Posts Tagged locomotion
Background and Objectives: High-intensity interval training (HIIT) is a promising strategy for improving gait and fitness after stroke, but optimal parameters remain unknown. We tested the effects of short vs long interval type and over-ground vs treadmill mode on training intensity.
Methods: Using a repeated measures design, 10 participants with chronic hemiparesis performed 12 HIIT sessions over 4 weeks, alternating between short and long-interval HIIT sessions. Both protocols included 10 minutes of over-ground HIIT, 20 minutes of treadmill HIIT and another 10 minutes over-ground. Short-interval HIIT involved 30 second bursts at maximum safe speed and 30-60 second rest periods. Long-interval HIIT involved 4-minute bursts at ~90% of peak heart rate (HRpeak) and 3-minute recovery periods at ~70% HRpeak.
Results: Compared with long-interval HIIT, short-interval HIIT had significantly faster mean overground speeds (0.75 vs 0.67 m/s) and treadmill speeds (0.90 vs 0.51 m/s), with similar mean treadmill HR (82.9 vs 81.8%HRpeak) and session perceived exertion (16.3 vs 16.3), but lower overground HR (78.4 vs 81.1%HRpeak) and session step counts (1481 vs 1672). For short-interval HIIT, training speeds and HR were significantly higher on the treadmill vs. overground. For long-interval HIIT, the treadmill elicited HR similar to overground training at significantly slower speeds.
Conclusions: Both short and long-interval HIIT elicit high intensities but emphasize different dosing parameters. From these preliminary findings and previous studies, we hypothesize that overground and treadmill short-interval HIIT could be optimal for improving gait speed and overground long-interval HIIT could be optimal for improving gait endurance.
[ARTICLE] Minimal Clinically Important Difference of the 6-Minute Walk Test in People With Stroke – Full Text
Background and Purpose: The 6-minute walk test (6MWT) is commonly used in people with stroke. The purpose of this study was to estimate the minimal clinically important difference (MCID) of the 6MWT 2 months poststroke.
Methods: We performed a secondary analysis of data from a rehabilitation trial. Participants underwent physical therapy between 2 and 6 months poststroke and the 6MWT was measured before and after. Two anchors of important change were used: the modified Rankin Scale (mRS) and the Stroke Impact Scale (SIS). The MCID for the 6MWT was estimated using receiver operating characteristic curves for the entire sample and for 2 subgroups: initial gait speed (IGS) <0.40 m/s and ≥0.40 m/s.
Results: For the entire sample, the estimated MCID of the 6MWT was 71 m with the mRS as the anchor (area under the curve [AUC] = 0.66) and 65 m with the SIS as the anchor (AUC = 0.59). For participants with IGS <0.40 m/s, the estimated MCID was 44 m with the mRS as the anchor (AUC = 0.72) and 34 m with the SIS as the anchor (AUC = 0.62). For participants with IGS ≥0.40 m/s, the estimated MCID was 71 m with the mRS as the anchor (AUC = 0.59) and 130 m with the SIS as the anchor (AUC = 0.56).
Discussion and Conclusions: Between 2 and 6 months poststroke, people whose IGS is <0.40 m/s and experience a 44-m improvement in the 6MWT may exhibit meaningful improvement in disability. However, we were not able to estimate an accurate MCID for the 6MWT in people whose IGS was ≥0.40 m/s. MCID values should be estimated across different levels of function and anchors of importance.
Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A232).
