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Factors Associated With Health-Related Quality of Life After Stroke
Neurorehabilitation and Neural Repair, 2011
Background. In line with patient-centered health care, it is necessary to understand patients' perceptions of health. How stroke survivors perceive their health at different time points after stroke and which factors are associated with these feelings provide important information about relevant rehabilitation targets. Objective. This study aimed to identify the independent factors of health-related quality of life (HRQoL) from a biopsychosocial perspective using the methods of multivariate regression at 3 different time points poststroke. Methods. Included in the study were 99 patients from stroke units with diagnosed first-ever stroke. At admission and at 6 weeks, 3 months, and 1 year poststroke, HRQoL was assessed using the EuroQoL-5D Visual Analogue Scale (EQ-5D VAS). Consequences in Body Functions and Activities and Participation, and Environmental Factors were documented using 155 categories of the International Classification of Functioning, Disability and Health (ICF) Core Set for Stroke. Results. For a period of 1 year, problems with recreation and leisure, personality functions, energy and drive functions, and gait pattern functions were repeatedly associated with worse HRQoL. Whereas Body Functions and Activities and Participation explained more than three-fourths of the variances of HRQoL at 6 weeks and 3 months (R 2 = 0.80-0.93), the variation at 1 year was best explained by either Body Functions or Environmental Factors (R 2 = 0.51). Conclusions. The results indicate the importance of Body Functions and Activities and Participation (mainly personality functions and recreation and leisure) on HRQoL within 3 months poststroke, but increased impact of Environmental Factors on HRQoL at 1 year.
Archives of Physical Medicine and Rehabilitation, 2011
Barclay-Goddard R, Lix LM, Tate R, Weinberg L, Mayo NE. Health-related quality of life after stroke: does response shift occur in self-perceived physical function? Arch Phys Med Rehabil 2011;92:1762-9. Objective: To determine whether response shift (a change in the self-perceived meaning of health-related quality of life [HRQL]) was present in a model of physical function over time poststroke.
Self-rated health determinants in post-stroke individuals
Journal of Rehabilitation Medicine, 2020
The main question studied was: Could variables in func tion and disability, which have potential to be modified by rehabilitation, be determinants of selfrated health in chronic poststroke individuals? The variables investiga ted were: emotional function, motor recovery level, ma nual and locomotion skill, and participation. Only emo tional function determined the selfrated health of the subjects. Individuals with impaired emotional function were 6.6 times more likely to assess their own health as poor. Assessment of selfrated health and emotional function is recommended, since these factors can help to improve clinical decisionmaking in the rehabilitation process in chronic poststroke individuals. Objective: To investigate whether variables of func tion and disability, which have potential to be modi fied by rehabilitation, are determinants of self-rated health in poststroke individuals in the chronic pha se. Design: Crosssectional exploratory study. Methods: The dependent variable was selfrated health. The independent variables were organized according to the International Classification of Fun ctioning, Disability and Health components: Body structure and function (emotional function and mo tor recovery level), Activity (manual and locomo tion skill), and Participation (participation). Logistic regression analysis was performed to identify signi ficant associations between the independent variables and self-rated health (α = 5%). Results: Sixtythree individuals were included in the study: 44 (70%) rated their own health as good ("excellent"/"very good"/"good") and 19 (30%) as poor ("fair"/"poor"). Significant association with self-rated health was identified only for emotional function. Individuals with impaired emotional fun ction were 6.6 times more likely to assess their own health as poor (odds ratio (OR) 6.56; 95% confiden ce interval 1.53-28.21). Conclusion: Emotional function was found to be a determinant of selfrated health in poststroke indi viduals in the chronic phase and, therefore, must be assessed carefully in order to help provide integral healthcare and improve clinical decision-making. Fu ture studies should investigate whether enhancing emotional function is associated with improvements in selfrated health in poststroke individuals.
