The insidious impact of under-diagnosed proximal weakness induced by statins.

Introduction: This Perspective reassesses the consensus opinion that statin-associated muscle symptoms (SAMS) occur in <1% of users and associated myopathic proximal muscle weakness is even more rare.Areas covered: Of the over 180,000 participants in clinical trials and large registries of statin users, only a few studies have included a standard manual muscle test (MMT), dynamometry or a focused questionnaire to assess for proximal weakness and related disability in daily and recreational activities. Formal strength testing suggests, however, that weakness can be demonstrated in at least 10% of users.Expert opinion: Reporting inaccuracies about SAMS, confirmation bias among experts and physicians, absence of a standard questionnaire regarding the potential consequences of weakness on physical capacity, and the failure to routinely perform an objective assessment of strength may have led to under-diagnosis of statin-induced myopathy. A brief MMT before cholesterol-lowering agents are started and at follow-up visits, a 12-week withdrawal of the statin in the presence of new paresis without an alternative cause, and the exam finding that strength recovers off the statin are necessary to assess the incidence of drug-induced proximal weakness and inform alternative therapeutic strategies.

Provocative Walking Test of Strength for Diagnosis, Management, and Outcome Assessment of Symptomatic Lumbar Spinal Stenosis.

Background. Clinical care and randomized trials of rehabilitation or surgery for symptomatic lumbar spinal stenosis with neurogenic claudication (LSS) are complicated by the lack of standard criteria for diagnosis and outcome measurement. Objective. To evaluate whether manual muscle testing (MMT) can detect transient lower-extremity weakness provoked by walking in patients with likely LSS. Methods. A total of 19 patients with symptoms and MRI findings suggestive of LSS were tested for a decline in lower-extremity strength, using the British Medical Council scale of MMT, by comparing strength at rest to a change in strength within 60 s of completing a 400-foot walk. They were retested after reclining supine for another 2 minutes. This examination was repeated following decompressive lumbar surgery. Results. All patients developed bilateral weakness in the distribution of their LSS, but always including the hip extensors and knee flexors, when tested immediately after the provocative walking test. Most patients were not aware of weakness or change in gait during the walking task. They recovered to baseline strength after resting supine. The patients did not improve with physical therapy. When examined within 8 weeks after decompressive laminectomy, no one developed weakness during the 400-foot walk, and daily lower-extremity pain had resolved. Conclusions. A careful repetitive motor examination can detect transient paraparesis in patients with definite LSS. This finding supports the diagnosis and the functional severity of LSS while providing an objective outcome measurement for physical therapy and surgical interventions that goes beyond symptoms of pain.

DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data.

Detailed behavioral analysis is key to understanding the brain-behavior relationship. Here, we present deep learning-based methods for analysis of behavior imaging data in mice and humans. Specifically, we use three different convolutional neural network architectures and five different behavior tasks in mice and humans and provide detailed instructions for rapid implementation of these methods for the neuroscience community. We provide examples of three dimensional (3D) kinematic analysis in the food pellet reaching task in mice, three-chamber test in mice, social interaction test in freely moving mice with simultaneous miniscope calcium imaging, and 3D kinematic analysis of two upper extremity movements in humans (reaching and alternating pronation/supination). We demonstrate that the transfer learning approach accelerates the training of the network when using images from these types of behavior video recordings. We also provide code for post-processing of the data after initial analysis with deep learning. Our methods expand the repertoire of available tools using deep learning for behavior analysis by providing detailed instructions on implementation, applications in several behavior tests, and post-processing methods and annotated code for detailed behavior analysis. Moreover, our methods in human motor behavior can be used in the clinic to assess motor function during recovery after an injury such as stroke.

Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice.

