Fun and games: a scoping review of enjoyment and intensity assessment in studies of game-based interventions for gait rehabilitation in neurological disorders.

PURPOSE: Exergames are used to promote gait rehabilitation in patients with neurological disorders because they are believed to heighten patient enjoyment and training intensity. This scoping review evaluated whether and how studies support these claims. METHODS: A search for studies published up until October 2023 involving virtual reality or exergames for patients with neurological disorders (stroke, Parkinson's disease, multiple sclerosis, spinal cord injury) was conducted on PubMed and Scopus, with additional articles identified through backward and forward citation searching. Studies collecting gait measurements, with at least five participants and a control group were included. Data extracted were rationale, and whether participants' enjoyment of the intervention and training intensity were assessed. RESULTS: 1060 records were identified with 58 included in this review. There were 34 articles on stroke, 11 on multiple sclerosis, and 13 on Parkinson's disease. Participant enjoyment and greater training intensity were important rationales but were only evaluated in 12 and seven of the included studies, respectively. CONCLUSION: Results highlight that participant enjoyment and heightened training intensity are commonly cited rationales for using exergames in gait rehabilitation, but these effects are assumed and not routinely measured or analysed. Greater consistency is needed in the design and execution of exergaming studies for neurological disorders.

Participant enjoyment and heightened training intensity are commonly cited rationales for using exergames in gait rehabilitation, but these effects are assumed and not routinely measured or analysed.There are no agreed-upon conceptual frameworks nor validated measures of enjoyment, and this concept is commonly conflated with adherence.Intervention adherence could be improved by considering participant capabilities, opportunities and motivation at the design stage.Whether exergames increase adherence and training intensity because they are more enjoyable and motivating remains an open question.

Do integrated stroke units affect patient and family experience of care transitions?

PURPOSE: Patients and families identify discharge from hospital as highly challenging. Less is known about experiences of transition between acute services and inpatient rehabilitation. We aimed to understand the experiences of patients and families as they transition to inpatient rehabilitation services, before and after the opening of a new integrated stroke and rehabilitation unit (ISU). MATERIALS AND METHODS: Adults were recruited 7 days after transfer to inpatient rehabilitation, in two 6-month periods before and after the opening of the ISU. Their experiences of care continuity were evaluated with a survey. Univariate analyses compared survey data pre- and post-ISU. A subset of participants completed semi-structured interviews that underwent thematic analysis. RESULTS: 150 patients were recruited (median age 60 years, range 20-92 years, 72 female). There were no differences between pre- and post-ISU survey scores for patient or family experiences (all p > 0.3). Interview analysis identified 3 major themes: "Whanaungatanga - the foundation of patient experience", "In the dark and out of control", and "A nice view…but I want to be able to do more." CONCLUSIONS: Implementation of an integrated stroke and rehabilitation unit maintained levels of patient and family satisfaction. Interviews identified important themes for services planning to improve patient experience.

Inpatient transitions can be a challenging experience for patients and family members.A new ward environment that eliminated the transition from acute to inpatient rehabilitation services had little effect on patient and family experiences.Relational aspects of inpatient care are more salient for patients and families than the physical environment.Services planning to improve patient experience should prioritise investing in staff alongside improvements to the ward environment.

Accuracy of Physiotherapist Predictions for Independent Walking After Stroke.

