Depression has always been a main issue among all individuals throughout the 21st century. It is one of the major causes that lead individuals to have suicidal thoughts, addiction to drugs, self-injury and reckless behaviour. It does not only affect individuals’ mental state, but also influences the quality of life and increasing the risk to acquire diseases such as cardiovascular diseases, diabetes, stroke and obesity. Countless research examined the relationship between sleep quality and depression, and age with depression separately, with debates going on about determining sleep quality or depression as a causation to each other. But these researches did not consider both of the factors together. Therefore, this paper aimed to determine the relationship between sleep quality, age and depression.
Sleep quality is an important determinant for a good physical and mental health. Hayashino (2010) and her team found out that comorbid conditions is highly associated with depression and sleep via a population-based survey. Le Blanc et al. (2007) discovered a correlation between low quality of life, sleeping problems such as insomnia and having depression at the same time. Another research conducted by Demirci and team (2015) found out that sleep quality and depression affect each other in a two-way relationship via their experiment of determining the consequence of using smartphone among university students. Sleep problems such as insomnia and hypersomnia can lead to lower productivity at work, which causes workers to not perform well due to the experience of depression which lead them complete their task unsuccessfully (Nutt, Wilson, & Paterson, 2008).
On the other hand, depression can also be a determinant for sleep quality. In a sample of 500 high school students in Japan, children and adolescents which experienced poor mental health conditions reported to have sleep disturbances in their daily lives (Kaneita et al., 2009). Whereas among depressive patients in a hospital in UK, three quarters of them reported difficulties in waking up and falling asleep in their daily routine (Yates et al. 2007). Disturbed sleep may lead to a lower quality of life in individuals. Chen, Burley, & Gotlib (2012) conducted a research on 44 girls with/without depression, and girls with higher risk of depression reported having more sleeping difficulties. However, this research has its weakness. As girls with high-depression risk may have a negative perception towards their sleep difficulties, they may or may not have severe sleep disturbances at all. Thus, they might be bias in their reports. Nevertheless, many researches still manage to establish a correlation between sleep quality and depression.
Age is a moderating variable for depression risk in a person. Depression is categorized as an equal-opportunity mood disorder that can to begin at any age. This disorder can be divided into two categories, which is early-onset and late-onset. Early-onset involves observing depression among children, adolescents and young adults where as late onset involves adults which age from 65 years old and above. ADAA (Anxiety and Depression Association of America) reported that 2 to 3 percent among children between 6 and 12 years old experiences serious depression. Whereas in year 2014, 6 to 8 percent (2.8 million) adolescents aged between 12 to 17 years old experienced at least one major depressive episode throughout their schooling life (Yeh & Chiao, 2016). Young adults aged from 18 to 22 years old are one of the most vulnerable groups to have suicidal thoughts due to depression. In 2016, 13.15 percent of young adults who experienced major depressive episodes committed suicide.
Adults and the Elderly (>65 years old) are age groups which reported to have acquire major depressive disorder at the highest rate (O’Neil, 2007). Adults hold responsibilities financially, important roles to play within a family, which acts as a ‘backbone’ to a family structure, which made work stress the major cause of depression among adults (Melchior et al., 2007). Whereas in the elderly, statistics indicated the depression rate is highest in this group. In America alone, 6.5 million out of 35 million elderly (18.14%) acquires depression due to many reasons, such as life expectancy, lost of friends, having physical illness and has limited financial resources (Huen and Hein, 2005). Consequences for not treating depression in this age group are obtaining chronic diseases, having suicidal thoughts along with memory decline (Vink, Aartsen, & Schoevers, 2008).
One common problem faced by all age groups with depression is the inability to manage stress (Hammen, 2005). But age is not a major contributor to depression, but rather just a phase in life where different kinds of problems will hit respective age groups. Therefore, more research that links other factors and age to depression has to be conducted.
The aim of this paper is to determine the impact of both sleep quality and age on depression among late-onset age groups (34-84 years-old) in the United States. It is hypothesized that people with better qualities of sleep has a lower level of depression, and the relationship is moderated by age, which different life stages yields different results.
One thousand two hundred and fifty-five participants were involved in the research. Participants are eligible to the research if they completed the MIDUS II Project 1 phone interview and self-administered questionnaire, were in the MAINRDD and are living in the continental of the United States. However, only 1172 questionnaires were completed, and 1168 of them are valid. Participants are comprised of 505 males and 663 females, with an age range from 34-84 years old, with a mean age of 54.49. The recruitment of participants is a two-step process. First, recruitment packets will be distributed to households at designated data collection sites by the staff, and participants will be assigned to one of the data collection spots: UCLA, University of Wisconsin or Georgetown University. Next, participants will be contacted by research staff on questions they have about the questionnaire and to facilitate an interview with individuals that acquire major depression and has sleeping problems. Verbal and written consent were given to participants for the conduct of an interview, and it is approved by the Health Science Institutional Review Boards and all the other authorities involved.