PURPOSE OF REVIEW: Measurements obtained during real-world activity by wearable motion sensors may contribute more naturalistic accounts of clinically meaningful changes in impairment, activity, and participation during neurologic rehabilitation, but obstacles persist. Here we review the basics of wearable sensors, the use of existing systems for neurological and rehabilitation applications and their limitations, and strategies for future use. RECENT FINDINGS: Commercial activity-recognition software and wearable motion sensors for community monitoring primarily calculate steps and sedentary time. Accuracy declines as walking speed slows below 0.8 m/s, less so if worn on the foot or ankle. Upper-extremity sensing is mostly limited to simple inertial activity counts. Research software and activity-recognition algorithms are beginning to provide ground truth about gait cycle variables and reveal purposeful arm actions. Increasingly, clinicians can incorporate inertial and other motion signals to monitor exercise, activities of daily living, and the practice of specific skills, as well as provide tailored feedback to encourage self-management of rehabilitation. Efforts are growing to create a compatible collection of clinically relevant sensor applications that capture the type, quantity, and quality of everyday activity and practice in known contexts. Such data would offer more ecologically sound measurement tools, while enabling clinicians to monitor and support remote physical therapies and behavioral modification when combined with telemedicine outreach.

Locomotor Training and Strength and Balance Exercises for Walking Recovery After Stroke: Response to Number of Training Sessions.

BACKGROUND: Evidence-based guidelines are needed to inform rehabilitation practice, including the effect of number of exercise training sessions on recovery of walking ability after stroke. OBJECTIVE: The objective of this study was to determine the response to increasing number of training sessions of 2 interventions-locomotor training and strength and balance exercises-on poststroke walking recovery. DESIGN: This is a secondary analysis of the Locomotor Experience Applied Post-Stroke (LEAPS) randomized controlled trial. SETTING: Six rehabilitation sites in California and Florida and participants' homes were used. PARTICIPANTS: Participants were adults who dwelled in the community (N=347), had had a stroke, were able to walk at least 3 m (10 ft) with assistance, and had completed the required number of intervention sessions. INTERVENTION: Participants received 36 sessions (3 times per week for 12 weeks), 90 minutes in duration, of locomotor training (gait training on a treadmill with body-weight support and overground training) or strength and balance training. MEASUREMENTS: Talking speed, as measured by the 10-Meter Walk Test, and 6-minute walking distance were assessed before training and following 12, 24, and 36 intervention sessions. RESULTS: Participants at 2 and 6 months after stroke gained in gait speed and walking endurance after up to 36 sessions of treatment, but the rate of gain diminished steadily and, on average, was very low during the 25- to 36-session epoch, regardless of treatment type or severity of impairment. LIMITATIONS: Results may not generalize to people who are unable to initiate a step at 2 months after stroke or people with severe cardiac disease. CONCLUSIONS: In general, people who dwelled in the community showed improvements in gait speed and walking distance with up to 36 sessions of locomotor training or strength and balance exercises at both 2 and 6 months after stroke. However, gains beyond 24 sessions tended to be very modest. The tracking of individual response trajectories is imperative in planning treatment.

A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training.

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions. Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices. A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover. On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Behavioral self-management strategies for practice and exercise should be included in neurologic rehabilitation trials and care.

PURPOSE OF REVIEW: Rehabilitation trials and postacute care to lessen impairments and disability after stroke, spinal cord injury, and traumatic brain injury almost never include training to promote long-term self-management of skills practice, strengthening and fitness. Without behavioral training to develop self-efficacy, clinical trials, and home-based therapy may fail to show robust results. RECENT FINDINGS: Behavioral theories about self-management and self-efficacy for physical activity have been successfully incorporated into interventions for chronic diseases, but rarely for neurologic rehabilitation. The elements of behavioral training include education about the effects of practice and exercise that are relevant to the person, goal setting, identification of possible barriers, problem solving, feedback about performance, tailored instruction, decision making, and ongoing personal or social support. Mobile health and telerehabilitation technologies offer new ways to remotely enable such training by monitoring activity from wearable wireless sensors and instrumented exercise devices to allow real-world feedback, goal setting, and instruction. SUMMARY: Motivation, sense of responsibility, and confidence to practice and exercise in the home can be trained to increase adherence to skills practice and exercise both during and after formal rehabilitation. To optimize motor learning and improve long-term outcomes, self-management training should be an explicit component of rehabilitation care and clinical trials.

