Introduction. Wrist (Colles') and forearm fractures commonly occur when a person falls on the outstretched forearm and the force exceeds bone strength. There is lack of experimental evidence testing the available force prediction models and assessing factors that determine forearm forces during a fall.<p> Objective. The primary objective was to compare experimentally measured force peaks (F1max-E and F2max-E) to the force peaks that were predicted by an engineering based force prediction model (F1max-M and F2max-M), at heights greater than 5cm. The second objective was to describe the relationships between the experimentally measured peak forces and forearm bone and muscle strength properties, body mass, and stature as a function of fall height.<p> Methods. Using 3D motion tracking, we assessed the first (F1max) and second (F2max) peak forces from 10 young adults (5 male; 5 female) who volunteered to fall from heights up to 25cm onto a foam covered force plate. Peripheral QCT was used to determine the bone strength index (BSIc), strength-strain index (SSIp), and muscle cross sectional area (MCSA) of each participant. Two 2x8 between-within factorial ANOVAs determined the difference between the experimental and model force peaks, with post hoc analyses at all fall heights. Pearson's correlation was used to determine the relationship between the pQCT-derived bone and muscle strength indices and the force peaks.<p> Results. There was no significant differences between F1max-E and F1max-M across all fall heights, but the model significantly over-predicted the F2max-E across all fall heights. After controlling F1max-E and F2max-E for body mass, the force peaks appeared to be weakly related to the anthropometric as well as bone and muscle strength outcomes (r=0.2-0.7, p>0.05). The relationship between bone and muscle strength outcomes appeared to have a tendency to get stronger at higher fall heights.<p> Conclusion. The model predicted experimental F1max, but not experimental F2max. This study presents preliminary pilot results. Larger sample size is needed to confirm whether incorporating bone and muscle strength estimates into fall force prediction models could enhance forearm fracture risk assessments.

Introduction. Wrist (Colles') and forearm fractures commonly occur when a person falls on the outstretched forearm and the force exceeds bone strength. There is lack of experimental evidence testing the available force prediction models and assessing factors that determine forearm forces during a fall.<p> Objective. The primary objective was to compare experimentally measured force peaks (F1max-E and F2max-E) to the force peaks that were predicted by an engineering based force prediction model (F1max-M and F2max-M), at heights greater than 5cm. The second objective was to describe the relationships between the experimentally measured peak forces and forearm bone and muscle strength properties, body mass, and stature as a function of fall height.<p> Methods. Using 3D motion tracking, we assessed the first (F1max) and second (F2max) peak forces from 10 young adults (5 male; 5 female) who volunteered to fall from heights up to 25cm onto a foam covered force plate. Peripheral QCT was used to determine the bone strength index (BSIc), strength-strain index (SSIp), and muscle cross sectional area (MCSA) of each participant. Two 2x8 between-within factorial ANOVAs determined the difference between the experimental and model force peaks, with post hoc analyses at all fall heights. Pearson's correlation was used to determine the relationship between the pQCT-derived bone and muscle strength indices and the force peaks.<p> Results. There was no significant differences between F1max-E and F1max-M across all fall heights, but the model significantly over-predicted the F2max-E across all fall heights. After controlling F1max-E and F2max-E for body mass, the force peaks appeared to be weakly related to the anthropometric as well as bone and muscle strength outcomes (r=0.2-0.7, p>0.05). The relationship between bone and muscle strength outcomes appeared to have a tendency to get stronger at higher fall heights.<p> Conclusion. The model predicted experimental F1max, but not experimental F2max. This study presents preliminary pilot results. Larger sample size is needed to confirm whether incorporating bone and muscle strength estimates into fall force prediction models could enhance forearm fracture risk assessments.

