Derungs Adrian, Schuster-Amft Corina, Amft Oliver
Lehrstuhl für Digital Health, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.
Research Department, Reha Rheinfelden, Rheinfelden, Switzerland.
Front Bioeng Biotechnol. 2018 Oct 17;6:136. doi: 10.3389/fbioe.2018.00136. eCollection 2018.
Physical activity (PA) is essential in stroke rehabilitation of hemiparetic patients to avoid health risks, and moderate to vigorous PA could promote patients' recovery. However, PA assessments are limited to clinical environments. Little is known about PA in unguided free-living. Wearable sensors could reveal patients' PA during rehabilitation, and day-long long-term measurements over several weeks might reveal recovery trends of affected and less-affected body sides. We investigated PA in an observation study during outpatient rehabilitation in a day-care center. PA of affected and less-affected body sides, including upper and lower limbs were derived using wearable motion sensors. In this analysis we focused on PA during free-living and clinician guided therapies, and investigated differences between body-sides. Linear regressions were used to estimate metabolic equivalents for each limb at comparable scale. Non-parametric statistics were derived to quantify PA differences between body sides. We analyzed 102 full-day movement data recordings from eleven hemiparetic patients during individual rehabilitation periods up to 79 days. The comparison between free-living and clinician guided therapy showed on average 16.1 % higher PA in the affected arm during therapy and 5.3 % higher PA in the affected leg during therapy. Average differences between free-living and therapy in the less-affected side were below 4.5 %. We analyzed PA of patients with a hemiparesis in two distinct rehabilitation settings, including free-living and clinician guided therapies over several weeks and compared MET values of affected and less-affected body sides. In particular, we investigated PA using individual regression models for each limb. We demonstrated that wearable motion sensors provide insights in patient's PA during rehabilitation. Although, no clear PA trends were found, our analysis showed patients' tendency to sedentary behavior, confirming previous lab study results. Our PA analysis approach could be used beyond clinical rehabilitation to devise personalized patient and limb-specific exercise recommendations in future remote rehabilitation.
体力活动(PA)对于偏瘫患者的中风康复至关重要,可避免健康风险,而中度至剧烈的体力活动能够促进患者康复。然而,体力活动评估仅限于临床环境。对于无指导的自由生活状态下的体力活动了解甚少。可穿戴传感器能够揭示患者康复期间的体力活动情况,数周的全天长期测量可能会揭示受影响和受影响较小身体部位的恢复趋势。我们在日间护理中心的门诊康复期间进行了一项观察性研究,以调查体力活动情况。使用可穿戴运动传感器得出受影响和受影响较小身体部位(包括上肢和下肢)的体力活动情况。在本分析中,我们重点关注自由生活和临床医生指导治疗期间的体力活动,并研究身体部位之间的差异。使用线性回归来估计每个肢体在可比规模下的代谢当量。采用非参数统计来量化身体两侧的体力活动差异。我们分析了11名偏瘫患者在长达79天的个体康复期间的102份全天运动数据记录。自由生活与临床医生指导治疗之间的比较显示,治疗期间患侧手臂的体力活动平均高出16.1%,患侧腿部的体力活动平均高出5.3%。受影响较小一侧在自由生活和治疗之间的平均差异低于4.5%。我们在两种不同的康复环境中分析了偏瘫患者的体力活动情况,包括数周的自由生活和临床医生指导治疗,并比较了受影响和受影响较小身体部位的代谢当量值。特别是,我们使用每个肢体的个体回归模型来研究体力活动。我们证明,可穿戴运动传感器能够提供患者康复期间体力活动的相关信息。尽管未发现明确的体力活动趋势,但我们的分析显示了患者久坐行为的倾向,证实了之前实验室研究的结果。我们的体力活动分析方法可用于临床康复之外,以便在未来的远程康复中制定个性化的患者和肢体特定运动建议。