BACKGROUND: The use of prediction tools in stroke rehabilitation research and clinical practice is increasing, but it is not clear whether these prediction tools out-perform clinician predictions. OBJECTIVE: This study aimed to compare physiotherapist predictions for independent walking with the Time to Walking Independently after STroke (TWIST) prediction tool. METHODS: Adults with new lower limb weakness and unable to walk independently (Functional Ambulation Category [FAC] < 4) were recruited. At 1 week post-stroke, the treating physiotherapist was asked to predict whether their patient would achieve independent walking by 4, 6, 9, 12, 16, or 26 weeks, or remain dependent. Predictions were also made using the TWIST prediction tool, but not shared. Binary logistic regressions were conducted with the time independent walking was achieved as the dependent variable and independent variables were the physiotherapist and TWIST predictions. RESULTS: Ninety-one participants were included (median age 71 years, 36 [40%] female). Most participants (67 [74%]) were non-ambulatory (FAC = 0) at 1-week post-stroke. Thirty-seven physiotherapists were recruited. Physiotherapists made accurate predictions for time taken to achieve independent walking for 39 participants (43%). Prediction accuracy was not related to physiotherapist confidence or years of stroke-specific experience. TWIST out-performed physiotherapist predictions (Physiotherapists 76%-77%, TWIST 86%-88% accurate) for participants who achieved independent walking by 4, 6, and 9 weeks post-stroke. Accuracy of physiotherapist and TWIST predictions was similar for 16 and 26 weeks post-stroke. CONCLUSIONS: The TWIST prediction tool is more accurate than physiotherapists at predicting whether a patient will achieve independent walking by 4, 6, or 9 weeks post-stroke, but not for 16 or 26 weeks post-stroke. TWIST may be useful to inform early rehabilitation and discharge planning. Clinical Trial Registration-URL: www.anzctr.org.au Unique Identifier: ACTRN12617001434381.

Biomarkers of Motor Outcomes After Stroke.

Predicting motor outcomes after stroke based on clinical judgment alone is often inaccurate and can lead to inefficient and inequitable allocation of rehabilitation resources. Prediction tools are being developed so that clinicians can make evidence-based, accurate, and reproducible prognoses for individual patients. Biomarkers of corticospinal tract structure and function can improve prediction tool performance, particularly for patients with initially moderate to severe motor impairment. Being able to make accurate predictions for individual patients supports rehabilitation planning and communication with patients and families.

Do lower limb motor-evoked potentials predict walking outcomes post-stroke?

BACKGROUND: This observational study examined whether lower limb (LL) motor-evoked potentials (MEPs) 1 week post-stroke predict recovery of independent walking, use of ankle-foot orthosis (AFO) or walking aid, at 3 and 6 months post-stroke. METHODS: Non-ambulatory participants were recruited 5 days post-stroke. Transcranial magnetic stimulation was used to determine tibialis anterior MEP status and clinical assessments (age, National Institutes of Health Stroke Scale (NIHSS), ankle dorsiflexion strength, LL motricity index, Berg Balance Test) were completed 1 week post-stroke. Functional Ambulation Category (FAC), use of AFO and walking aid were assessed 3 months and 6 months post-stroke. MEP status, alone and combined with clinical measures, and walking outcomes at 3 and 6 months were analysed with Pearson χ2 and multivariate binary logistic regression. RESULTS: Ninety participants were included (median age 72 years (38-97 years)). Most participants (81%) walked independently (FAC ≥ 4), 17% used an AFO, and 49% used a walking aid 3 months post-stroke with similar findings at 6 months. Independent walking was better predicted by age, LL strength and Berg Balance Test (accuracy 92%, 95% CI 85% to 97%) than MEP status (accuracy 73%, 95% CI 63% to 83%). AFO use was better predicted by NIHSS and MEP status (accuracy 88%, 95% CI 79% to 94%) than MEP status alone (accuracy 76%, 95% CI 65% to 84%). No variables predicted use of walking aids. CONCLUSIONS: The presence of LL MEPs 1-week post-stroke predicts independent walking at 3 and 6 months post-stroke. However, the absence of MEPs does not preclude independent walking. Clinical factors, particularly age, balance and stroke severity, more strongly predict independent walking than MEP status. LL MEP status adds little value as a biomarker for walking outcomes.

Applications of Repetitive Transcranial Magnetic Stimulation to Improve Upper Limb Motor Performance After Stroke: A Systematic Review.

