Raschka Tamara, To Jackrite, Hähnel Tom, Sapienza Stefano, Ibrahim Alzhraa, Glaab Enrico, Gaßner Heiko, Steidl Ralph, Winkler Jürgen, Corvol Jean-Christophe, Klucken Jochen, Fröhlich Holger
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany.
Bonn-Aachen International Center for IT, University of Bonn, Friedrich Hirzebruch-Allee 6, 53115, Bonn, Germany.
Sci Rep. 2025 Jul 15;15(1):25541. doi: 10.1038/s41598-025-09088-7.
Digital technologies for monitoring motor symptoms of Parkinson's Disease (PD) underwent a strong evolution during the past years. Although it has been shown for several devices that derived digital gait features can reliably discriminate between healthy controls and people with PD, the specific gait tasks best suited for monitoring motor symptoms and especially their progression, remain unclear. Furthermore, the potential benefit as endpoint in a clinical trial context has not been investigated so far. In this study we employed a digital gait device manufactured by Portabiles HCT, which has been used by 339 patients within the LuxPark cohort (n = 161, Luxembourg) as well as within routine clinical care visits at the University Medical Center Erlangen (n = 178, Erlangen, Germany). Linear (mixed) models were used to assess the association of task-specific digital gait features with disease progression and motor symptom severity measured by several clinical scores. Furthermore, we employed machine learning to evaluate whether digital gait assessments were prognostic for patient-level motor symptom progression. Overall, digital gait features derived from Portabiles digital gait device were found to effectively monitor motor symptoms and their longitudinal progression. At the same time the prognostic performance of digital gait features was limited. However, we could show a strong reduction in required sample size, if digital gait features were employed as surrogates for traditional endpoints in a clinical trial context. Thus, Portabiles digital gait device provides an effective way to objectively monitor motor symptoms and their progression in PD. Furthermore, the digital gait device bears strong potential as an alternative and easily assessable endpoint predictor in a clinical trial context.
用于监测帕金森病（PD）运动症状的数字技术在过去几年中经历了重大发展。尽管已经有几种设备表明，从数字步态特征中得出的结果能够可靠地区分健康对照者和帕金森病患者，但最适合监测运动症状尤其是其进展情况的具体步态任务仍不明确。此外，到目前为止，尚未研究其在临床试验背景下作为终点指标的潜在益处。在本研究中，我们使用了Portabiles HCT制造的数字步态设备，LuxPark队列中的339名患者（n = 161，卢森堡）以及埃尔朗根大学医学中心的常规临床护理就诊患者（n = 178，德国埃尔朗根）都使用过该设备。线性（混合）模型用于评估特定任务的数字步态特征与通过几种临床评分测量的疾病进展和运动症状严重程度之间的关联。此外，我们使用机器学习来评估数字步态评估是否可预测患者水平的运动症状进展。总体而言，发现Portabiles数字步态设备得出的数字步态特征能够有效监测运动症状及其纵向进展。同时，数字步态特征的预后性能有限。然而，我们可以证明，如果在临床试验背景下将数字步态特征用作传统终点指标的替代指标，则所需样本量会大幅减少。因此，Portabiles数字步态设备提供了一种客观监测帕金森病运动症状及其进展的有效方法。此外，在临床试验背景下，数字步态设备作为一种替代且易于评估的终点指标预测器具有很大潜力。
