DOI: 10.5176/2315-4330_WNC13.61
Authors: Kultida Panidchakul and Suneerut Boonsin
Abstract:
Background: The prevalence of stroke has been increasing. Post-stroke patients often have deformities which impact their quality of life.
Objective: To determine whether demographic data, ability of daily living, depression, and social supports could accurately predict quality of life in post-stroke patients.
Methods: Descriptive research method was used in the study. One hundred and three adults with stroke were selected from Saraburi hospital by using purposive sampling technique, and completed questionnaires relating to Socio- demographic characteristics, Barthel Activity of daily Living index, Thai geriatric Depression Scale, Social Support Measure, and World Health Organization Quality of Life Brief. The overall reliability was 0.93.
Data analysis: Data were analyzed by using percentage arithmetic means, standard deviation, Chi-square, Pearson’s Product Moment Correlation Coefficient, and Stepwise Multiple Regression.
Results: For the demographic data, the average age of the participants were 62.59 years, 56.3{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} were males, and 63.1{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} were married. The majority of the subjects graduated from elementary school (71.8{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465}). The average monthly income of the subjects was 2231.07 bath. The length of time since diagnosis with stroke was 54.01 months. The major symptoms of participants were weakness (100{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465}), imbalance movement (39.8{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465}), aspirate and constipation (20.4{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465}) respectively. The study revealed that most of the participants (75.7{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465}) were able to perform their daily living activities. For depression level, the subjects showed depression at a normal level (65{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465}). Regarding social support, the subjects have overall social support at a moderate level (M = 87.35, SD = 19.61). When each type of social support was explored, it was found that most of the subjects received material and financial support (M =18.37, SD = 4.32). Regarding their quality of life, the subjects perceived their quality of life at a moderate level (M =78.59, SD = 15.2).
Chi-square and Pearson’s Product Moment Correlation Coefficient were conducted to explore the relationships of these variables and quality of life. The results showed that social support, ability of daily living, and educational level were statistically significant positive relationships with the quality of life (r=0.74, p<0.01, r=0.27, p<0.01, r=0.28, p<0.01,) respectively. There was statistically significant negative relationships between length of diagnosis with stroke, depression level and quality of life (r= -0.21, p<0.05, r = -0.23, p<0.05). These results implied that the participants who had received more social support, more independence to perform their daily living, higher education, lower depression level, and shorter periods of stroke diagnosis were more likely to have a better quality of life. Stepwise Multiple Regression analysis showed that 55.7 percent (Multiple R2 = .557) of the variances of quality of life was counted for by three significant predictors, i.e., appraisal support, social support and Network, and material and financial support.
Conclusion: This study finding will provide guidance for health care professionals for on-going development of support systems to strengthen the ability of post-stroke patients for improving their quality of life.
Keywords: quality of life, social support, post-stroke patients
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