目的:根据异质成年人群的社区可部署的运动损伤和功能测试来确定运动表现的类别。
方法:获得了16项针对肢体和全身运动障碍和功能的测试。线性回归分析用于将每个测试的性能分为落在年龄和性别预测值之内或之外。潜在类别分析用于确定3类电机性能。分类变量采用卡方检验和Fisher精确检验,和方差分析和Kruskal-Wallis检验用于连续变量,以评估人口统计学特征与潜在类别之间的关系。
方法:一般社区。
方法:个人(N=118;50名男性)参与了研究。配额抽样用于招募自我鉴定为健康(n=44)或目前患有慢性健康状况的人,包括关节炎(n=19),多发性硬化症(n=18),帕金森病(n=17),中风(n=18),或低功能(n=2)。
方法:不适用。
方法:电机性能的潜在类别。
结果:在整个样本中,确定了3种潜在的运动性能类别,它们聚集了运动性能下降的个体:(1)在大多数测试(预期类别)的预测值内,(2)某些测试的外部预测值(中等等级),和(3)在大多数测试(严重类)的预测值之外。使用以下社区可部署的电机性能测试,可以根据落在预测值之外的百分比来区分各个类别:10米步行测试(22%,80%,和100%),6分钟步行测试(14.5%,37.5%,和100%),槽钉板试验(23%,38%,和100%),和改性物理性能测试(3%,54%,和96%)。
结论:在这个异质的成年人组中,我们发现了三种不同的电机性能,将样本聚类到预期测试分数组中,中度考试成绩不足组,和一个被切断的考试成绩不足组。根据电机性能测试,我们建立了可部署的社区,易于管理的测试可以准确地预测已建立的运动性能集群。
OBJECTIVE: To determine classes of motor performance based on community deployable motor impairment and functional tests in a heterogeneous adult population.
METHODS: Sixteen tests of limb-specific and whole-body measures of motor impairment and function were obtained. Linear regression analysis was used to dichotomize performance on each test as falling within or outside the age- and sex-predicted values. Latent class analysis was used to determine 3 classes of motor performance. The chi-square test of association and the Fisher exact test were used for categorical variables, and analysis of variance and the Kruskal-Wallis test were used for continuous variables to evaluate the relationship between demographic characteristics and latent classes.
METHODS: General community.
METHODS: Individuals (N=118; 50 men) participated in the study. Quota sampling was used to recruit individuals who self-identified as healthy (n=44) or currently living with a preexisting chronic health condition, including arthritis (n=19), multiple sclerosis (n=18), Parkinson disease (n=17), stroke (n=18), or low functioning (n=2).
METHODS: Not applicable.
METHODS: Latent classes of motor performance.
RESULTS: Across the entire sample, 3 latent classes of motor performance were determined that clustered individuals with motor performance falling: (1) within predicted values on most of the tests (expected class), (2) outside predicted values on some of the tests (moderate class), and (3) outside predicted values on most of the tests (severe class).The ability to distinguish between the respective classes based on the percent chance of falling outside predicted values was achieved using the following community deployable motor performance tests: 10-meter walk test (22%, 80%, and 100%), 6-minute walk test (14.5%, 37.5%, and 100%), grooved pegboard test (23%, 38%, and 100%), and modified physical performance test (3%, 54%, and 96%).
CONCLUSIONS: In this heterogeneous group of adults, we found 3 distinct classes of motor performance, with the sample clustering into an expected test score group, a moderate test score deficiency group, and a severed test score deficiency group. Based on the motor performance tests, we established that community deployable, easily administered testing could accurately predict the established clusters of motor performance.