Publications by authors named "Janne M Veerbeek"

Background: Upper limb impairment post-stroke often leads to a predominant use of the less affected arm and consequent learned disuse of the affected side, hindering upper limb outcome. Wearable sensors such as accelerometers, combined with smart reminders (i.e.

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Visuospatial neglect (VSN) is a negative, strong, and independent predictor of poor outcome after stroke, and is associated with poorer upper limb (UL) motor recovery in terms of function or capacity (ie, in standardized, lab-based testing). Although the main aim of stroke rehabilitation is to re-establish optimal functioning in daily life, the impact of VSN on UL performance (ie, in unstructured, everyday environments) is largely unknown. In this proof of principle study, the impact of VSN on the strength of the association between UL motor function (Jamar Hand Dynamometer) and UL performance (Upper Limb Lucerne ICF-based Multidisciplinary Observation Scale) was investigated in 65 (hyper)acute first-ever stroke patients.

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Introduction: Stroke-induced upper limb disabilities can be characterized by both motor impairments and activity limitations, commonly assessed using Fugl-Meyer Motor Assessment for Upper Extremity (FMMA-UE) and Action Research Arm Test (ARAT), respectively. The relationship between the two assessments during recovery is largely unstudied. Expectedly they diverge over time when recovery of impairment (restitution) plateaus, but compensation-driven improvements still occur.

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Background And Objectives: Robot technology to support upper limb (UL) rehabilitation poststroke has rapidly developed over the past 3 decades. We aimed to assess the effects of UL-robots (UL-RTs) on recovery of UL motor functioning and capacity poststroke when compared with any non-UL-RT and to identify variables that are associated with the found effect sizes (ESs).

Methods: Randomized controlled trials (RCTs) comparing UL-RTs with any other intervention on patients with UL limitations poststroke were identified in electronic searches from PubMed, Wiley/Cochrane Libraries, Embase, Cumulative Index of Nursing and Allied Health Literature, Web of Science, SportDISCUS, Physiotherapy Evidence Database (PEDro), and Google Scholar from inception until August 1, 2022.

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Importance: Flow can be described as a subjective state that people report when they fully engage in an activity and experience pleasure, satisfaction, and enjoyment. Flow experiences are measured to determine the extent to which patients engage in therapy activities. Several flow questionnaires are used in neurorehabilitation.

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Purpose: Assessing feasibility and initial impact of the Home-Graded Repetitive Arm Supplementary Program combined with in-home accelerometer-based feedback (AH-GRASP) on perceived and actual daily-life upper limb (UL) activity in stroke survivors during the chronic phase with good UL motor function but low perceived daily-life activity.

Material And Methods: A 4-week intervention program (4 contact hours, 48 h self-practice) encompassing task-oriented training, behavioral techniques, phone-based support, monitoring, and weekly feedback sessions using wrist-worn accelerometery was implemented using a pre-post double baseline repeated measures design. Feasibility, clinical assessments, patient-reported outcomes, and accelerometer data were investigated.

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Background: Mobility is a key priority for stroke survivors. Worldwide consensus of standardized outcome instruments for measuring mobility recovery after stroke is an essential milestone to optimize the quality of stroke rehabilitation and recovery studies and to enable data synthesis across trials.

Methods: Using a standardized methodology, which involved convening of 13 worldwide experts in the field of mobility rehabilitation, consensus was established through an defined survey-based approach followed by group discussions.

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Background: Mobility is a key priority for stroke survivors. Worldwide consensus of standardized outcome instruments for measuring mobility recovery after stroke is an essential milestone to optimize the quality of stroke rehabilitation and recovery studies and to enable data synthesis across trials.

Methods: Using a standardized methodology, which involved convening of 13 worldwide experts in the field of mobility rehabilitation, consensus was established through an defined survey-based approach followed by group discussions.

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Background Laboratory-based assessments have shown that stroke recovery is heterogeneous between patients and affected domains such as motor and language function. However, laboratory-based assessments are not ecologically valid and do not necessarily reflect patients' daily life performance. Therefore, we aimed to give an innovative view on stroke recovery by profiling daily life performance recovery across domains in patients with early subacute stroke and determine their interrelatedness, taking stroke localization into account.

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Introduction: About 77% of adults with stroke have upper limb impairments. Many scales are available to measure the impairment and activity level of the affected limb. However, an observational scale to assess dependency on others in upper limb performance during daily life activities instead of laboratory settings is lacking.

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Background: Autoregulation of the cerebral vasculature keeps brain perfusion stable over a range of systemic mean arterial pressures to ensure brain functioning, e.g., in different body positions.

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Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into and classes is paramount.

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Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data.

