The feasibility of the adapted H-GRASP program for perceived and actual daily-life upper limb activity in the chronic phase post-stroke

(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. Results: Of the 34 individuals approached, nineteen were included (recruitment rate 56%). Two dropped out, one due to increased UL pain (retention rate 89%). Seven (41%) achieved the prescribed exercise target (120 min/day, six days/week). Positive patient experiences and improvements in UL capacity, self-efficacy, and contribution of the affected UL to overall activity (p Conclusions: A home-based UL exercise program with accelerometer-based feedback holds promise for enhancing perceived and actual daily-life UL activity for our subgroup of chronic stroke survivors. Implementing a home-based exercise program with accelerometer-based feedback and telephone supervision may effectively improve upper limb activity after stroke.This program is most suitable for individuals with mild upper limb impairment and should be tailored to their abilities, preferences, and limitations to enhance engagement.The AH-GRASP program shows promising recruitment and retention rates, safety, and positive patient experiences.To improve adherence, consider dividing exercises into shorter sessions that accommodate patient’s schedules, attention and endurance levels. Implementing a home-based exercise program with accelerometer-based feedback and telephone supervision may effectively improve upper limb activity after stroke. This program is most suitable for individuals with mild upper limb impairment and should be tailored to their abilities, preferences, and limitations to enhance engagement. The AH-GRASP program shows promising recruitment and retention rates, safety, and positive patient experiences. To improve adherence, consider dividing exercises into shorter sessions that accommodate patient’s schedules, attention and endurance levels.</p

DataSheet1_Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.PDF

Background: 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. We here develop and validate a solution for various sensor configurations specifically for a stroke population.Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations.Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN.Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.</p

Additional file 2: of Is upper limb virtual reality training more intensive than conventional training for patients in the subacute phase after stroke? An analysis of treatment intensity and content

Data file. (XLS 25 kb

Additional file 1: of Does sensorimotor upper limb therapy post stroke alter behavior and brain connectivity differently compared to motor therapy? Protocol of a phase II randomized controlled trial

SPIRIT checklist; checklist based on the SPIRIT guidelines. (DOCX 19 kb

Additional file 3: of Does sensorimotor upper limb therapy post stroke alter behavior and brain connectivity differently compared to motor therapy? Protocol of a phase II randomized controlled trial

Outcome measures. (DOCX 26 kb

A cross-sectional study comparing lateral and diagonal maximum weight shift in people with stroke and healthy controls and the correlation with balance, gait and fear of falling

<div><p>Impaired balance is common post stroke and can be assessed by means of force-platforms measuring center of pressure (COP) displacements during static standing, or more dynamically during lateral maximum weight shift (MWS). However, activities of daily life also include diagonal MWS and since force platforms are nowadays commercially available, investigating lateral and diagonal MWS in a clinical setting might be feasible and clinically relevant. We investigated lateral and diagonal MWS while standing in patients with stroke (PwS) and healthy controls (HC), evaluated MWS towards the affected and the non-affected side for PwS and correlated MWS with measures of balance, gait and fear of falling. In a cross-sectional observational study including 36 ambulatory sub-acute inpatients and 32 age-matched HC, a force platform (BioRescue, RM Ingénierie, France) was used to measure lateral and diagonal MWS in standing. Clinical outcome measures collected were Berg Balance Scale and Community Balance and Mobility Scale (CBMS) for balance, 10-meter walk test (10MWT) for gait speed and Falls Efficacy Scale–international version for fear of falling. MWS for PwS towards the affected side was significantly smaller compared to HC (lateral: p = 0.029; diagonal-forward: p = 0.000). MWS for PwS was also significantly reduced towards the affected side in the diagonal-forward direction (p = 0.019) compared to the non-affected side of PwS. Strong correlations were found for MWS for PwS in the diagonal-forward direction towards the affected side, and clinical measures of balance (CBMS: r = 0.66) and gait speed (10MWT: r = 0.66). Our study showed that ambulatory sub-acute PwS, in comparison to HC, have decreased ability to shift their body weight diagonally forward in standing towards their affected side. This reduced ability is strongly related to clinical measures of balance and gait speed. Our results suggest that MWS in a diagonal-forward direction should receive attention in rehabilitation of ambulatory sub-acute PwS in an inpatient setting.</p></div

Comparison between patients with stroke (n = 36) and healthy controls (n = 32) for force platform parameters.

<p>Comparison between patients with stroke (n = 36) and healthy controls (n = 32) for force platform parameters.</p

Participant characteristics.

<p>Participant characteristics.</p

Spearman’s rho correlation coefficients of the force platform parameters and clinical outcome measures.

<p>Spearman’s rho correlation coefficients of the force platform parameters and clinical outcome measures.</p

Comparison between affected and non-affected side for force platform parameters.

<p>Comparison between affected and non-affected side for force platform parameters.</p