Relationships Between Personal, Depression and Social Network Factors and Sleep Quality in Community-Dwelling Older Adults : Journal of Nursing Research

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Relationships Between Personal, Depression and Social Network Factors and Sleep Quality in Community-Dwelling Older Adults

Yao, Ke-Wu; Yu, Shu*; Cheng, Shun-Ping**; Chen, I-Ju***

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Journal of Nursing Research 16(2):p 131-139, June 2008. | DOI: 10.1097/01.JNR.0000387298.37419.ff
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Deep sleep duration (slow-wave sleep-stages 3 and 4) and the proportion of REM (rapid eye movement) sleep both decline with aging (Floyd, 2002; Hoffman, 2003). It is therefore generally accepted that sleep disturbances or insomnia increase with age. Sleep problems have indeed emerged as a critical issue arising from the growth in elderly populations worldwide. Epidemiological studies have indicated that 15 to 45.3% of older people complain of frequent sleep problems or disturbances, and that the incidence of sleep complaints is rising (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989; Hoffman, 2003; Sukegawa et al., 2003). Difficulties initiating and maintaining sleep are the most reported sleep disturbances among older people (Hoffman, 2003; Hsu, 2001). Older people generally spend more time in bed (sleep latency), but less time asleep, and are more easily aroused from sleep than younger persons (Lin, Su, & Chang, 2003).

Tamakoshi, Ohno, and the Japan Collaborative Cohort (JACC) Study Group (2004) reported that sleep patterns were correlated with all-cause mortality risk. Better sleep patterns have been related to better physical and mental health (Taira et al., 2002), and better sleep quality is needed to promote overall quality of life in the senior population (Hoffman, 2003). Sleep conditions have also been shown to influence the quality of life of individuals and their families, and also to influence healthcare costs (Montgomery & Dennis, 2004).

Sleep quality is frequently used as an assessment indicator to evaluate sleep conditions. Buysse et al. (1989) defined “sleep quality” as both quantitative aspects of sleep (such as sleep duration, sleep latency, number of arousals) and more subjective aspects (such as the “depth” or “restfulness” of sleep). Numerous studies have examined the relationships between sleep quality or subjective insomnia and other variables, including age (Doi, Minowa, Uchiyama, & Okawa, 2001; Livingston, Blizzard, & Mann, 1993; Morgan, Dallosso, Ebrahim, & Fentem, 1988), gender (Buysse et al., 1991; Doi et al., 2001), marital status (Morgan et al., 1988), living arrangements (Livingston et al., 1993; Rodin, Mcavay, & Timko, 1988) and health status (Morgan et al., 1988). Relationships between sleep quality and lifestyle behaviors, including smoking, coffee or alcohol consumption (Cheek, Shaver, & Lentz, 2004; Gislason, Reynisdottir, Kristbjarnarson, & Benediktsdottir, 1993; Hoffman, 2003), and exercise (Cheek et al., 2004; Li et al., 2004) have also been examined. Depressive tendencies also represent a factor of influence in sleep quality (Hsu, 2001; Lin et al., 2003) discussed recently.

Humans are undoubtedly social animals. The conditions of human interaction influence physiological and psychological functions. Reported negative physiologic effects of poor social support or interaction include elevated stress hormones and negative effects on the neuroendocrine system, cardiovascular activity, autonomic functions, and hypothalamic-pituitary-adrenal functions (Cacioppo et al., 2002; House, Landis, & Umberson, 1988; Seeman, 2000). The negative influences of poor social relationships on psychological well being, health status, and mental health have also been examined (Abbey, Abramis, & Caplan, 1985; Seeman, 2000) as well as personal factors associated with sleep. However, research on the relationship between social relationship factors and sleep is rare in the existing literature.

