The prevalence of poor sleep quality and sleep problems varies greatly depending on the population. For example, in a study in 8 different countries, the prevalence of poor sleep quality ranged from 3.9% to over 40%.10 In a study of Norwegians using DSM-V, a prevalence of 7.1% (8.6% for women, 5.5% for men)11 was found. Higher prevalence has been reported in Canadians (13.4%).12 Given this variability, the accuracy, the precision, and the criteria used to define sleep problems are fundamental.

The reported prevalence of insomnia in Spain varies depending on the used methodology. For example, the type of questions is responsible for variations between 11.3% and 22.9%.13,14 Other data show insomnia according to DSM-IV at 6.4%, and complaints related with insomnia at 20.8%, with higher incidence in women (23.9%) than men (17.6%).15 Finally, as expected, in a clinical population, the prevalence of poor sleep quality is higher, as was found in over 90% of anxious and depressive patients.6

The higher incidence of problems and shorter sleep duration in women is constant in the literature,11,16 and has been also found in the Spanish population.17 The National Health Survey 2011-2012 found an average sleep duration that ranged from 7.03 to 7.11hours in women between 45-74 years. In men, these same averages range from 7.19 to 7.61hours.18 In a meta-analysis of sex differences in prevalence of insomnia, Zhang and Wing16 found that women are 1.41 times more likely to suffer from insomnia.

These differences could be due to hormonal changes related to menopause symptoms and associated with physical, physiological and psychological changes that may increase the incidence of problems related to sleep.19 Insomnia increases in the perimenopausal state, and slightly decreases in the postmenopausal, but remains above the premenopausal levels. Moreover, insomnia contributes to psychopathology, mainly anxiety and depression, which increase during menopause and decrease later.20,21

Aging contributes progressively to lower sleep quality in men and women.22,23 Throughout adulthood an attenuation of motivational pressure (tendency to fall asleep based on the elapsed wakefulness) is observed, leading to a lower recovery of lost sleep that affects its subjective quality. Similarly, after 40 years old people tend to go to bed earlier and wake up earlier.24

Due to the significant impact of the problems related to quality of sleep, and the influence of factors such as age and sex, it is particularly important to analyze what the actual prevalence of these problems in the population is, especially in the age groups where this impact is greater. There is also a lack of studies for Spain. Up to date data are needed in order to reveal the current situation accurately and establish the variations due to age and gender. Therefore, our objective is to study the prevalence of poor sleep quality and the characteristics of sleep quality components in a population based sample of adult subjects living in the Region of Murcia.

The sample comprised 2144 subjects born between 1939 and 1966, participants in the Murcia Twin Registry (MTR).25 The MTR is a population based registry of adult multiples in the Region of Murcia (Spain). The registry was born in 2006 based on an agreement between the University of Murcia and the Murcia Health Council. The general goal of this initiative was to constitute a basic research tool for the analysis of the relative contribution of genetic and environmental factors to the development of complex phenotypes, focusing on health and health-related behaviors. Participation in the MTR is voluntary, subjected to informed consent, and not remunerated. Information about the individuals comes from the databases available at the regional health system. People who were born on the same day and share their surnames are selected from these databases and contacted as potential twins. They are included in the MTR if they meet the inclusion criteria: pairs with both members alive at the time of incorporation, residence in the Region of Murcia, and no conditions or disability that may limit their voluntary participation. The MTR development strategy has followed a step-by-step process and data collection has been done in different sub-samples (female-female, male-male, opposite-sex pairs) and moments, and using alternative methods (face-to-face or telephone interview) depending on the availability of resources. Global cooperation rate across data collection waves and sub-samples is 72.5%. More detailed information about recruitment procedures and data collection is provided elsewhere.25,26 The MTR sample has been shown to be comparable regarding main health conditions to reference representative samples of the general population in the region and Spain, accounting for the generalizability of results.27

The MTR data collection and analytical procedures have been approved by the Committee of Research Ethics of the University of Murcia and meet the legal requirements of confidentiality and personal data protection. Since the objective of this work is not the analysis of the relative contribution of genetic and environmental factors on the studied phenotypes, it does not impact on the zygosity data of twins and no genetic analyses are carried out.

The study data were collected from the three sub-cohorts (female pairs, male and opposite-sex) providing data at different times between 2009 and 2011 through face-to-face or telephone interviews. Mean age of the subjects with available data for this study was 53.7 years (SD: 7.4; range: 43-71) at the time of data collection and 54.7% of the sample were women. Data collection and administration of the questionnaires was carried out at all times by trained personnel.

Subjects answered to an inventory developed specifically for the MTR that incorporates socio-demographic health-related information, together with several standard scales (e.g., zygosity questionnaire and Pittsburgh Sleep Quality Questionnaire). This inventory is based on the National Health Survey questionnaire,18 and questions are the same regardless of administration method. For this study we have selected from the demographic datasets (age and sex), menopausal status, and sleep quality information. Menopausal status was assessed through self-report on whether it had already begun or had passed, and in case of a hit, if it had started naturally or by other causes (disease/drug therapy/surgery) and the age of onset.

The subjective quality of sleep was assessed by a self-report questionnaire through the Pittsburgh Sleep Quality Index,28,29 which provides seven partial scores and an overall score on sleep characteristics referring to the previous month. The overall score is known as the Pittsburgh Sleep Quality Index in (PSQI). The questionnaire has proven reliable and valid30 and it is validated in its Spanish version.29 The seven partial scores are: 1) sleep quality, 2) sleep latency, 3) sleep duration, 4) habitual sleep efficiency (percentage of time that the patient believes to be asleep of the total time lying down), 5) sleep disturbance (frequency of disturbances such as snoring or toileting), 6) use of sleeping medication, and 7) daytime dysfunction (such as daytime sleepiness or fatigue). Subjects with a score >5 on a general scale can be classified as having poor sleep quality.28,29

Dependent variables were the overall score in the PSQI scores and the different subscales that compose it. The dependent variables were categorized in a binary fashion. The global PSQI Index was dichotomized as good (PSQI ≤5) or poor (PSQI >5) sleep quality according to the standard criteria for this scale.28,29 All subscales were also dichotomized. For quantitative scales the cut-off points were defined, according to the scoring system of the PSQI, depending on whether the score on the sub-scale added 0 or ≥1 points to the global index (1 or 2 points in the case of disturbances) (duration: ≥7hours vs. <7hours; disturbances: 0-9 vs. 10 or more; latency ≤15minutes vs. >15minutes; efficiency ≥85% vs. <85%). Discrete variables were categorized as yes/no or good/bad answers [daytime dysfunction (yes/no); subjective quality (good or fairly good/bad or very bad; use of sleeping medication (yes/no)].

To assess the association between age (quantitative) and gender with respect to PSQI and its various subscales logistic regression analyses were performed. Bivariate analyses were also performed to analyze the effect of age and sex together. The analysis of subjects from the same family produces dependence on data, which is why regression analyses were performed by adjusting the estimates of standard error. This method provides robust statistical estimates for the characteristics of the model. Analyses were performed using the statistical package STATA12. The level of statistical significance was set at α=0.05 with a confidence interval (CI) of 95%.