Prevalence and Detection of Obstructive Sleep Apnea Early after Stroke.

Background: Obstructive sleep apnea (OSA) negatively impacts post-stroke recovery. This study's purpose: examine the prevalence of undiagnosed OSA and describe a simple tool to identify those at-risk for OSA in the early phase of stroke recovery. Methods: This was a cross-sectional descriptive study of people ∼15 days post-stroke. Adults with stroke diagnosis admitted to inpatient rehabilitation over a 3-year period were included if they were alert/arousable, able to consent/assent to participation, and excluded if they had a pre-existing OSA diagnosis, other neurologic health conditions, recent craniectomy, global aphasia, inability to ambulate 150 feet independently pre-stroke, pregnant, or inability to understand English. OSA was deemed present if oxygen desaturation index (ODI) of >=15 resulted from overnight oximetry measures. Prevalence of OSA was determined accordingly. Four participant characteristics comprised the "BASH" tool (body mass index >=35, age>=50, sex=male, hypertension=yes). A receiver operator characteristics (ROC) curve analysis was performed with BASH as test variable and OSA presence as state variable. Results: Participants (n=123) were 50.4% male, averaged 64.12 years old (sd 14.08), and self-identified race as 75.6% White, 20.3% Black/African American, 2.4%>1 race, and 1.6% other; 22% had OSA. ROC analysis indicated BASH score >=3 predicts presence of OSA (sensitivity=0.778, specificity=0.656, area under the curve =0.746, p<0.001). Conclusions: Prevalence of undiagnosed OSA in the early stroke recovery phase is high. With detection of OSA post-stroke, it may be possible to offset untreated OSA's deleterious impact on post-stroke recovery of function. The BASH tool is an effective OSA screener for this application.

Speed- and Endurance-Based Classifications of Community Ambulation Post-Stroke Revisited: The Importance of Location in Walking Performance Measurement.

BACKGROUND: Gait speed or 6-minute walk test are frequently used to project community ambulation abilities post-stroke by categorizing individuals as household ambulators, limited, or unlimited community ambulators. However, whether improved clinically-assessed gait outcomes truly translate into enhanced real-world community ambulation remains uncertain. OBJECTIVE: This cross-sectional study aimed to examine differences in home and community ambulation between established categories of speed- and endurance-based classification systems of community ambulation post-stroke and compare these with healthy controls. METHODS: Sixty stroke survivors and 18 healthy controls participated. Stroke survivors were categorized into low-speed, medium-speed, or high-speed groups based on speed-based classifications and into low-endurance, medium-endurance, or high-endurance groups based on the endurance-based classification. Home and community steps/day were quantified using Global Positioning System and accelerometer devices over 7 days. RESULTS: The low-speed groups exhibited fewer home and community steps/day than their medium- and high-speed counterparts (P < .05). The low-endurance group took fewer community steps/day than the high-endurance group (P < .05). Despite vast differences in clinical measures of gait speed and endurance, the medium-speed/endurance groups did not differ in their home and community steps/day from the high-speed/endurance groups, respectively. Stroke survivors took 48% fewer home steps/day and 77% fewer community steps/day than healthy controls. CONCLUSIONS: Clinical classification systems may only distinguish home ambulators from community ambulators, but not between levels of community ambulation, especially beyond certain thresholds of gait speed and endurance. Clinicians should use caution when predicting community ambulation status through clinical measures, due to the limited translation of these classification systems into the real world.

Moderate to Vigorous Intensity Locomotor Training After Stroke: A Systematic Review and Meta-analysis of Mean Effects and Response Variability.

