Schönfeldová Júlia, Cohen Chen, Otmazgin Ortal, Saban William
Center for Accessible Neuropsychology, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 69978, Israel.
Department of Occupational Therapy, Gray Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, 69978, Israel.
Sci Rep. 2025 Jul 24;15(1):26880. doi: 10.1038/s41598-025-12787-w.
Motor outcomes in Parkinson's disease (PD) have long been the primary diagnostic criteria and treatment targets. While non-motor outcomes of PD impact daily well-being, they are rarely targeted by interventions or utilized for classification. Despite promising evidence, the contributions of perceived social support (PSS) to PD detection and well-being in real-world settings remain unclear. Using remote monitoring technologies, we investigated the relationship between PSS and three non-motor measures-cognition, anxiety, and depression-in 92 participants: 45 PD and 47 matched-controls. To examine the specificity of PSS to non-motor features, we also examined the associations between PSS and three motor-related measures: disease severity, duration, or stage. Moreover, we developed machine-learning classifiers (ML) based on only non-motor features to identify disease status (PD/controls) in two cohorts: low and high PSS. PSS was significantly associated with non-motor measures in PD, with stronger correlations than in matched-controls in real-world settings. However, no significant correlations were found between PSS and the three motor-related measures, demonstrating PSS's limitations. While the ML classification models performed low in high-PSS, they classified 13% better in a low-PSS cohort (AUC = 0.8), demonstrating moderate-high discriminatory performance. Taken together, our findings underscore the role of PSS in PD, highlighting its distinct contributions to non-motor classification models and the daily well-being of patients.
帕金森病（PD）的运动结果长期以来一直是主要的诊断标准和治疗目标。虽然PD的非运动结果会影响日常生活幸福感，但它们很少成为干预的目标或用于分类。尽管有一些有前景的证据，但在现实环境中，感知社会支持（PSS）对PD检测和幸福感的贡献仍不清楚。我们使用远程监测技术，调查了92名参与者（45名PD患者和47名匹配对照）中PSS与三种非运动指标（认知、焦虑和抑郁）之间的关系。为了检验PSS对非运动特征的特异性，我们还研究了PSS与三种运动相关指标（疾病严重程度、病程或分期）之间的关联。此外，我们基于仅非运动特征开发了机器学习分类器（ML），以在两个队列（低PSS和高PSS）中识别疾病状态（PD/对照）。在现实环境中，PSS与PD中的非运动指标显著相关，且相关性比匹配对照更强。然而，未发现PSS与三种运动相关指标之间存在显著相关性，这表明了PSS的局限性。虽然ML分类模型在高PSS队列中的表现较低，但在低PSS队列中分类效果好13%（AUC = 0.8），显示出中高区分性能。综上所述，我们的研究结果强调了PSS在PD中的作用，突出了其对非运动分类模型和患者日常生活幸福感的独特贡献。