Clinical Epidemiology and Global Health, 2021
This study piloted the World Health Organization Quality of Life short version form (WHOQOL-BREF) to compare the quality of life (QoL) in post-stroke patients who followed the medical stroke rehabilitation program and those whom did not. This study determined which quality of life domains were mostly affected by stroke, and whether there are influencing factors other than rehabilitation. Methods: A cross-sectional study was conducted at Public Hospital in Surabaya. Patients with stroke less than two years since the first attack were included in this study (n = 52). Cronbach's alpha test was performed to assess the internal consistency of WHOQOL-BREF questionnaire. The independent t-test and ANOVA were used to compare the differences between patient's characteristics and the six domains of QoL (i.e., perception of QoL, perception of health, physical of health, psychological health, social relationship, and environmental health). Multiple linear regression was performed to assess the influential factors of QoL. Results: post-stroke patients aged more than 50 years old (76.9%), male (55.8%). But, only 48.1% of those patients participating in medical rehabilitation programs. Age group was significantly associated with QoL in the psychological health domain (p = 0.021); participating in medical stroke rehabilitation programs was significantly associated with social relationship domain (p = 0.026) and the education level was significantly associated to physical health (p = 0.005), psychological heath (p = 0.035) and perception of health (p = 0.003). Conclusion: Lower education level was significantly associated with a low perception of health, one of QoL domains; and it was the most influential factor of QoL among post-stroke patients.
Quality of life for patients poststroke and the factors affecting it
Journal of Stroke and …, 2005
Objective: We sought to assess quality of life (QOL) and the factors affecting QOL for patients with stroke. Methods: This study was designed as a controlled prospective study. The study group consisted of 88 patients who had experienced a stroke and the control group consisted of ...
Predictors of Health-Related Quality of Life in Stroke Survivors After Inpatient rehabilitation
2019
Background. Disordered and decreased quality of life is the most important consequence of stroke for stroke survivors. The aim of the study was to determine the predictors of the health-related quality of life (HRQOL) in stroke survivors six months after discharge from inpatient rehabilitation. Methods. We conducted prospective cohort study which involved 136 (48.5% males and 51.5% females) survivors. We examined seven potential predictors of HRQOL: age, sex, stroke type, stroke side, functional status (Barthel Index-BI and modified Rankin Scale-mRS), cognition (Mini-Mental State Examination; MMSE) and stroke severity (National Institutes of Health Stroke Scale; NIHSS). HRQOL was assessed by Stroke Impact Scale (SIS) 3.0. Using Pearson's correlation and multiple logistic regression analysis we described the relationships between mRS, NIHSS, BI, MMSE and HRQOL. Results. Baseline mRS and NIHSS scores negatively correlated with seven SIS domains except with strength. Baseline BI scores positively correlated with seven SIS domains except mobility and baseline MMSE scores positive correlated with memory, ADL, hand function and participation role and negatively correlated with emotion, communication and mobility domains. Decrease of both mRS and NIHSS scores during the observed period positively correlated with increase of all SIS domains. Ischaemic stroke positively correlated with emotion and communication and stroke in brainstem negatively correlated with communication domain. Memory domain positively correlated with female sex and with stroke in the right hemisphere. Age wasn't significantly associated with any SIS domain. Conclusion. We conclude that major factors in predicting the improvement of strength, physical functioning, mobility, hand function, ADL and participation role were increase of BI and decrease of mRS and NIHSS scores. Female sex, stroke in right hemisphere and increase of BI and MMSE scores predicted better memory. Baseline mRS and NIHSS scores were predictors for improvement of hand function and increase of mRS predicted decrease of hand function and participation role.
