Pohl Johannes, Verheyden Geert, Held Jeremia Philipp Oskar, Luft Andreas Ruediger, Easthope Awai Chris, Veerbeek Janne Marieke
Lake Lucerne Institute, Data Analytics and Rehabilitation Technology (DART), Vitznau, Switzerland.
Department of Rehabilitation Sciences, KU Leuven, Leuven Brain Institute, Leuven, Belgium.
J Neuroeng Rehabil. 2025 Jan 29;22(1):14. doi: 10.1186/s12984-024-01512-9.
Construct 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 coherent understanding of motor recovery. Defining sensor-based AU metrics for intentional upper limb movements could be crucial in mitigating bias 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.
In this 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. Using clinical measures, upper limb motor function (Fugl-Meyer Assessment), capacity (Action Research Arm Test, Box & Block Test), and perceived performance (14-item Motor Activity Log) were assessed. Additionally, individuals wore five movement sensors (trunk, wrists, and ankles) for three days. Thirteen AU metrics were computed based on functional movements during non-walking periods. Construct validity across clinical measures and AU metrics was determined by Spearman's rank correlations for each time point. Criterion responsiveness was examined by correlating patient-reported Global Rating of Perceived Change (GRPC) scores and observed change in upper limb measures and AU metrics. Optimal cut-off values for minimal important change (MIC) were estimated by ROC curve analysis.
Ninety-three individuals participated. At D3 and D10, correlations between clinical measures and AU metrics showed variability (range r: 0.44-0.90). All following time points showed moderate-to-high positive correlations between clinical measures and affected AU metrics (range r: 0.57-0.88). Unilateral nonaffected AU duration was negatively correlated with clinical measures (range r: -0.48 to -0.77). Responsiveness across outcomes was highest between D10-D28 within moderate to strong relations between GRPC and clinical measures (r: range 0.60-0.73), whereas relations were weaker for AU metrics (range r: 0.28-0.43) Eight MIC values were estimated for clinical measures and nine for AU metrics, showing moderate to good accuracy (66-87%).
We present reference data on the construct validity and responsiveness of clinical upper limb measures and specified sensor-based AU metrics within the first year after stroke. The MIC values can be used as a benchmark for clinical stroke rehabilitation.
This trial was registered on clinicaltrials.gov; registration number NCT03522519.
上肢结局测量指标的结构效度和反应性对于解释中风后的运动恢复至关重要。评估临床上肢测量指标与基于传感器的手臂使用情况（AU）之间的关联，有助于对运动恢复形成连贯的理解。定义基于传感器的有意上肢运动的AU指标，对于减轻与步行相关活动的偏差可能至关重要。在此，我们研究了排除步态和非功能性上肢运动后，一组全面的临床测量指标和基于传感器的AU指标的测量特性。
在这项前瞻性纵向队列研究中，对运动功能受损的个体在首次中风后的第3±2天（D3）、第10±2天（D10）、第28±4天（D28）、第90±7天（D90）和第365±14天（D365）进行测量。使用临床测量指标评估上肢运动功能（Fugl-Meyer评估）、能力（动作研究手臂测试、箱块测试）和自我感知表现（14项运动活动日志）。此外，个体佩戴五个运动传感器（躯干、手腕和脚踝）三天。基于非步行期间的功能运动计算了13个AU指标。通过每个时间点的Spearman等级相关性确定临床测量指标和AU指标之间的结构效度。通过将患者报告的整体感知变化（GRPC）评分与观察到的上肢测量指标和AU指标变化进行关联，检查标准反应性。通过ROC曲线分析估计最小重要变化（MIC）的最佳截断值。
93名个体参与了研究。在D3和D10时，临床测量指标与AU指标之间的相关性显示出变异性（范围r：0.44 - 0.90）。所有后续时间点显示临床测量指标与受影响的AU指标之间存在中度至高度正相关（范围r：0.57 - 0.88）。单侧未受影响的AU持续时间与临床测量指标呈负相关（范围r：-0.48至-0.77）。在GRPC与临床测量指标之间存在中度至强关系的D10 - D28期间，各结局的反应性最高（r：范围0.60 - 0.73），而AU指标的关系较弱（范围r：0.28 - 0.43）。估计了8个临床测量指标的MIC值和9个AU指标的MIC值，显示出中度至良好的准确性（66 - 87%）。
我们提供了中风后第一年内临床上肢测量指标和特定基于传感器的AU指标的结构效度和反应性的参考数据。MIC值可作为临床中风康复的基准。
本试验在clinicaltrials.gov上注册；注册号NCT03522519。
