Can Sleep Loss be Used As a Health Indicator Based on Women's Wellness Decisions?

BACKGROUND:  Sleep loss is an important determinant of health status owing to its relationships with biological processes such as molecular, immune, and neural changes which are important etiological mechanisms for the development of cardiovascular and metabolic diseases, and increased risk of accidents. Sleep loss can have serious health consequences in women including adverse effects on physical and mental health, and severity of chronic disease. It is estimated that 1 in 3 Americans 16 years or older are sleeping less than 7 hours per night. Sleep loss is associated with increased healthcare cost and greater mortality due to its profound correlations with elevated chronic disease.

METHODS:  The study focused on health/sleep related questions captured by the Behavioral Risk Factor Surveillance System to determine if there were significant epidemiological patterns of reported sleep variations in women and if these variations in sleep can be related to a woman’s nutrition, physical activity level, body mass index (BMI), and her access to healthcare. The study design was a retrospective, quantitative, epidemiological study designed to identify indicators for women’s positive and negative wellness decisions that could impact sleep quality. Statistical measures included Pearson’s Chi-square, Mantel-Haenszel Chi-square, and multiple regression.

RESULTS:  Data analysis revealed sufficient evidence to reject the null hypothesis and accept the alternative hypothesis in which the prevalence of perceived insufficient rest or sleep in women is different in women self-reporting her health status (χ2 (1, N=8,556) = 158.452, p<0.000), fruit/vegetable intake (χ2 (1, N=4835) = 16.217, p<0.000), physical activity (χ2 (1, N=8,556) = 5.584, p<0.019), body mass index (χ2 (1, N=8,409) = 12.768, p<0.000), and access to healthcare (χ2 (1, N=8,556) = 101.269, p<0.000). It also showed that the co-variants do have a significant effect on the perceived reporting of sleep by adult women and when combined in logistic regression models can strengthen the prediction of perceived rest in adult women (chi square = 279.491,  p< 0.000). The results also revealed that fruit (χ2 (1, N=8,496) = 2.300, p<0.129) and vegetable (χ2 (1, N=8,531) = 6.888, p<0.005) intake looked at as separate independent variables showed different outcomes of the data and that fruit intake alone is not significant to predicting sleep quality.

CONCLUSIONS:  The study identified possible risk factors from the BRFSS cross-sectional survey that relate to sleep loss in women. It revealed that behavioral practices and access to health services do influence sleep quality in women.