Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland.
Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore.
Ann Clin Transl Neurol. 2022 Apr;9(4):432-443. doi: 10.1002/acn3.51493. Epub 2022 Feb 27.
Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is the second most frequent recessive ataxia and commonly features reduced upper limb coordination. Sensitive outcome measures of upper limb coordination are essential to track disease progression and the effect of interventions. However, available clinical assessments are insufficient to capture behavioral variability and detailed aspects of motor control. While digital health metrics extracted from technology-aided assessments promise more fine-grained outcome measures, these have not been validated in ARSACS. Thus, the aim was to document the metrological properties of metrics from a technology-aided assessment of arm and hand function in ARSACS.
We relied on the Virtual Peg Insertion Test (VPIT) and used a previously established core set of 10 digital health metrics describing upper limb movement and grip force patterns during a pick-and-place task. We evaluated reliability, measurement error, and learning effects in 23 participants with ARSACS performing three repeated assessment sessions. In addition, we documented concurrent validity in 57 participants with ARSACS performing one session.
Eight metrics had excellent test-retest reliability (intraclass correlation coefficient 0.89 ± 0.08), five low measurement error (smallest real difference % 25.4 ± 5.7), and none strong learning effects (systematic change η -0.11 ± 2.5). Significant correlations (ρ 0.39 ± 0.13) with clinical scales describing gross and fine dexterity and lower limb coordination were observed.
This establishes eight digital health metrics as valid and robust endpoints for cross-sectional studies and five metrics as potentially sensitive endpoints for longitudinal studies in ARSACS, thereby promising novel insights into upper limb sensorimotor control.
常染色体隐性痉挛性共济失调（ARSACS）是第二常见的常染色体隐性共济失调，常见的上肢协调能力下降。上肢协调的敏感结局指标对于跟踪疾病进展和干预效果至关重要。然而，现有的临床评估不足以捕捉行为的可变性和运动控制的详细方面。虽然从技术辅助评估中提取的数字健康指标有望提供更精细的结局指标，但这些指标尚未在 ARSACS 中得到验证。因此，本研究旨在记录 ARSACS 手臂和手部功能的技术辅助评估中指标的计量学特性。
我们依赖于虚拟钉插入测试（VPIT），并使用先前建立的 10 个数字健康指标的核心集，描述了在取放任务中上肢运动和握持力模式。我们在 23 名 ARSACS 参与者中进行了 3 次重复评估，评估了可靠性、测量误差和学习效应。此外，我们在 57 名 ARSACS 参与者中进行了一次评估，记录了同时的有效性。
8 项指标具有极好的重测信度（组内相关系数 0.89±0.08），5 项指标具有较低的测量误差（最小真实差异 %25.4±5.7），没有指标具有较强的学习效应（系统变化 η-0.11±2.5）。观察到与描述粗大和精细灵巧性以及下肢协调的临床量表显著相关（ρ0.39±0.13）。
这确立了 8 项数字健康指标作为横断面研究的有效和可靠的终点，5 项指标作为 ARSACS 纵向研究中潜在敏感的终点，从而有望为上肢感觉运动控制提供新的见解。
