Adaptation of a 3-factor model for the Pittsburgh Sleep Quality Index in Portuguese older adults
Introduction
Currently, sleep problems constitute a global epidemic that threatens the health and quality of life of approximately 45% of the world's population (Wade et al., 2008, WASM, 2016). Sleep deprivation and poor sleep quality have a high negative impact on health in the short and long term. Poor sleep quality has a negative impact on attention, memory and learning (WASM, 2016). It has also been associated with several serious health problems such as obesity, diabetes, and some cancers (Gottlieb et al., 2005, Gümüştekín et al., 2004, Taheri et al., 2004, WASM, 2016). In addition, many psychological disorders such as depression, anxiety and psychosis are also associated with sleep difficulties (Beusterien et al., 1999, WASM, 2016, Zammit et al., 1999).
Although the majority of sleep disorders are easily prevented or treated, fewer than one-third of those affected seeks professional assistance (WASM, 2016). However, sleep is a basic need of all people, just like eating and drinking, being crucial to ensure good health and quality of life (WASM, 2016). In a comprehensive epidemiological studies, it was found that more than 50% of older adults have insomnia complaints (Foley et al., 1995, Neikrug and Ancoli-Israel, 2010), and sleep improvement was associated with health improvement (Foley et al., 1999, Neikrug and Ancoli-Israel, 2010). However, other studies have also shown that the rates of sleep disorders are lower in healthy older adults (Neikrug and Ancoli-Israel, 2010, Vitiello et al., 2002). So, what changes over the lifespan is not an intrinsic ability to sleep well, but comorbidities related to aging, and not necessarily caused by aging itself (Neikrug and Ancoli-Israel, 2010).
Thus, the ability to identify any difficulties in sleep as soon as possible is essential for the screening of other important comorbidities to act in maintaining good quality of life and well-being of older people. The sleep assessment instrument most commonly used in clinical and research environments is the Pittsburg Sleep Quality Index - PSQI (Mollayeva et al., 2016). It is a self-assessment questionnaire with 19 items that measures sleep disorders through seven components that together make up a Sleep Quality score (Buysse et al., 1989). Several studies have examined the one-dimensionality of the PSQI and raised concerns about the factorial structure of the instrument (Mollayeva et al., 2016). Through a systematic review and meta-analysis it was found that eight out of eleven studies that factor analyzed the PSQI reported that a single factor model poorly fit the resulting data, and the PSQI is best represented by a model with two or three factors (Mollayeva et al., 2016).
Relatedly, analysis of the instrument using a Portuguese sample (João et al., 2017) found poor reliability (Cronbach's alpha). As demonstrated by Mollayeva et al. (2016), most studies using factor analysis achieved better results with a model with two or three factors. We understand that it is necessary to adapt a three factor model for the PSQI as, previously, reported by Cole et al. (2006), which will give an upgrade in our sample.
Section snippets
Pittsburgh Sleep Quality Index
The PSQI assesses sleep quality over a one-month period. The questionnaire consists of 19 self-rated questions and five (5) questions that are to be answered by bedmates or roommates. These last five questions are used only for clinical information and, therefore, they are not tabulated in the scoring or reported in this article. The 19 self-rated questions are grouped into seven (7) components, with each one scored on a scale that ranges from 0 to 3 (see more detail in the original study,
Results
The PSQI-PT global score ranged from 0 to 18 with a mean of 5.98 (SD±3.45). The sociodemographic characteristics are in Table 1. The distribution of the global sleep quality (GSQ) scores is the same for the categories of sociodemographic variables (Table 1), except for gender and self-assessed healthiness (“Do you consider yourself a healthy person?”). The regression analysis showed that gender (β=0.195, t=2.72, p=0.004) and self-assessed healthiness (β=0.257, t=3.85, p<0.001) significantly
Discussion
The present study examined the factor structure of the PSQI in a sample of older Portuguese adults using a cross-validation approach. Consistent with findings reported by Mollayeva et al. (2016), the current study found that the single-factor model is not the optimal factor structure for the PSQI. However, one must understand that the original scale author chooses to use the global PSQI score because it demonstrated acceptable internal consistency in diverse populations and clinical settings,
Conclusion
This research it is proposed the adaptation of 3-factor model for the PSQI in Portuguese older adults. The Cronbach's α was improved without “use of sleep medications” component. Therefore, the adaptation of the model is similar to the original model proposed by Cole et al. (2006), with the only change being to use the component "use of medications to sleep" as a complementary qualitative assessment of health rather than including it in the model. This will allow a more specific evaluation of
Acknowledgements
This study was supported by the Foundation for Science and Technology – Portugal (CIEO – Research Centre for Spatial and Organizational Dynamics, University of Algarve, Portugal). N.B. Becker received a doctoral fellowship from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). Process BEX 1990/15-2.
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