Morgan Catherine, Masullo Alessandro, Mirmehdi Majid, Isotalus Hanna Kristiina, Jovan Ferdian, McConville Ryan, Tonkin Emma L, Whone Alan, Craddock Ian
Translational Health Sciences, University of Bristol, Bristol, UK.
Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Bristol, UK.
Digit Biomark. 2023 Aug 14;7(1):92-103. doi: 10.1159/000530953. eCollection 2023 Jan-Dec.
Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications.
Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed.
3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, = 0.018) and speed (U = 9,965, < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant.
We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.
技术有望追踪帕金森病（PD）的疾病进展以及对神经保护疗法的反应。从坐起到站立（STS）的转换是一个频繁发生的事件，对帕金森病患者很重要。本研究的目的是展示一种自动方法，使用真实生活中的自由活动数据集来量化STS持续时间和速度，并研究结果的临床相关性，包括当帕金森病患者停药时STS参数是否会发生变化。
从24名参与者在自然环境中两人一组居住5天期间收集了85小时的视频数据。从视频数据中提取骨骼关节；估计头部轨迹并用于估计STS的持续时间和速度参数。
平均每人每小时观察到3.14次STS转换。自动测量和手动测量的STS持续时间之间存在显著相关性（皮尔逊相关系数rho = 0.419，P = 0.042），自动测量的STS速度与手动测量的STS持续时间之间也存在显著相关性（皮尔逊相关系数rho = 0.780，P < 0.001）。金标准临床评定量表评分与STS持续时间和STS速度之间均存在显著且强烈的相关性；当参与者手持物品进行STS转换时，未观察到这些相关性。在队列水平上，对照组和帕金森病患者服药时的STS持续时间（U = 6263，P = 0.018）和速度（U = 9965，P < 0.001）存在显著差异。在个体水平上，只有两名帕金森病患者在停药时STS速度明显变慢；停药在个体水平上未显著改变任何参与者的STS持续时间。
我们展示了一种新颖的方法，可自动量化并在生态学上验证两个STS参数，这两个参数与测量帕金森病疾病严重程度的金标准临床工具相关。