BACKGROUND: Noninvasive brain stimulation (NIBS) is a promising technique for improving upper limb motor performance post-stroke. Its application has been guided by the interhemispheric competition model and typically involves suppression of contralesional motor cortex. However, the bimodal balance recovery model prompts a more tailored application of NIBS based on ipsilesional corticomotor function. OBJECTIVE: To review and assess the application of repetitive transcranial magnetic stimulation (rTMS) protocols that aimed to improve upper limb motor performance after stroke. METHODS: A PubMed search was conducted for studies published between 1st January 2005 and 1st November 2022 using rTMS to improve upper limb motor performance of human adults after stroke. Studies were grouped according to whether facilitatory or suppressive rTMS was applied to the contralesional hemisphere. RESULTS: Of the 492 studies identified, 70 were included in this review. Only 2 studies did not conform to the interhemispheric competition model, and facilitated the contralesional hemisphere. Only 21 out of 70 (30%) studies reported motor evoked potential (MEP) status as a biomarker of ipsilesional corticomotor function. Around half of the studies (37/70, 53%) checked whether rTMS had the expected effect by measuring corticomotor excitability (CME) after application. CONCLUSION: The interhemispheric competition model dominates the application of rTMS post-stroke. The majority of recent and current studies do not consider bimodal balance recovery model for the application of rTMS. Evaluating CME after the application rTMS could confirm that the intervention had the intended neurophysiological effect. Future studies could select patients and apply rTMS protocols based on ipsilesional MEP status.

The TWIST Tool Predicts When Patients Will Recover Independent Walking After Stroke: An Observational Study.

BACKGROUND: The likelihood of regaining independent walking after stroke influences rehabilitation and hospital discharge planning. OBJECTIVE: This study aimed to develop and internally validate a tool to predict whether and when a patient will walk independently in the first 6 months post-stroke. METHODS: Adults with stroke were recruited if they had new lower limb weakness and were unable to walk independently. Clinical assessments were completed one week post-stroke. The primary outcome was time post-stroke by which independent walking (Functional Ambulation Category score ≥ 4) was achieved. Cox hazard regression identified predictors for achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. The cut-off and weighting for each predictor was determined using ß-coefficients. Predictors were assigned a score and summed for a final TWIST score. The probability of achieving independent walking at each time point for each TWIST score was calculated. RESULTS: We included 93 participants (36 women, median age 71 years). Age < 80 years, knee extension strength Medical Research Council grade ≥ 3/5, and Berg Balance Test < 6, 6 to 15, or ≥ 16/56, predicted independent walking and were combined to form the TWIST prediction tool. The TWIST prediction tool was at least 83% accurate for all time points. CONCLUSIONS: The TWIST tool combines routine bedside tests at one week post-stroke to accurately predict the probability of an individual patient achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. If externally validated, the TWIST prediction tool may benefit patients and clinicians by informing rehabilitation decisions and discharge planning.

The effects of unilateral step training and conventional treadmill training on gait asymmetry in patients with chronic stroke.

BACKGROUND: Step length asymmetry is common after stroke. Unilateral step training (UST) can improve step length asymmetry for patients who take a longer step with their paretic leg (P-long). UST has not been tested with patients who take a shorter step with their paretic leg (P-short). RESEARCH QUESTION: Does training patients according to the direction of their asymmetry improve step length asymmetry? METHODS: Adults 18 years and older with asymmetrical gait at least 6 months post-stroke completed three 20 min treadmill training sessions at least 48 h apart: Conventional treadmill; UST with the non-paretic leg stationary on the side of the treadmill and the paretic leg stepping on the moving treadmill belt (P-stepping); and UST with the paretic leg stationary on the side of the treadmill and the non-paretic leg stepping on the moving belt (NP-stepping). Spatiotemporal gait parameters before, immediately, 10 min and 30 min after training were recorded at self-selected and fastest walking pace. Asymmetry values for each parameter were calculated. RmANOVAs were used to investigate the effects of training type on spatiotemporal parameters and paired-samples t-tests used to investigate potential contributors to training effects on asymmetry. RESULTS: Twenty participants (16 male, median age 65 (43-80) years; 11 P-long, 9 P-short) were included. Improvements in step length asymmetry were observed immediately after both Conventional (9.1 %; 95 % CI 2.7-15.4%) and P-stepping (11.6 %; 95 % CI 5.3-17.8 %) treadmill training in participants who take a shorter step with their paretic leg, however effects were only sustained after Conventional training. Step length asymmetry did not improve for P-long participants with any training type. SIGNIFICANCE: The effectiveness of unilateral step training may be related to the direction of step length asymmetry. Further investigation is required before considering using unilateral step training as a rehabilitation tool for gait asymmetry after stroke.

