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BackgroundChronic pain and somatosensory impairment are common following a stroke. It is possible that an interaction exists between pain and somatosensory impairment and that a change in one may influence the other. We therefore investigated the presence of chronic pain and self-reported altered somatosensory ability in individuals with stroke, aiming to determine if chronic pain is more common in stroke survivors with somatosensory impairment than in those without.MethodsStroke survivors were invited to complete an online survey that included demographics, details of the stroke, presence of chronic pain, and any perceived changes in body sensations post-stroke.ResultsSurvivors of stroke (n = 489) completed the survey with 308 indicating that they experienced chronic pain and 368 reporting perceived changes in somatosensory function. Individuals with strokes who reported altered somatosensory ability were more likely to experience chronic pain than those who did not (OR = 1.697; 95% CI 1.585, 2.446). Further, this difference was observed for all categories of sensory function that were surveyed (detection of light touch, body position, discrimination of surfaces and temperature, and haptic object recognition).ConclusionsThe results point to a new characteristic of chronic pain in strokes, regardless of nature or region of the pain experienced, and raises the potential of somatosensory impairment being a rehabilitation target to improve pain-related outcomes for stroke survivors.

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain–computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings. [ABSTRACT FROM AUTHOR]

The most difficult clinical questions in stroke rehabilitation are "What is this patient's potential for recovery?" and "What is the best rehabilitation strategy for this person, given her/his clinical profile?" Without answers to these questions, clinicians struggle to make decisions regarding the content and focus of therapy, and researchers design studies that inadvertently mix participants who have a high likelihood of responding with those who do not. Developing and implementing biomarkers that distinguish patient subgroups will help address these issues and unravel the factors important to the recovery process. The goal of the present paper is to provide a consensus statement regarding the current state of the evidence for stroke recovery biomarkers. Biomarkers of motor, somatosensory, cognitive and language domains across the recovery timeline post-stroke are considered; with focus on brain structure and function, and exclusion of blood markers and genetics. We provide evidence for biomarkers that are considered ready to be included in clinical trials, as well as others that are promising but not ready and so represent a developmental priority. We conclude with an example that illustrates the utility of biomarkers in recovery and rehabilitation research, demonstrating how the inclusion of a biomarker may enhance future clinical trials. In this way, we propose a way forward for when and where we can include biomarkers to advance the efficacy of the practice of, and research into, rehabilitation and recovery after stroke.

Background: Mild stroke affects more than half the stroke population, yet there is limited evidence characterizing cognition over time in this population, especially with predictive approaches applicable at the individual-level. We aimed to identify patterns of recovery and the best combination of demographic, clinical, and lifestyle factors predicting individual-level cognitive state at 3- and 12-months after mild stroke. Methods: In this prospective cohort study, the Montreal Cognitive Assessment (MoCA) was administered at 3–7 days, 3- and 12-months post-stroke. Raw changes in MoCA and impairment rates (defined as MoCA<24 points) were compared between assessment time-points. Trajectory clusters were identified using variations of ≥1 point in MoCA scores. To further compare clusters, additional assessments administered at 3- and 12-months were included. Gamma and Quantile mixed-effects regression were used to predict individual MoCA scores over time, using baseline clinical and demographic variables. Model predictions were fitted for each stroke survivor and evaluated using model cross-validation to identify the overall best predictors of cognitive recovery. Results: Participants' (n = 119) MoCA scores improved from baseline to 3-months (p<0.001); and decreased from 3- to 12-months post-stroke (p = 0.010). Cognitive impairment rates decreased significantly from baseline to 3-months (p<0.001), but not between 3- and 12-months (p = 0.168). Nine distinct trajectory clusters were identified. Clinical characteristics between clusters at each time-point varied in cognitive outcomes but not in clinical and/or activity participation outcomes. Cognitive performance at 3- and 12-months was best predicted by younger age, higher physical activity levels, and left-hemisphere lesion side. Conclusion: More than half of mild-stroke survivors are at risk of cognitive decline one year after stroke, even when preceded by a significantly improving pattern in the first 3-months of recovery. Physical activity was the only modifiable factor independently associated with cognitive recovery. Individual-level prediction methods may inform the timing and personalized application of future interventions to maximize cognitive recovery post-stroke. [ABSTRACT FROM AUTHOR]