The Specific Requirements of Neural Repair Trials for Stroke.

Novel molecular, cellular, and pharmacological therapies to stimulate repair of sensorimotor circuits after stroke are entering clinical trials. Compared with acute neuroprotection and thrombolysis studies, clinical trials for repair in subacute and chronic hemiplegic participants have a different time course for delivery of an intervention, different mechanisms of action within the milieu of the injury, distinct relationships to the amount of physical activity and skills practice of participants, and need to include more refined outcome measures. This review examines the biological interaction of targeted rehabilitation with neural repair strategies to optimize outcomes. We suggest practical guidelines for the incorporation of inexpensive skills training and exercise at home. In addition, we describe some novel outcome measurement tools, including wearable sensors, to obtain the more detailed outcomes that may identify at least some minimal level of success from cellular and regeneration interventions. Thus, proceeding in the shadow of acute stroke trial designs may unnecessarily limit the mechanisms of action of new repair strategies, reduce their impact on participants, and risk missing important behavioral outcomes.

SIRRACT: An International Randomized Clinical Trial of Activity Feedback During Inpatient Stroke Rehabilitation Enabled by Wireless Sensing.

BACKGROUND: Walking-related disability is the most frequent reason for inpatient stroke rehabilitation. Task-related practice is a critical component for improving patient outcomes. OBJECTIVE: To test the feasibility of providing quantitative feedback about daily walking performance and motivating greater skills practice via remote sensing. METHODS: In this phase III randomized, single blind clinical trial, patients participated in conventional therapies while wearing wireless sensors (triaxial accelerometers) at both ankles. Activity-recognition algorithms calculated the speed, distance, and duration of walking bouts. Three times a week, therapists provided either feedback about performance on a 10-meter walk (speed only) or walking speed feedback plus a review of walking activity recorded by the sensors (augmented). Primary outcomes at discharge included total daily walking time, derived from the sensors, and a timed 15-meter walk. RESULTS: Sixteen rehabilitation centers in 11 countries enrolled 135 participants over 15 months. Sensors recorded more than 1800 days of therapy, 37 000 individual walking bouts, and 2.5 million steps. No significant differences were found between the 2 feedback groups in daily walking time (15.1 ± 13.1 vs 16.6 ± 14.3 minutes, P = .54) or 15-meter walking speed (0.93 ± 0.47 vs 0.91 ± 0.53 m/s, P = .96). Remarkably, 30% of participants decreased their total daily walking time over their rehabilitation stay. CONCLUSIONS: In this first trial of remote monitoring of inpatient stroke rehabilitation, augmented feedback beyond speed alone did not increase the time spent practicing or improve walking outcomes. Remarkably modest time was spent walking. Wireless sensing, however, allowed clinicians to audit skills practice and provided ground truth regarding changes in clinically important, mobility-related activities.

Predictors and biomarkers of treatment gains in a clinical stroke trial targeting the lower extremity.

