinics, respectively, were given recommended drugs. After the intervention, intervention clinics provided better case management than controls: 88 versus 50% (P < 0.01) received recommended drugs; 83 versus 12% (P < 0.005) were correctly case managed; 68 versus 46% (P = 0.06) were adequately counselled; 84 versus 58% experienced good staff attitude (P = 0.07); and 92 versus 86% (P = 0.4) were consulted privately. A syndrome packet cost US$1.50; the incremental cost was US$6.80. The total intervention cost equalled 0.3% of annual district health expenditure. Interpretation: A simple and affordable health service intervention achieved substantial improvements in STD case management. Although this is a critical component of STD control and can reduce HIV transmission, community-level interventions to influence health-seeking behaviour are also needed. (C) 2000 Lippincott Williams & Wilkins.

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BACKGROUND: Previous studies have shown positive effects from noninvasive ventilation (NIV) or supplemental oxygen on exercise capacity in patients with COPD. However, the best adjunct for promoting physiologic adaptations to physical training in patients with severe COPD remains to be investigated. METHODS: Twenty-eight patients (mean +/- SD age 68 +/- 7 y) with stable COPD (FEV(1) 34 +/- 9% of predicted) undergoing an exercise training program were randomized to either NIV (n = 14) or supplemental oxygen (n = 14) during group training to maintain peripheral oxygen saturation (S(pO2)) >= 90%. Physical training consisted of treadmill walking (at 70% of maximal speed) 3 times a week, for 6 weeks. Patients were assessed at baseline and after 6 weeks. Assessments included physiological adaptations during incremental exercise testing (ratio of lactate concentration to walk speed, oxygen uptake [(V) over dot(O2)], and dyspnea), exercise tolerance during 6-min walk test, leg fatigue, maximum inspiratory pressure, and health-related quality of life. RESULTS: Two patients in each group dropped out due to COPD exacerbations and lack of exercise program adherence, and 24 completed the training program. Both groups improved 6-min walk distance, symptoms, and health-related quality of life. However, there were significant differences between the NIV and supplemental-oxygen groups in lactate/speed ratio (33% vs -4%), maximum inspiratory pressure (80% vs 23%), 6-min walk distance (122 m vs 47 m), and leg fatigue (25% vs 11%). In addition, changes in S(pO2)/speed, (V) over dot(O2), and dyspnea were greater with NIV than with supplemental-oxygen. CONCLUSIONS: NIV alone is better than supplemental oxygen alone in promoting beneficial physiologic adaptations to physical exercise in patients with severe COPD.

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This study examined the effects of intermittent hypoxic training (IHT) on skeletal muscle monocarboxylate lactate transporter (MCT) expression and anaerobic performance in trained athletes. Cyclists were assigned to two interventions, either normoxic (N; n = 8; 150 mmHg PIO2) or hypoxic (H; n = 10; ∼3000 m, 100 mmHg PIO2) over a three week training (5×1 h-1h30.week-1) period. Prior to and after training, an incremental exercise test to exhaustion (EXT) was performed in normoxia together with a 2 min time trial (TT). Biopsy samples from the vastus lateralis were analyzed for MCT1 and MCT4 using immuno-blotting techniques. The peak power output (PPO) increased (p<0.05) after training (7.2% and 6.6% for N and H, respectively), but VO2max showed no significant change. The average power output in the TT improved significantly (7.3% and 6.4% for N and H, respectively). No differences were found in MCT1 and MCT4 protein content, before and after the training in either the N or H group. These results indicate there are no additional benefits of IHT when compared to similar normoxic training. Hence, the addition of the hypoxic stimulus on anaerobic performance or MCT expression after a three-week training period is ineffective.