BACKGROUND: Exercise is a critical treatment modality in persons with Type II Diabetes Mellitus (T2DM), however people with this disease experience chronic fatigue and a decreased exercise capacity, which affects their ability or willingness to participate in physical activity. Studies suggest that this exercise intolerance may be partly due to a reduced exercising muscle blood flow (MBF), and in particular to a reduced ability of red blood cells (RBCs) to evoke ATP-mediated vasodilation and an increase in MBF as they traverse areas of high O2 demand. Additional evidence suggests that caffeine may attenuate this impairment by enhancing the release of ATP from RBCs. HYPOTHESIS: Persons with T2DM would have reduced Forearm Blood Flow (FBF), oxygen consumption (VO2), and exercise tolerance responses to exercise compared to control (CON) subjects, and caffeine would attenuate these impairments. METHODS: T2DM (n = 4) and CON (n = 4) participants performed rhythmic forearm handgrip exercise at an intensity equivalent to 17.5 kg until “task failure” or 20 minutes of exercise was reached, after having consumed either a caffeine (5mg/kg; Caff) or placebo (Pl) capsule. FBF (Doppler and Echo ultrasound of the brachial artery), VO2 and lactate efflux (deep venous blood sampling), forearm vascular conductance (FVK), mean arterial pressure (MAP) and heart rate (HR) were quantified for each minute of exercise. RESULTS: Steady state FBF was similar across groups and treatment conditions (mean ± SE ml/min; CONCaff 553.80 ± 82.35, CONPl 583.42 ± 112.62, T2DMCaff 523.33 ± 105.39, T2DMPl 569.08 ± 134.20, NS), and this was due to similar MAP and FVK (across groups and treatment conditions, NS). VO2 and Time to Task Failure (TTF) were not different between groups and treatment conditions (NS), although TTF tended to be improved with caffeine versus placebo (10.00 ± 2.02 vs 8.24 ± 1.79 min, P=0.295). There was a strong positive relationship between FBF and TTF (r2=0.763; P=0.005). CONCLUSIONS: In the exercise model utilized, persons with T2DM do not have impaired cardiovascular responsiveness or reduced exercise tolerance, and caffeine does not provide any benefit. Differences in exercising MBF may be an underlying mechanism regarding differences in exercise tolerance. / Thesis (Master, Kinesiology & Health Studies) -- Queen's University, 2009-09-16 16:19:42.537

Laktatkonzentrationszeitkurven nach akuter körperlicher Belastung und im Stufentest haben sich ebenso wie der Dauerleistungstest in der sportwissenschaftlichen Leistungsdiagnostik etabliert. Beide Verfahren erlauben, die Leistungsfähigkeit von Sportlern einzuschätzen und die Trainingsbelastung optimal entsprechend der Sportdisziplin zu steuern. Der Unterarm-Ischämie-Test dient dazu, Muskelerkrankungen auf der Basis von Laktat- und Ammoniakkonzentration zu differenzieren. Die Laktat-Konzentrations-Zeitkurven nach akuter Belastung, im Stufentest oder im Dauerleistungstest sowie im Unterarm-Ischämietest werden vorwiegend deskriptiv durch Parameter wie die maximale Laktatkonzentration oder Leistung bei bestimmter Laktatkonzentration ausgewertet. Das Ziel der vorliegenden Untersuchung ist, pharmakokinetische Modelle für auf der Laktatkonzentration basierende Tests zu prüfen und deren praktische Relevanz zu diskutieren. Dabei können der akute Belastungstest und der Vorderarm-Ischämietest wegen eines ähnlichen Laktatprofils (Peak-Kurven), der Dauerleistungstest (kumulative Kurven) und der Stufentest (stetig monoton steigende Kurven) getrennt betrachtet werden. Die Routinelaktatprofile von 13 männlichen Freizeitsportlern (Alter: 20-35 Jahre), die sich einem 3 Minuten dauernden akuten ergometrischen Leistungstest unterzogen, wurden zur Modellbildung herangezogen. Der Unterarm-Ischämietest wurde bei acht Patienten im Alter zwischen 20 und 45 Jahren, bei denen bei Aufnahme die Verdachtsdiagnose einer Muskelerkrankung des Glukose- oder Eiweißstoffwechsels bestand, durchgeführt. Wird die Laktatkonzentrationszeitkurve, oder im Ischämietest auch der Ammoniakkonzentrationzeitkurve, als Summe eines Produktions- und Eliminationsvorgangs, dagestellt, lassen sich zusätzliche Informationen gewinnen. Blutlaktatkonzentrationen (Cb(t)) nach akuter ergometrischer Belastung wurden mittels nichtlinearer Regression an das Grundmodell Cb(t)=Co+B*(exp(-Lp*t )-exp(-Le*t)) angepasst, wobei Co der Ruhelaktatkonzentration und B einer Konstanten entsprechen. Die Laktatproduktionsrate (Lp) und Laktateliminationsrate (Le) differenzieren zwischen der Laktatproduktion einerseits und der Laktatelimination andererseits. Der Quotient Lp/P (P=erbrachte Leistung) stellt ein standardisiertes Maß für die Laktatproduktion im Muskel dar. Dagegen charakterisiert der Quotient Le/P die Elimination des Laktats aus dem Blutkompartiment. Über die Beziehung HWZp = ln2/Lp und HWZe = ln2/Le können die entsprechenden Halbwertszeiten berechnet werden. Eine reine Laktatproduktionszeitkurve (Cp(t)) lässt sich über die Beziehung Cp(t) = Cb(t) + Le*( ∫Cb(t)-Co*t) rekonstruieren und zeigt anschaulich den Verlauf der Laktatproduktion. Die Produktionszeitkurve erreicht im Verlauf der Elimination asymptotisch einen Maximalwert (Pm) und kann, identische Verteilungsvolumina des Laktats (Vdl) vorausgesetzt, über die Beziehung Ml = Vdl * Pm Informationen über die insgesamt freigesetzte Laktatmenge (Ml) geben. 