Tejada-Illa Clara, Pegueroles Jordi, Claramunt-Molet Mireia, Pi-Cervera Ariadna, Heras-Delgado Ainhoa, Gascón-Fontal Jesus, Idelsohn-Zielonka Sebastian, Rico Mari, Vidal Nuria, Martín-Aguilar Lorena, Caballero-Ávila Marta, Lleixà Cinta, Collet-Vidiella Roger, Llansó Laura, Carbayo Álvaro, Vesperinas Ana, Querol Luis, Pascual-Goñi Elba
Institut de Recerca Sant Pau (IR SANT PAU), Barcelona, Spain.
Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Sant Quintí 77 (2nd Floor), 08041, Barcelona, Spain.
J Neuroeng Rehabil. 2025 Jul 13;22(1):159. doi: 10.1186/s12984-025-01694-w.
The clinical status and treatment response of patients with peripheral neuropathies (PNs) rely on subjective and inaccurate clinical scales. Wearable sensors have been evaluated successfully in other neurological conditions to study gait and balance. Our aim was to explore the ability of biomechanical analysis using wearable technology to monitor disease activity in PN.
We conducted a single-center, longitudinal study to analyze gait parameters in PN patients and healthy controls using wearable biomechanical sensors. We used a novel technology that registers and integrates data from multiple wearable inertial sensors placed at different locations and plantar insoles. This system allows measuring kinematics, spatio-temporal parameters and plantar pressure. Patients wore the wearable system while performing the 2-min walking test (2MWT).
We included 37 chronic inflammatory demyelinating polyneuropathy (CIDP) patients, 3 chronic ataxic neuropathy, ophthalmoplegia, immunoglobulin M [IgM] paraprotein (CANOMAD) patients, 21 monoclonal gammopathy patients of undetermined significance associated with IgM (IgM-MGUS) patients, 7 patients with autoimmune nodopathies, 11 patients with hereditary neuropathies, and 50 healthy controls. First, we analyzed the sensor's ability to detect changes in ataxia and steppage gait severity and found significant differences in spatiotemporal and angular variables of the gait cycle. Second, we found correlations between biomechanical features and clinical scales and with the specific gait phenotype they associated with. Finally, we demonstrated that this technology is able to capture clinically significant changes in gait features over time.
Our study provides proof-of-concept that wearable technology effectively detects and grades gait impairment, captures clinically relevant changes, and could enhance gait assessment in routine care and clinical research for patients with PN.
周围神经病变（PNs）患者的临床状况和治疗反应依赖于主观且不准确的临床量表。可穿戴传感器已在其他神经系统疾病中成功用于研究步态和平衡。我们的目的是探索使用可穿戴技术进行生物力学分析以监测PN疾病活动的能力。
我们进行了一项单中心纵向研究，使用可穿戴生物力学传感器分析PN患者和健康对照的步态参数。我们采用了一种新技术，该技术可记录并整合来自放置在不同位置的多个可穿戴惯性传感器和足底鞋垫的数据。该系统能够测量运动学、时空参数和足底压力。患者在进行2分钟步行测试（2MWT）时佩戴可穿戴系统。
我们纳入了37例慢性炎症性脱髓鞘性多发性神经病（CIDP）患者、3例慢性共济失调性神经病、眼肌麻痹、免疫球蛋白M [IgM]副蛋白血症（CANOMAD）患者、21例意义未明的单克隆丙种球蛋白病伴IgM（IgM-MGUS）患者、7例自身免疫性结节病患者、11例遗传性神经病患者以及50名健康对照。首先，我们分析了传感器检测共济失调和跨阈步态严重程度变化的能力，发现步态周期的时空和角度变量存在显著差异。其次，我们发现生物力学特征与临床量表之间以及与它们相关的特定步态表型之间存在相关性。最后，我们证明了该技术能够捕捉步态特征随时间的临床显著变化。
我们的研究提供了概念验证，即可穿戴技术能有效检测和分级步态障碍，捕捉临床相关变化，并可在PN患者的常规护理和临床研究中加强步态评估。