BACKGROUND AND PURPOSE: This meta-analysis quantified mean effects of moderate to vigorous intensity locomotor training (LT mv ) on walking outcomes in subacute and chronic stroke, and the magnitude of variability in LT mv response. METHODS: Databases were searched for randomized trials comparing LT mv with no intervention, nongait intervention, or low-intensity gait training. Comfortable gait speed (CGS), fastest gait speed (FGS), 6-minute walk test (6MWT), walking activity (steps per day), and adverse effect/event (AE) data were extracted. Pooled estimates were calculated for mean changes, AE relative risks, and the standard deviation of response (SD response ) to LT mv versus control groups, stratified by study chronicity where possible. RESULTS: There were 19 eligible studies (total N = 1096): 14 in chronic stroke (N = 839) and 5 in subacute stroke (N = 257). Compared with control interventions, LT mv yielded significantly greater increases in CGS (chronic, +0.06 m/s [95% confidence interval (CI), 0.01-0.10]; subacute, +0.16 [0.12-0.19]; subacute vs chronic, P = 0.03), FGS (chronic, +0.07 m/s [0.02-0.13]; subacute, +0.21 [0.01, 0.41]; P = 0.04), and 6MWT (chronic, +33 m [24-42]; subacute, +51 [26-77]; P = 0.054) but not steps/day (+260 [-1159 to 1679]). There were no treatment-related serious AEs among 398 LT mv participants in 14 AE-reporting studies. SD response estimates indicated substantial response variability: CGS, 0.11 m/s [0.00-0.15]; FGS, 0.14 m/s [-0.00 to 0.20]; and 6MWT, 41 m [27-51]. DISCUSSION AND CONCLUSIONS: LT mv improves mean walking capacity outcomes in subacute and chronic stroke and does not appear to have high risk of serious harm. Response magnitude varies within and between chronicity subgroups, and few studies have tested effects on daily walking activity or non-serious AEs.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1 available at: http://links.lww.com/JNPT/A452 ).

Research from Low-Income and Middle-Income Countries will Benefit Global Health and the Physiotherapy Profession, but it Requires Support.

Disparities in research publications are common in the physiotherapy and rehabilitation fields.1 A small proportion of published research arises from low-income and middle-income countries (LMICs),1,2 home to 85% of the world's population. Systems-level, institutional-level, and individual-level factors contribute to these disparities. With urgent and unified actions, global health and the standard of physiotherapy research in LMICs can be improved and strengthened. In this editorial, we will discuss the challenges encountered by researchers from LMICs in conducting and publishing high-quality research and propose potential strategies to address these challenges.

Modeling Cues May Reduce Sway Following Sit-To-Stand Transfer for People with Parkinson's Disease.

Cues are commonly used to overcome the effects of motor symptoms associated with Parkinson's disease. Little is known about the impact of cues on postural sway during transfers. The objective of this study was to identify if three different types of explicit cues provided during transfers of people with Parkinson's disease results in postural sway more similar to healthy controls. This crossover study had 13 subjects in both the Parkinson's and healthy control groups. All subjects completed three trials of uncued sit to stand transfers. The Parkinson's group additionally completed three trials of sit to stand transfers in three conditions: external attentional focus of reaching to targets, external attentional focus of concurrent modeling, and explicit cue for internal attentional focus. Body worn sensors collected sway data, which was compared between groups with Mann Whitney U tests and between conditions with Friedman's Tests. Sway normalized with modeling but was unchanged in the other conditions. Losses of balance presented with reaching towards targets and cueing for an internal attentional focus. Modeling during sit to stand of people with Parkinson's disease may safely reduce sway more than other common cues.

Sleep Quantity and Quality During Inpatient Rehabilitation After Stroke.