Social support and interaction have been found to play a critical role in promoting optimal aging (McReynolds & Rossen, 2004). Albert, Im, Raveis (2002) have also stated that weak social resources represent one cause of disability in older adults. Similarly, Seeman (2000) has highlighted the importance of social relationships to health and illness among older adults. Several studies have also examined older adults' perceived social support (Coventry, Gillespie, Heath, & Martin, 2004) and its relationship to healthpromoting behavior (Seeman, 2000; Suwonnaroop & Zauszniewski, 2002). However, social relationship, support, and social interaction were often used in the existing literature. Social scientists used social networks to comprehend the complexities of relationships between members of social systems (Antonucci & Akiyama, 1987; Hall & Wellman, 1985). Basically, social networks include the two major dimensions of social structure and social functioning. Social structure can be measured by describing the size, composition, proximity, density, reachability and homogeneity of social relationships. Support, ascertain, intensity and providence have typically been used to represent social functioning (Antonucci & Akiyama, 1987). The influences and significance of social networks on experiences of illness, health, quality of life, and the nature of healthcare have been reported (Hall & Wellman, 1985; Jang, Haley, Small, & Mortimer, 2002; Sapp et al., 2003).

Although the importance of sleep quality and the influence of social networks on health have been described, information on the association between social networks and sleep is limited. Cacioppo et al. (2002) reported that loneliness retarded sleep functions among college students, but the inclusion of factors such as shyness, anxiety, and hostility in the definition of “loneliness” may have somewhat obscured the influence of social network on sleep. Tamakoshi et al. (2004) also proposed that social contact influences sleep duration. Meaningful contact with other people is a basic human need (Dupertuis, Aldwin, & Bossé, 2001). However, there has been little discussion of the connection between social networks and sleep in older adults.

Nurses should be aware of the status and nature of sleep problems as part of their work to improve quality of life in elderly patients under their care. However, due to a lack of relevant professional skills, nurses often do not assess sleep conditions in detail and are, therefore, limited in their ability to help older adults establish healthy sleep patterns. The fact that knowledge and evidence-based intervention strategies are essential for proper sleep management in clinical practice cannot be ignored (Hoffman, 2003). To enrich clinical nursing knowledge and encourage effective nursing interventions among older populations, it is important to clarify the nature of sleep conditions and influencing factors. Therefore, we designed this study to describe sleep conditions and sleep quality as well as to explore the influence of social networks and personal and depression factors on sleep quality in elderly people.


Population and Sample

Older individuals (aged 65 years and above) living independently in Peitou, a suburban district in Taipei City, Taiwan, formed the target population for this study. Subjects were selected by a two-stage random sampling process. First, seven administrative subdivisions (“li”) were randomly selected from the district. Next, 40 subjects were randomly selected from each selected district. A total of 280 qualified candidates were selected. After excluding candidates who had address errors (n = 30), had moved their residence (n = 14), declined to participate (n = 21), were not at home after three visits (n = 26), or died prior to the interview (n = 2), a total of 187 older adults were registered as study subjects, giving a response rate of 66.8%. A trained research assistant (RA) visited subjects at their homes, explained the study, and invited them to participate. Once participants had consented to be interviewed and granted permission, they were interviewed by the RA using a structured questionnaire. Subjects spent 20 to 30 minutes on average answering questions.


Data were collected in a three-part questionnaire consisting of sections addressing personal, depression, and social network information as well as in the Chinese version of the Pittsburgh Sleep Quality Index (C-PSQI).

Personal information included demographic characteristics, disease history, lifestyle behaviors (alcohol, coffee or tea consumption, cigarette smoking, and exercise). With the exception of age and education level, all demographic and disease history variables were dichotomous. Questions about habitual alcohol, coffee or tea consumption, and cigarette smoking behaviors in the past 6 months had yes/no answers. “Yes” indicated that subjects drank alcohol, coffee or tea 3 or more days per week or smoked on a daily basis. “No” indicated the absence or occasional practice of such behavior. Exercise status was assessed by asking about the weekly frequency of exercise sessions longer than 30 minutes over the past 6 months. Frequency was rated on a five-point Likert scale, with answers ranging from 1 (exercise 7 times a week) to 5 (no exercise), i.e., the lower the score, the more frequent the respondent exercised.