19 gesunde Probanden unterzogen sich einem standardisierten Stufentest unter folgenden Bedingungen: Vor Beginn der Bergtour (Meereshöhe, SLa), nach Ankunft auf 1700 m (1700a), nach 10tägigem moderaten Training (Bergwandern zwischen 1700 und 3000m, 6 h pro Tag) auf 1700 m (1700b) sowie nach 4 Wochen (kein spezifisches Training) erneut auf Meereshöhe (SLb). Primäres Ziel der Auswertung war, mit der Potenzfunktion der allgemeinen Form Y(x) = A+ B * X^C den funktionalen Zusammenhang zwischen Laktatkonzentration und Belastung bzw. Herzfrequenz und Belastung zu beschreiben. Neben den modellabhängigen Faktoren (Ordinatenabschnitt, Steigungsfaktor, Exponent) ließen sich durch die AUC(70-280) (Area under the curve 70 bis 280 Watt Leistung) das Ausmaß der Laktatproduktion, die entsprechende mittlere Konzentration (Cm) und durch die Laktatkonzentrationen bei 70 und 280 Watt (LT-70, LT-280) der Laktatanstieg charakterisieren. Der Dauerleistungstest hat sich neben dem akuten ergometrischen Belastungstest in der sportmedizinischen Leistungsdiagnostik als Methode etabliert. Bisher konzentrierte sich die Auswertung auf die maximalen Blutlaktatkonzentrationen im steady state. Die Autoren schlagen verschiedene Modelle vor, sowohl empirische als auch mechanistische, um die Laktatkonzentrationszeitkurve im Dauerleistungstest zu beschreiben. Neben der maximalen Konzentration können nach Berechnung der Modellkurven durch nichtlineare Regression Konzentrationen zu definierten Bedingungen (z. B. LT20 = Laktat nach 20 Minuten) oder die Steigung der Kurve beurteilt werden. Darüber hinaus lässt sich die AUC (Area under the curve) als Ausmaß für die Laktatbildung während des Dauerleistungstests mit der Trapezregel bestimmen. Zusammenfassend zeigen die Untersuchungen, dass in allen Verfahren der Laktatdiagnostik, dem akuten Belastungstest, der Standardergometrie, dem Laktatischämietest und dem Dauerleistungstest, signifikante und praktikable pharmakokinetische Modelle berechnet werden können. Sie erlauben es, die Ergebnisse mittels Modellparametern zu quantifizieren und zu vergleichen. / Lactate concentration versus time curves following acute physical exercise, the standard exercise test using increasing levels of work load and the steady state exercise test have been established methods to characterise the fitness of athletes and to control training intensity. The ischemic forearm exercise test (IFET) is used to detect metabolic disorders of muscles based on lactate and ammonia concentration during exercise under ischemia. Lactate concentration curves following acute exercise, standard ergometries and steady state tests as well as IFET are generally analysed descriptively, i. e. maximum lactate concentrations or work load with regard to defined lactate concentrations are used. The primary objective of this study was to assess pharmacokinetic models for lactate in exercise tests and to discuss the relevance in sports science. For practical purpose, the models used in acute and IFET (asymmetric peak curves), the steady state exercise test (cumulative curves) and standard exercise tests (continuously increasing function) are dealt with separately. Routine lactate profiles of 13 male nonprofessional athletes (age: 20-35) years who underwent an acute ergometry lasting 3 minutes were used to assess different pharmacokinetic models. An IFET was performed in 8 patients (Age: 20-45 years) supposed to have disorders of glucose metabolism or lack of myoadenylate deaminase. Lactate concentration versus time curves were fitted by means of non-linear regression to different kinetic models. The modified basic curve Cb(t)=Co+B*(exp(-Lp*t )-exp(-Le*t)), where Cb denotes the baseline concentration, B a constant, Le denotes the lactate elimination constant and Lp the “absorption or production” constant, yielded remarkable nonlinear regression results in for both test settings. Lactate concentration versus time curves in acute exercise tests are mostly assessed descriptively by means of parameters such as maximum concentration or workload with regard to specified lactate levels. Additional diagnostic information can be obtained, if production and elimination processes of the concentration versus time curve are separated. Production rate (Lp) and elimination rate (Le) of lactate are to define the shape of the curve. The ratio Lp/P (P=performance, work load), where Lp denotes the workload of the ergometer, can be considered as a standardized criterion of lactate production in the muscle. On the contrary, the ratio Le/P characterizes the elimination process from the vascular compartment. The corresponding half-lives [Tp, Te] are obtained using the relations Tp = ln2/Lp and Te = ln2/Le. The absolute lactate production versus time curve [Cp(t)] is given by the following equation: Cp(t) = Cb(t) + Le*( ∫Cb(t)-Co*t). The production versus time curve reaches a maximum value (Pm) after termination of the elimination process. If lactate has identical volumes of distribution (Vdl), Pm characterizes the total amount of lactate production (Ml) due to the relation Ml = Vdl*Pm. Nineteen healthy