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 ).

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.

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.

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.

Sleep problems worsen health-related quality of life and participation during the first 12 months of stroke rehabilitation.

OBJECTIVE: Evaluate the impact of self-reported sleep problems on post-stroke recovery. DESIGN: Cross-sectional secondary analysis of longitudinal data from the Locomotor Experience Applied Post-Stroke (LEAPS) rehabilitation and recovery study (phase-III single-blind randomized controlled clinical trial). Group medians were compared for three sleep problem groups across three time points. SETTING: Outpatient and in-home physical therapy. SUBJECTS: Adults during the first year following stroke (n = 408, 380, 360 at 2, 6, 12 months, respectively). INTERVENTIONS: The original study compared effects of locomotor training with body weight support in the year post-stroke. This analysis evaluated function in three sleep/functional-impact groups: no sleep problems, sleep problems with no-to-minimal-impact and sleep problems with moderate-to-quite-a-bit of impact. MAIN MEASURES: Participants' responses regarding if they had "a sleep problem, such as insomnia" and, if so, what the impact was on their function. Stroke Impact Scale subscales for strength, hand function, mobility, ADLs, memory, communication, emotion, participation, and percent recovery. RESULTS: About 25% of people with stroke reported sleep difficulty, 10% perceived sleep problems negatively impact function. Groups self-reporting worse sleep performed worse in all functional subscales (except self-perceived percent recovery) during the first year post-stroke. CONCLUSION: Self-reported poor sleep adversely effects post-stroke functional recovery.

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.