Identity, social engagement and community participation impact physical activity levels of stroke survivors: A mixed-methods study

Background The prognosis of the walking ability of individuals with stroke affects the choice of rehabilitation program. Identifying patients who will need assistance with ambulation at discharge allows clinicians to deliver rehabilitation programs focused on educating caregivers and adjusting the patient’s environment to allow safe transfer within the home. The primary objective of this study was to develop and internally validate a prediction model of walking dependence for patients with stroke admitted to a rehabilitation facility. The secondary objective was to establish a prediction model of restricted walking speed. Methods This retrospective cohort study included 476 individuals with subacute stroke who were admitted to a rehabilitation facility. Model 1 was developed to predict the probability of walking dependence. Model 2 was developed to predict restricted walking speed. Walking dependence was defined as a functional independence measure walk score of 5 or less. Restricted walking speed was defined as the ability to walk at 0.93 m/s or less. Potential predictors, including age, leg strength of the affected side, sitting balance, cognitive function, and urinary function, were selected based on the literature and analyzed using logistic regression analysis. Models were internally validated using the bootstrap method. Model performance was assessed by discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer–Lemeshow (H–L) goodness-of-fit test and calibration plots). Results A total of 236 patients (49.6%) walked dependently at discharge. Of the 240 individuals who achieved walking independence, 121 (50.4%) had restricted walking speed. In model 1, older age, poor leg strength, sitting balance, cognitive function, and urinary incontinence were significantly associated with walking dependence at discharge. The AUCs of models 1 and 2 were 0.93 (95% confidence interval (CI) = .90–.95) and 0.69 (95%CI = .62–.76), respectively. Both models had good calibration confirmed by the H-L test. Conclusions The internally validated prediction model of walking dependence had good discrimination and calibration, while the prediction model of restricted walking speed had poor discrimination. The prediction model for walking dependence developed in this study may be useful for planning rehabilitation strategies and setting realistic goals for patients.

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A predictive model for social participation of middle-aged and older adult stroke survivors: the China Health and Retirement Longitudinal Study

Liu,

Li,

Ding

et al. 2024

Front. Public Health

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ObjectiveThis study aims to develop and validate a prediction model for evaluating the social participation in the community middle-aged and older adult stroke survivors.MethodsThe predictive model is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which focused on individuals aged 45 years or older. The study utilized subjects from the CHARLS 2015 and 2018 wave, eighteen factors including socio-demographic variables, behavioral and health status, mental health parameters, were analyzed in this study. To ensure the reliability of the model, the study cohort was randomly split into a training set (70%) and a validation set (30%). The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify the most effective predictors of the model through a 10-fold cross-validation. The logistic regression model was employed to investigate the factors associated with social participation in stroke patients. A nomogram was constructed to develop a prediction model. Calibration curves were used to assess the accuracy of the nomogram model. The model’s performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA).ResultA total of 1,239 subjects with stroke from the CHARLS database collected in 2013 and 2015 wave were eligible in the final analysis. Out of these, 539 (43.5%) subjects had social participation. The model considered nineteen factors, the LASSO regression selected eleven factors, including age, gender, residence type, education level, pension, insurance, financial dependence, physical function (PF), self-reported healthy,cognition and satisfaction in the prediction model. These factors were used to construct the nomogram model, which showed a certain extent good concordance and accuracy. The AUC values of training and internal validation sets were 0.669 (95%CI 0.631–0.707) and 0.635 (95% CI 0.573–0.698), respectively. Hosmer–Lemeshow test values were p = 0.588 and p = 0.563. Calibration curves showed agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had predictive performance.ConclusionThe nomogram constructed in this study can be used to evaluate the probability of social participation in middle-aged individuals and identify those who may have low social participation after experiencing a stroke.

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Identity, social engagement and community participation impact physical activity levels of stroke survivors: A mixed-methods study

Cited by 4 publications

References 41 publications

Similarities and differences in factors associated with high and low sedentary behavior after stroke: a mixed methods study

Similarities and differences in factors associated with high and low sedentary behavior after stroke: a mixed methods study

Development and internal validation of two prediction models of walking dependence and restricted walking speed in individuals with subacute stroke

A predictive model for social participation of middle-aged and older adult stroke survivors: the China Health and Retirement Longitudinal Study

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