Posts Tagged progressive resistance training

[ARTICLE] Progressive resistance training increases strength after stroke but this may not carry over to activity: a systematic review – Full Text

Article Outline

  1. Introduction
  2. Method
    1. Identification and selection of studies
    2. Assessment of characteristics of the studies
      1. Quality
      2. Participants
      3. Intervention
      4. Outcome measures
    3. Data analysis
  3. Results
    1. Flow of trials through the review
    2. Characteristics of included trials
      1. Quality
      2. Participants
      3. Intervention
      4. Outcome measures
    3. Effect of intervention
      1. Strength
      2. Activity
  4. Discussion
  5. Appendix 1. Supplementary data
  6. References

Abstract

Question

Does progressive resistance training improve strength and activity after stroke? Does any increase in strength carry over to activity?

Design

Systematic review of randomised trials with meta-analysis.

Participants

Adults who have had a stroke.

Intervention

Progressive resistance training compared with no intervention or placebo.

Outcome measures

The primary outcome was change in strength. This measurement had to be of maximum voluntary force production and performed in muscles congruent with the muscles trained in the intervention. The secondary outcome was change in activity. This measurement had to be a direct measure of performance that produced continuous or ordinal data, or with scales that produced ordinal data.

Results

Eleven studies involving 370 participants were included in this systematic review. The overall effect of progressive resistance training on strength was examined by pooling change scores from six studies with a mean PEDro score of 5.8, representing medium quality. The effect size of progressive resistance training on strength was 0.98 (95% CI 0.67 to 1.29, I2 = 0%). The overall effect of progressive resistance training on activity was examined by pooling change scores from the same six studies. The effect size of progressive resistance training on activity was 0.42 (95% CI –0.08 to 0.91, I2 = 54%).

Conclusion

After stroke, progressive resistance training has a large effect on strength compared with no intervention or placebo. There is uncertainty about whether these large increases in strength carry over to improvements in activity.

via Progressive resistance training increases strength after stroke but this may not carry over to activity: a systematic review – Journal of Physiotherapy

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[ARTICLE] A novel approach for robot-assisted upper-limb rehabilitation – Full Text

This study presented a novel control approach for rehabilitation robotic system using the hybrid system theory and the subject’s bio-damping and bio-stiffness parameters. Resistance training was selected as a paradigm. The proposed control architecture incorporated the physical therapist’s behavior intervention, the stroke survivor’s muscle strength changes, and the robotic device’s motor control into a unified framework. The main focuses of this research were to (i) automatically monitor the subject’s muscle strength changes using the online identified bio-damping/stiffness parameters; (ii) make decisions on the modification of the desired resistive force so as to coincide with the subject’s muscle strength changes; and (iii) generate accommodating plans when the safety-related issues such as spasticity and the abnormal robotic working states happen during the execution of training tasks. A Barrett WAM compliant manipulator-based resistance training system and two experiments including four scenarios were developed to verify the proposed approach. Experimental results with healthy subjects showed that the hybrid system–based control architecture could administrate the subject’s muscle strength changes and the robotic device’s interventions in an automated and safe manner.

Patients suffering from strokes need to receive intensive rehabilitation training to improve range of motion, movement coordination, and muscle strength. These therapy programs are traditionally conducted by physical therapists. However, the efficacy of therapist-centered rehabilitation therapy often relies on the therapist’s subjective clinical experience. In recent years, there is an increasing interest in using robotic devices to help providing rehabilitation training because these devices can provide a variety of highly repetitive movements and training protocols for stroke patients.1

Human–robot interactive control approaches, that specify how rehabilitation robotic devices adaptively interact with stroke survivors in an automated and safe manner, are one of the major challenges in developing robot-assisted training system. The control strategies explored in some early rehabilitation robotic devices, such as the proportional integral derivative controller for MIME,2 the impedance controller for MIT-Manus,3 and the admittance controller for GENTLE/s,4are mainly concentrated on providing constant assistance while not integrating stroke patients’ feedback into the control loop. It is a common hypothesis in the field of robot-assisted rehabilitation that the control approaches, that can close the loop via the patients and further adapt robotic devices’ assistance to the stroke patients’ progress, will be more efficient.5 This issue is typically addressed using assist-as-needed6,7 or user-cooperative control strategies.8,9 In the study by Hussain et al.,6 an adaptive seamless assist-as-needed control scheme is developed for the robotic gait training, which learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. Riener et al.8 presents a patient-cooperative strategy for robotic gait training, and results with healthy subjects show that subjects train more actively and only get support as much as needed. Nonetheless, the potential issue of these methods is that they focus on low-level robotic motor controllers. The training tasks update, the safety-related issues (e.g. spasticity and twitch) monitoring, the robotic working states (e.g. joint torque, voltage, workload, and end-effector velocity) detection, the therapeutic progress assessment, and the decision-making behaviors are all administered by physical therapists.

In the last few years, hierarchical supervisory control strategies have been developed. These approaches incorporate the training tasks update and the physical therapist’s behavior decision into the low-level robotic motor controller. Denève et al.10 merge a high-level sequential controller into a robotic-assisted upper limb rehabilitation system, by which three different low-level control laws for passive, active, and resistance modes can be switched. In the study by Varol et al.,11 a three-level hierarchical supervisory control architecture is proposed, which consists of the lowest level robotic joint torque controller, the middle-level torque references generator, and the high-level intent recognizer. Fuzzy-based hierarchical supervisory control strategies are also presented in our previous studies.12,13 Especially in the study,13 a high-level safety supervisory controller is designed to monitor spasticity-related issues. Great improvements have been made in the current hierarchical supervisory control architecture for incorporating therapists’ behavior-decision experiences. Unfortunately, these supervisory control methods are statically designed concerning some predefined situations, and the absence of dynamic mechanism makes them incapable of dealing with the extended/unexpected events and the complex training tasks coordination. Besides, few rehabilitation robotic control system design takes the safety-related issues into consideration.