The Chinese version of the Geriatric Depression Scale (GDS-C), which has thirty questions using yes/no answers, was used to screen participants for depression (Brink et al., 1982). High scores indicated more depressive symptoms, and subjects with scores ≥ 11 were likely to have depression (Yesavage et al., 1983). The translated Chinese version of GDS was previously developed and confirmed as having satisfactory validity and reliability (Chiu et al., 1994).

Social network, consisting of social structure and functioning, was used in this study to refer collectively to six items. In this study, family members, relatives/friends and social groups represented the principal social network resources, while subjective perceptions of relationship and support obtained from these resources represented social structure and functioning. Initially, three of the six items asked of subjects inquired about their perception of their relationships with family members, relatives/friends, and social groups. The remaining three items inquired about support received from family members, relatives/friends, and social groups. A five point scale was used to rate subjects' perceptions of social relationships. Higher scores indicated that subjects perceived better relationships and lower scores indicated poorer perceived relationships. As to social support, a five point scale was also used to describe subjects' perception of social support from family members, relatives/friends, and social groups. Higher scores indicated that subjects perceived higher support and lower scores indicated poorer perceived support. Content validity of social networks was re-recognized for this study in response to suggestions from experts. The Cronbach's α for the social network was .33 when these six items were tested. After removing relationships with social groups and support from social groups, the Cronbach's α rose to .60 and .66, respectively. Hence, four items were chosen to represented social network in this study.

C-PSQI, the Chinese version of the Pittsburgh Sleep Quality Index (PSQI), collected data about sleep conditions and sleep quality. Buysse et al. (1989) originally developed the PSQI to evaluate older persons' sleep quality and also to identify “good” or “poor” sleepers. PSQI has been adopted in previous studies to measure sleep quality (Doi et al., 2001; Hsu, 2001). The PSQI contains seven evaluative components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, sleeping medication, and daytime dysfunction. Each component is scored from 0 to 3. High scores in each component indicate poor sleep quality. The sum of seven components is calculated as the global PSQI score (with potential scores ranging from 0 to 21). Higher scores indicate lower sleep quality. The PSQI global score was used to identify “poor” sleepers (> 5) (Buysse et al., 1991). Content validity and internal consistency of C-PSQI were rechecked for this study based on expert suggestions and the attained Cronbach's α of .85.

Data Analysis

Questionnaires were used to collect data, which was entered directly into personal computers. SPSS for Window 12.0 software was used for statistical analyses. Frequency, percentage, mean, and standard deviation were used to describe participant demographic data, disease histories, lifestyle behaviors, GDS-C scores, relationships with family members, relationships with relatives/friends, support from family, support from relatives/friends. The same analysis was carried out for each component and for the total C-PSQI score.

Because existing evidence could not provide a comprehensive theoretical construct of sleep quality, stepwise multiple regression analysis was chosen to identify variables influencing C-PSQI scores and then provide information to develop a theoretical analysis of sleep quality. Personal, depression and social network variables were all included in the regression model. Education level was transferred into dummy variables before analysis. Scores for relationships with family, relationships with relatives/friends, support from family, and support from relatives/friends were changed to a 3-point scale because of the small case numbers observed in several analyses as a result of using the 5-point format.


Personal, Depression, and Social Network Factors

Approximately three-fourths of subjects were aged between 65 and 75, with a mean of 72.13 years (SD = 4.93). Over fifty percent did not attend junior high school. Most had a spouse. More than three-fourths had a chronic disease or medical condition. Most reported no habitual alcohol use, cigarette smoking or coffee/tea consumption. Over twenty percent did not exercise at all. The GDS-C score was 9.1 ± 5.75 (range: 0-25), and 29.9% were identified as having depressive symptoms (GDS-C ≥ 11).

A distinctly higher proportion of participants' relationships with family was perceived to be good (82.9%) compared to those reporting good relationships with relatives/friends (38.0%). Similar findings were also identified in terms of social support, with 68.4% of subjects frequently perceived good support from family members and one-third reporting frequently perceiving support from relatives/friends. Personal information, depression, and social network results are shown in Table 1.