Implementing the PREP2 Algorithm to Predict Upper Limb Recovery Potential After Stroke in Clinical Practice: A Qualitative Study.

OBJECTIVE: Predicting motor recovery after stroke is a key factor when planning and providing rehabilitation for individual patients. The Predict REcovery Potential (PREP2) prediction tool was developed to help clinicians predict upper limb functional outcome. In parallel to further model validation, the purpose of this study was to explore how PREP2 was implemented in clinical practice within the Auckland District Health Board (ADHB) in New Zealand. METHODS: In this case study design using semi-structured interviews, 19 interviews were conducted with clinicians involved in stroke care at ADHB. To explore factors influencing implementation, interview content was coded and analyzed using the consolidated framework for implementation research. Strategies identified by the Expert Recommendations for Implementing Change Project were used to describe how implementation was undertaken. RESULTS: Implementation of PREP2 was initiated and driven by therapists. Key factors driving implementation were as follows: the support given to staff from the implementation team; the knowledge, beliefs, and self-efficacy of staff; and the perceived benefits of having PREP2 prediction information. Twenty-six Expert Recommendations for Implementing Change strategies were identified relating to 3 areas: implementation team, clinical/academic partnerships, and training. CONCLUSIONS: The PREP2 prediction tool was successfully implemented in clinical practice at ADHB. Barriers and facilitators to implementation success were identified, and implementation strategies were described. Lessons learned can aid future development and implementation of prediction models in clinical practice. IMPACT: Translating evidence-based interventions into clinical practice can be challenging and slow; however, shortly after its local validation, PREP2 was successfully implemented into clinical practice at the same site in New Zealand. In parallel to further model validation, organizations and practices can glean useful lessons to aid future implementation.

Neurochemical balance and inhibition at the subacute stage after stroke.

Stroke is a leading cause of death and disability worldwide with many people left with impaired motor function. Evidence from experimental animal models of stroke indicates that reducing motor cortex inhibition may facilitate neural plasticity and motor recovery. This study compared primary motor cortex (M1) inhibition measures over the first 12 wk after stroke with a cohort of age-similar healthy controls. The excitation-inhibition ratio and gamma-aminobutyric acid (GABA) neurotransmission within M1 were assessed using magnetic resonance spectroscopy and threshold hunting paired-pulse transcranial magnetic stimulation respectively. Upper limb impairment and function were assessed with the Fugl-Meyer Upper Extremity Scale and Action Research Arm Test. Patients with a functional corticospinal pathway had motor-evoked potentials on the paretic side and exhibited better recovery from upper limb impairment and recovery of function than patients without a functional corticospinal pathway. Compared with age-similar controls, the neurochemical balance in terms of the excitation-inhibition ratio was greater within contralesional M1 in patients with a functional corticospinal pathway. There was evidence for elevated long-interval inhibition in both ipsilesional and contralesional M1 compared with controls. Short-interval inhibition measures differed between the first and second phases, with evidence for elevation of the former only in ipsilesional M1 and no evidence of disinhibition for the latter. Overall, findings from transcranial magnetic stimulation indicate an upregulation of GABA-mediated tonic inhibition in M1 early after stroke. Therapeutic approaches that aim to normalize inhibitory tone during the subacute period warrant further investigation.NEW & NOTEWORTHY Magnetic resonance spectroscopy indicated higher excitation-inhibition ratios within motor cortex during subacute recovery than age-similar healthy controls. Measures obtained from adaptive threshold hunting paired-pulse transcranial magnetic stimulation indicated greater tonic inhibition in patients compared with controls. Therapeutic approaches that aim to normalize motor cortex inhibition during the subacute stage of recovery should be explored.