The 6-minute walk test (6MWT) is commonly used in people with stroke undergoing rehabilitation.1–3 Although originally developed and validated as a submaximal oxygen consumption test for individuals with cardiac or pulmonary disease,4 , 5 the 6MWT is a valid6–11and reliable12 , 13 measure of walking endurance and is highly recommended by the Academy of Neurologic Physical Therapy for use with people with stroke and other neurologic conditions across the continuum of care.14 More recently, the 6MWT has been used to predict community walking activity.15
An important psychometric property of any outcome measure is its sensitivity to change and responsiveness. Liang and colleagues16 , 17 define sensitivity to change as the ability of an instrument to measure change regardless of whether or not that change is important; it is the amount of change that exceeds measurement error and patient variability. Responsiveness is the ability of an instrument to measure important change. In particular, the minimal detectable change ([MDC] an index of sensitivity to change) and the minimal clinically important difference ([MCID] an index of responsiveness) are useful for clinicians and researchers when interpreting scores and/or change on an outcome measure. The MDC is an estimate of the measurement error and random fluctuation in the test score in patients who are stable.18 , 19
Although MDC is useful for interpreting change scores, it is not ideal, as it provides only the information that the change has exceeded measurement error and variability in patients who are stable. Conversely, the MCID is more useful clinically as it provides an index of important change. The MCID involves an anchor-based approach to estimating how much change in an outcome measure is clinically important and meaningful. The anchor is some external variable that is judged to be important.20 External anchors can be patients’ perception of important change, clinicians’ perception of important change, or an objective marker of important change (eg, discharge home).20 For example, Fulk and colleagues21 used patient and therapist’s perception of important change measured with a Global Rating of Change Scale as an anchor to estimate clinically important change in the Arm Motor Ability Test. When estimating the MCID of gait speed, Tilson and colleagues22 used a 1-point improvement on the modified Rankin Scale (mRS) as the anchor of important improvement in disability.
Unfortunately, there is limited research on the sensitivity to change and responsiveness of the 6MWT in people with stroke. In people with chronic stroke, the MDC is estimated to be 29 m,12 ,23 while in people with stroke undergoing inpatient rehabilitation 30 days poststroke, the MDC is estimated to be 54 m.10 To the best of our knowledge, the MCID of the 6MWT has been reported for people with stroke in only 1 other study. Using data from a completed rehabilitation trial, Perera and colleagues24 estimated meaningful change in the 6MWT using 3 different methodologies. They used an anchor-based approach using decline on 2 items of the 36-Item Short Form Health Survey (walking 1 block and climbing a flight of stairs) as the anchors. Using a distribution-based approach, they calculated standard error of measurement, and they multiplied mean baseline 6MWT distance by a small (0.2) and medium effect size (0.5). Limitations in their findings are that the anchor-based approach used was in relation to decline in performance on the anchor and so should not be applied when trying to interpret improvement. The distribution-based methods Perera and colleagues24 used to estimate change in the 6MWT were based on patients whose condition was stable and are indices of sensitivity to change not responsiveness (ie, important change). However, the MCID of the 6MWT has been reported for other patient populations and has been estimated to be between 14.0 m and 156 m in people with chronic obstructive pulmonary disease, lung disease (lung disease), coronary artery disease, fibromyalgia, and older adults.25–28
The purpose of this research study was to estimate the MCID of the 6MWT in people with stroke undergoing outpatient rehabilitation 2 months poststroke using an anchor-based approach. Based on the MDC values reported in the literature, we hypothesized that the MCID would be greater than the reported MDC values.[…]
[Abstract] Early robot-assisted gait retraining in non-ambulatory patients with stroke: a single blind randomized controlled trial
BACKGROUND: Restoration of walking function is a primary concern of neurorehabilitation with respect to the aspired social and vocational reintegration. To date, the best practice for improving gait early after stroke is still object of debate. On one hand, repetitive task-specific approaches with higher intensities of walking have been observed to result in greater improvements of gait after stroke. Conversely there is some evidence that conventional gait training would be more effective for facilitating walking ability after stroke.
AIM: To compare the effects of an early treatment protocol of add-on robot-assisted gait training with add-on conventional overground physiotherapy for improving locomotion in non-ambulatory adult stroke patients.
DESIGN: Single-blind randomized controlled trial.
SETTING: Neurorehabilitation hospital.
POPULATION: Seventy-four subacute patients with first-ever ischemic stroke.
METHODS: The patients were randomized into two groups. The training program consisted of forty, 2-hour sessions (including 45 minutes basic training, 45 minutes add-on training plus rest periods), five days a week, for eight consecutive weeks. Patients allocated to the add-on robot-assisted gait training were treated by means of the Lokomat. Patients allocated to the add-on conventional overground gait training aimed at improving postural control during gait, body weight transfer, stability during the stance phase, free swing phase, adequate heel contact and gait pattern. Primary outcome was the modified Emory Functional Ambulation Profile. Secondary outcomes were the Rivermead Motor Index, the Mobility Milestones and the Hochzirl Walking Aids Profile.
RESULTS: No significant difference was observed between groups with regards to age (P=0.661), time from stroke onset (P=0.413) and the primary outcome (P=0.854) at baseline evaluation. As to the primary outcome, no significant differences were found between groups at the end of the study. As During the 8-week training, within-group comparisons showed significant improvements of mean modified Emory Functional Ambulation Profile in both groups (P<0.001).