volunteers were exposed to a standardized exercise test at sea level (SLa), at an altitude of 1700 m before (1700a) and after a moderate 10 day mountain training (1700b), with a final control four weeks later at sea level (SLb). Vital signs, blood lactate and arterial oxygen saturation were determined prior, during or after the exercise test. The primary aim of the study was to fit the power function Y(X) = A+ B * X^C as a model for lactate versus workload and heart rate versus workload data. Apart from model characteristics (intercept, slope, exponent) the extent of lactate production could be estimated by the model independent characteristic AUC(70-280) (Area under the curve between 70 and 280 Watt) and the corresponding average concentration (Cm). The degree of lactate increase was characterized by means of the lactate concentration at 70 and 280 Watt (LT-70, LT-280), respectively. Apart from the standard and acute exercise test the steady state exercise test has gained increasing relevance in practice of sports medicine. So far, lactate curves of steady state tests were characterised by means of maximum. The author suggests several models, both empirical and mechanistic models, in order to fit lactate concentration versus time curves of the steady state ergometry. In addition to the maximum lactate concentration fitted nonlinear regression curves allow to assess the concentrations at defined conditions (e.g. LT20=lactate after 20 minutes of steady state workload, EC50 of the Emax model) or the slope of the curve. Moreover, the AUC(0-tx) – a measure for the extent of lactate production – can be calculated using the trapezoidal rule. In conclusion, in all lactate based tests, acute and standard ergometry, ischemic forearm test and steady state exercise test, concentration versus time data were fitted suitable pharmacokinetic models which allow to quantify and compare the results.

Despite over a century of inquiry, the mechanisms that achieve the close matching of oxygen supply to demand during exercise remain elusive. It has been proposed that in addition to its role as the primary oxygen carrier, the red blood cell (RBC) functions as a roving oxygen sensor, linking the oxygen demand at the muscle with oxygen delivery via the circulation (Ellsworth et al. 1995). It is hypothesised that the RBC would release adenosine triphosphate (ATP) in proportion to the number of unoccupied binding sites on the haemoglobin molecule as it traverses regions of high oxygen demand such as the microcirculation of active skeletal muscle. ATP would then stimulate the release of vasodilatory substances from the endothelium which would diffuse to neighbouring vascular smooth muscle resulting in vasodilation and an increase in blood flow in accordance with the oxygen demand set by the muscle. The first step in establishing a role for this mechanism during exercise in humans is to determine whether ATP increases in the venous blood draining an active muscle bed. Based on the handful of published studies, there is an increase in ATP concentration in the femoral vein during knee extensor exercise. However the response has not been studied in other vascular beds in humans. As such, the main aim of this thesis was to measure the ATP response to dynamic handgrip exercise. Secondary aims were to determine whether the response was modified by hypoxia, and to provide information about the timing of the changes in ATP concentration during a bout of handgrip exercise. These questions were addressed in Studies 3 and 4. Because blood flow is central to this hypothesis, a substantial portion of this thesis was also associated with the measurement of forearm blood flow (FBF) using venous occlusion strain gauge plethysmography (VOSGP), and this was conducted in Studies 1 and 2. VOSGP is based on the assumption that with venous outflow prevented, any increase in limb volume is proportional to the rate of arterial inflow. The rate of arterial inflow is determined as the slope of the change in limb volume over time. The slope must be calculated over the initial linear portion of this relationship, when arterial inflow is unaffected by the inevitable rise in venous pressure associated with venous occlusion. VOSGP was initially used to measure blood flow at rest and in response to pharmacological interventions which produced only modest increases in arterial inflow (Joyner et al. 2001). However, measurement of the high rates of arterial inflow that occur with exercise may challenge the limits of this technique. Tschakovsky et al. (1995) reported a marked reduction in arterial inflow over the first four cardiac cycles during venous occlusion following static handgrip exercise that elevated blood flow to 22-24 mL/min/100mL. Only during the first cardiac cycle was arterial inflow unaffected by cuff inflation. As such, the window for measuring high rates of arterial inflow may be very brief. Therefore Study 1 aimed to determine whether blood flow could be measured using VOSGP across the range of arterial inflows that occur with dynamic handgrip exercise. Participants (n = 7) completed four, five-minute bouts of dynamic handgrip exercise at 15, 30, 45, and 60% of maximum voluntary contraction (MVC). FBF was