The National Institute of Health Stroke Scale (NIHSS) is used worldwide to classify stroke severity as 'mild', 'moderate', or 'severe' based on neurological impairment. Yet, stroke survivors argue that the classification of 'mild' does not represent the holistic experience and impact of stroke on their daily lives. In this observational cohort study, we aimed to identify different types of impairment profiles among stroke survivors classified as 'mild'. We used survivors of mild stroke' data from the START longitudinal stroke cohort (n = 73), with measures related to sensorimotor, cognition, depression, functional disability, physical activity, work, and social adjustment over 12 months. Given the multisource, multigranular, and unlabeled nature of the data, we utilized a structure-adapting, unsupervised machine learning approach, the growing self-organizing map (GSOM) algorithm, to generate distinct clinical profiles. These diverse impairment profiles revealed that survivors of mild stroke experience varying degrees of impairment and impact (cognitive, depression, physical activity, work/social adjustment) at different time points, despite the uniformity implied by their NIHSS-classified 'mild' stroke. This emphasizes the necessity of creating a holistic and more comprehensive representation of survivors of mild stroke' needs over the first year after stroke to improve rehabilitation and poststroke care. [ABSTRACT FROM AUTHOR]

Stroke recovery is multifaceted and complex. Machine learning approaches have potential to identify patterns of brain activity associated with clinical outcomes, providing new insights into recovery. We aim to use machine learning to characterise the contribution of and potential interaction between resting state functional connectivity networks in predicting touch discrimination outcomes in a well-phenotyped, but small, stroke cohort. We interrogated and compared a suite of automated machine learning approaches to identify patterns of brain activity associated with clinical outcomes. Using feature reduction, the identification of combined 'golden features', and five-fold cross-validation, two golden features patterns emerged. These golden features identified patterns of resting state connectivity involving interactive relationships: 1. The difference between right insula and right superior temporal lobe correlation and left cerebellum and vermis correlation; 2. The ratio between right inferior temporal lobe and left cerebellum correlation and left frontal inferior operculum and left supplementary motor area correlation. Our findings demonstrate evidence of the potential for automated machine learning to provide new insights into brain network patterns and their interactions associated with the prediction of quantitative touch discrimination outcomes, through the automated identification of robust associations and golden feature brain patterns, even in a small cohort of stroke survivors. [ABSTRACT FROM AUTHOR]

Cognitive deficits are a common and debilitating consequence of stroke, yet our understanding of the structural neurobiological biomarkers predicting recovery of cognition after stroke remains limited. In this longitudinal observational study, we set out to investigate the effect of both focal lesions and structural connectivity on poststroke cognition. Sixty‐two patients with stroke underwent advanced brain imaging and cognitive assessment, utilizing the Montreal Cognitive Assessment (MoCA) and the Mini‐Mental State Examination (MMSE), at 3‐month and 12‐month poststroke. We first evaluated the relationship between lesions and cognition at 3 months using voxel‐based lesion‐symptom mapping. Next, a novel correlational tractography approach, using multi‐shell diffusion‐weighted magnetic resonance imaging (MRI) data collected at both time points, was used to evaluate the relationship between the white matter connectome and cognition cross‐sectionally at 3 months, and longitudinally (12 minus 3 months). Lesion‐symptom mapping did not yield significant findings. In turn, correlational tractography analyses revealed positive associations between both MoCA and MMSE scores and bilateral cingulum and the corpus callosum, both cross‐sectionally at the 3‐month stage, and longitudinally. These results demonstrate that rather than focal neural structures, a consistent structural connectome underpins the performance of two frequently used cognitive screening tools, the MoCA and the MMSE, in people after stroke. This finding should encourage clinicians and researchers to not only suspect cognitive decline when lesions affect these tracts, but also to refine their investigation of novel approaches to differentially diagnosing pathology associated with cognitive decline, regardless of the aetiology. [ABSTRACT FROM AUTHOR]