CONCLUSIONS: Our results support the hypothesis that an early treatment protocol of robot-assisted gait retraining is not superior to add-on conventional gait training intervention for improving locomotion in non- ambulatory stroke patients.
CLINICAL REHABILITATION IMPACT: This study might help to better understand the role of robot-assisted gait training in early phase stroke rehabilitation.
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via Early robot-assisted gait retraining in non-ambulatory patients with stroke: a single blind randomized controlled trial – European Journal of Physical and Rehabilitation Medicine 2018 Mar 29 – Minerva Medica – Journals
[Abstract] Efficacy of the Regent Suit-based rehabilitation on gait EMG patterns in hemiparetic subjects: a pilot study
Background: The recovery of the functional limb mobility of patients with cerebral damages can take great benefit of the role offered by proprioceptive rehabilitation. Recently have been developed a special Regent Suit (RS) for rehabilitative applications. Actually, there are preliminary studies which describes the effects of RS on gait recovery of stroke patients in acute stage, but none in chronic stage. Moreover, it is known that motor recovery does not always reflect improvements of the muscle activity and coactivity.
AIM: To investigate the effects of proprioceptive stimulation induced by the Regent Suit (RS) on the EMG patterns during gait in post-stroke chronic patients.
Design: Randomized controlled trial.
Setting: S. Maugeri Foundation, Telese Terme (BN), Italy.
Population: Patients have been randomly assigned into two equal groups of 20 patients: experimental group and traditional group. Further, a control group of 20 healthy subjects have been enrolled.
Settings: The traditional group attended a rehabilitation program composed by neuro-motor exercises without the RS, the experimental group performed the same rehabilitation program while wearing the RS. the NIH Stroke Scale (NIHSS), the Barthel Index (BI), the Functional Independent Measure (FIM) and the Berg Balance Scale (BBS) have been evaluated. EMG analysis has been performed considering the muscle activation timing over the gait of the Soleus, Tibialis Anterior, Semitendinosus and Vastus Lateralis muscles by decomposing the EMG signals into Gaussian pulses. Then, the symmetry of muscle activation and the muscle synergy patterns over the gait cycle have been assessed.
Results: The proprioceptive stimulation of the RS-based treatment induces higher and remarkable restoration of the normal muscle activation timing, also increasing the muscle symmetry and reducing the pathological muscle coactivation on both affected and non-affected sides.
Conclusions: These results suggest confirm that a RS-based treatment is more effective than usual care in improving the EMG patterns during locomotion and daily living activities in chronic post-stroke subjects.
Clinical Rehabilitation Impact: The proprioceptive rehabilitation Regent Suit based has an impact on motor function in stroke patients during gait.
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Locomotion is a natural task that has been assessed for decades and used as a proxy to highlight impairments of various origins. So far, most studies adopted classical linear analyses of spatio-temporal gait parameters. Here, we use more advanced, yet not less practical, non-linear techniques to analyse gait time series of healthy subjects. We aimed at finding more sensitive indexes related to spatio-temporal gait parameters than those previously used, with the hope to better identify abnormal locomotion. We analysed large-scale stride interval time series and mean step width in 34 participants while altering walking direction (forward vs. backward walking) and with or without galvanic vestibular stimulation. The Hurst exponent αand the Minkowski fractal dimension D were computed and interpreted as indexes expressing predictability and complexity of stride interval time series, respectively. These holistic indexes can easily be interpreted in the framework of optimal movement complexity. We show that αand D accurately capture stride interval changes in function of the experimental condition. Walking forward exhibited maximal complexity (D) and hence, adaptability. In contrast, walking backward and/or stimulation of the vestibular system decreased D. Furthermore, walking backward increased predictability (α) through a more stereotyped pattern of the stride interval and galvanic vestibular stimulation reduced predictability. The present study demonstrates the complementary power of the Hurst exponent and the fractal dimension to improve walking classification. Our developments may have immediate applications in rehabilitation, diagnosis, and classification procedures.
The stride interval of normal human walking is the time period between consecutive heel strikes of the same foot . For more than two decades, a line of research focused on the understanding of the nature of the subtle variations observed in stride intervals and the origin of typical long-range structures in these variations. Today, these investigations are of paramount importance since they could provide a better understanding of the physiological mechanisms involved in normal human walking and in alterations observed in clinical practice. The nature of these stride interval variations could arise either from noisy neural processes that result in errors in the motor output or from alterations in the motor command that account for balance instabilities .
