METHODS: Participants with IMIDs and NDDs (Parkinson\'s disease (PD), Huntington\'s disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren\'s syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning.
RESULTS: Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis.
CONCLUSIONS: Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.
方法:患有IMID和NDD(帕金森病(PD),亨廷顿病(HD),类风湿性关节炎(RA),系统性红斑狼疮(SLE),原发性干燥综合征(PSS),和炎症性肠病(IBD))在家连续佩戴下背部IMU长达10天。同时,参与者每天最多四次完成PRO(身体疲劳(PF)和精神疲劳(MF))。宏(体积,可变性,模式,和加速度矢量大小)和微(速度,节奏,可变性,不对称,和姿势控制)从加速度计数据中提取步态特征。使用广义线性混合效应模型(GLMM)和具有机器学习的二元分类来评估这些度量与PRO的关联。
结果:记录72名参与者的数据:PD=13,HD=9,RA=12,SLE=9,PSS=14,IBD=15。对于GLMM,PF的非步行回合长度(以秒为单位)的变异性返回最高条件R2为0.165,而MF的最高边际R2为0.0018。对于机器学习分类器,当前分析的最高准确性是由微步态特征返回的,具有受试者内交叉验证方法和MF为56.90%(精度=43.9%,召回率=51.4%)。总的来说,加速度矢量大小,bout长度变化,姿势控制,和步态节律是未来分析最有趣的特征。
结论:反直觉,结果表明,典型的步态测量与异常疲劳之间存在微弱的关系。然而,COVID-19大流行等因素可能影响了步态行为。因此,需要对更大的队列进行进一步调查,以充分了解步态与异常疲劳之间的关系.