Rudroff Thorsten
PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland.
Brain Sci. 2025 May 21;15(5):533. doi: 10.3390/brainsci15050533.
Digital biomarkers for fatigue monitoring in neurological disorders represent an innovative approach to bridge the gap between mechanistic understanding and clinical application. This perspective paper examines how smartphone-derived measures, analyzed through artificial intelligence methods, can transform fatigue assessment from subjective, episodic reporting to continuous, objective monitoring. The proposed framework for smartphone-based digital phenotyping captures passive data (movement patterns, device interactions, and sleep metrics) and active assessments (ecological momentary assessments, cognitive tests, and voice analysis). These digital biomarkers can be validated through a multimodal approach connecting them to neuroimaging markers, clinical assessments, performance measures, and patient-reported experiences. Building on the previous research on frontal-striatal metabolism in multiple sclerosis and Long-COVID-19 patients, digital biomarkers could enable early warning systems for fatigue episodes, objective treatment response monitoring, and personalized fatigue management strategies. Implementation considerations include privacy protection, equity concerns, and regulatory pathways. By integrating smartphone-derived digital biomarkers with AI analysis approaches, the future envisions fatigue in neurological disorders no longer as an invisible, subjective experience but rather as a quantifiable, treatable phenomenon with established neural correlates and effective interventions. This transformative approach has significant potential to enhance both clinical care and the research for millions affected by disabling fatigue symptoms.
用于神经系统疾病疲劳监测的数字生物标志物是一种创新方法，可弥合机制理解与临床应用之间的差距。这篇观点论文探讨了通过人工智能方法分析的源自智能手机的测量指标如何将疲劳评估从主观的、偶发性报告转变为连续的、客观的监测。所提出的基于智能手机的数字表型分析框架可获取被动数据（运动模式、设备交互和睡眠指标）以及主动评估（生态瞬时评估、认知测试和语音分析）。这些数字生物标志物可通过将它们与神经影像标志物、临床评估、性能指标以及患者报告的体验相联系的多模态方法进行验证。基于先前对多发性硬化症和长新冠患者额叶 - 纹状体代谢的研究，数字生物标志物可实现疲劳发作的早期预警系统、客观的治疗反应监测以及个性化的疲劳管理策略。实施方面的考虑因素包括隐私保护、公平性问题和监管途径。通过将源自智能手机的数字生物标志物与人工智能分析方法相结合，未来设想神经系统疾病中的疲劳不再是一种无形的、主观的体验，而是一种具有既定神经关联和有效干预措施的可量化、可治疗的现象。这种变革性方法对于改善数百万受致残性疲劳症状影响者的临床护理和研究具有巨大潜力。