Clinical determinants of long-term quality of life after stroke
Age and Ageing, 2007
Objectives: to determine factors that independently predict health-related quality of life (HRQOL) 1 and 3 years after stroke. Methods: subjects numbering 397, from a population-based register of first-ever strokes were assessed for HRQOL using the Short Form 36 (SF36) 1 year after stroke. Physical (PHSS) and mental health (MHSS) summary scores were derived from the eight domains of HRQOL in the SF36. Multivariate stepwise regression analyses were conducted to determine independent predictors of these scores; β coefficients with 95% CI were obtained. β coefficient is the difference between average value of the variable (e.g. male) and average value under consideration (e.g. female). Demographic and stroke risk factors, neurological impairments and cognitive impairment (MMSE <24) were included in the models. Similar analyses were undertaken on 150 subjects 3 years post-stroke. Results: a year after stroke, independent predictors of the worst PHSS were of females (β coefficient −3.3 : 95% CI −5.7 to −0.8), manual workers (−3.2: −5.9 to −0.4), diabetes (−4.2: −7.7 to −0.8), right hemispheric lesions (−4.9: −8.7 to −1.2), urinary incontinence (−7.8: −11.6 to −4.1) and cognitive impairment (−2.7: −5.5 to −0.1); the worst MHSS were associated with being Asian (−11.8: −20.6 to −3.0), ischaemic heart disease (−2.7: −5.4 to −0.03), cognitive impairment (−3.04: −5.8 to −0.3). Subjects aged 65-75 years (5.4 : 2.5 to −8.4) had better MHSS than those <65 years. Three years post-stroke, independent predictors of worse PHSS were hypertension (−8.7: −13.5 to −3.9), urinary incontinence (−8.1: −15 to −1.1) and cognitive impairment (−8.3: −13.2 to −3.5). Conclusions: determinants of HRQOL vary both over time after stroke and whether physical or psychosocial aspects of HRQOL are being considered. This study provides valuable information on factors predicting long-term HRQOL, which can be taken into consideration in audits of clinical practice or in future interventional studies aiming to improve HRQOL after stroke.
Response shift influenced estimates of change in health-related quality of life poststroke
Journal of Clinical Epidemiology, 2004
The interpretability of changes in perceived health status over time is threatened if people experience a response shift. This study assessed whether the recovery process following stroke altered individuals' perceptions of past health status and the impact that change in internal standards (response shift) had on ratings over time. We hypothesized that individuals with stroke would experience changes in internal standards, not experienced by the control group. Two other hypotheses related to objective criterion measures also were tested.
Modeling health-related quality of life in people recovering from stroke
Quality of Life Research, 2013
Background The Wilson-Cleary (W-C) model of healthrelated quality of life (HRQL) has not been tested in stroke, and a better understanding of the components of HRQL during recovery would lead to a more integrated and person-centered approach to health management and outcome optimization for this vulnerable population. Objective To enhance our understanding for how QOL emerges from the sequelae of stroke during the recovery period, the aim was to empirically test a biopsychosocial conceptual model of HRQL for people recovering from stroke. Methods We present a multi-site longitudinal study of an inception cohort of 678 persons recruited at stroke onset and studied at key intervals over the first post-stroke year. As the most pronounced recovery after stroke occurs in the first 3 months, this time frame was chosen as the focus of this analysis. The measures for this study were chosen for their relevance to key constructs of stroke impact and for their optimal psychometric properties. Multiple measures for each of the W-C rubrics were available from instruments such as the Stroke Impact Scale, RAND-36, HUI, and EQ-5D, among others. A structural equation model (SEM) was fit using MPlus. To minimize potential bias arising from the missing data, multiple imputation was performed on the longitudinal data using SAS proc MI. Results Of the 678 subjects who entered the cohort, 618 were interviewed at 1 month post-stroke and 533 at 3 months (486 and 454 had data at 6 and 12 months, respectively). A 3-month model with paths from biological factors to symptoms and symptoms to function fits well (CFI:0.966, RMSEA:0.044), though one model with paths from function to health perception did not (CFI:0.934, RMSEA:0.058). Allowing additional paths across non-adjacent rubrics improved fit considerably (CFI:0.962, RMSEA:0.044). A final model included emotional well-being under the symptom rubric (CFI:0.955, RMSEA:0.047). Including social support as an environmental factor had little impact on the model. Total variance in health perception explained was 76.3 %. Conclusion These results emphasize that to optimize overall HRQL during the crucial first 3 months of recovery, interventions need to continue to focus on comorbid health conditions and on reducing stroke impairments. A function-only focus too soon in the recovery process may not produce the desired impact to optimize HRQL.