measured using VOSGP at rest, and following five minutes of dynamic handgrip exercise. The slope of the change in limb volume was measured over the first one, two, three, and four consecutive cardiac cycles following the onset of occlusion. FBF was 2.5 ± 0.5 at rest, and 16.5 ± 4.9, 24.9 ± 9.4, 44.1 ± 22.0, and 57.8 ± 14.9 mL/min/100mL following five minutes of exercise at 15, 30, 45, and 60% MVC, respectively. At rest, arterial inflow decreased across the four cardiac cycles (P = 0.017 for the main effect), however post-hoc pairwise comparisons revealed no significant differences between any of the cardiac cycles. In contrast, the inclusion of two, three, or four cardiac cycles at 30 and 60% MVC, and three or four cardiac cycles at 15 and 45% MVC resulted in reductions in calculated arterial inflow compared with using the first cardiac cycle alone (P > 0.05). The inclusion of just two cardiac cycles resulted in a 9-26% reduction in calculated arterial inflow depending on the workload. This reduction was even more pronounced when three (19-40%) or four (26-50%) cardiac cycles were included. In conclusion, resting FBF can be calculated over at least four cardiac cycles during venous occlusion at rest. However, exercising FBF should be calculated from the first cardiac cycle only following dynamic handgrip exercise across the range of intensities used in this study. This extends the findings of Tschakovsky et al. (1995) who demonstrated this effect following handgrip exercise at a single intensity. Study 2 was designed to establish the FBF response to dynamic handgrip exercise, whether the workloads produced different blood flow responses, and to establish the within- and between-day reproducibility of FBF measured using VOSGP. In Part A (within-day reproducibility), participants (n = 7) completed three trials of dynamic handgrip exercise at four intensities (15, 30, 45, and 60% MVC), with each exercise trial separated by 10 minutes of rest. In Part B (between-day reproducibility) participants (n = 7) completed three trials of dynamic handgrip exercise at 15, 30, and 45% MVC on three separate days within a two week period. FBF was measured at rest, and each minute of exercise during brief (5-7 second) pauses in contractions. FBF response. FBF increased from rest at all workloads (P > 0.05), and then plateaued between Minutes 1 to 5 at the 15 and 30% MVC workloads and between Minutes 2 and 5 at the 45% workload (P > 0.05 for each minute compared to Minute 5). Too few participants completed the 60% workload to permit any statistical analysis. FBF reached values of 13.0 ± 2.0, 26.8 ± 8.4, 44.8 ± 14.9, and 52.9 ± 5.1 mL/min/100mL in the final minute of exercise at the 15, 30, 45, and 60% MVC workloads. FBF was different between the 15, 30, and 45% workloads by Minute 3 (P > 0.05). Reproducibility. The within-day test-retest reliability of exercising FBF was poor to moderate (ICC = 0.375-0.624) with individual coefficients of variation (CVs) ranging from 6-25%, 9-23%, and 9-31% for the 15, 30, and 45% MVC workloads, respectively. The between-day test-retest reliability for resting FBF was moderate (ICC = 0.644, P > 0.05; individual CVs between 1 and 31%). Between-day test-retest reliability for exercising FBF was poor to moderate (ICC = 0.381-0.614), with individual CVs ranging from 14-24%, 8-23%, and 6-18% for the 15, 30, and 45% workloads, respectively. It was concluded from this study that VOSGP provides adequately reproducible measurements to detect changes in FBF of the magnitude seen between workloads in this study. However, the variability in the measurement precludes its use when smaller differences are of interest. Based on the previous findings reporting an increase in ATP concentration during dynamic knee extensor exercise in the leg (Gonzalez-Alonso et al. 2002; Yegutkin et al. 2007), Study 3 was designed to determine whether ATP concentration increased in the venous effluent during dynamic handgrip exercise in the forearm. Since the deoxygenation of haemoglobin is a primary stimulus for ATP release from red blood cells, a further aim was to determine whether this response was augmented by systemic hypoxia. Participants (n = 6) completed four, five-minute bouts of dynamic handgrip exercise at 30, 45, 65, and 85% MVC under normoxia (inspired oxygen fraction = 0.21) and hypoxia (inspired oxygen fraction = 0.12). Blood samples for the determination of ATP concentration were drawn at rest and 180 seconds after the onset of exercise at each workload from a catheter inserted into a forearm vein. Venous plasma ATP concentration at rest was 0.28 ± 0.11 μM/L and remained unchanged during exercise at workloads up to 85% MVC (P > 0.05). Systemic hypoxia, sufficient to reduce arterial oxygen saturation to 83 ± 2%, also failed to alter the plasma ATP concentration (P = 0.148). The lack of a change in ATP concentration was unexpected but there are several possible explanations. It is possible, although unlikely, that ATP was not released in the forearm microcirculation. The previous demonstration that ATP increased in response to static handgrip exercise (Forrester and Lind 1969) would suggest that this was probably not the case. When considered in the context of the