| Summary: | 碩士 === 中國醫藥大學 === 藥學系碩士班 === 106 === Background
Sleep disturbance, including insomnia (as the most common type), sleep- related breathing disorders, central disorder of hypersomnolence, circadian rhythm sleep-wake disorders, parasomnias, sleep-related movement disorders and other sleep disorders, might affect patients’daily function status, and health-related quality of life. According to the data obtained from 2017 Taiwan Society of Sleep Medicine, the prevalence of insomnia among Taiwanese is 11.3%. It implies that one out of every ten people are suffering from insomnia. The reasons associated with insomnia include stress in living, comorbidities, sleep and life habits and medication use. However, there’re very limited studies exploring the associations between medication use, and sleep characteristics and health-related quality of life among patients encountering sleep disturbance.
Objective
To explore the factors associated with medication use and sleep characteristics and health-related quality of life among patients who claimed to encounter sleep disturbance.
Methods
This is a piggy-packed study from a cross-sectional study. The 3-P (Predisposing factor-precipitating factor-perpetuating factor) model proposed by Spielman was adapted to come up with the conceptual framework. Accordingly, the relevant variables, including disease health statues and medication use recorded in medical records during three- month period prior the index dates, were extracted from the obtained databases. We grouped the variables into the aforementioned 3-P factors to perform the secondary data analysis. In particular, those participants with the total score of Pittsburgh sleep quality index greater than 5 were grouped as “sleep disturbance” group, whereas those with equal or less than 5 were in the “non-sleep disturbance” group. The corresponding descriptive, inferential analysis approaches, e.g, t-test, single variable regression and multivariable regression, were performed to compare the differences between the two groups and explore the associated factors. Then, the propensity scores were used to perform the 1:1 matching and to identify the corresponding non-sleep disturbance patients based upon some basic demographic characteristics (age, gender, education level, BMI and individual monthly income). The subgroup analysis focusing on those “sleep disturbance” group patients were conducted to explore the associated factors with self-reported health status and their different levels of sleep disturbance (i.e., severe sleep disturbance=Athens Insomnia Scale≧6; mild sleep disturbance=Athens Insomnia scale <6). The inconsistent responses toward the similar items listed in Pittsburgh sleep quality index and Athens Insomnia scale were compared. All the analyses were performed in SPSS, whereas the p<0.05 is recognized as the significant difference.
Results
Of 869 recruited patients, 777 patients with complete medical record and medication use information were further included for analysis. Of them, 195 patients in either sleep disturbance or non-sleep disturbance group after matching were identified. The regression analysis findings showed that depress tendency and sleep disturbance, and depress tendency affected their health utility and EQ-VAS score, as well as the severity levels of sleep disturbance. The statsticial significant factors associated with medication persistence were marriage status, health utilization amounts, and medication use for chronic diseases. Moreover, sleep quality, degree of stress, depress and anxiety tendency, medication use and health- related quality of life, were statsitically significant associated with different severity levels of sleep distrubence through the subgroup analysis. In addition, the participants’ religion believes, score of dysfunctional beliefs and attitudes toward sleep, experience of adverse drug reaction and medication adherence were also associated with the level of health utility and EQ-VAS among patients encountering insomnia.
Conclusion
Sleep disturbance and some other variables were associated with EQ- VAS and utility, where some the other variables might also be associated upon the findings derived from univariate regressions due to the possibility of potential type II error for smaller sample size. Regardless, the finding of the current study can be provided to facilitate health- care providers’ and patients’ and their familty’s decision making in oder to come up with the better quality of care and enhance the professional environment.
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