Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
Physiol Meas. 2018 Apr 26;39(4):044005. doi: 10.1088/1361-6579/aab512.
To better understand the longitudinal characteristics of Parkinson's disease (PD) through the analysis of finger tapping and memory tests collected remotely using smartphones.
Using a large cohort (312 PD subjects and 236 controls) of participants in the mPower study, we extract clinically validated features from a finger tapping and memory test to monitor the longitudinal behaviour of study participants. We investigate any discrepancy in learning rates associated with motor and non-motor tasks between PD subjects and healthy controls. The ability of these features to predict self-assigned severity measures is assessed whilst simultaneously inspecting the severity scoring system for floor-ceiling effects. Finally, we study the relationship between motor and non-motor longitudinal behaviour to determine if separate aspects of the disease are dependent on one another.
We find that the test performances of the most severe subjects show significant correlations with self-assigned severity measures. Interestingly, less severe subjects do not show significant correlations, which is shown to be a consequence of floor-ceiling effects within the mPower self-reporting severity system. We find that motor performance after practise is a better predictor of severity than baseline performance suggesting that starting performance at a new motor task is less representative of disease severity than the performance after the test has been learnt. We find PD subjects show significant impairments in motor ability as assessed through the alternating finger tapping (AFT) test in both the short- and long-term analyses. In the AFT and memory tests we demonstrate that PD subjects show a larger degree of longitudinal performance variability in addition to requiring more instances of a test to reach a steady state performance than healthy subjects.
Our findings pave the way forward for objective assessment and quantification of longitudinal learning rates in PD. This can be particularly useful for symptom monitoring and assessing medication response. This study tries to tackle some of the major challenges associated with self-assessed severity labels by designing and validating features extracted from big datasets in PD, which could help identify digital biomarkers capable of providing measures of disease severity outside of a clinical environment.
通过分析使用智能手机远程采集的手指敲击和记忆测试，更好地了解帕金森病 (PD) 的纵向特征。
我们使用 mPower 研究中的一个大型队列（312 名 PD 患者和 236 名对照者），从手指敲击和记忆测试中提取经过临床验证的特征，以监测研究参与者的纵向行为。我们研究 PD 患者和健康对照者之间与运动和非运动任务相关的学习率差异。评估这些特征预测自我评估严重程度测量的能力，同时检查严重程度评分系统的地板-天花板效应。最后，我们研究运动和非运动纵向行为之间的关系，以确定疾病的不同方面是否相互依赖。
我们发现，最严重患者的测试表现与自我评估的严重程度测量具有显著相关性。有趣的是，病情较轻的患者没有显著相关性，这表明 mPower 自我报告严重程度系统中的地板-天花板效应导致了这一结果。我们发现，练习后的运动表现比基线表现更能预测严重程度，这表明在新运动任务开始时的表现不如测试后学习的表现更能代表疾病严重程度。我们发现 PD 患者在短期和长期分析中，交替手指敲击（AFT）测试的运动能力均存在明显障碍。在 AFT 和记忆测试中，我们发现 PD 患者的纵向表现变异性更大，而且与健康对照组相比，需要更多次测试才能达到稳定状态。
我们的研究结果为 PD 中的客观评估和量化纵向学习率铺平了道路。这对于症状监测和评估药物反应尤其有用。本研究通过设计和验证从 PD 大数据集中提取的特征来解决与自我评估严重程度标签相关的一些主要挑战，这有助于确定能够在临床环境之外提供疾病严重程度测量的数字生物标志物。
