Department of Neurology, Medical School, Aeginition Hospital, National and Kapodistrian University of Athens, Athens, Greece.
Adv Clin Chem. 2024;123:221-253. doi: 10.1016/bs.acc.2024.06.005. Epub 2024 Jun 22.
Digital biomarker (DB) assessments provide objective measures of daily life tasks and thus hold promise to improve diagnosis and monitoring of Parkinson's disease (PD) patients especially those with advanced stages. Data from DB studies can be used in advanced analytics such as Artificial Intelligence and Machine Learning to improve monitoring, treatment and outcomes. Although early development of inertial sensors as accelerometers and gyroscopes in smartphones provided encouraging results, the use of DB remains limited due to lack of standards, harmonization and consensus for analytical as well as clinical validation. Accordingly, a number of clinical trials have been developed to evaluate the performance of DB vs traditional assessment tools with the goal of monitoring disease progression, improving quality of life and outcomes. Herein, we update current evidence on the use of DB in PD and highlight potential benefits and limitations and provide suggestions for future research study.
数字生物标志物 (DB) 评估可提供日常生活任务的客观测量，因此有望改善帕金森病 (PD) 患者，尤其是晚期患者的诊断和监测。来自 DB 研究的数据可用于人工智能和机器学习等高级分析，以改善监测、治疗和结果。虽然智能手机中的惯性传感器（如加速度计和陀螺仪）的早期开发提供了令人鼓舞的结果，但由于缺乏分析和临床验证的标准、协调和共识，DB 的使用仍然受到限制。因此，已经开发了许多临床试验来评估 DB 与传统评估工具的性能，以监测疾病进展、提高生活质量和结果。在此，我们更新了 PD 中 DB 使用的现有证据，并强调了潜在的益处和局限性，并为未来的研究提供了建议。
