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.

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.

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.

The Impact of Sleep Disorders on Functional Recovery and Participation Following Stroke: A Systematic Review and Meta-Analysis.

BACKGROUND: Adequate sleep is vital for health and quality of life. People with stroke and a concomitant sleep disorder may have poorer outcomes than those without a sleep disorder. OBJECTIVE: To systematically evaluate the published literature to determine the impact of sleep disorders on physical, functional recovery at the activity and participation level after stroke. METHODS: A systematic review was conducted using PubMed, CINAHL, Scopus, and PsycINFO. Studies were selected that reported outcomes on physical, functional recovery at the activity and participation levels in participants with stroke and a diagnosed sleep disorder. A meta-analysis was performed on included studies that reported Barthel Index (BI) and modified Rankin Scale (mRS) scores. Results: A total of 33 studies were included in the systematic review with 9 of them in the meta-analysis. The mean mRS score was 0.51 points higher in participants with stroke and sleep disorders versus participants with stroke without sleep disorder [95% CI: 0.23-0.78]. The mean BI score was 10.2 points lower in participants with stroke and sleep disorders versus participants with stroke without sleep disorder [95% CI: -17.9 to -2.6]. CONCLUSIONS: People with stroke and a sleep disorder have greater functional limitations and disability than those without a sleep disorder. Rehabilitation professionals should screen their patients with stroke for potential sleep disorders and further research is needed to develop sleep and rehabilitation interventions that can be delivered in combination. PROSPERO registration number: CRD42019125562.

Minimal Clinically Important Difference of the 6-Minute Walk Test in People With Stroke.

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).

Predicting Home and Community Walking Activity Poststroke.

BACKGROUND AND PURPOSE: Walking ability poststroke is commonly assessed using gait speed categories developed by Perry et al. The purpose of this study was to reexamine factors that predict home and community ambulators determined from real-world walking activity data using activity monitors. METHODS: Secondary analyses of real-world walking activity from 2 stroke trials. Home (100-2499 steps/d), most limited community (2500-4499 steps/d), least limited community (5000-74 999), and full community (≥7500 steps/d) walking categories were developed based on normative data. Independent variables to predict walking categories were comfortable and fast gait speed, 6-minute walk test, Berg Balance Scale, Fugl Meyer, and Stroke Impact Scale. Data were analyzed using multivariate analyses to identify significant variables associated with walking categories, bootstrap method to select the most stable model and receiver-operating characteristic to identify cutoff values. RESULTS: Data from 441 individuals poststroke were analyzed. The 6-minute walk test, Fugl Meyer, and Berg Balance Scale combined were the strongest predictors of home versus community and limited versus unlimited community ambulators. The 6-minute walk test was the strongest individual variable in predicting home versus community (receiver-operating characteristic area under curve=0.82) and limited versus full community ambulators (receiver-operating characteristic area under curve=0.76). A comfortable gait speed of 0.49 m/s discriminated between home and community and a comfortable gait speed of 0.93 m/s discriminated between limited community and full community ambulators. CONCLUSIONS: The 6-minute walk test was better able to discriminate among home, limited community, and full community ambulators than comfortable gait speed. Gait speed values commonly used to distinguish between home and community walkers may overestimate walking activity.

Self-Selected and Maximal Walking Speeds Provide Greater Insight Into Fall Status Than Walking Speed Reserve Among Community-Dwelling Older Adults.

OBJECTIVE: To determine the degree to which self-selected walking speed (SSWS), maximal walking speed (MWS), and walking speed reserve (WSR) are associated with fall status among community-dwelling older adults. DESIGN: WS and 1-year falls history data were collected on 217 community-dwelling older adults (median age = 82, range 65-93 years) at a local outpatient PT clinic and local retirement communities and senior centers. WSR was calculated as a difference (WSRdiff = MWS - SSWS) and ratio (WSRratio = MWS/SSWS). RESULTS: SSWS (P < 0.001), MWS (P < 0.001), and WSRdiff (P < 0.01) were associated with fall status. The cutpoints identified were 0.76 m/s for SSWS (65.4% sensitivity, 70.9% specificity), 1.13 m/s for MWS (76.6% sensitivity, 60.0% specificity), and 0.24 m/s for WSRdiff (56.1% sensitivity, 70.9% specificity). SSWS and MWS better discriminated between fallers and non-fallers (SSWS: AUC = 0.69, MWS: AUC = 0.71) than WSRdiff (AUC = 0.64). CONCLUSIONS: SSWS and MWS seem to be equally informative measures for assessing fall status in community-dwelling older adults. Older adults with SSWSs less than 0.76 m/s and those with MWSs less than 1.13 m/s may benefit from further fall risk assessment. Combining SSWS and MWS to calculate an individual's WSR does not provide additional insight into fall status in this population. TO CLAIM CME CREDITS: Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME CME OBJECTIVES:: Upon completion of this article, the reader should be able to: (1) Describe the different methods for calculating walking speed reserve and discuss the potential of the metric as an outcome measure; (2) Explain the degree to which self-selected walking speed, maximal walking speed, and walking speed reserve are associated with fall status among community-dwelling older adults; and (3) Discuss potential limitations to using walking speed reserve to identify fall status in populations without mobility restrictions. LEVEL: Advanced ACCREDITATION: : The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The Association of Academic Physiatrists designates this activity for a maximum of 1.5 AMA PRA Category 1 Credit(s). Physicians should only claim credit commensurate with the extent of their participation in the activity.

Self-Selected Walking Speed is Predictive of Daily Ambulatory Activity in Older Adults.