Table 1:
Personal, Depression and Social Network Statistics (N = 187)

Sleep Conditions and Sleep Quality

Most subjects went to bed around 10 p.m. and arose between 5 and 6 a.m. Subjects spent 7.64 hours (SD = 1.16) in bed, and sleep duration averaged 7.09 hours (SD = 1.52 hours). These subjects took 30.57 minutes (SD = 64.31 minutes) to fall asleep (sleep latency). Our subjects also had daytime naps averaging 73.67 minutes (SD = 38.79 minutes). According to the classification developed by Buysee et al. (1989), 71.1% of subjects were “good sleepers” (C-PSQI global scores ≤ 5). Among observed components of the C-PSQI, the best sleep quality (lowest score) was observed for habitual sleep efficiency, and the worst quality was for sleep latency. Sleep quality data are shown in Table 2.

Table 2:
Sleep Quality in Community-Dwelling Older Adults (N = 187)

Factors Influencing Sleep Quality

Results of the stepwise regression model showed that four predictor variables (depression tendency, relationships with relatives/friends, college or above education, and habitual alcohol consumption) accounted for 46.1% of sleep quality variance (F = 38.91, p < .000). The prediction equation showed that sleep quality was likely better in older adults with fewer tendencies towards depression, good relationships with relatives/friends, an education at a level below the college level, and alcohol consumption (Table 3).

Table 3:
Stepwise Multiple Regression Analysis of the Chinese Version of the Pittsburgh Sleep Quality Index Global Scores Among the Elderly (N = 187)


Sleep Quality in Different Populations

The percentage of good sleepers (C-PSQI ≤ 5) in this study (71.1%) was higher than the 60.1%, 57%-66.7%, and 62.1% of healthy older adults studied in other ethnic populations studied by Buysse et al. (1991), Livingston et al. (1993), and Morgan et al. (1988), respectively. Different sampling methods (random versus convenient sampling) might have contributed to variations in results.

A study of older Chinese immigrants in Seattle, USA, found that 66.3% slept more than 7 hours every night (Hsu, 2001) and this current study also found that Chinese elderly living independently averaged 7.09 hours (SD = 1.52). The average sleep duration was slightly less in Taiwan than in the USA (Buysse et al., 1989; Buysse et al., 1991) and Iceland (Gislason, Reynisdottir, Kristbjarnarson, & Benediktsdottir, 1993). Sleep latency (30.57 ± 64.31) for our study subjects was longer than for older Americans (Buysse et al., 1991), suggesting ethnic variations in sleep characteristics. Longer daytime sleep (73.67 ± 38.79) was found in this study, indicating that duration of the daytime naps might contribute to observed ethnic variations. In oriental medicine, heat and humidity are harmful to the harmony of body, and midday naps are good for health (Hsu, 2001). Barbar and his colleagues (2000) also highlighted the influence of cultural differences on daytime napping. However, Lamarche, Driver, Wiebe, Crawford, and De Koninck (2007) reported that daytime sleepiness was not related with nocturnal sleep. Therefore, more studies are suggested to investigate the influences of duration of daytime naps on sleep quality as well as of cross-cultural populations.

Relationships Between Depression, Personal Factors, and Sleep Quality

Poor sleepers have been found to have depression tendencies (Henderson et al., 1995; Hsu, 2001). In our study, depression (GDS-C score) explained 32.7% of variances in sleep quality. We thereby propose that depression is a key variable influencing sleep quality and note that treatment for depression is necessary not only for mental health, but also for promoting sleep quality.