findings from Study 4, the most plausible explanation is that a less than optimal blood sampling site may have hindered the measurement of a change in ATP. The blood flow response at the onset of dynamic exercise in the forearm is at least biphasic; Phase 1 describes the immediate, large increase in blood flow within 2 seconds of the onset of exercise and is believed to be governed by mechanical factors whereas Phase 2 has a latency of ~20 seconds and describes a further, slower increase until blood flow reaches steady state (Saunders et al. 2005b). The temporal characteristics of Phase 2, along with the fact that blood flow during this phase is closely related to the metabolic rate of the muscle, suggest regulation by metabolic factors. Currently there is scant evidence detailing the time course of vasodilator release, although it is important to demonstrate that the release of a vasodilatory substance precedes the blood flow response it is proposed to influence (Delp 1999). ATP is released from red blood cells in proportion to the offloading of oxygen and a reduction in the oxygen content of venous blood draining a muscle bed occurs within 10 seconds of the onset of exercise. Thus the release of ATP should follow soon thereafter. As such, Study 4 was designed to determine whether ATP increased in the venous effluent of the forearm following 30 and 180 seconds of dynamic handgrip exercise at 45% MVC; and whether this increase corresponded with a decrease in venous oxygen content. Participants (n = 10) completed two bouts of dynamic handgrip exercise at 45% MVC; the first was one minute in duration, and the second was four minutes in duration. Venous blood samples for the determination of ATP and venous oxygen content were drawn at rest and during exercise from a catheter inserted in a retrograde manner into the median cubital vein. Arterialised samples for the estimation of arterial blood gases and ATP concentration were obtained from the non-exercising hand. ATP concentration in arterialised blood from the non-exercising arm was 0.79 ± 0.30 μM/L at rest and remained unchanged at both time points during exercise (P > 0.05). ATP concentration in the venous blood of the exercising arm increased from 0.60 ± 0.17 μM/L at rest to 1.04 ± 0.33 μM/L 30 seconds after the onset of exercise (P > 0.05), and remained at this higher level after 180 seconds (0.92 ± 0.26 μM/L, P > 0.05 versus rest). This corresponded with a decrease in venous oxygen content from 103 ± 23 mL/L at rest to 68 ± 16 mL/L 30 seconds after the onset of exercise (P > 0.05) and 76 ± 15 mL/L (P > 0.05 versus rest) 180 seconds into exercise. Furthermore, at 180 seconds of exercise, ATP concentration was moderately and inversely related to venous oxygen content (r = -0.651, p > 0.05). In conclusion, this study provides the first evidence that ATP concentration is increased in the blood draining the exercising forearm muscles in response to dynamic handgrip exercise. The finding that ATP concentration was increased just 30 seconds after the onset of exercise is also novel, and particularly interesting in the context of the recently reported dynamic response characteristics of the forearm blood flow response. In conclusion, the work contained within this thesis provides several important findings. The first study has provided evidence that measuring high rates of arterial inflow using VOSGP is possible, but that the window for making these measurements is small, probably as brief as a single cardiac cycle. The second study demonstrated that while the reproducibility of forearm blood flow measurements using VOSGP is poor, it is adequate to detect the large changes that occurred between workloads. However, VOSGP cannot be used to detect more modest differences. Common to both Study 3 and 4 was the measurement of ATP at rest, and 180 seconds after the onset of dynamic handgrip exercise at 45% MVC. The primary difference was the position of the catheter which was inserted in an antegrade manner in Study 3, and in a retrograde manner in Study 4. Since ATP was unchanged in Study 3 but increased under similar conditions in Study 4, it is likely that ATP was also released during exercise in Study 3, but that a less than optimal blood sampling site precluded its measurement. This illustrates the necessity to sample blood from as close as possible to the probable site of ATP release, the muscle microcirculation. The most important and novel findings from this body of work come from Study 4. This is the first study to demonstrate an increase in ATP concentration in the forearm in response to dynamic handgrip exercise. However, the most novel finding was that ATP concentration was elevated just 30 seconds after the onset of exercise. Such an early increase has not previously been reported during dynamic exercise in any vascular bed. This is an important finding since establishing the time course for the release of vasodilatory substances is critical to our understanding of the mechanisms that regulate blood flow during exercise.

Thesis (Ph. D.)--Virginia Commonwealth University, 2009. / Prepared for: Dept. of Health and Human Performance. Title from resource description page. Includes bibliographical references.