In this article, we present a hybrid system–based control architecture using resistance training as a paradigm, which can incorporate the physical therapist’s behavior intervention, the stroke survivor’s muscle strength progress, and the robotic device’s motor control into a unified framework. In fact, over the years, the hybrid systems framework has been effectively used in many fields to model and analyze their performances, such as the power systems,14 the communication networks,15 and the coordinated control of assistive robotic devices for complex tasks.16

The primary focus of this article is to (i) automatically monitor the impaired limb’s muscle strength progress using the online identified bio-impedance changes; (ii) make decisions on the modification of the desired resistive force so as to coincide with the impaired limb’s muscle strength changes; and (iii) be aware of robotic working state/safety-related issues during the execution of training task and to generate accommodating plans when such events happen. The remainder of this article is organized as follows: “Methods” section presents the experimental setup and protocols, bio-impedance parameters identification, and controller development. “Results” section details the results of the proposed control strategy. Some discussions and conclusions are given in sections “Discussions” and “Conclusions”.

 

Experimental setup

The rehabilitation robotic system for upper limb muscle strength training used in the trials, shown in Figure 1(a), consisted of a Barrett WAM™ manipulator, a three dimensional (3-D) force sensor, and an external PC offered by Barrett Technology.17 The standard WAM™ is a four degree of freedom highly dexterous, back-drivable manipulator. Its human-like kinematics and high back drivability enable inherent force-control, haptic interaction, and rehabilitation application. In order to record the force interaction between the impaired limb and the WAM end-effector, a 3-D force sensor was designed and attached to the end-effector. Figure 1(b) and (c) shows the mechanical structure, appearance, and strain gauges distribution of the 3-D force sensor. Force data measured from the sensor must be transformed from the WAM tool frame into its world frame. The graphical user interface developed using Linux/GDK technology, shown in Figure 1(d), was used to display the actual training trajectory when the patient moved his arm in the XOZ vertical plane (O-XYZ coordinates shown in Figure 1(a)), where no reference trajectories were predefined except for several via points. The external PC, running with the Ubuntu Linux system and the Xenomai real-time module, was responsible for executing the control loop and sending high-level commands to the WAM-aided rehabilitation training system. Real-time communication between the external PC and motor Pucks™ was conducted via a high-speed controller area network bus.

figure

Figure 1. The Barrett WAM™ rehabilitation robotic system for upper limb muscle strength training and its attachments. (a) The Barrett WAM™ rehabilitation robotic system, (b) mechanical structure and appearance of the 3-D force sensor, (c) distribution of 16 strain gauges on the cross beam, and (d) the graphical user interface for muscle training in XOZ plane.

 

Continue —>  A novel approach for robot-assisted upper-limb rehabilitationInternational Journal of Advanced Robotic Systems – Guozheng Xu, Xiang Gao, Sheng Chen, Qiang Wang, Bo Zhu, Jinfei Li, 2017

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[ARTICLE] The Effect of High Intensity Training on Stroke Rehabilitation: A Systematic Review – Full Text

Abstract

Background and Purpose: Stroke is one of the leading causes of disability worldwide. Stroke can cause deficits in one’s ability to walk independently, cause deficits in balance, and lead to a variety of other health issues as a sequela of paresis and prolonged physical inactivity.1 The purpose of this systematic review is to evaluate the efficacy of high intensity training (HIT) for the rehabilitation of patients with stroke.

Methods: A systematic review was performed utilizing five databases using search terms “stroke rehabilitation” and “high intensity training”. Article titles and abstracts were screened to include key words “stroke”, “high intensity training”, “resistance training”, “interval training”, “power training”, or “step training”. Research studies using subjects with co-morbidities other than stroke and its residuum were excluded.

Results: After meeting the selection criteria, 10 studies were selected for review. A review of each article’s subject population, tests performed, intervention, and result, reveal that many types of high intensity training have a positive effect on functional and health outcomes in patients with stroke.

Conclusion: High intensity training (HIT) has a positive effect on the rehabilitative potential of patients with stroke. HIT is shown to improve patient’s respiratory function, walking ability, balance, functional ability and other key areas.

Introduction

Stroke can be defined as an acute neurologic dysfunction of vascular origin from a hemorrhagic or ischemic event causing a disruption of blood flow to tissues of the brain.2 Strokes are a global health issue affecting 16 million people each year. It is estimated that by the year 2030 there will be 77 million survivors of stroke worldwide. Each year, 114 of 100,000 people in the United States will suffer their first stroke, accounting for 75% of hospitalizations due to stroke. The remaining 25% of stroke hospitalizations are of patients with recurrent strokes. Patient risk factors for stroke include, but are not limited to hypertension, smoking, diabetes, obesity, dyslipidemia, and elevated homocysteine.3 The long-term implications of a stroke depend upon how early a stroke is recognized and treated. Clinical manifestations following a stroke can include a loss of balance, speech and visual deficits, cognitive dysfunction and hemiparesis. There is potential for the spontaneous recovery of certain deficits in the first few weeks following a stroke, however there is likelihood for long-term dysfunction. The most prevalent long-term dysfunction after a stroke are motor impairments secondary to hemiparesis; which reduces muscle mass and the force of muscle contraction causing lower limb weakness, loss of mobility and gait deficiencies of the affected side.2,3

Continue —>  https://www.linkedin.com/pulse/effect-high-intensity-training-stroke-rehabilitation-review-timothy

Figure 1: Flow diagram of Selection Process

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