Andreasen Sydney C, Wright Tamara R, Crenshaw Jeremy R, Reisman Darcy S, Knarr Brian A
Department of Biomechanics, Biomechanics Research Building, University of Nebraska at Omaha, Omaha, NE, United States.
Clinical Research Laboratory, Department of Physical Therapy, University of Delaware, Newark, DE, United States.
Front Sports Act Living. 2020 Nov 3;2:551542. doi: 10.3389/fspor.2020.551542. eCollection 2020.
Stroke survivors are more sedentary than the general public. Previous research on stroke activity focuses on linear quantities. Non-linear measures, such as Jensen-Shannon Divergence and Lempel-Ziv Complexity, may help explain when and how stroke survivors move so that interventions to increase activity may be designed more effectively. Our objective was to understand what factors affect a stroke survivor's physical activity, including weather, by characterizing activity by step counts, structure, and complexity. A custom MATLAB code was used to analyze clinical trial (NCT02835313, https://clinicaltrials.gov/ct2/show/NCT02835313) data presented as minute by minute step counts. Six days of data were analyzed for 142 participants to determine the regularity of activity structure across days and complexity patterns of varied cadences. The effect of steps on structure and complexity, the season's effect on steps, structure, and complexity, and the presence of precipitation's effect on steps and complexity were all analyzed. Step counts and regularity were linearly related ( < 0.001). Steps and complexity were quadratically related ( = 0.70 for mean values, 0.64 for daily values). Season affected complexity between spring and winter ( = 0. 019). Season had no effect on steps or structure. Precipitation had no effect on steps or complexity. Stroke survivors with high step counts are active at similar times each day and have higher activity complexities as measured through patterns of movement at different intensity levels. Non-linear measures, such as Jensen-Shannon Divergence and Lempel-Ziv Complexity, are valuable in describing a person's activity. Weather affects our activity parameters in terms of complexity between spring and winter.
中风幸存者比普通大众的久坐时间更长。先前关于中风活动的研究主要集中在线性数量上。非线性测量方法，如詹森-香农散度和莱姆佩尔-齐夫复杂度，可能有助于解释中风幸存者何时以及如何活动，从而更有效地设计增加活动量的干预措施。我们的目标是通过步数、结构和复杂度来描述活动，以了解影响中风幸存者身体活动的因素，包括天气。使用自定义的MATLAB代码分析了临床试验（NCT02835313，https://clinicaltrials.gov/ct2/show/NCT02835313）数据，这些数据以每分钟的步数呈现。对142名参与者的六天数据进行了分析，以确定不同日期活动结构的规律性以及不同节奏的复杂度模式。分析了步数对结构和复杂度的影响、季节对步数、结构和复杂度的影响以及降水对步数和复杂度的影响。步数与规律性呈线性相关（<0.001）。步数与复杂度呈二次相关（平均值=0.70，日值=0.64）。季节影响春季和冬季之间的复杂度（=0.019）。季节对步数或结构没有影响。降水对步数或复杂度没有影响。步数多的中风幸存者每天在相似的时间活跃，并且通过不同强度水平的运动模式测量，其活动复杂度更高。非线性测量方法，如詹森-香农散度和莱姆佩尔-齐夫复杂度，在描述一个人的活动方面很有价值。天气在春季和冬季之间的复杂度方面影响我们的活动参数。
