10 resultados para pelvic floor muscle training


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In young adults, improvements in the rate of force development as a result of resistance training are accompanied by increases in neural drive in the very initial phase of muscle activation. The purpose of this experiment was to determine if older adults also exhibit similar adaptations in response to rate of force development (RFD) training. Eight young (21-35 years) and eight older (60-79 years) adults were assessed during the production of maximum rapid contractions, before and after four weeks of progressive resistance training for the elbow flexors. Young and older adults exhibited significant increases (P<0.01) in peak RFD, of 25.6% and 28.6% respectively. For both groups the increase in RFD was accompanied by an increase in the root mean square (RMS) amplitude and in the rate of rise (RER) in the electromyogram (EMG) throughout the initial 100 ms of activation. For older adults, however, this training response was only apparent in the brachialis and brachioradialis muscles. This response was not observed in surface EMG recorded from the biceps brachii muscle during either RFD testing or throughout training, nor was it observed in the pronator teres muscle. The minimal adaptations observed for older adults in the bifunctional muscles biceps brachii and pronator teres are considered to indicate a compromise of the neural adaptations older adults might experience in response to resistance training.

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Background and Purpose—Severe upper limb paresis is a major contributor to disability after stroke. This study investigated the efficacy of a new nonrobotic training device, the Sensorimotor Active Rehabilitation Training (SMART) Arm, that was used with or without electromyography-triggered electrical stimulation of triceps brachii to augment elbow extension, permitting stroke survivors with severe paresis to practice a constrained reaching task.

Methods—A single-blind, randomized clinical trial was conducted with 42 stroke survivors with severe and chronic paresis. Thirty-three participants completed the study, of whom 10 received training using the SMART Arm with electromyography-triggered electrical stimulation, 13 received training using the SMART Arm alone, and 10 received no intervention (control). Training consisted of 12 1-hour sessions over 4 weeks. The primary outcome measure was “upper arm function,” item 6 of the Motor Assessment Scale. Secondary outcome measures included impairment measures; triceps muscle strength, reaching force, modified Ashworth scale; and activity measures: reaching distance and Motor Assessment Scale. Assessments were administered before (0 weeks) and after training (4 weeks) and at 2 months follow-up (12 weeks).

Results—Both SMART Arm groups demonstrated significant improvements in all impairment and activity measures after training and at follow-up. There was no significant difference between these 2 groups. There was no change in the control group.

Conclusions—Our findings indicate that training of reaching using the SMART Arm can reduce impairment and improve activity in stroke survivors with severe and chronic upper limb paresis, highlighting the benefits of intensive task-oriented practice, even in the context of severe paresis.

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Although it has long been supposed that resistance training causes adaptive changes in the CNS, the sites and nature of these adaptations have not previously been identified. In order to determine whether the neural adaptations to resistance training occur to a greater extent at cortical or subcortical sites in the CNS, we compared the effects of resistance training on the electromyographic (EMG) responses to transcranial magnetic (TMS) and electrical (TES) stimulation. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle of 16 individuals before and after 4 weeks of resistance training for the index finger abductors (n=8), or training involving finger abduction-adduction without external resistance (n=8). TMS was delivered at rest at intensities from 5% below the passive threshold to the maximal output of the stimulator. TMS and TES were also delivered at the active threshold intensity while the participants exerted torques ranging from 5 to 60% of their maximum voluntary contraction (MVC) torque. The average latency of MEPs elicited by TES was significantly shorter than that of TMS MEPs (TES latency=21.5+/-1.4 ms; TMS latency=23.4+/-1.4 ms; P

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It has long been believed that resistance training is accompanied by changes within the nervous system that play an important role in the development of strength. Many elements of the nervous system exhibit the potential for adaptation in response to resistance training, including supraspinal centres, descending neural tracts, spinal circuitry and the motor end plate connections between motoneurons and muscle fibres. Yet the specific sites of adaptation along the neuraxis have seldom been identified experimentally, and much of the evidence for neural adaptations following resistance training remains indirect. As a consequence of this current lack of knowledge, there exists uncertainty regarding the manner in which resistance training impacts upon the control and execution of functional movements. We aim to demonstrate that resistance training is likely to cause adaptations to many neural elements that are involved in the control of movement, and is therefore likely to affect movement execution during a wide range of tasks.