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke.

High interindividual variability in the recovery of upper limb (UL) function after stroke means it is difficult to predict an individual's potential for recovery based on clinical assessments alone. The functional integrity of the corticospinal tract is an important prognostic biomarker for recovery of UL function, particularly for those with severe initial UL impairment. This article presents a protocol for evaluating corticospinal tract function within 1 week of stroke. This protocol can be used to select and stratify patients in trials of interventions designed to improve UL motor recovery and outcomes after stroke. The protocol also forms part of the PREP2 algorithm, which predicts UL function for individual patients 3 months poststroke. The algorithm sequentially combines a UL strength assessment, age, transcranial magnetic stimulation, and stroke severity, within a few days of the stroke. The benefits of using PREP2 in clinical practice are described elsewhere. This article focuses on the use of a UL strength assessment and transcranial magnetic stimulation to evaluate corticospinal tract function.

PREP2 Algorithm Predictions Are Correct at 2 Years Poststroke for Most Patients.

Background. The PREP2 algorithm combines clinical and neurophysiological measures to predict upper-limb (UL) motor outcomes 3 months poststroke, using 4 prediction categories based on Action Research Arm Test (ARAT) scores. The algorithm was accurate at 3 months for 75% of participants in a previous validation study. Objective. This study aimed to evaluate whether PREP2 predictions made at baseline are correct 2 years poststroke. We also assessed whether patients' UL performance remained stable, improved, or worsened between 3 months and 2 years after stroke. Methods. This is a follow-up study of 192 participants recruited and assessed in the original PREP2 validation study. Participants who completed assessments 3 months poststroke (n = 157) were invited to complete follow-up assessments at 2 years poststroke for the present study. UL outcomes were assessed with the ARAT, upper extremity Fugl-Meyer Scale, and Motor Activity Log. Results. A total of 86 participants completed 2-year follow-up assessments in this study. PREP2 predictions made at baseline were correct for 69/86 (80%) participants 2 years poststroke, and PREP2 UL outcome category was stable between 3 months and 2 years poststroke for 71/86 (83%). There was no difference in age, stroke severity, or comorbidities among patients whose category remained stable, improved, or deteriorated. Conclusions. PREP2 algorithm predictions made within days of stroke are correct at both 3 months and 2 years poststroke for most patients. Further investigation may be useful to identify which patients are likely to improve, remain stable, or deteriorate between 3 months and 2 years.

Implementing biomarkers to predict motor recovery after stroke.

BACKGROUND: There is growing interest in using biomarkers to predict motor recovery and outcomes after stroke. The PREP2 algorithm combines clinical assessment with biomarkers in an algorithm, to predict upper limb functional outcomes for individual patients. To date, PREP2 is the first algorithm to be tested in clinical practice, and other biomarker-based algorithms are likely to follow. PURPOSE: This review considers how algorithms to predict motor recovery and outcomes after stroke might be implemented in clinical practice. FINDINGS: There are two tasks: first the prediction information needs to be obtained, and then it needs to be used. The barriers and facilitators of implementation are likely to differ for these tasks. We identify specific elements of the Consolidated Framework for Implementation Research that are relevant to each of these two tasks, using the PREP2 algorithm as an example. These include the characteristics of the predictors and algorithm, the clinical setting and its staff, and the healthcare environment. CONCLUSIONS: Active, theoretically underpinned implementation strategies are needed to ensure that biomarkers are successfully used in clinical practice for predicting motor outcomes after stroke, and should be considered in parallel with biomarker development.