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The purpose of this study was to test the hypothesis that athletes having a slower oxygen uptake ( VO(2)) kinetics would benefit more, in terms of time spent near VO(2max), from an increase in the intensity of an intermittent running training (IT). After determination of VO(2max), vVO(2max) (i.e. the minimal velocity associated with VO(2max) in an incremental test) and the time to exhaustion sustained at vVO(2max) ( T(lim)), seven well-trained triathletes performed in random order two IT sessions. The two IT comprised 30-s work intervals at either 100% (IT(100%)) or 105% (IT(105%)) of vVO(2max) with 30-s recovery intervals at 50% of vVO(2max) between each repeat. The parameters of the VO(2) kinetics (td(1), tau(1), A(1), td(2), tau(2), A(2), i.e. time delay, time constant and amplitude of the primary phase and slow component, respectively) during the T(lim) test were modelled with two exponential functions. The highest VO(2) reached was significantly lower ( P<0.01) in IT(100%) run at 19.8 (0.9) km(.)h(-1) [66.2 (4.6) ml(.)min(-1.)kg(-1)] than in IT(105%) run at 20.8 (1.0) km(.)h(-1) [71.1 (4.9) ml(.)min(-1.)kg(-1)] or in the incremental test [71.2 (4.2) ml(.)min(-1.)kg(-1)]. The time sustained above 90% of VO(2max) in IT(105%) [338 (149) s] was significantly higher ( P<0.05) than in IT(100%) [168 (131) s]. The average T(lim) was 244 (39) s, tau(1) was 15.8 (5.9) s and td(2) was 96 (13) s. tau(1) was correlated with the difference in time spent above 90% of VO(2max) ( r=0.91; P<0.01) between IT(105%) and IT(100%). In conclusion, athletes with a slower VO(2) kinetics in a vVO(2max) constant-velocity test benefited more from the 5% rise of IT work intensity, exercising for longer above 90% of VO(2max) when the IT intensity was increased from 100 to 105% of vVO(2max).

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The aim of this study was to investigate the synergistic effects of endurance training and hypoxia on endurance performance in normoxic and hypoxic conditions (approximately 3000 m above sea level) as well as on lactate and glucose metabolism during prolonged exercise. For this purpose, 14 well-trained cyclists performed 12 training sessions in conditions of normobaric hypoxia (HYP group, n = 7) or normoxia (NOR group, n = 7) over 4 weeks. Before and after training, lactate and glucose turnover rates were measured by infusion of exogenous lactate and stable isotope tracers. Endurance performance was assessed during incremental tests performed in normoxia and hypoxia and a 40 km time trial performed in normoxia. After training, performance was similarly and significantly improved in the NOR and HYP groups (training, P < 0.001) in normoxic conditions. No further effect of hypoxic training was found on markers of endurance performance in hypoxia (training x hypoxia interaction, n.s.). In addition, training and hypoxia had no significant effect on lactate turnover rate. In contrast, there was a significant interaction of training and hypoxia (P < 0.05) on glucose metabolism, as follows: plasma insulin and glucose concentrations were significantly increased; glucose metabolic clearance rate was decreased; and the insulin to glucagon ratio was increased after training in the HYP group. In conclusion, our results show that, compared with training in normoxia, training in hypoxia has no further effect on endurance performance in both normoxic and hypoxic conditions or on lactate metabolic clearance rate. Additionally, these findings suggest that training in hypoxia impairs blood glucose regulation in endurance-trained subjects during exercise.

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BACKGROUND: Physician training in smoking cessation counseling has been shown to be effective as a means to increase quit success. We assessed the cost-effectiveness ratio of a smoking cessation counseling training programme. Its effectiveness was previously demonstrated in a cluster randomized, control trial performed in two Swiss university outpatients clinics, in which residents were randomized to receive training in smoking interventions or a control educational intervention. DESIGN AND METHODS: We used a Markov simulation model for effectiveness analysis. This model incorporates the intervention efficacy, the natural quit rate, and the lifetime probability of relapse after 1-year abstinence. We used previously published results in addition to hospital service and outpatient clinic cost data. The time horizon was 1 year, and we opted for a third-party payer perspective. RESULTS: The incremental cost of the intervention amounted to US$2.58 per consultation by a smoker, translating into a cost per life-year saved of US$25.4 for men and 35.2 for women. One-way sensitivity analyses yielded a range of US$4.0-107.1 in men and US$9.7-148.6 in women. Variations in the quit rate of the control intervention, the length of training effectiveness, and the discount rate yielded moderately large effects on the outcome. Variations in the natural cessation rate, the lifetime probability of relapse, the cost of physician training, the counseling time, the cost per hour of physician time, and the cost of the booklets had little effect on the cost-effectiveness ratio. CONCLUSIONS: Training residents in smoking cessation counseling is a very cost-effective intervention and may be more efficient than currently accepted tobacco control interventions.