Introduction: A complex interaction between muscles, tendons, bones, joints and nerves are required for optimal function of the human hand. It is known that an individual’s grip strength is vital for performance of physical demanding tasks such as strength training with free weights. Strength training including a thicker grip around the bar may enhance the strength of the grip in the athlete without other special routines for grip strength development. The purpose of this investigation was to examine the difference in performance in multiple repetitions in two strength training exercises using two different sizes on the bar, to look for correlations between grip strength of the subjects hand and the amount of repetitions executed with two different size of the bar and finally the correlation of hand size and the amount of repetitions executed with two different size of the bar. Method: 15 strength training men (23,9 ± 4,1 years), underwent measurements of hand size, maximum grip strength, 1 repetition maximum (1RM), a 80 % of 1RM weight strength test with two different  bar sizes. Results: The results from the present investigation indicates a 21,1 % reduction of 80 % of 1 RM weight performance in repetitions executed in the bench press with the thicker diameter of the bar and a 66,2 % reduction in repetitions executed with a 80 % of 1 RM weight in the lying bench row with the thicker diameter of the bar. The size of the hand or the maximum grip strength does not influences the performance in the 80 % of 1 RM strength test. Conclusion: With support of the results from this present investigation the size of the bar diameter significant influences the performance in maximum repetitions executed in a set in strength training with free weights, in a rowing exercise the repetitions executed reduced with 66,2 % and in the bench press the reduction of executed repetitions were 21,1 % with the thicker diameter of the bar. The size of the hand do not influences the performance of maximal executed repetitions with the thicker bar diameter. Maximal grip strength has no influence of the performance according to the findings of this investigation.

Fractures in the distal of the forearm are the most popular fractures of the muscular skeletal system. The aim of this study was to evaluate the effectiveness of joints mobilization method after fractures in the distal of the forearm. The goals of the study: To evaluate the effectiveness of different physical therapeutics methods in reconstruction of hand joints’ amplitudes as well as local hand brawn and function. To estimate the influence of different physical therapeutics methods on the change of hand pain while reconstructing hand’s functions. Organization and methods of the study. The examination was performed with patients grouped into two groups according to casual selection: 1. experimental group – patients underwent active and passive exercises and the joints mobilization method; 2. control group – patients underwent active and passive physical therapeutics exercises. Each group consisted of 30 patients whose mean age accordingly is 52,6±2,3 and 51,6±3,4 years. The study was performed at Kaunas Red Cross Hospital. A closed reduction was performed on all the patients after fracture in the distal of the forearm. Immobilization period lasted for 5 – 6 weeks. The physical therapeutics was started 3 – 5 days after removing the immobilization. Active exercises, passive movements of wrist and fingers joints and mobilization of the joints were applied to the patients of the experimental group. Passive movements as well as active exercises of wrist and fingers joints... [to full text]

Optimal management of fractures, post-traumatic arthritis and instability of the distal radioulnar joint (DRUJ) requires an understanding of the forces existing across this joint as a function of the activities of daily living. However, such knowledge is currently incomplete. The goal of this research was to quantify the loads that occur at the DRUJ during forearm rotation and to determine the effect that individual muscles have on those loads. Human and cadaver studies were used to analyze the shear (A-P), transverse (M-L) and resultant forces at the DRUJ and to determine the role that 15 individual muscles had on those forces. Data for scaling the muscles forces came from EMG analysis measuring muscle activity at nine positions of forearm rotation in volunteers during isometric pronation and supination. Muscle orientations were determined from the marked muscle origin and insertion locations of nine cadaveric arms at various stages of forearm rotation. The roles that individual muscles played in DRUJ loading were analyzed by removing the muscle of interest from the analysis and comparing the results. The EMG portion of this study found that the pronator quadratus, pronator teres, brachioradialis, flexor carpi radialis and palmaris longus contribute significantly to forearm pronation. The supinator, biceps brachii, and abductor pollicis longus were found to contribute significantly to supination. The results of the DRUJ analysis affirm that large transverse forces pass from the radius to the ulnar head at all positions of forearm rotation during pronation and supination (57.5N-181.4N). Shear forces exist at the DRUJ that act to pull the radius away from the ulna in the AP direction and are large enough to merit consideration when examining potential treatment options (7.9N-99.5N). Individual muscle analysis found that the extensor carpi radialis brevis, extensor pollicis longus, extensor carpi ulnaris, extensor indicis and palmaris longus had minimal effect on DRUJ loading. Other than the primary forearm rotators (pronator quadratus, pronator teres, supinator, biceps brachii), the muscles that exhibited the largest influence on DRUJ loading were the abductor pollicis longus, brachialis, brachioradialis, extensor carpi ulnaris, flexor carpi radialis, and flexor carpi ulnaris.