Normal gait is characterized by the presence of autocorrelations in the stride interval when considering walking on a sufficiently long time scale [1, 3]. The origin of these autocorrelations may be attributed to neural central pattern generators (CPGs) [1, 3] or a super CPG coupled to a forced Van der Pol oscillator , and/or to the biomechanics of walking [5, 6]. For many years, gait analysis has been studied with classical methods adopting biomechanical models in which variability was not of interest. More recent techniques derived from chaos theory are well adapted to analyse time series that exhibit long-range autocorrelation. Importantly, they treat variability as a meaningful interpretable signal. Since the pioneering works of Hausdorff et al. [1, 3], long-range autocorrelations in time series are estimated by the Hurst or fractal exponent (α). A fractal, introduced in 1975 by the French mathematician Benoît Mandelbrot (1924–2010) , is defined as a geometrical structure that has a regular or an uneven shape repeated over all scales of measurement. It is characterized by a fractal dimension (D) greater than the spatial dimension of the structure . A famous example of such object is a snow flake. Objects that are statistically self-similar—parts of it show the same statistical properties at many scales—exhibit strong autocorrelation. The Hurst exponent α is a statistical measure of long-term memory of time series (see e.g.  for a review) and is usually associated to fractal-like behaviour. In particular, the peculiar behavior of the stride interval may be referred to as “fractal behavior” .
The theoretical model of optimal movement complexity  is based on the complementary concepts of predictability and complexity. Nature let us find optimal behavior in terms of skills and variability through evolution. As proposed by Lipsitz and Glodberger in their pioneering work, the optimal state of a biological system may be characterized by chaotic temporal variations in the steady state output that correspond to maximal complexity . Any deviation from healthy state, like senescence and disease, causes a loss in complexity (see also ). Too few practice results in high disorder (randomness, no predictability) and excessive practice leads to high order (periodic signal, maximal predictability). Adaptation of a system to external stimuli is maximal only at an intermediate state of predictability. Furthermore, a signal from a dynamical system also holds some inherent complexity. A decrease of complexity of a physiological system results from either a reduction in the number of structural components or an alteration in the coupling function between these components. For instance, a joint can become rigid with senescence, hence decreasing the degree of freedom of the system and consequently, its complexity. A holistic approach to study these mechanisms requires to associate specific measurements to these two concepts. The Hurst exponent α captures part of the story and is well suited to reflect predictability. While the Minkowski fractal dimension Dprovides good measurability of the “apparent rugosity” of fractals  and reflects complexity. Note that the quantification of a concept such as complexity may not be linked to a unique measure; entropy-related measures have also been shown to be relvant in that domain . Here, we use these parameters to complement the usual quantification of autocorrelation α in unusual and perturbed gait conditions in an attempt to probe adaptability in the framework of the model of optimal movement complexity .
As of today, the vast majority of studies explored autocorrelation in the stride interval during natural forward walking. In one notable exception however, Bollens et al.  also tested backward walking in a small sample of young healthy subjects. The authors did not find significant differences in long-range autocorrelation between both walking directions. However, backward walking measures revealed to be more sensitive than forward walking measures to classify elderly fallers compare to non-fallers . The study of backward walking under the perspective of fractal analyses is therefore promising to provide more reliable predictive index of fallers, as previously proposed for forward walking . Backward walking is also frequently used in sports and in rehabilitation settings, and a better understanding of the variability of stride interval in this condition is needed since it is believed that backward walking is at least partly controlled by specialized neural circuits .
The vestibular system provides an essential sensory contribution to the maintenance of balance during human walking . Individuals with vestibular disorders show a decreased walking stability accompanied by an increased risk to fall . Therefore, perturbing the vestibular system of healthy subjects with galvanic vestibular stimulation (GVS) is a well targeted mean to probe gait: it is standardized, well tolerated by subjects, generated by currently affordable electrostimulators, and easy to implement when a large number of stride intervals are recorded with an instrumented treadmill. The use of GVS is also an increasingly common clinical intervention on locomotion [20–22].