This will be a correlational design. The subject variable for the research is sleep quality, and the score for Pittsburgh Sleep Questionnaire were recorded. The moderating variable is the age of participants. Whereas the dependent variable of the research is depression score, which is determined by the Center for Epidemiological Studies Depression Inventory (CES-D). So, this research examines how sleep quality affects depression with age as a moderating variable.
Participants are required to fill in a self-administered questionnaire (SAQ) which consists of two components. This included the Center for Epidemiological Studies Depression Inventory (CES-D) and Pittsburgh Sleep Questionnaire (PSQ).
This inventory consists of 20 items and 8 subscales. The 8 subscales measures sadness, lost of interest, appetite, sleep, thinking, guilt, tired, movement, and suicidal ideation respectively. Participants are required to rate their score from a scale of 0 (rarely or none of the time) to 3 (most or all of the time). The total score to be obtained ranges from 0 to 60. 4 items of the questionnaire were remarked with “R” which will be reverse-coded so that higher scores can reflect higher standing in the scale. Scale scores were computed by summing across all items for which there were no missing data. A higher score indicates a higher depression score whereas a lower score indicates a healthier mental state. The CES-D inventory has a high internal consistency. The validity and reliability has also been found similar to the general population.
This questionnaire is a two-paged document which consists of seven components, which are subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication and daytime dysfunction respectively. Some questions are to be answered by the participant’s bed partner or sleep mate. The summation of scores form the global sleep quality score. PSQ scores were used to determine sleep quality as they have a high reliability and validity.
IBM SPSS 24 was used to compute all the data. Descriptive statistics analysis will be performed to obtain basic information such as the total number of participants involved (N), maximum and minimum value, mean (M) and standard deviation (SD).
Next, Shapiro-wilk’s test will be performed to test the normality of the data, reduce the skewness and remove outliers, which all data that exceeds the significant value of (p < .05) will be considered as violating the assumption. But due to the large sample size (N = 1255), the central limit theorem could also be applied to remove outliers, that is if the assumption of normality is violated. A z-score is also used to test the presence of outliers.
The primary analysis for this research is multiple linear regression, and the p-value threshold for a significant data will be α = .05.
References
Chen, M. C., Burley, H. W., & Gotlib, I. H. (2012). Reduced sleep quality in healthy girls at risk for depression. Journal of sleep research, 21(1), 68-72.
Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of behavioral addictions, 4(2), 85-92.
Hammen, C. (2005). Stress and depression. Annu. Rev. Clin. Psychol., 1, 293-319.
Hayashino, Y., Yamazaki, S., Takegami, M., Nakayama, T., Sokejima, S., & Fukuhara, S. (2010).
Association between number of comorbid conditions, depression, and sleep quality using the Pittsburgh Sleep Quality Index: results from a population-based survey. Sleep medicine, 11(4), 366-371.
Heun, R., & Hein, S. (2005). Risk factors of major depression in the elderly. European Psychiatry, 20(3), 199-204.
Kaneita, Y., Yokoyama, E., Harano, S., Tamaki, T., Suzuki, H., Munezawa, T., … & Ohida, T. (2009). Associations between sleep disturbance and mental health status: a longitudinal study of Japanese junior high school students. Sleep medicine, 10(7), 780-786.
LeBlanc, M., Beaulieu-Bonneau, S., Mérette, C., Savard, J., Ivers, H., & Morin, C. M. (2007).
Psychological and health-related quality of life factors associated with insomnia in a population-based sample. Journal of psychosomatic research, 63(2), 157-166.
Melchior, M., Caspi, A., Milne, B. J., Danese, A., Poulton, R., & Moffitt, T. E. (2007). Work stress precipitates depression and anxiety in young, working women and men. Psychological medicine, 37(8), 1119-1129.
Nutt, D., Wilson, S., & Paterson, L. (2008). Sleep disorders as core symptoms of depression. Dialogues in clinical neuroscience, 10(3), 329.
O’Neil, M. (2007). Depression in the elderly. The Journal of Continuing Education in Nursing, 38(1), 14-15.
Stewart, R., Besset, A., Bebbington, P., Brugha, T., Lindesay, J., Jenkins, R., … & Meltzer, H. (2006). Insomnia comorbidity and impact and hypnotic use by age group in a national survey population aged 16 to 74 years. Sleep, 29(11), 1391-1397.
Vink, D., Aartsen, M. J., & Schoevers, R. A. (2008). Risk factors for anxiety and depression in the elderly: a review. Journal of affective disorders, 106(1-2), 29-44.
Yates, W. R., Mitchell, J., Rush, A. J., Trivedi, M., Wisniewski, S. R., Warden, D., … & Gaynes,N. (2007). Clinical features of depression in outpatients with and without co-occurring general medical conditions in STAR* D: confirmatory analysis. Primary care companion to the Journal of clinical psychiatry, 9(1), 7.
Yeh, P. M., & Chiao, C. H. (2016). The influences of spiritual wellbeing, Parental rearing attitude and coping strategies on USA college students’ anxiety and depression. Nova Science Publishers, Inc, 400, 117-130.
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