Department of Exercise Science, University of South Carolina, 1300 Wheat St, Columbia, SC 29208 (USA).
Department of Physical Therapy Education, SUNY Upstate Medical University, Syracuse, New York.
Phys Ther. 2020 May 18;100(5):807-817. doi: 10.1093/ptj/pzaa020.
What contributes to free-living walking after stroke is poorly understood. Studying the characteristics of walking may provide further details that guide interventions.
The objectives of this study were to examine how the walking characteristics of bouts per day, median steps per bout, maximum steps per bout, and time spent walking differ in individuals with various walking speeds, walking endurance, and daily steps and to identify cutoffs for differentiating ambulators who were active versus inactive.
This study involved a cross-sectional analysis of data from the Locomotor Experience Applied Post-Stroke trial.
Participants were categorized by walking speed, walking endurance (via the 6-minute walk test), and daily steps (via 2 consecutive days of objective activity monitoring). Differences in walking characteristics were assessed. Linear regression determined which characteristics predicted daily step counts. Receiver operating characteristic curves and areas under the curve were used to determine which variable was most accurate in classifying individuals who were active (≥5500 daily steps).
This study included 252 participants with chronic stroke. Regardless of categorization by walking speed, walking endurance, or daily steps, household ambulators had significantly fewer bouts per day, steps per bout, and maximum steps per bout and spent less time walking compared with community ambulators. The areas under the curve for maximum steps per bout and bouts per day were 0.91 (95% confidence interval = 0.88 to 0.95) and 0.83 (95% confidence interval = 0.78 to 0.88), respectively, with cutoffs of 648 steps and 53 bouts being used to differentiate active and inactive ambulation.
Activity monitoring occurred for only 2 days.
Walking characteristics differed based on walking speed, walking endurance, and daily steps. Differences in daily steps between household and community ambulators were largely due to shorter and fewer walking bouts. Assessing and targeting walking bouts may prove useful for increasing stepping after stroke.
人们对导致中风后自由行走的因素知之甚少。研究行走特征可以提供更多细节,从而为干预措施提供指导。
本研究旨在探讨不同行走速度、行走耐力和每日步数的个体之间,每日行走回合数、每个回合的平均步数、每个回合的最大步数和行走时间的差异,并确定区分活跃和不活跃步行者的区分界限。
本研究为 Locomotor Experience Applied Post-Stroke 试验数据的横断面分析。
根据行走速度、行走耐力(通过 6 分钟步行测试)和每日步数(通过 2 天的客观活动监测)对参与者进行分类。评估行走特征的差异。线性回归确定哪些特征可以预测每日步数。使用受试者工作特征曲线和曲线下面积来确定哪个变量在区分活跃(每日步数≥5500 步)个体方面最准确。
本研究纳入了 252 名慢性中风患者。无论根据行走速度、行走耐力还是每日步数进行分类,与社区步行者相比,居家步行者每日行走回合数、每个回合的平均步数和最大步数以及行走时间明显减少。最大步数和回合数的曲线下面积分别为 0.91(95%置信区间:0.88 至 0.95)和 0.83(95%置信区间:0.78 至 0.88),用于区分活跃和不活跃行走的最佳截断值分别为 648 步和 53 回合。
仅进行了 2 天的活动监测。
行走特征根据行走速度、行走耐力和每日步数而有所不同。居家和社区步行者之间每日步数的差异主要归因于行走回合数短且少。评估和靶向行走回合可能有助于增加中风后的步数。