Objective: To identify sleep patterns and their association with recovery after stroke during inpatient rehabilitation, and to determine if clinical outcomes are different between participants demonstrating abnormal sleep patterns as compared to those with normal sleep patterns. Methods: Cohort study in which participants were undergoing inpatient rehabilitation after a stroke. Sleep quantity and quality was measured using an actigraph that participants wore for up to 7 nights during the first week of inpatient rehabilitation. Medicare Quality Indicators (GG code), Barthel Index, gait speed, and Berg balance scale were collected at admission and discharge. Participants were categorized into groups based on meeting or not meeting recommended sleep quantity and quality guidelines. Association between sleep patterns and outcomes were assessed using Pearson correlation and differences in outcomes and length of stay between participants who met or did not meet sleep quantity and quality guidelines were determined using independent sample t-test. Results: 69 participants were in the study. Sleep quantity and quality was poor for all the participants. None of the participants met all the sleep quantity and quality guidelines. There were moderate to small associations (-0.42 to 0.22) between some sleep quantity and quality parameters and clinical outcomes. Participants who's sleep efficiency (SE) was <85% had a significantly longer length of stay compared to those who's SE was >=85% (17.4 vs. 21.5 days, p<0.05). Conclusions: People with stroke undergoing inpatient rehabilitation have poor sleep quantity and quality. There is a small to moderate association between sleep patterns and clinical outcomes and participants with poor sleep quality had longer length of stay compared to those with good sleep quality. Further research is necessary to better understand the complex relationship between sleep and recovery after stroke. Impact: Sleep is associated with functional recovery during inpatient rehabilitation after stroke.

Association between Self-Reported Prior Night's Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning.

Failure to obtain the recommended 7−9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night's sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7−9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night's sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night's sleep using single-task gait.

Walk the Talk: Current Evidence for Walking Recovery After Stroke, Future Pathways and a Mission for Research and Clinical Practice.

Achieving safe, independent, and efficient walking is a top priority for stroke survivors to enable quality of life and future health. This narrative review explores the state of the science in walking recovery after stroke and potential for development. The importance of targeting walking capacity and performance is explored in relation to individual stroke survivor gait recovery, applying a common language, measurement, classification, prediction, current and future intervention development, and health care delivery. Findings are summarized in a model of current and future stroke walking recovery research and a mission statement is set for researchers and clinicians to drive the field forward to improve the lives of stroke survivors and their carers.

Study Protocol: Sleep Effects on Poststroke Rehabilitation Study.

BACKGROUND: A range of sleep disturbances and disorders are problematic in people after stroke; they interfere with recovery of function during poststroke rehabilitation. However, studies to date have focused primarily on the effects of one sleep disorder-obstructive sleep apnea (OSA)-on stroke recovery. OBJECTIVES: The study protocol for the SLEep Effects on Poststroke Rehabilitation (SLEEPR) Study is presented with aims of characterizing proportion of non-OSA sleep disorders in the first 90 days after stroke, evaluating the effect of non-OSA sleep disorders on poststroke recovery, and exploring the complex relationships between stroke, sleep, and recovery in the community setting. METHODS: SLEEPR is a prospective cohort observational study across multiple study sites following individuals from inpatient rehabilitation through 90 days poststroke, with three measurement time points (inpatient rehabilitation; i.e., ~15 days poststroke, 60 days poststroke, and 90 days poststroke). Measures of sleep, function, activity, cognition, emotion, disability, and participation will be obtained for 200 people without OSA at the study's start through self-report, capacity assessments, and performance measures. Key measures of sleep include wrist actigraphy, sleep diaries, overnight oximetry, and several sleep disorders screening questionnaires (Insomnia Severity Index, Cambridge-Hopkins Restless Legs Questionnaire, Epworth Sleepiness Scale, and Sleep Disorders Screening Checklist). Key measures of function and capacity include the 10-meter walk test, Stroke Impact Scale, Barthel index, and modified Rankin scale. Key performance measures include leg accelerometry (e.g., steps/day, sedentary time, upright time, and sit-to-stand transitions) and community trips via GPS data and activity logs. DISCUSSION: The results of this study will contribute to understanding the complex interplay between non-OSA sleep disorders and poststroke rehabilitation; they provide insight regarding barriers to participation in the community and return to normal activities after stroke. Such results could lead to strategies for developing new stroke recovery interventions.