PREP2: A biomarker-based algorithm for predicting upper limb function after stroke.

Objective: Recovery of motor function is important for regaining independence after stroke, but difficult to predict for individual patients. Our aim was to develop an efficient, accurate, and accessible algorithm for use in clinical settings. Clinical, neurophysiological, and neuroimaging biomarkers of corticospinal integrity obtained within days of stroke were combined to predict likely upper limb motor outcomes 3 months after stroke. Methods: Data from 207 patients recruited within 3 days of stroke [103 females (50%), median age 72 (range 18-98) years] were included in a Classification and Regression Tree analysis to predict upper limb function 3 months poststroke. Results: The analysis produced an algorithm that sequentially combined a measure of upper limb impairment; age; the presence or absence of upper limb motor evoked potentials elicited with transcranial magnetic stimulation; and stroke lesion load obtained from MRI or stroke severity assessed with the NIHSS score. The algorithm makes correct predictions for 75% of patients. A key biomarker obtained with transcranial magnetic stimulation is required for one third of patients. This biomarker combined with NIHSS score can be used in place of more costly magnetic resonance imaging, with no loss of prediction accuracy. Interpretation: The new algorithm is more accurate, efficient, and accessible than its predecessors, which may support its use in clinical practice. While further work is needed to potentially incorporate sensory and cognitive factors, the algorithm can be used within days of stroke to provide accurate predictions of upper limb functional outcomes at 3 months after stroke. www.presto.auckland.ac.nz.

The TWIST Algorithm Predicts Time to Walking Independently After Stroke.

BACKGROUND AND OBJECTIVE: The likelihood of regaining independent walking after stroke is of concern to patients and their families and influences hospital discharge planning. The objective of this study was to explore factors that could be combined in an algorithm for predicting whether and when a patient will walk independently after stroke. METHODS: Adults with new lower limb weakness were recruited within 3 days of having a stroke. Clinical assessment, transcranial magnetic stimulation, and magnetic resonance imaging were completed 1 to 2 weeks poststroke. Classification and regression tree (CART) analysis was used to identify factors that predicted whether a patient achieved independent walking by 6 or 12 weeks, or remained dependent at 12 weeks. RESULTS: We recruited 41 patients (24 women; median age 72 years, range 43-96 years). The CART analysis results were used to create the Time to Walking Independently after STroke (TWIST) algorithm, which made accurate predictions for 95% of patients. Patients with a trunk control test score >40 at 1 week walked independently within 6 weeks. Patients with a trunk control test score <40 only achieved independent walking by 12 weeks if they also had hip extension strength of Medical Research Council grade 3 or more. Neurophysiological and neuroimaging measures did not predict independent walking after stroke. CONCLUSIONS: In this exploratory study, the TWIST algorithm accurately predicted whether and when an individual patient walked independently after stroke using simple bedside measures 1 week poststroke. Further work is required to develop and validate this algorithm in a larger study.

Proportional Recovery From Lower Limb Motor Impairment After Stroke.

BACKGROUND AND PURPOSE: In people with preserved corticospinal tract (CST) function after stroke, upper limb impairment resolves by ≈70% within 3 months. This is known as the proportional recovery rule. Patients without CST function do not fit this rule and have worse upper limb outcomes. This study investigated resolution of motor impairment in the lower limb (LL). METHODS: Patients with stroke and LL weakness were assessed 3 days and 3 months after stroke with the LL Fugl-Meyer. CST integrity was determined in a subset of patients using transcranial magnetic stimulation to test for LL motor-evoked potentials and magnetic resonance imaging to measure CST lesion load. Linear regression analyses were conducted to predict resolution of motor impairment (ΔFugl-Meyer) including factors initial impairment, motor-evoked potential status, CST lesion load, and LL therapy dose. RESULTS: Thirty-two patients completed 3-month follow-up and recovered 74% (95% confidence interval, 60%-88%) of initial LL motor impairment. Initial impairment was the only significant predictor of resolution of motor impairment. There was no identifiable cluster of patients who did not fit the proportional recovery rule. Measures of CST integrity did not predict proportional LL recovery. CONCLUSIONS: LL impairment resolves by ≈70% within 3 months after stroke. The absence of a nonfitter group may be because of differences in the neuroanatomical organization of descending motor tracts to the upper limb and LL. Proportional recovery of the LL is not influenced by therapy dose providing further evidence that it reflects a fundamental biological process.

Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency.

BACKGROUND AND PURPOSE: Several clinical measures and biomarkers are associated with motor recovery after stroke, but none are used to guide rehabilitation for individual patients. The objective of this study was to evaluate the implementation of upper limb predictions in stroke rehabilitation, by combining clinical measures and biomarkers using the Predict Recovery Potential (PREP) algorithm. METHODS: Predictions were provided for patients in the implementation group (n=110) and withheld from the comparison group (n=82). Predictions guided rehabilitation therapy focus for patients in the implementation group. The effects of predictive information on clinical practice (length of stay, therapist confidence, therapy content, and dose) were evaluated. Clinical outcomes (upper limb function, impairment and use, independence, and quality of life) were measured 3 and 6 months poststroke. The primary clinical practice outcome was inpatient length of stay. The primary clinical outcome was Action Research Arm Test score 3 months poststroke. RESULTS: Length of stay was 1 week shorter for the implementation group (11 days; 95% confidence interval, 9-13 days) than the comparison group (17 days; 95% confidence interval, 14-21 days; P=0.001), controlling for upper limb impairment, age, sex, and comorbidities. Therapists were more confident (P=0.004) and modified therapy content according to predictions for the implementation group (P<0.05). The algorithm correctly predicted the primary clinical outcome for 80% of patients in both groups. There were no adverse effects of algorithm implementation on patient outcomes at 3 or 6 months poststroke. CONCLUSIONS: PREP algorithm predictions modify therapy content and increase rehabilitation efficiency after stroke without compromising clinical outcome. CLINICAL TRIAL REGISTRATION: URL: http://anzctr.org.au. Unique identifier: ACTRN12611000755932.

Proportional Motor Recovery After Stroke: Implications for Trial Design.

BACKGROUND AND PURPOSE: Recovery of upper-limb motor impairment after first-ever ischemic stroke is proportional to the degree of initial impairment in patients with a functional corticospinal tract (CST). This study aimed to investigate whether proportional recovery occurs in a more clinically relevant sample including patients with intracerebral hemorrhage and previous stroke. METHODS: Patients with upper-limb weakness were assessed 3 days and 3 months poststroke with the Fugl-Meyer scale. Transcranial magnetic stimulation was used to test CST function, and patients were dichotomized according to the presence of motor evoked potentials in the paretic wrist extensors. Linear regression modeling of Δ Fugl-Meyer score between 3 days and 3 months was performed, with predictors including initial impairment (66 - baseline Fugl-Meyer score), age, sex, stroke type, previous stroke, comorbidities, and upper-limb therapy dose. RESULTS: One hundred ninety-two patients were recruited, and 157 completed 3-month follow-up. Patients with a functional CST made a proportional recovery of 63% (95% confidence interval, 55%-70%) of initial motor impairment. The recovery of patients without a functional CST was not proportional to initial impairment and was reduced by greater CST damage. CONCLUSIONS: Recovery of motor impairment in patients with intact CST is proportional to initial impairment and unaffected by previous stroke, type of stroke, or upper-limb therapy dose. Novel interventions that interact with the neurobiological mechanisms of recovery are needed. The generalizability of proportional recovery is such that patients with intracerebral hemorrhage and previous stroke may usefully be included in interventional rehabilitation trials. CLINICAL TRIAL REGISTRATION: URL: http://www.anzctr.org.au. Unique identifier: ANZCTR12611000755932.