Previously, autocorrelations in stride interval time series have been identified not only in healthy young adults  but also in children  and elderly , and even—although significantly modified—in several neurodegenerative conditions. In particular, the cases of Huntington’s disease , amyotrophic lateral sclerosis , and Parkinson’s disease have been studied [26, 27], with a hope of connecting the observed modifications of fractal behavior to some relevant evaluation of the risk of falling . Here, we hypothesize that the combined effects of walking direction and the application of GVS on long-range autocorrelations in the stride interval could enhance the sensitivity of fractal analysis to identify impaired gait. We measured α and D during forward and backward walking, with and without the application of binaural and monaural GVS. We speculate that these two indexes should be able to capture differences between experimental conditions and therefore provide better indexes to classify patients.[…]
[Abstract+References] Forced Use of the Paretic Leg Induced by a Constraint Force Applied to the Nonparetic Leg in Individuals Poststroke During Walking
Background. Individuals with stroke usually show reduced muscle activities of the paretic leg and asymmetrical gait pattern during walking. Objective. To determine whether applying a resistance force to the nonparetic leg would enhance the muscle activities of the paretic leg and improve the symmetry of spatiotemporal gait parameters in individuals with poststroke hemiparesis. Methods. Fifteen individuals with chronic poststroke hemiparesis participated in this study. A controlled resistance force was applied to the nonparetic leg using a customized cable-driven robotic system while subjects walked on a treadmill. Subjects completed 2 test sections with the resistance force applied at different phases of gait (ie, early and late swing phases) and different magnitudes (10%, 20%, and 30% of maximum voluntary contraction [MVC] of nonparetic leg hip flexors). Electromyographic (EMG) activity of the muscles of the paretic leg and spatiotemporal gait parameters were collected. Results. Significant increases in integrated EMG of medial gastrocnemius, medial hamstrings, vastus medialis, and tibialis anterior of the paretic leg were observed when the resistance was applied during the early swing phase of the nonparetic leg, compared with baseline. Additionally, resistance with 30% of MVC induced the greatest level of muscle activity than that with 10% or 20% of MVC. The symmetry index of gait parameters also improved with resistance applied during the early swing phase. Conclusion. Applying a controlled resistance force to the nonparetic leg during early swing phase may induce forced use on the paretic leg and improve the spatiotemporal symmetry of gait in individuals with poststroke hemiparesis.
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via Forced Use of the Paretic Leg Induced by a Constraint Force Applied to the Nonparetic Leg in Individuals Poststroke During WalkingNeurorehabilitation and Neural Repair – Chao-Jung Hsu, Janis Kim, Elliot J. Roth, William Z. Rymer, Ming Wu, 2017
[REVIEW] Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed? – Full Text
We summarize content from the opening thematic session of the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM). Scientific discoveries from the past 20 years of research are covered, highlighting the impacts of rapid technological, computational, and financial growth on motor control research. We discuss spinal-level communication mechanisms, relationships between muscle structure and function, and direct cortical movement representations that can be decoded in the control of neuroprostheses. In addition to summarizing the rich scientific ideas shared during the session, we reflect on research infrastructure and capacity that contributed to progress in the field, and outline unresolved issues and remaining open questions.
At the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM), the opening thematic session was chaired by Dr. Fay Horak (Oregon Health & Science University). Presentations and discussions covered insights from 20 years of research in the field of motor control, delivered by Drs. Zev Rymer (Rehabilitation Institute of Chicago), Andy Biewener (Harvard University), Andy Schwartz (University of Pittsburgh), and Daofen Chen (National Institute of Neurological Disorders and Stroke). Presentation themes included the impact of technological advancements on motor control research, unresolved issues in muscle biology and neurophysiology, and changes in the scientific funding landscape. This brief review summarizes content presented by each speaker, along with discussions from the audience.