Excessive alcohol consumption is recognized as an unhealthy behavior, and several studies have confirmed that older drinkers are at greater risk of harm than younger adults (Stevenson, 2005; Stevenson & Masters, 2005). However, our study found that subjects who drank alcohol (8.6% of participants) were good sleepers, in contrast with previous findings (Hoffman, 2003). Stevenson and Masters (2005) recommended that health professionals pay attention to older adults who consume alcohol because it may blur sleep problems. Therefore, we believe that it is worthwhile for future studies of older populations to examine in greater depth the association between alcohol consumption variables (e.g., time, duration and amount of alcohol consumption) and sleep quality.

Although gender has been previously identified as an influencing factor on sleep quality (Doi et al., 2001; Livingston et al., 1993; Morgan et al., 1988), the results of this study did not find such, but rather was consistent with the findings of Buysse et al. (1991) and Lin et al. (2003), which found no significant association. Overall, differences in ethnicity, sampling methods, research settings and instruments might contribute to inconsistencies between our findings and those of others regarding sleep quality.

This study found that subjects who had graduated at the college level or above had poorer sleep quality, results that were consistent with those of Chen & Wang (1995), who found that older women with lower educational levels enjoyed better sleep quality. Factors associated with personal education levels, such as cognition, attitude, psychosocial status, and previous lifestyle and occupation, might have a bearing on results. In addition, the influence of sleep habits prior to retirement cannot be ignored. We believe that multi-disciplinary collaboration between health professionals, psychologists, and behavior scientists is needed to determine the mechanisms of sleep quality and explain the relationships between sleep quality and education status.

Relationships Between Social Networks and Sleep Quality

Using regression analysis, this study found that relationships with relatives/friends accounted for 5.9% of the variance in sleep quality. Our results here might relate to the importance of connections and interactions with kin in traditional Chinese culture. That “relationship with family” factors did not appear in the regression model may be explained by the high proportion of subjects who perceived their relationships with family to be good (82.9%), compared with those who perceived them as fair (11.2%) or poor (5.9%). These heavily weighted results may have interfered with the explanatory power of the regression model. The high percentage of participant family support in our study reflects the findings of Coventry et al. (2004) and Prezza and Pacilli (2002), whose studies identified family support as critical among older individuals because the support from friends tends to decline with age.

No relevant literature regarding the influence of social networks on sleep quality was found. This study nevertheless identified a significant relationship between subject social networks and sleep quality. However, the degree of influence of social networks on sleep quality cannot be definitively assessed. Because most subjects perceived relatively good relationships with family members, the discriminating role that relationships with relatives/friends play in maintaining sleep quality among subjects was explored in this study. In addition, a relationship between social networks and depression has been previously reported (Bisschop, Kriegsman, Beekman, & Deeg, 2004; Jang et al., 2002). The indirect effects of social network on sleep quality can not be ignored either. More studies are needed to ascertain mediating or moderating effects of social networks on sleep quality. Nurses are encouraged to assess actively the condition of social contacts among the elderly in their care and to assist their strengthening relationships with family members as well as with relatives/friends. Therefore, family dynamics and social support networks should be emphasized in senior services (McReynolds & Rossen, 2004; Seeman, 2000).

A major limitation of this study was the crosssectional nature of analysis. We were not able to determine a causal direction for the variables studied. The use of several dichotomous variables to represent the personal data, as well as the limited number of items (4) used to represent social networks instead of a scale was also a cause of weakness in this study. In addition, the high prevalence of good sleepers, research setting and case numbers may have limited the extent to which we can generalize findings. To extend this analysis, we believe that more communities of older people should be examined and more cases recruited across national and ethnic backgrounds.

To our knowledge, this is the first study to explore relationships between social networks and sleep quality among older adults who live independently in the community. Personal factors and depression tendencies were found to play a critical role in explaining sleep quality among older people in the community studied. We also identified an association between social network and sleep quality among this population. Supporting and improving the sleep quality of elderly patients is a critical task in nursing care. This study suggests the high importance of considering and valuing social network factors equal to depression and personal factors as important determinants of sleep quality among older people. Study findings enrich the knowledge base and, therefore, highlight specific personal and social network factors that may assist nurses to assess sleep conditions more comprehensively.


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older adult; social network; depression; sleep quality.

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