Limited time for patient encounters prevents reliable evaluation of all neurological functions in routine clinical practice. Quantifying neurological disability in a patient-autonomous manner via smartphones may remedy this problem, if such tests provide reliable, disease-relevant information. We developed a smartphone version of the cognitive processing speed test, the Symbol-Digit Modalities Test (SDMT), and assessed its clinical utility. The traditional SDMT uses identical symbol-number codes, allowing memorization after repeated trials. In the phone app, the symbol-number codes are randomly generated. In 154 multiple sclerosis (MS) patients and 39 healthy volunteers (HV), traditional and smartphone SDMT have good agreement (Lin's coefficient of concordance [CCC] = 0.84) and comparable test-retest variance. In subjects with available volumetric MRI and digitalized neurological examinations (112 MS, 12 HV), the SDMT scores were highly associated with T2 lesion load and brain parenchymal fraction, when controlled for relevant clinical characteristics. The smartphone SDMT association with clinical/imaging features was stronger (R-squared = 0.75, p < 0.0001) than traditional SDMT (R-squared = 0.65, p < 0.0001). In the longitudinal subcohort, improvements from testing repetition (learning effects), were identifiable using non-linear regression in 14/16 subjects and, on average, peaked after 8 trials. Averaging several post-learning SDMT results significantly lowers the threshold for detecting true decline in test performance. In conclusion, smartphone, self-administered SDMT is a reliable substitute of the traditional SDMT for measuring processing speed in MS patients. Granular measurements at home increase sensitivity to detect true performance decline in comparison to sporadic assessments in the clinic.

中文翻译：

---

基于智能手机的符号数字模态测试可可靠地测量多发性硬化症患者的认知功能

患者接触时间有限，无法在常规临床实践中可靠评估所有神经系统功能。如果这种测试能够提供可靠的，与疾病相关的信息，则通过智能手机以患者自主方式对神经功能障碍进行量化可能会解决该问题。我们开发了智能手机版本的认知处理速度测试，符号数字模态测试（SDMT），并评估了其临床实用性。传统的SDMT使用相同的符号编号代码，允许在反复试验后记忆。在电话应用程序中，符号编号代码是随机生成的。在154例多发性硬化症（MS）患者和39例健康志愿者（HV）中，传统和智能手机SDMT具有良好的一致性（林氏一致性系数[CCC] = 0.84），并且具有可比的重测差异。在控制了相关临床特征的情况下，在具有可用体积MRI和数字化神经系统检查（112 MS，12 HV）的受试者中，SDMT得分与T2病变负荷和脑实质分数高度相关。具有临床/影像功能的智能手机SDMT关联性（R平方= 0.75，p <0.0001）比传统SDMT（R平方= 0.65，p <0.0001）强。在纵向亚组中，使用非线性回归在14/16受试者中可以确定测试重复（学习效果）带来的改善，平均而言，在8次试验后达到峰值。平均几个学习后的SDMT结果会大大降低检测测试性能真正下降的阈值。总之，智能手机 自我管理的SDMT可以替代传统SDMT来测量MS患者的处理速度。与临床中的零星评估相比，在家进行粒度测量可提高检测真实性能下降的敏感性。