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Background: Concurrent validity and responsiveness of upper limb outcome measures are essential to interpret motor recovery poststroke. Evaluating the associations between clinical upper limb measures and sensor-based arm use (AU) fosters a comprehensive understanding of motor recovery. Defining sensor-based AU metrics for intentional upper limb movements could be crucial in mitigating bias arising from walking-related activities. Here, we investigate the measurement properties of a comprehensive set of clinical measures and sensor-based AU metrics when gait and non-functional upper limb movements are excluded. Methods: In a prospective, longitudinal cohort study, individuals with motor impairment were measured at days 3±2 (D3), 10±2 (D10), 28±4 (D28), 90±7 (D90), and 365±14 (D365) after their first stroke. Upper limb function, activity capacity, and performance were assessed using the Fugl-Meyer Assessment, Action Research Arm Test, Box & Block Test, and the 14-item Motor Activity Log. For three days, individuals wore five movement sensors (trunk, wrists, and ankles). Thirteen AU metrics were computed based on functional movements during non-walking periods. Concurrent validity across clinical and AU metrics was determined by Spearman's rank correlations for each time point. Criterion-based responsiveness was examined by correlating patient-reported Global Rating of Perceived Change (GRPC) scores (1-7) and observed change in upper limb outcome. Optimal cut-off values for minimal important change (MIC) were estimated by ROC curve analysis. Results: Ninety-three individuals participated. At D3 and D10, correlations between clinical measures and AU-metrics presented variability (range rs 0.44-0.90, p<0.01). All time points following showed strong positive associations between capacity measures and affected AU metrics (range rs 0.73-0.94, p<0.01), whereas unilateral nonaffected AU metrics had low-to-high negative associations (range rs 0.48-0.77). Responsiveness across outcomes was highest between D10-D28 within moderate-to-strong relations between GRPC and clinical measures (rs range 0.60-0.73, p<0.01), whereas relations were weaker for AU-metrics (rs range 0.28-0.43, p<0.05). Eight MIC values were estimated for clinical measures and nine for AU metrics, showing moderate to good accuracy (66-87%). Conclusions: We present reference data on concurrent validity and responsiveness of clinical upper limb measures and specified AU metrics within the first year poststroke. Estimated MIC values can be used as a benchmark for clinical stroke rehabilitation. Trial registration: This trial was registered on clinicaltrials.gov; registration number NCT03522519.
Background: Concurrent validity and responsiveness of upper limb outcome measures are essential to interpret motor recovery poststroke. Evaluating the associations between clinical upper limb measures and sensor-based arm use (AU) fosters a comprehensive understanding of motor recovery. Defining sensor-based AU metrics for intentional upper limb movements could be crucial in mitigating bias arising from walking-related activities. Here, we investigate the measurement properties of a comprehensive set of clinical measures and sensor-based AU metrics when gait and non-functional upper limb movements are excluded. Methods: In a prospective, longitudinal cohort study, individuals with motor impairment were measured at days 3±2 (D3), 10±2 (D10), 28±4 (D28), 90±7 (D90), and 365±14 (D365) after their first stroke. Upper limb function, activity capacity, and performance were assessed using the Fugl-Meyer Assessment, Action Research Arm Test, Box & Block Test, and the 14-item Motor Activity Log. For three days, individuals wore five movement sensors (trunk, wrists, and ankles). Thirteen AU metrics were computed based on functional movements during non-walking periods. Concurrent validity across clinical and AU metrics was determined by Spearman's rank correlations for each time point. Criterion-based responsiveness was examined by correlating patient-reported Global Rating of Perceived Change (GRPC) scores (1-7) and observed change in upper limb outcome. Optimal cut-off values for minimal important change (MIC) were estimated by ROC curve analysis. Results: Ninety-three individuals participated. At D3 and D10, correlations between clinical measures and AU-metrics presented variability (range rs 0.44-0.90, p<0.01). All time points following showed strong positive associations between capacity measures and affected AU metrics (range rs 0.73-0.94, p<0.01), whereas unilateral nonaffected AU metrics had low-to-high negative associations (range rs 0.48-0.77). Responsiveness across outcomes was highest between D10-D28 within moderate-to-strong relations between GRPC and clinical measures (rs range 0.60-0.73, p<0.01), whereas relations were weaker for AU-metrics (rs range 0.28-0.43, p<0.05). Eight MIC values were estimated for clinical measures and nine for AU metrics, showing moderate to good accuracy (66-87%). Conclusions: We present reference data on concurrent validity and responsiveness of clinical upper limb measures and specified AU metrics within the first year poststroke. Estimated MIC values can be used as a benchmark for clinical stroke rehabilitation. Trial registration: This trial was registered on clinicaltrials.gov; registration number NCT03522519.
Background People who survive a stroke in many cases require upper-limb rehabilitation (ULR), which plays a vital role in stroke recovery practices. However, rehabilitation services in the Global South are often not affordable or easily accessible. For example, in Bangladesh, the access to and use of rehabilitation services is limited and influenced by cultural factors and patients’ everyday lives. In addition, while wearable devices have been used to enhance ULR exercises to support self-directed home-based rehabilitation, this has primarily been applied in developed regions and is not common in many Global South countries due to potential costs and limited access to technology. Objective Our goal was to better understand physiotherapists’, patients’, and caregivers’ experiences of rehabilitation in Bangladesh, existing rehabilitation practices, and how they differ from the rehabilitation approach in the United Kingdom. Understanding these differences and experiences would help to identify opportunities and requirements for developing affordable wearable devices that could support ULR in home settings. Methods We conducted an exploratory study with 14 participants representing key stakeholder groups. We interviewed physiotherapists and patients in Bangladesh to understand their approaches, rehabilitation experiences and challenges, and technology use in this context. We also interviewed UK physiotherapists to explore the similarities and differences between the 2 countries and identify specific contextual and design requirements for low-cost wearables for ULR. Overall, we remotely interviewed 8 physiotherapists (4 in the United Kingdom, 4 in Bangladesh), 3 ULR patients in Bangladesh, and 3 caregivers in Bangladesh. Participants were recruited through formal communications and personal contacts. Each interview was conducted via videoconference, except for 2 interviews, and audio was recorded with consent. A total of 10 hours of discussions were transcribed. The results were analyzed using thematic analysis. Results We identified several sociocultural factors that affect ULR and should be taken into account when developing technologies for the home: the important role of family, who may influence the treatment based on social and cultural perceptions; the impact of gender norms and their influence on attitudes toward rehabilitation and physiotherapists; and differences in approach to rehabilitation between the United Kingdom and Bangladesh, with Bangladeshi physiotherapists focusing on individual movements that are necessary to build strength in the affected parts and their British counterparts favoring a more holistic approach. We propose practical considerations and design recommendations for developing ULR devices for low-resource settings. Conclusions Our work shows that while it is possible to build a low-cost wearable device, the difficulty lies in addressing sociotechnical challenges. When developing new health technologies, it is imperative to not only understand how well they could fit into patients’, caregivers’, and physiotherapists’ everyday lives, but also how they may influence any potential tensions concerning culture, religion, and the characteristics of the local health care system.
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