Department of Mechanical Engineering, University of Bristol, University Walk, Bristol, BS8 1TR, UK.
Institute of Bio-Sensing Technology, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK.
BMC Med Inform Decis Mak. 2019 Aug 16;19(1):162. doi: 10.1186/s12911-019-0881-1.
There is growing interest in sensor-based assessment of upper limb tremor in multiple sclerosis and other movement disorders. However, previously such assessments have not been found to offer any improvement over conventional clinical observation in identifying clinically relevant changes in an individual's tremor symptoms, due to poor test-retest repeatability.
We hypothesised that this barrier could be overcome by constructing a tremor change metric that is customised to each individual's tremor characteristics, such that random variability can be distinguished from clinically relevant changes in symptoms. In a cohort of 24 people with tremor due to multiple sclerosis, the newly proposed metrics were compared against conventional clinical and sensor-based metrics. Each metric was evaluated based on Spearman rank correlation with two reference metrics extracted from the Fahn-Tolosa-Marin Tremor Rating Scale: a task-based measure of functional disability (FTMTRS B) and the subject's self-assessment of the impact of tremor on their activities of daily living (FTMTRS C).
Unlike the conventional sensor-based and clinical metrics, the newly proposed 'change in scale' metrics presented statistically significant correlations with changes in self-assessed impact of tremor (max R>0.5,p<0.05 after correction for false discovery rate control). They also outperformed all other metrics in terms of correlations with changes in task-based functional performance (R=0.25 vs. R=0.15 for conventional clinical observation, both p<0.05).
The proposed metrics achieve an elusive goal of sensor-based tremor assessment: improving on conventional visual observation in terms of sensitivity to change. Further refinement and evaluation of the proposed techniques is required, but our core findings imply that the main barrier to translational impact for this application can be overcome. Sensor-based tremor assessments may improve personalised treatment selection and the efficiency of clinical trials for new treatments by enabling greater standardisation and sensitivity to clinically relevant changes in symptoms.
人们对基于传感器的上肢震颤评估在多发性硬化症和其他运动障碍中的应用越来越感兴趣。然而，由于测试-重测重复性差，以前的研究发现，这种评估方法并不能在识别个体震颤症状的临床相关变化方面优于传统的临床观察。
我们假设，通过构建一种针对每个个体震颤特征定制的震颤变化指标，可以克服这一障碍，从而区分随机变异性和症状的临床相关变化。在一个由 24 名多发性硬化症震颤患者组成的队列中，将新提出的指标与传统的临床和基于传感器的指标进行了比较。基于 Spearman 等级相关系数对每个指标进行评估，该系数与从 Fahn-Tolosa-Marin 震颤评定量表中提取的两个参考指标相关：基于任务的功能障碍测量（FTMTRS B）和患者对震颤对日常生活活动影响的自我评估（FTMTRS C）。
与传统的基于传感器和临床的指标不同，新提出的“量表变化”指标与震颤对日常生活活动影响的自我评估变化呈统计学显著相关（最大 R>0.5，经假发现率校正后有统计学意义（p<0.05））。与基于任务的功能表现变化的相关性方面，它们也优于所有其他指标（R=0.25 与传统临床观察的 R=0.15，两者均为 p<0.05）。
所提出的指标实现了基于传感器的震颤评估的一个难以实现的目标：在对变化的敏感性方面优于传统的视觉观察。需要进一步改进和评估所提出的技术，但我们的核心发现表明，该应用的转化障碍可以克服。基于传感器的震颤评估可以通过提高对症状的临床相关变化的标准化和敏感性，改善新疗法的个性化治疗选择和临床试验的效率。