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Introduction: At hospital stroke units, the time available to assess the patient's limitations in activities and participation is limited, although being essential for discharge planning. Till date, there is no quick-to-perform instrument available that captures the patient's actual performance during daily activities from a motor, cognitive, and communication perspective within the International Classification of Functioning, Disability and Health (ICF) framework. Therefore, the aim was to develop and validate a shortened version of the Lucerne ICF-Based Multidisciplinary Observation Scale (Short-LIMOS) that observes the patient's performance across ICF-domains and is applicable in the context of an acute stroke unit.

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Background: To reduce healthcare costs, it has become increasingly important to shorten the length of stay in acute stroke units. The goal of this study was to develop and externally validate a decision tree model applicable < 48 h poststroke for discharge home from an acute stroke unit with a short length of stay, and to assess the inappropriate home discharge rate.

Methods: A prospective study including two samples of stroke patients admitted to an acute stroke unit.

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Objective: The 'Early Prediction of Functional Outcome after Stroke' (EPOS) model was developed to predict the presence of at least some upper limb capacity (Action Research Am Test [ARAT] ≥10/57) at 6 months based on assessments on days 2, 5 and 9 after stroke. External validation of the model is the next step towards clinical implementation. The objective here is to externally validate the EPOS model for upper limb outcome 3 months poststroke in Switzerland and extend the model using an ARAT cut-off at 32 points.

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Introduction: The Early Prediction of Functional Outcome after Stroke (EPOS) model for independent gait is a tool to predict between days 2 and 9 poststroke whether patients will regain independent gait 6 months after stroke. External validation of the model is important to determine its clinical applicability and generalizability by testing its performance in an independent cohort. Therefore, this study aimed to perform a temporal and geographical external validation of the EPOS prediction model for independent gait after stroke but with the endpoint being 3 months instead of the original 6 months poststroke.

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Introduction: Many stroke survivors require continuous outpatient rehabilitation therapy to maintain or improve their neurological functioning, independ-ence, and quality of life. In Switzerland and many other countries, the shutdown to contain SARS-CoV-2 infections led to mobility restrictions and a decrease in therapy delivery. This study investigated the impact of the COVID-19 shutdown on stroke survivors' access to therapy, physical activity, functioning and mood.

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Background: A previously shown 'mismatch' group of patients with good observed upper limb (UL) motor function but low perceived UL activity at six months post stroke tends to use the affected UL less in daily life than would be expected based on clinical tests, and this mismatch may also be present at 12 months. We aimed to confirm this group in another cohort, to investigate the evolution of this group from six to 12 months, and to determine factors on admission to inpatient rehabilitation and at 6 months that can discriminate between mismatch and good match groups at 12 months.

Methods: Persons after stroke were recruited on rehabilitation admission and re-assessed at six and 12 months.

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Deficits in interjoint coordination, such as the inability to move out of synergy, are frequent symptoms in stroke subjects with upper limb impairments that hinder them from regaining normal motor function. Kinematic measurements allow a fine-grained assessment of movement pathologies, thereby complementing clinical scales, like the Fugl-Meyer Motor Assessment of the Upper Extremity (FMMA-UE). The study goal was to investigate the effects of the performed task, the tested arm, the dominant affected hand, upper limb function, and age on spatiotemporal parameters of the elbow, shoulder, and trunk.

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Outcome measures are key to tailor rehabilitation goals to the stroke patient's individual needs and to monitor poststroke recovery. The large number of available outcome measures leads to high variability in clinical use. Currently, an internationally agreed core set of motor outcome measures for clinical application is lacking.

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Background: Gait and balance impairments are common in neurological diseases, including stroke, and negatively affect patients' quality of life. Improving balance and gait are among the main goals of rehabilitation. Rehabilitation is mainly performed in clinics, which lack context specificity; therefore, training in the patient's home environment is preferable.

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Upper limb functions are severely affected in 23% of the chronic stroke patients, compromising their life quality. To re-enable hand use, providing a degree of functionality and motivating against learned non-use, we propose a robotic supernumerary limb, the SoftHand X (SHX), consisting of a robotic hand, a gravity support system, and different sensors to detect the patient's intent for controlling the robotic hand. In this paper, this novel compensational approach is introduced and experimentally evaluated in stroke patients, assessing its efficacy, usability and safety.

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The accurate assessment of upper limb motion impairment induced by stroke - which represents one of the primary causes of disability world-wide - is the first step to successfully monitor and guide patients' recovery. As of today, the majority of the procedures relies on clinical scales, which are mostly based on ordinal scaling, operator-dependent, and subject to floor and ceiling effects. In this work, we intend to overcome these limitations by proposing a novel approach to analytically evaluate the level of pathological movement coupling, based on the quantification of movement complexity.

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