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Strategics for the control of human movement are constrained by the neuroanatomical characteristics of the motor system. In particular, there is evidence that the capacity of muscles for producing force has a strong influence on the stability of coordination in certain movement tasks. In the present experiment, our aim was to determine whether physiological adaptations that cause relatively long-lasting changes in the ability of muscles to produce force can influence the stability of coordination in a systematic manner. We assessed the effects of resistance training on the performance of a difficult coordination task that required participants to synchronize or syncopate movements of their index finger with an auditory metronome. Our results revealed that training that increased isometric finger strength also enhanced the stability of movement coordination. These changes were accompanied by alterations in muscle recruitment patterns. In Particular, the trained muscles were recruited in a more consistent fashion following the programme of resistance training. These results indicate that resistance training produces functional adaptations of the neuroanatomical constraints that underlie the control of voluntary movement.

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This exploratory study was undertaken to investigate the mechanisms that contributed to improvements in upper limb function following a novel training program. Surface electromyography (EMG) was used to examine training-induced changes in the pattern of triceps and biceps activation during reaching tasks in stroke survivors with severe paresis in the chronic stage of recovery. The EMG data were obtained in the context of a single blind randomised clinical trial conducted with 42 stroke survivors with minimal upper limb muscle activity and who were more than 6 months post-stroke. Of the 33 participants who completed the study, 10 received training of reaching using a non-robotic upper limb training device, the SMART Arm, with EMG triggered functional electrical stimulation (EMG-stim), 13 received training of reaching using the SMART Arm alone, and 10 received no intervention. Each intervention group engaged in 12 1-h training sessions over a 4-week period. Clinical and laboratory measures of upper limb function were administered prior to training (0 weeks), at completion (4 weeks) and 2 months (12 weeks) after training. The primary outcome measure was 'upper arm function' which is Item 6 of the Motor Assessment Scale (MAS). Laboratory measures consisted of two multijoint reaching tasks to assess 'maximum isometric force' and 'maximum distance reached'. Surface EMG was used to monitor triceps brachii and biceps brachii during the two reaching tasks. To provide a comparison with normal values, seven healthy adults were tested on one of the reaching tasks according to the same procedure. Study findings demonstrated a statistically significant improvement in upper limb function for stroke participants in the two training groups compared to those who received no training however no difference was found between the two training groups. For the reaching tasks, all stroke participants, when compared to normal healthy adults, exhibited lower triceps and biceps activation and a lower ratio of triceps to biceps activation. Following training, stroke participants demonstrated increased triceps activation and an increased ratio of triceps to biceps activation for the task that was trained. Better performance was associated with greater triceps activation and a higher ratio of triceps to biceps activation. The findings suggest that increased activation of triceps as an agonist and an improved coordination between triceps and biceps could have mediated the observed changes in arm function. The changes in EMG activity were small relative to the changes in arm function indicating that factors, such as the contribution of other muscles of reaching, may also be implicated.

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Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

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There is a perception amongst some of those learning computer programming that the principles of object-oriented programming (where behaviour is often encapsulated across multiple class files) can be difficult to grasp, especially when taught through a traditional, didactic ‘talk-and-chalk’ method or in a lecture-based environment.
We propose a non-traditional teaching method, developed for a government funded teaching training project delivered by Queen’s University, we call it bigCode. In this scenario, learners are provided with many printed, poster-sized fragments of code (in this case either Java or C#). The learners sit on the floor in groups and assemble these fragments into the many classes which make-up an object-oriented program.
Early trials indicate that bigCode is an effective method for teaching object-orientation. The requirement to physically organise the code fragments imitates closely the thought processes of a good software developer when developing object-oriented code.
Furthermore, in addition to teaching the principles involved in object-orientation, bigCode is also an extremely useful technique for teaching learners the organisation and structure of individual classes in Java or C# (as well as the organisation of procedural code). The mechanics of organising fragments of code into complete, correct computer programs give the users first-hand practice of this important skill, and as a result they subsequently find it much easier to develop well-structured code on a computer.
Yet, open questions remain. Is bigCode successful only because we have unknowingly predominantly targeted kinesthetic learners? Is bigCode also an effective teaching approach for other forms of learners, such as visual learners? How scalable is bigCode: in its current form can it be used with large class sizes, or outside the classroom?