国家神经系统疾病临床医学研究中心数据管理与共享系统

发表杂志：Journal of Rehabilitation M edicine

发表类型: Research Article

影响因子(IF): 3.5

PMID: 38616713 Pubmed原文链接 全文下载

Background: Cumulative evidence consistently demonstrated that stroke patients have a lower health-related quality of life (HRQoL) than healthy individuals.1-3 Previous studies have identified the impact of different factors on HRQoL after stroke, including demographic factors (age, female gender, and low education level) and clinical factors (stroke severity, stroke type, smoking, and depression).4-6 However, data are scarce on the impact of walking speed at admission on stroke patients’ future quality of life and function. Walking speed is a simple performance measure that has been recognized as a reliable and sensitive indicator for the assessment of physiologic performance and prediction of clinical outcomes, and indeed has been regarded as the “sixth vital sign”.7 Several previous studies have demonstrated that walking speed is a consistent predictor of community mobility, cognitive impairment, all-cause mortality, hospitalization, and disability.8-11 Furthermore, a previous cross-sectional study showed significant correlations between walking speed and physical domains of quality of life, and the community ambulators, whose walking speed was more than 0.8 m/sec, had a higher quality of life than the household or limited community ambulators.12 However, inadequate data exist on walking speed at admission for stroke patients and their association with longitudinal HRQoL and functional outcome. Therefore, the purpose of this study was to assess the relationship between the walking speed at admission with functional outcome and HRQoL amongst people with stroke after 3 months. We hypothesized that a slower walking speed at admission would be associated with a worse HRQoL and function outcome after 3 months. Purpose: This study aimed to analyze the association between walking speed at admission with functional outcome and HRQoL at 3-month follow-up in patients with stroke.

Baseline Data Collection： Data collected at baseline included sociodemographic information (age, sex, education, body mass index, living status, marital status, income, health insurance, occupation class, current smoking, and current drinking), medical histories (hypertension, diabetes, hyperlipidaemia, atrial fibrillation, coronary artery disease, heart failure, peripheral arterial disease, arthritis), intravenous thrombolysis, rehabilitation status. Physical activity was defined according to the classification of this variable in the CNSR-III database. Briefly, participants’ self-reported levels of physical activity were measured by the duration and perceived intensity of weekly participation in certain physical activities, including farm work, work, housework, transport-related physical activity, leisure exercise, or sports. Physical activity patterns were defined as follows: None, no physical activity; Low, not reporting any moderate- or vigorous-intensity physical activities; Medium, reporting less than 4 h/week of moderate-intensity physical activity and less than 80 min/week of vigorous-intensity physical activity; High, reporting at least 4h/week of moderate-intensity physical activity or at least 80 min/week of vigorous-intensity physical activity. Stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS) on a scale ranging from 0 to 42, with a lower value indicating lesser stroke severity (13). The degree of functional disability was assessed with the modified Rankin Scale (mRS) (score 0–5) (14). The Montreal Cognitive Assessment Scale (MoCA) is a screening tool for detecting mild cognitive impairment. The score ranges from 0 to 30 where a value below 26 can indicate cognitive impairment (15). Gait speed was assessed by the 10MWT within 2 weeks of hospitalization for acute stroke and before the rehabilitation programme. Calculating a comfortable gait speed involved measuring the time (in seconds) needed to travel the middle 10 m of a 14-m walkway. A walking aid, crutches, and foot orthoses were permitted in this test and to be supported by one personal assistant in the event of instability or balance problems. If walking aids or support were required, they were provided at retest to ensure that testing procedures were equivalent. Calculating a comfortable gait speed involved measuring the time (in seconds) needed to travel the middle 10m of a 14-m walkway. The timing starts when the subject’s first foot crosses the 2-m mark and stops when the first foot crosses the 12-m mark. The test was provided without any motivation (16). The test was repeated 3 times. The average of the 3 walking tests was recorded as the gait speed. The 10MWT demonstrated excellent inter-rater reliability, test–retest reliability, construct validity, and sensitivity to change in people with acute stroke (16, 17). Outcomes and Follow-Up： The main clinical outcome was HRQoL after 3 and 12 months of follow-up. EuroQol five dimensions (EQ-5D), developed by the EuroQol Group, was used to measure HRQoL (18), which included a health state descriptive system with 5 dimensions and a visual analogue scale (VAS) score. Statistical Analysis: Continuous variables are expressed as the means with standard deviation (SD) or median with interquartile range (IQR), as appropriate. Categorical data are presented as proportions. Patients were divided into 3 groups according to the tertiles of gait speed, with Q1 as the lowest tertile and Q3 as the highest tertile. Baseline characteristics were analysed by χ2 statistics or Fisher’s exact test for the categorical variables and 1-way ANOVA or Kruskal‒Wallis test for the continuous variables, as appropriate. Multivariate linear regression analysis was used to determine the associations between gait speed and HRQoL. Multivariate logistic regression analysis was used to evaluate the associations of gait speed with the 5 dimensions of the EQ-5D-3L, using odds ratios (ORs) and 95% confidence intervals (CIs).