Background Implementing evidence into clinical practice is a key focus of healthcare improvements to reduce unwarranted variation. Dissemination of evidence-based recommendations and knowledge brokering have emerged as potential strategies to achieve evidence implementation by influencing resource allocation decisions. The aim of this study was to determine the effectiveness of these 2 research implementation strategies to facilitate evidence-informed healthcare management decisions for the provision of inpatient weekend allied health services. Methods and findings This multicentre, single-blinded (data collection and analysis), 3-group parallel cluster randomised controlled trial with concealed allocation was conducted in Australian and New Zealand hospitals between February 2018 and January 2020. Clustering and randomisation took place at the organisation level where weekend allied health staffing decisions were made (e.g., network of hospitals or single hospital). Hospital wards were nested within these decision-making structures. Three conditions were compared over a 12-month period: (1) usual practice waitlist control; (2) dissemination of written evidence-based practice recommendations; and (3) access to a webinar-based knowledge broker in addition to the recommendations. The primary outcome was the alignment of weekend allied health provision with practice recommendations at the cluster and ward levels, addressing the adoption, penetration, and fidelity to the recommendations. The secondary outcome was mean hospital length of stay at the ward level. Outcomes were collected at baseline and 12 months later. A total of 45 clusters (n = 833 wards) were randomised to either control (n = 15), recommendation (n = 16), or knowledge broker (n = 14) conditions. Four (9%) did not provide follow-up data, and no adverse events were recorded. No significant effect was found with either implementation strategy for the primary outcome at the cluster level (recommendation versus control [beta] 18.11 [95% CI -8,721.81 to 8,758.02] p = 0.997; knowledge broker versus control [beta] 1.24 [95% CI -6,992.60 to 6,995.07] p = 1.000; recommendation versus knowledge broker [beta] -9.12 [95% CI -3,878.39 to 3,860.16] p = 0.996) or ward level (recommendation versus control [beta] 0.01 [95% CI 0.74 to 0.75] p = 0.983; knowledge broker versus control [beta] -0.12 [95% CI -0.54 to 0.30] p = 0.581; recommendation versus knowledge broker [beta] -0.19 [-1.04 to 0.65] p = 0.651). There was no significant effect between strategies for the secondary outcome at ward level (recommendation versus control [beta] 2.19 [95% CI -1.36 to 5.74] p = 0.219; knowledge broker versus control [beta] -0.55 [95% CI -1.16 to 0.06] p = 0.075; recommendation versus knowledge broker [beta] -3.75 [95% CI -8.33 to 0.82] p = 0.102). None of the control or knowledge broker clusters transitioned to partial or full alignment with the recommendations. Three (20%) of the clusters who only received the written recommendations transitioned from nonalignment to partial alignment. Limitations include underpowering at the cluster level sample due to the grouping of multiple geographically distinct hospitals to avoid contamination. Conclusions Owing to a lack of power at the cluster level, this trial was unable to identify a difference between the knowledge broker strategy and dissemination of recommendations compared with usual practice for the promotion of evidence-informed resource allocation to inpatient weekend allied health services. Future research is needed to determine the interactions between different implementation strategies and healthcare contexts when translating evidence into healthcare practice. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12618000029291., Author(s): Mitchell N. Sarkies 1,2,3,*, Lauren M. Robins 3, Megan Jepson 3, Cylie M. Williams 3, Nicholas F. Taylor 4,5, Lisa O'Brien 6, Jenny Martin 7, Anne Bardoel 8, Meg [...]

