Sleep is fundamental to human health and well-being. Middle-aged especially perimenopausal women show a higher tendency to exhibit sleeping problems than those in other age groups (Clark, Flowers, Boots, & Shettar, 1995; Soares, 2005). Hsu and Lin (2005) found that 42.1% of 197 perimenopausal women in the community had poor sleep quality. The top two symptoms of perimenopausal problems, as identified by traditional Chinese medicine clinics, were sleep pattern disturbances and difficulty falling asleep (Tsao, Su, & Chiu, 2002). Middle-aged women with poor sleep quality often suffer from psychological distress, physical and mental disorders, and postmenopausal symptoms (Shaver & Zenk, 2000).
Fatigue was another common symptom among perimenopausal women (Chen, Ho, & Chien, 2000; Lee & Wang, 2000). Fatigue is an inherent and a subjective feeling of tiredness. Fatigue affects one's physiological, psychological, and emotional status. Cahill (1999) found that women have a higher prevalence of fatigue than men, which may be explained by the greater number of life events faced by women in comparison with men. Sung (2006) indicated that perimenopausal women typically experience mild to moderate levels of fatigue and frequently suffer from insomnia (Sung, 2006).
A positive correlation between level of fatigue and sleep quality was identified among cancer patients in at least two studies (Miaskowski & Lee, 1999; Morriss, 1993). However, previous perimenopausal studies addressed sleep problems and related factors only and did not include fatigue (Hsu & Lin, 2005; Tsao, Chou, et al., 2002). A few previous studies investigated the relationship between the two of most common perimenopausal complaints, sleep disturbance and fatigue. It is crucial to investigate both sleep and fatigue for perimenopausal women. Therefore, the purpose of this study was to explore sleep quality and fatigue and their related factors in perimenopausal women.
Study Design and Samples
This was a cross-sectional study that used a purposive sampling method to recruit women from gynecologic clinics and a menopausal support group at a regional teaching hospital in northern Taiwan. A group of healthy perimenopausal women aged between 42 and 58 years were recruited. Women with any of the following were excluded from the study: mental disorders, surgically induced menopause by hysterectomy or oophorectomy, and chronic diseases (e.g., cancer, diabetes mellitus, heart disease, or sleep apnea syndrome).
Tools used in this study included a demographic questionnaire, the Perimenopausal Disturbance Scale, the Perimenopausal Fatigue Scale, the Hospital Anxiety and Depression Scale, and the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI).
Demographic Data Questionnaire
The demographic questionnaire contained information on age, marital status, education level, employment status, religious affiliation, physical condition (menopausal status, long-term drug use, hormone or nutritional supplement intake, and history of chronic diseases), and lifestyle (participation in social activities, regular exercise, alcohol/tea/coffee consumption, nap habits).
Perimenopausal Disturbance Scale
Perceived perimenopausal disturbances were evaluated by measuring an individual's subjective perimenopausal symptoms using a scale developed by Tsao (2002), Tsao, Chou, et al. (2002), and Tsao, Su, et al. (2002), which included 39 items scored according to a 4-point scale. Thirty-eight of the items addressed the five main categories of measured symptoms, including vasomotor, muscular-skeletal, urologic, reproductive, and psychological symptoms. A higher score indicated greater discomfort from perimenopausal symptoms and a stronger corresponding impact on daily life. Cronbach's α was .90 for the current study.
Perimenopausal Fatigue Scale
This questionnaire was modified from Sung's (2006) Perimenopausal Fatigue Scale, which consisted of 16 items scored on an 11-point Likert scale. Questions 13 to 16 were open-ended questions. Respondents were asked to describe (a) the perimenopausal symptom with the greatest impact on fatigue, (b) their feelings of fatigue, (c) their approaches toward relieving fatigue, and (d) the consequences of fatigue. Cronbach's α value for the scale used in this study was .90.
Hospital Anxiety and Depression Scale
We adopted Zigmond and Snaith's (1983) Hospital Anxiety and Depression Scale. This is a self-assessment scale that uses two subscales to detect levels of anxiety and depression. The scale features 14 questions and a 4-point response scale corresponding to each question. A total score lower than 8 indicates no anxiety or depression problems. A score between 8 and 10 represents borderline/potential problems. A total score of 11 or more identifies a definite case of anxiety and/or depression problems. Cronbach's α for anxiety and depression subscales were .88 and .90, respectively.