Bibliographische Beschreibung Krupnik, Viktoria Koordination zwischen Atmung und rhythmischen Unterarm-Trackingbewegungen bei unterschiedlichen mentalen Bedingungen der Trackingaufgabe Universität Leipzig, Dissertation 55 Seiten, 49 Lit., 9 Abb., 2 Tab., 1 Artikel Referat: Präzisionsbewegungen kommt eine steigende Rolle im täglichen Leben zu, z.B. bei der Bedienung von Smartphones und Computern. Sie unterliegen, wie alle motorischen Aktionen, der Koordination. Koordination bezeichnet die gegenseitige Beeinflussung des zeitlichen Ablaufs gleichzeitig ablaufender motorischer Prozesse, welche die Verschmelzung zu einem gemeinsamen Rhythmus oder die Einstellung stabiler Frequenz- bzw. Phasenbeziehungen zur Folge hat. Von besonderem Interesse ist die Koordination intendierter Bewegungen mit der stetig ablaufenden Atmung, die die Funktionsziele beider Prozesse beeinträchtigen kann. Wir untersuchten Atmungs-Bewegungs-Koordination (MRC) bei visuell geführten Folgebewegungen des Unterarms unter zwei hauptsächlichen Fragestellungen: a) Beeinträchtigt MRC die Genauigkeit der Folgebewegung? b) Wie beeinflussen erhöhte Genauigkeitsanforderung, Üben und aufgabenbezogene Belastung die Stärke der MRC? Ausgangshypothese war, dass die Folgegenauigkeit durch MRC verschlechtert wird. Außerdem vermuteten wir, dass die Stärke der MRC durch erhöhte Aufmerksamkeit und wiederholtes Üben verstärkt, durch höhere aufgabenspezifische Belastung (höhere Komplexität der Bewegung) dagegen verringert wird. 35 Probanden führten 8 Versuche unter verschiedenen Bedingungen durch: positive (gleichsinnige) Signal- Response-Beziehung (SRR), negative (gegensinnige) SRR als aufgabenbezogene Belastung, strenge (Leistungsanforderung) und weniger strenge (lockere) Instruktion. Die Versuche mit positiver und negativer SRR wurden zur Untersuchung von Übungseffekten je dreimal vorgenommen. Während die Stärke der MRC unter allen Bedingungen gleich blieb, variierte das Phasenkopplungsmuster. Unter positiver SRR und weniger strenger Instruktion wurde eine bestimmte Phasenbeziehung zur Atemperiode bevorzugt. Bei negativer SRR und strenger Instruktion zeigte sich ein engeres Kopplungsmuster mit zwei bevorzugten Phasenbeziehungen zur Atemperiode. MRC verbesserte die Folgegenauigkeit unter allen Versuchsbedingungen mit Ausnahme derjenigen mit lockerer Instruktion. Zur Verbesserung der Folgegenauigkeit trug vor allem ein geringerer Amplitudenfehler bei. Die Ergebnisse zeigen, dass erhöhte Konzentration die Phasenkopplung zwischen Folgebewegungen und Atmung verstärkt und die MRC-bedingte Verbesserung der Folgegenauigkeit weiter steigert.:I. INHALTSVERZEICHNIS Seite I. Inhaltsverzeichnis …………………………………………………………………. 3 II. Abkürzungsverzeichnis …………………………………………………………… 5 1. Einleitung ……………………………………………………………………………. 6 1.1. Einführung in die Thematik: Koordination in der Motorik ………………………… 6 1.1.1. Kennzeichen stabiler Koordination ………………………………………………… 8 1.1.2. Koordination der Atmung mit nichtrhythmischen Bewegungen ………………… 9 1.1.3. Wechselseitigkeit der MRC …………………………………………………………. 9 1.1.4. Worin könnte die biologische Funktion der Atmungs-Bewegungs-Koordination bestehen? ……………………………………………………………………………. 10 1.1.5. Einflüsse auf die Stärke bzw. Stabilität der Atmungs-Bewegungs-Koordination.. 11 1.2. Methodische Betrachtungen: Visuell geführte Folgebewegungen (Tracking-Tests) ……………………………………………………………………… 13 2. Zielstellungen der Arbeit …………………………………………………………. 15 3. Publikation …………………………………………………………………………. 16 4. Zusätzliches Material ………………………………………………………………. 31 4.1. Zusätzliche Probandendaten ………………………………………………………. 31 4.1.1. Charakterisierung der Probanden …………………………………………………. 31 4.1.2. Befindlichkeitsbezogene Auskünfte der Probanden ……………………………… 33 4.1.3. Zusammenfassende Bewertung der zusätzlichen Probandendaten ……………. 34 4.2. Versuchsplanung …………………………………………….……………………….. 34 4.3. Ergänzungen zum Versuchsaufbau ………………………………………………… 35 4.4. Beurteilung der Koordinationsstärke ………………………………………………. 37 4.4.1. Bestimmung des MRC-Grades ……………………………………………………. 37 4.4.2. Analyse der Verteilung der relativen Phase ………………………………………. 39 4.5. Zusätzliche graphische Darstellungen von Ergebnissen ………………………… 39 4.5.1. Verteilung der relativen Phasen ……………………………………………………. 39 4.5.2. Vergleich der Amplitudenabweichungen in koordinierten und nicht- koordinierten Abschnitten …………………………………………………………… 42 5. Zusammenfassung ………………………………………………………………… 44 6. Literaturverzeichnis ......................................................................................... 48 Anhang III. Publikationsverzeichnis …………………………………………………………… 52 IV. Erklärung über die eigenständige Abfassung der Arbeit …………………… 53 V. Beitrag der Autoren zur Publikation ……………………………………………. 54 VI. Danksagung …………………………………………………………………………. 55