Considerable changes have occurred in the fields of biomechanics and motor control over the past 20 years, changes made possible by rapid technological advances in computing power and memory along with reduced physical size of biotechnology hardware. Because of these changes, research approaches have been reshaped and new questions have emerged. Previously, motor control research was constrained to laboratory-based assessments of individual neurons, muscles or joints, captured from low sample sizes. In the past, reliance on large, expensive, external recording devices, such as optical motion capture systems, understandably limited the feasibility of large-scale, multivariate research. Today, whole-body kinematic recordings using body-worn inertial measurement units, wireless electromyography (EMG), electroencephalography (EEG), and functional near infrared spectroscopy (fNIRS) systems, and electrode arrays for neural network recordings are increasingly commonplace. Alongside these technical leaps, sociocultural bounds have expanded research inclusion, as evidenced in the representation of speakers at the 2016 BANCOM meeting. In contrast to the 1996 meeting, which included three invited female speakers, 13 women were included as speakers in 2016. Such advancements will continue to shape our scientific landscape, driving innovation through new technologies and perspectives.[…]
[ARTICLE] The Efficacy of State of the Art Overground Gait Rehabilitation Robotics: A Bird’s Eye View – Full Text
To date, rehabilitation robotics has come a long way effectively aiding the rehabilitation process of the patients suffering from paraplegia or hemiplegia due to spinal cord injury (SCI) or stroke respectively, through partial or even full functional recovery of the affected limb. The increased therapeutic outcome primarily results from a combination of increased patient independence and as well as reduced physical burden on the therapist. Especially for the case of gait rehabilitation following SCI or stroke, the rehab robots have the potential to significantly increase the independence of the patient during the rehabilitation process without the patient’s safety being compromised. An intensive gait-oriented rehabilitation therapy is often effective irrespective of the type of rehabilitation paradigm. However, eventually overground gait training, in comparison with body-weight supported treadmill training (BWSTT), has the potential of higher therapeutic outcome due its associated biomechanics being very close to that of the natural gait. Recognizing the apparent superiority of the overground gait training paradigms, a through literature survey on all the major overground robotic gait rehabilitation approaches was carried out and is presented in this paper. The survey includes an in-depth comparative study amongst these robotic approaches in terms of gait rehabilitation efficacy.
Robotic exoskeletons and bionic prostheses have moved from science fiction to science reality in the last decade. These robotic devices for assisting human movement are now technically feasible given recent advancements in robotic actuators, sensors, and computer processors. However, despite the ability to build robotic hardware that is wearable by humans, we still do not have optimal controllers to allow humans to move with coordination and grace in synergy with the robotic devices. We consider the history of robotic exoskeletons and bionic limb prostheses to provide a better assessment of the roadblocks that have been overcome and to gauge the roadblocks that still remain. There is a strong need for kinesiologists to work with engineers to better assess the performance of robotic movement assistance devices. In addition, the identification of new performance metrics that can objectively assess multiple dimensions of human performance with robotic exoskeletons and bionic prostheses would aid in moving the field forward. We discuss potential control approaches for these robotic devices, with a preference for incorporating feedforward neural signals from human users to provide a wider repertoire of discrete and adaptive rhythmic movements.
[Abstract] Factors Influencing the Efficacy of Aerobic Exercise for Improving Fitness and Walking Capacity After Stroke: A Meta-Analysis with Meta-Regression.
To assess the influence of dosing parameters and patient characteristics on the efficacy of aerobic exercise (AEX) post-stroke.
A systematic review was conducted using Pubmed, MEDLINE, CINAHL, PEDro and Academic Search Complete.
Studies were selected that compared AEX to a non-aerobic control group among ambulatory persons with stroke.
Extracted outcome data included: peak oxygen consumption during exercise testing (VO2peak), walking speed and walking endurance (6-minute walk test). Independent variables of interest were: AEX mode (seated or walking), AEX intensity (moderate or vigorous), AEX volume (total hours), stroke chronicity and baseline outcome scores.
Significant between-study heterogeneity was confirmed for all outcomes. Pooled AEX effect size estimates (AEX change – control change) from random effects models were: VO2peak, 2.2 mL/kg/min [95% CI: 1.3, 3.1]; walking speed, 0.06 m/s [95% CI: 0.01, 0.11]; and 6-minute walk test distance, 29 m [95% CI: 15, 42]. From meta-regression, greater VO2peak effect sizes were significantly associated with higher AEX intensity and higher baseline VO2peak. Greater effect sizes for walking speed and the 6-minute walk test were significantly associated with a walking AEX mode. In contrast, seated AEX did not have a significant effect on walking outcomes.
AEX significantly improves aerobic capacity post-stroke, but may need to be task specific to impact walking speed and endurance. Higher AEX intensity is associated with better outcomes. Future randomized studies are needed to confirm these results.
Source: Factors Influencing the Efficacy of Aerobic Exercise for Improving Fitness and Walking Capacity After Stroke: A Meta-Analysis with Meta-Regression – Archives of Physical Medicine and Rehabilitation