Chinese Version of the Pittsburgh Sleep Quality Index
We used the translated (Chinese) version of the Pittsburgh Sleep Quality Index of Tsai et al. (2005) originally developed by Buysse, Reynolds III, Monk, Berman, and Kupfer (1989). This translated version, known as the CPSQI, addressed seven measurement indicators including subject sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. A score lower than 5 indicates good sleep quality, and higher scores (greater than 5) correspond to progressively worsening sleep quality (Tsai et al., 2005). The Cronbach's α value for this study was .80.
Procedures and Ethics
This study was approved by a research ethics committee (institutional review board) of a regional teaching hospital. Informed consent was obtained from each participant before her enrollment. Study participants were informed that participation was wholly voluntary and that they could withdraw at any time. Of the total 91 returned questionnaires, 6 incomplete questionnaires were discarded, leaving 85 replies as valid and giving a valid response rate of 93.4%. Using PASS 6.0 to calculate sample power (with α set to .05, N set at 85, and sample power was 0.8), the sample size and the interpretability were determined as appropriate.
Study variables were described for sleep quality, fatigue, and related factors. Data were analyzed using the Statistical Package for the Social Sciences for Windows (Version 15.0; SPSS Inc., Chicago, IL). To describe the distribution of demographic data, sleep quality, fatigue, perimenopausal disturbance, and the Hospital Anxiety and Depression Scale, we used descriptive statistics expressed in terms of mean, standard deviation, and range. We tested the differences between sleep quality and demographic data using a t test or a one-way ANOVA and the Scheffe's method. We tested correlation among sleep quality, fatigue, and related factors using Pearson's product-moment correlation. Stepwise multiple regression was used to identify predictors of sleep quality, and significance level p value was set at .05.
Participant ages ranged between 45 and 58 years, with an average of 52.73 ± 3.65 years. The largest segment of participants were married (including cohabitation) (78.8%), having at least a high school degree (35.3%), stating occupation as "housewife" (49.4%), having religious beliefs (85.9%), in postmenopausal status (64.7%), not taking Chinese or Western medicine (60%), not taking hormone therapy (64.7%), not taking nutritional supplements (e.g., Soy Bean Isoflavones, etc.) (76.5%), free of chronic diseases (80%), exercising regularly (50.6%), holding membership in perimenopausal support groups (65.9%), not consuming alcohol on a regular basis (80%), not consuming coffee on a regular basis (47.1%), not consuming tea on a regular basis (37.6%), and not in the habit of taking naps (52.9%) (Table 1).
The mean CPSQI score was 7.71 ± 4.66, and 62.4% of women were identified as having poor sleep quality (CPSQI > 5). Nearly one fifth (17.7%) of participants required more than 30 minutes to fall asleep. One third (32.9%) reported less than once a week of being incapable of falling asleep within 30 minutes during the past month and an average sleep duration of 6.1 ± 1.25 hours. The highest rating for habitual sleep efficiency was "more than 85%," which described 60% of participants. Almost one third (28.2%) of participants reported having used sleep-assistance drugs. Nearly half (44.7%) complained of daytime dysfunction such as an inability to clear their mind for daily work. Another 63.5% indicated suffering from sleep disturbance at least one night per week. The top three events that disturbed sleep quality, in order of importance, were "getting up to use the bathroom," "waking up in the middle of the night or early morning," and "coughing or snoring loudly" (Table 2).
Distribution of Perimenopausal Fatigue, Perimenopausal Disturbance, Anxiety, and Depression
Findings showed a mean score of perimenopausal fatigue of 3.02 ± 2.41, indicating a mild level of fatigue. The mean score of extreme fatigue for the most recent 2-week period was 4.09 ± 3.08, which is close to a moderate level. The mean score for self-reported perimenopausal symptoms was between 0.05 and 2.5, and the mean score was 0.89 ± 0.55. This indicates that the level of suffering from perimenopausal symptoms was mostly mild to moderate. With regard to anxiety and depression, people without anxiety or depression accounted for 62.4% to 75.3% of the entire sample. However, 15.3% of the women indicated having anxiety problems and 9.4% reported having depression (Table 1).
