Concurrent Validity and Responsiveness of Clinical Upper Limb Outcome Measures and Sensor-based Arm Use Metrics within the First Year after Stroke: A Longitudinal Cohort Study

Author:

Pohl Johannes1,Verheyden Geert2,Held Jeremia Philipp Oskar3,Luft Andreas4,Awai-Easthope Chris1,Veerbeek Janne Marieke5

Affiliation:

1. Data Analytics and Rehabilitation Technology (DART), Lake Lucerne Institute

2. KU Leuven

3. Valens Clinics, Reha Center Triemli

4. University of Zurich and University Hospital Zurich, Department of Neurology, Zurich, Switzerland

5. Lake Lucerne Institute

Abstract

Abstract 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.

Publisher

Research Square Platform LLC

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