The Relationship Between Insomnia and Health-Related Quality of Life in Patients With Chronic Illness

Measures of sleep

Insomnia was defined as the complaint of initiating or maintaining sleep (CIMS) using a 6-point categorical scale, with responses ranging from “all of the time” to “never.”²⁷ Mild insomnia was defined by responses to 2 of the MOS sleep items indicating difficulty in initiating or maintaining sleep over the prior 4 weeks “some” or “a good bit” of the time; severe insomnia was defined by difficulty “most” or “all” of the time. By inquiring about sleep over the prior 4 weeks, the 2-item measure of DSM-III captures key elements of the DSM-IV definition²⁸ and is similar to definitions used in other epidemiologic investigations of insomnia.^29,30

The measure provides a simple tool for the identification of chronic insomnia in busy primary care practice. In addition, the validity of this 2-item categorical measure is supported by excellent concordance between this measure and a continuous 5-item measure of insomnia (details available on request from the author). The 2-item measure, it is important to note, does not include the DSM-IV item pertaining to impairment of daytime functioning, as this item is closely related to the dependent variables of interest in this study.

Measures of potential confounders

We used an indicator of poverty by dichotomizing per capita household income (in 1985 dollars) at a cut-off point of 200% of the poverty level. Alcohol and smoking status were assessed with a 3-point scale: no history of use, past user, or current user. Frequency of exercise was assessed with the question, “How often do you exercise?” on a 6-point Likert-type scale, with responses ranging from “daily or almost daily” to “almost never or never.” Subjects were overweight if body mass index (BMI) exceeded 25 and obese if BMI exceeded 30.^31,32

We identified 16 common medical conditions comorbid to the index conditions by using data from the MOS standardized health examination. Data on medications were excluded because they do not reflect contemporary patterns of medication use and because of collinearity between medication and comorbidity variables already in the model.

Statistical analysis

We used multiple linear regression to identify the association between insomnia and HRQOL. We adjusted for sociodemographic characteristics, health habits, index conditions, severity of index conditions,¹⁹ a count of the 16 medical comorbidities,³⁴ and study location. To account for the potentially nonlinear relationship between age and HRQOL, we included 3 dummy variables for age: 40 to 55 years, 56 to 65 years, and older than 65 years (age younger than 40 years was the holdout category). Similarly, we included dummy variables for education (less than 12 years or exactly 12 years; more than 12 years was the holdout category) and exercise (at least 4 times a week or less than once a week; 1 to 3 times a week was the holdout category).

We report the average deviation in HRQOL values for mild and severe insomnia and for 2 comparison conditions: clinical depression and congestive heart failure (CHF). These conditions were selected because they are representative of conditions with predominant effects on physical functioning (CHF) and mental health (depression).^17,24,25 The average deviation in HRQOL is represented by the regression coefficients corresponding to the terms for these conditions in each HRQOL model.¹⁷ Because all patients in the current study had at least 1 of the 5 physician-identified conditions, we used the subgroup of patients with mild hypertension¹⁹ and without insomnia as the reference group.

We also performed a subset analysis of 2197 patients who had completed a screening version of the DIS for anxiety disorders (generalized anxiety disorder, phobia, or panic disorder) at the baseline health evaluation. In this patient subset, we constructed a series of regression models and examined the change in average deviation in HRQOL associated with insomnia with the addition of groups of covariates (sociodemographics, health habits, medical conditions, depression, and anxiety) to a base model including only insomnia and study location variables.

We also examined whether our results were robust by using logistic regression. Because most of the dependent variables are highly skewed (and thus may not satisfy the distributional assumptions of linear regression), we dichotomized each dependent variable as categorical (lowest tertile versus middle and upper tertiles) and determined the odds ratios for mild and severe insomnia associated with the lowest tertile of each HRQOL measure. Because we assessed the significance of both mild and severe insomnia in 8 different HRQOL domains, we used the Bonferroni correction to adjust for multiple comparisons (only P values ≥.003 were considered statistically significant).

Finally, we checked for selected 2-way interaction terms to determine whether the association between insomnia and HRQOL differed significantly by age, gender, race, education, and burden of comorbidity.^28,35,36