Differences in Demographic Variables, Anxiety, and Depression Among Quality of Sleep
With regard to sleep quality, results showed significant differences in long-term drug use, hormone intake, nutritional supplement intake, menopausal status, suffering from anxiety, and suffering from depression (t = 5.43, p < .01; t = −3.15, p < .01; t = −3.33, p < .001; F = 4.33, p < .05; F = 20.20 and 12.73, p < .001; Table 1).
Relationships Between Quality of Sleep, Perimenopausal Fatigue, and Perimenopausal Disturbance
The current results show that perimenopausal fatigue (r = .63, p < .01) and perimenopausal disturbances (r = .61, p < .01) correlated significantly and positively with quality of sleep (Table 3). On the basis of the results of one-way ANOVA, t test, and Pearson's correlation test, long-term drug use, hormone intake, intake of nutritional supplements, menopausal status, perimenopausal disturbance, perimenopausal fatigue, anxiety, and depression were examined to identify sleep quality predictors. Using a stepwise multiple regression model, analysis found significant predictors for sleep quality to be perimenopausal fatigue and depression. Together, these variables explained 43% of sleep quality variance (Table 4).
The mean CPSQI score for participants was 7.71 ± 4.66, and 62.4% of the participants were identified as having poor sleep quality. This study showed a higher prevalence of poor sleep quality than found by Hsu and Lin (2005). The reason may be the different settings in which participants were recruited (urban versus rural). In this study, 41.2% felt dissatisfied with their personal sleep quality, a finding consistent with prior studies (Kronenberg, 1990; Shin et al., 2005) that demonstrated that an individual's feelings are likely to influence general sleep quality.
In all, 95.3% reported encountering sleep disturbances several nights per week, a finding similar to Lei (2003). "Getting up to use the bathroom" was the most frequent reason for sleep disturbance. Frequent night urination resulted in awakening and adverse impacts on sleep quality, a finding consistent with previous studies (Asplund & Aberg, 2005; Hsu & Lin, 2005; Jones & Czajkowski, 2000). In the research by Chen, Dai, and Huang (2007) on nocturia, the most common complaint among study participants was "lack of enough sleep at night," indicating that less frequent trips to the bathroom should correlate with better sleep quality. Therefore, controlling nocturia is an important issue that lends itself to further investigation.
This study showed that a significant difference in quality of sleep among participants with the following characteristics: long-term drug use, hormonal or nutritional supplement intake, and menopausal status. This is a finding that is also consistent with prior studies (Kronenberg, 1990; Shin et al., 2005). Such may reflect the side effects of prescription medications taken by the participants. Therefore, assessing the type and the side effects of medications that subjects are taking is recommended.
Participants experienced mild to moderate levels of fatigue: 15.3% reported anxiety and 9.4% reported depression. This finding was consistent with previous studies (Lu, Tseng, Lin, Luh, & Shu, 2009; Sung, 2006). "Perimenopausal fatigue" and "depression" together explained 43% of sleep quality variance. This finding indicates that individuals with higher scores on the Perimenopausal Fatigue Scale and who suffer from depression are more likely to experience poor sleep quality. Therefore, healthcare providers should emphasize fatigue evaluation work on perimenopausal women with sleep problems.
Eighty-five women aged between 45 and 58 years were recruited from a regional teaching hospital's clinic. Results showed "fatigue" and "depression" to be significantly associated with sleep quality and explained 43% of sleep quality variance. Assessing the sleep quality of perimenopausal women who complain of fatigue and depression is a priority. In addition, health providers should integrate factors (e.g., fatigue, long-term drug, hormone and nutritional supplements use, menopausal status, anxiety, and/or depression) in assessments of sleep quality in perimenopausal women. As this study targeted a hospital population, it may be subject to regional limitations. It is recommended that further discussion and understanding focus on other/more inclusive areas. Also, this study was a cross-sectional study and used questionnaires, which may have been subject to bias from respondents when answering questions. Reliability and validity may be increased if future research studies include research methods using more objective approaches, such as polysomnography.
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