Implementation of evidence-informed rehabilitation of the upper limb is variable, and outcomes for stroke survivors are often suboptimal. We established a national partnership of clinicians, survivors of stroke, researchers, healthcare organizations, and policy makers to facilitate change. The objectives of this study are to increase access to best-evidence rehabilitation of the upper limb and improve outcomes for stroke survivors. This prospective pragmatic, knowledge translation study involves four new specialist therapy centers to deliver best-evidence upper-limb sensory rehabilitation (known as SENSe therapy) for survivors of stroke in the community. A knowledge-transfer intervention will be used to upskill therapists and guide implementation. Specialist centers will deliver SENSe therapy, an effective and recommended therapy, to stroke survivors in the community. Outcomes include number of successful deliveries of SENSe therapy by credentialled therapists; improved somatosensory function for stroke survivors; improved performance in self-selected activities, arm use, and quality of life; treatment fidelity and confidence to deliver therapy; and for future implementation, expert therapist effect and cost-effectiveness. In summary, we will determine the effect of a national partnership to increase access to evidence-based upper-limb sensory rehabilitation following stroke. If effective, this knowledge-transfer intervention could be used to optimize the delivery of other complex, evidence-based rehabilitation interventions. [ABSTRACT FROM AUTHOR]

To identify the specific motor learning (ML) theories underpinning evidence-based, task-focused upper limb models of therapy for children with unilateral cerebral palsy; and to document the strategies used in the operationalisation of these theories. This scoping review searched for relevant studies using eight electronic databases. A list of 68 ML strategies and accompanying definitions was developed for data extraction. Three classifications; adequate, inadequate or not described were used to rate the description of ML strategies. A corresponding colour-coding system was used to provide a visual summary. There is a limited description of the ML theories and strategies used to operationalise these theories in existing models of evidence-based upper limb therapy. Of 103 therapy protocols included, only 24 explicitly described the guiding ML theory. When described, there was significant variation in the underlying theories, leading to significantly different focus and content of therapy. Of the 68 ML strategies, only three were adequately described. To support treatment fidelity and the implementation of evidence-based, task-focused models of upper limb therapy in clinical practice, future research needs to provide explicit details about the underlying theories and strategies used in the operationalisation of these theories. Evidence-based models of upper limb therapy purport to be based on motor learning theory, however, most provide a very limited description of the theories and strategies used. Dosage of practice is only one element that is specific to a therapy approach and other elements guided by the principles of type of task and type of feedback should be considered. To support the implementation of evidence-based approaches in clinical practice, and improve treatment fidelity, it is important for researchers to define the theories that guide therapy approaches and explicitly describe the strategies used to operationalise these theories. [ABSTRACT FROM AUTHOR]

The process of examining treatment fidelity is critical to the successful implementation of rehabilitation interventions. Videotaping is considered the 'gold standard' assessment; however, may be considered resource-intensive and intrusive for intervention providers and patients. An audit checklist is an alternative approach recommended in the literature. The purpose of this study was to develop a documentation audit checklist for assessing treatment fidelity during delivery of SENSe therapy, a complex rehabilitation intervention targeting upper limb somatosensory impairment post-stroke. Checklist development comprised: content determination and design; checklist testing via audit of 38 therapy records from an existing data set; and exploration of rater agreement between two assessors, using a subset of 10 therapy records. The developed audit checklist comprised 29 components core to the delivery of SENSe therapy. Six SENSe therapy records were delivered with high fidelity (>80% adherence to core components), and 32 with moderate fidelity (51–79%). Rater agreement was 80% across the subset of 10 records. Findings highlight the importance of using a theoretically-guided approach to checklist development, with the use of rater agreement to identify areas for refinement. A documentation audit checklist was developed that can be used to evaluate treatment fidelity of complex rehabilitation interventions. Development of an audit checklist that evaluates clinician documentation of therapy delivery, is feasible as one strategy to measure and enhance the treatment fidelity of complex rehabilitation interventions. The process of audit checklist development should be structured and based on conceptual frameworks, to ensure it accurately measures quality of delivery and adherence to core intervention components. Audit checklists can be used to support clinicians delivering complex rehabilitation interventions. [ABSTRACT FROM AUTHOR]