Daily ambulatory activity is associated with health and functional status in older adults; however, assessment requires multiple days of activity monitoring. The objective of this study was to determine the relative capabilities of self-selected walking speed (SSWS), maximal walking speed (MWS), and walking speed reserve (WSR) to provide insight into daily ambulatory activity (steps per day) in community-dwelling older adults. Sixty-seven older adults completed testing and activity monitoring (age 80.39 [6.73] years). SSWS (R2 = .51), MWS (R2 = .35), and WSR calculated as a ratio (R2 = .06) were significant predictors of daily ambulatory activity in unadjusted linear regression. Cutpoints for participants achieving < 8,000 steps/day were identified for SSWS (≤ 0.97 m/s, 44.2% sensitivity, 95.7% specificity, 10.28 +LR, 0.58 -LR) and MWS (≤ 1.39 m/s, 60.5% sensitivity, 78.3% specificity, 2.79 +LR, 0.50 -LR). SSWS may be a feasible proxy for assessing and monitoring daily ambulatory activity in older adults.

Development of the RT-GAIT, a Real-Time feedback device to improve Gait of individuals with stroke.

Regaining the ability to walk is a major rehabilitation goal after a stroke. Recent research suggests that, in people with stroke, task-oriented and intensive rehabilitation strategies can drive cortical reorganization and increase activity levels. This paper describes development and pilot testing of a novel wearable device for Real-Time Gait and Activity Improving Telerehabilitation (RT-GAIT), designed for use with such rehabilitation strategies. The RT-GAIT provides auditory or tactile feedback to the individual wearing the platform. The feedback is based on the amount of time spent in stance phase on each foot, as measured by the pressure sensors embedded into the insoles. The system was initially bench-validated using sensor signals collected in a previous study. Next, a clinical case study was conducted with one post-stroke individual. The results of the case study suggest that the RT-GAIT device can potentially improve the gait parameters. Mean difference in stance times between the healthy limb and paretic limb was improved by 48% and the standard deviation for the same was improved by 87.5%, between baseline measurements and the measurements taken after the treatment with the RT-GAIT.

Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury.

BACKGROUND: Advances in sensor technologies and signal processing techniques provide a method to accurately measure walking activity in the home and community. Activity monitors geared toward consumer or patient use may be an alternative to more expensive monitors designed for research to measure stepping activity. OBJECTIVE: The objective of this study was to examine the accuracy of 2 consumer/patient activity monitors, the Fitbit Ultra and the Nike+ Fuelband, in identifying stepping activity in people with stroke and traumatic brain injury (TBI). Secondarily, the study sought to compare the accuracy of these 2 activity monitors with that of the StepWatch Activity Monitor (SAM) and a pedometer, the Yamax Digi-Walker SW-701 pedometer (YDWP). DESIGN: A cross-sectional design was used for this study. METHOD: People with chronic stroke and TBI wore the 4 activity monitors while they performed the Two-Minute Walk Test (2MWT), during which they were videotaped. Activity monitor estimated steps taken were compared with actual steps taken counted from videotape. Accuracy and agreement between activity monitor estimated steps and actual steps were examined using intraclass correlation coefficients (ICC [2,1]) and the Bland-Altman method. RESULTS: The SAM demonstrated the greatest accuracy (ICC [2,1]=.97, mean difference between actual steps and SAM estimated steps=4.7 steps) followed by the Fitbit Ultra (ICC [2,1]=.73, mean difference between actual steps and Fitbit Ultra estimated steps=-9.7 steps), the YDWP (ICC [2,1]=.42, mean difference between actual steps and YDWP estimated steps=-28.8 steps), and the Nike+ Fuelband (ICC [2,1]=.20, mean difference between actual steps and Nike+ Fuelband estimated steps=-66.2 steps). LIMITATIONS: Walking activity was measured over a short distance in a closed environment, and participants were high functioning ambulators, with a mean gait speed of 0.93 m/s. CONCLUSIONS: The Fitbit Ultra may be a low-cost alternative to measure the stepping activity in level, predictable environments of people with stroke and TBI who can walk at speeds ≥0.58 m/s.

EMG control of a bionic knee prosthesis: exploiting muscle co-contractions for improved locomotor function.

This paper presents the development and experimental evaluation of a volitional control architecture for a powered-knee transfemoral prosthesis that affords the amputee user with direct control of knee impedance using measured electromyogram (EMG) potentials of antagonist muscles in the residual limb. The control methodology incorporates a calibration procedure performed with each donning of the prosthesis that characterizes the co-contraction levels as the user performs volitional phantom-knee flexor and extensor contractions. The performance envelope for EMG control of impedance is then automatically shaped based on the flexor and extensor calibration datasets. The result is a control architecture that is optimized to the user's current co-contraction activity, providing performance robustness to variation in sensor placement or physiological changes in the residual-limb musculature. Experimental results with a single unilateral transfemoral amputee user demonstrate consistent and repeatable control performance for level walking at self-selected speed over a multi-week, multi-session period of evaluation.

Using decision trees to measure activities in people with stroke.

Improving community mobility is a common goal for persons with stroke. Measuring daily physical activity is helpful to determine the effectiveness of rehabilitation interventions. In our previous studies, a novel wearable shoe-based sensor system (SmartShoe) was shown to be capable of accurately classify three major postures and activities (sitting, standing, and walking) from individuals with stroke by using Artificial Neural Network (ANN). In this study, we utilized decision tree algorithms to develop individual and group activity classification models for stroke patients. The data was acquired from 12 participants with stroke. For 3-class classification, the average accuracy was 99.1% with individual models and 91.5% with group models. Further, we extended the activities into 8 classes: sitting, standing, walking, cycling, stairs-up, stairs-down, wheel-chair-push, and wheel-chair-propel. The classification accuracy for individual models was 97.9%, and for group model was 80.2%, demonstrating feasibility of multi-class activity recognition by SmartShoe in stroke patients.