BACKGROUND Stroke units provide patients with a multiparametric monitoring of vital functions, while no instruments are actually available for a continuous monitoring of patients motor performance. Our aim was to develop an actigraphic index able both to identify the paretic limb and continuously monitor the motor performance of stroke patients in the stroke unit environment. METHODS Twenty consecutive acute stroke patients (mean age 69.2 years SD 10.1, 8 males and 12 females) and 17 bed-restrained patients (mean age 70.5 years SD 7.3, 7 males and 10 females) hospitalized for orthopedic diseases of the lower limbs, but not experiencing neurological symptoms, were enrolled. This last group represented our control group. The motor activity of arms was recorded for 24 h using two programmable actigraphic systems showing off as wrist-worn watches. The firmware segmented the acquisition in epochs of 1 minute and for each epoch calculates two motor activity indices: MAe1 (Epoch-related Motor Activity index) and MAe2 (Epoch-related Motor Activity index 2). MAe1 is defined as the standard deviation of the acceleration module and MAe2 as the module of the standard deviation of acceleration components. To describe the 24 h motor performance of each limb, we calculated the mean value of MAe1 and MAe2 (respectively MA1_24h and MA2_24h). Then we obtained two Asymmetry Rate Indices: AR1_24h and AR2_24h to show the motor activity prevalence. AR1_24h refers to the asymmetry index between the values of MAe1 of both arms and AR2_24h to MAe2 values. The stroke patients were clinically evaluated by NIHSS at the beginning (NIHSST0) and at the end (NIHSST1) of the 24 h actigraphic recordings. RESULTS Both MA1_24h and MA2_24h indices were smaller in the paretic than in the unaffected arm (respectively p = 0.004 and p = 0.004). AR2_24h showed a better capability (95% of paretic arms correctly identified, Phi Coefficient: 0.903) to discriminate the laterality of the clinical deficit than AR1_24h (85% of paretic arms correctly identified, Phi Coefficient: 0,698). We also found that AR1_24h did not differ between the two groups of patients while AR2_24h was greater in stroke patients than in controls and positively correlated with NIHSS total scores (r: 0.714, p < 0.001 for NIHSS, IC95%: 0.42-0.90) and with the sub-score relative to the paretic upper limb (r: 0.812, p < 0.001, IC95%: 0.62-0.96). CONCLUSIONS Our data show that actigraphic monitoring of upper limbs can detect the laterality of the motor deficit and measure the clinical severity. These findings suggest that the above described actigraphic system could implement the existing multiparametric monitoring in stroke units.

中文翻译：

---

急性卒中患者上肢运动的活动测量。

背景技术中风单元为患者提供生命功能的多参数监视，而实际上没有仪器可用于连续监视患者的运动表现。我们的目的是开发一种能同时识别卒中肢体并持续监测卒中患者在卒中单元环境中的运动表现的活动指数。方法连续住院的下肢骨科疾病的急性卒中患者20例（平均年龄69.2岁，标准10.1，男8例，女性12例）和17例卧床不起的患者（平均年龄70.5岁，标准差7.3，男7例，女性10例）。未出现神经系统症状的患者被纳入研究。最后一组代表我们的对照组。使用两个可编程的手腕运动系统记录了腕部运动，记录了手臂的运动情况，持续了24小时。固件在1分钟的时间段内对采集进行了细分，并为每个时间段计算了两个运动活动指数：MAe1（与Epoch相关的运动活动指数）和MAe2（与Epoch相关的运动活动指数2）。MAe1被定义为加速度模块的标准偏差，MAe2被定义为加速度分量的标准偏差的模块。为了描述每个肢体24小时运动表现，我们计算了MAe1和MAe2的平均值（分别为MA1_24h和MA2_24h）。然后，我们获得了两个不对称率指标：AR1_24h和AR2_24h，以显示运动活动的普遍程度。AR1_24h是指两个臂的MAe1的值与AR2_24h到MAe2的值之间的不对称指数。在24小时活动记录的开始（NIHSST0）和结束（NIHSST1）时，通过NIHSS对中风患者进行了临床评估。结果射血分数MA1_24h和MA2_24h均比未患病臂小（分别为p = 0.004和p = 0.004）。与AR1_24h（正确识别的85％的平臂，Phi系数：0,698）相比，AR2_24h显示出更好的能力（95％正确识别的平臂，Phi系数：0.903）来区分临床缺陷的偏侧性。我们还发现两组患者之间的AR1_24h并无差异，而卒中患者中的AR2_24h高于对照组，并且与NIHSS总得分呈正相关（r：0.714，NIHSS，p <0.001，IC95％：0.42-0.90）和相对于假肢上肢的子得分（r：0.812，p <0.001，IC95％：0.62-0.96）。结论我们的数据表明上肢的活动学监测可以检测出运动缺陷的偏侧性，并可以测量临床严重程度。这些发现表明，上述书法系统可以在笔划单元中实现现有的多参数监视。