BACKGROUND AND PURPOSE: Behavioral measures are often used to distinguish subgroups of patients with stroke (eg, to predict treatment gains, stratify clinical trial enrollees, or select rehabilitation therapy). In studies of the upper extremity, measures of brain function using functional magnetic resonance imaging (fMRI) have also been found useful, but this approach has not been examined for the lower extremity. The current study hypothesized that an fMRI-based measure of cortical function would significantly improve prediction of treatment-induced lower extremity behavioral gains. Biomarkers of treatment gains were also explored. METHODS: Patients with hemiparesis 1 to 12 months after stroke were enrolled in a double-blind, placebo-controlled, randomized clinical trial of ropinirole+physical therapy versus placebo+physical therapy, results of which have previously been reported (NCT00221390).(15) Primary end point was change in gait velocity. Enrollees underwent baseline multimodal assessment that included 19 measures spanning 5 assessment categories (medical history, impairment, disability, brain injury, and brain function), and also underwent reassessment 3 weeks after end of therapy. RESULTS: In bivariate analysis, 8 baseline measures belonging to 4 categories (medical history, impairment, disability, and brain function) significantly predicted change in gait velocity. Prediction was strongest, however, using a multivariate model containing 2 measures (leg Fugl-Meyer score and fMRI activation volume within ipsilesional foot sensorimotor cortex). Increased activation volume within bilateral foot primary sensorimotor cortex correlated positively with treatment-induced leg motor gains. CONCLUSIONS: A multimodal model incorporating behavioral and fMRI measures best predicted treatment-induced changes in gait velocity in a clinical trial setting. Results also suggest potential use of fMRI measures as biomarkers of treatment gains.

Prediction of responders for outcome measures of locomotor Experience Applied Post Stroke trial.

The Locomotor Experience Applied Post Stroke rehabilitation trial found equivalent walking outcomes for body weight-supported treadmill plus overground walking practice versus home-based exercise that did not emphasize walking. From this large database, we examined several clinically important questions that provide insights into recovery of walking that may affect future trial designs. Using logistic regression analyses, we examined predictors of response based on a variety of walking speed-related outcomes and measures that captured disability, physical impairment, and quality of life. The most robust predictor was being closer at baseline to the primary outcome measure, which was the functional walking speed thresholds of 0.4 m/s (household walking) and 0.8 m/s (community walking). Regardless of baseline walking speed, a younger age and higher Berg Balance Scale score were relative predictors of responding, whether operationally defined by transitioning beyond each speed boundary or by a continuous change or a greater than median increase in walking speed. Of note, the cutoff values of 0.4 and 0.8 m/s had no particular significance compared with other walking speed changes despite their general use as descriptors of functional levels of walking. No evidence was found for any difference in predictors based on treatment group.

Wearable motion sensors to continuously measure real-world physical activities.

PURPOSE OF REVIEW: Rehabilitation for sensorimotor impairments aims to improve daily activities, walking, exercise, and motor skills. Monitoring of practice and measuring outcomes, however, is usually restricted to laboratory-based procedures and self-reports. Mobile health devices may reverse these confounders of daily care and research trials. RECENT FINDINGS: Wearable, wireless motion sensor data, analyzed by activity pattern-recognition algorithms, can describe the type, quantity, and quality of mobility-related activities in the community. Data transmission from the sensors to a cell phone and the Internet enable continuous monitoring. Remote access to laboratory quality data about walking speed, duration and distance, gait asymmetry and smoothness of movements, as well as cycling, exercise, and skills practice, opens new opportunities to engage patients in progressive, personalized therapies with feedback about the performance. Clinical trial designs will be able to include remote verification of the integrity of complex physical interventions and compliance with practice, as well as capture repeated, ecologically sound, ratio scale outcome measures. SUMMARY: Given the progressively falling cost of miniaturized wearable gyroscopes, accelerometers, and other physiologic sensors, as well as inexpensive data transmission, sensing systems may become as ubiquitous as cell phones for healthcare. Neurorehabilitation can develop these mobile health platforms for daily care and clinical trials to improve exercise and fitness, skills learning, and physical functioning.

New evidence for therapies in stroke rehabilitation.

Neurologic rehabilitation aims to reduce impairments and disabilities so that persons with serious stroke can return to participation in usual self-care and daily activities as independently as feasible. New strategies to enhance recovery draw from a growing understanding of how types of training, progressive task-related practice of skills, exercise for strengthening and fitness, neurostimulation, and drug and biological manipulations can induce adaptations at multiple levels of the nervous system. Recent clinical trials provide evidence for a range of new interventions to manage walking, reach and grasp, aphasia, visual field loss, and hemi-inattention.