Risk Factors for Developing Comorbid Sleeping Problems: Results of a Survey of 1,925 Women Over 50 With a Chronic Health Condition

Abstract

Objective: To test the association between sleeping problems and multiple epidemiological factors among women over 50 with a chronic condition. Method: The Medical Outcomes Study Sleep Scale (MOS-SS) was employed to measure sleep problems among 1,925 participants with chronic conditions who also responded to questions about health service use, self-care and demographics. Results: About 43% of women reported sleeping problems. Women were more likely to have a sleeping problem if they reported some difficulties with available income, odds ratio (OR) = 1.61; 95% confidence interval (CI): [1.27, 2.04]; p < .005, or were struggling with available income (OR = 2.84; 95% CI: [2.04, 3.96]; p < .005). Women were less likely to have sleeping problems if they were highly physically active (OR = .63; 95% CI: [0.51, 0.79]; p < .005). Discussion: Medical professionals should be aware of the significant risk of sleeping problems among mid-age and older women with chronic health conditions, particularly those who have financial concerns, are sedentary, or are not highly physically active.

Keywords

chronic conditions physical activity gerontology sleeping problems women’s health

Introduction

Sleeping problems—a set of conditions signified by symptoms which include difficulty initiating and maintaining sleep and poor quality sleep (Ohayon, 2002)—present a significant societal and economic burden internationally (Cunnington, Junge, & Fernando, 2013; Daley, Morin, LeBlanc, Grégoire, & Savard, 2009; Léger & Bayon, 2010; Sarsour, Kalsekar, Swindle, Foley, & Walsh, 2011). Estimates of the prevalence of sleeping problems vary widely (6%-56%), nonetheless sleeping problems are consistently reported to be high in most international studies among the general adult population (Bin, Marshall, & Glozier, 2012; Léger & Bayon, 2010; Léger, Poursain, Neubauer, & Uchiyama, 2008; Stranges, Tigbe, Gómez-Olivé, Thorogood, & Kandala, 2012). A recent review of international literature found sleeping problems were associated with workplace absenteeism, automobile accidents, decreased productivity, decreased quality of life, and increased health service use (Léger & Bayon, 2010), demonstrating significant health impacts for those affected.

Sleeping problems are also frequently associated with comorbid chronic health conditions (Budhiraja, Roth, Hudgel, Budhiraja, & Drake, 2011; Taylor et al., 2007) particularly among older adults (Foley, Ancoli-Israel, Britz, & Walsh, 2004; Koyanagi et al., 2014). Evidence suggests that the socioeconomic costs of conditions that frequently manifest concomitantly with sleeping problems such as depression (Lépine & Briley, 2011), anxiety (Hoffman, Dukes, & Wittchen, 2008), diabetes (Hex, Bartlett, Wright, Taylor, & Varley, 2012), and obesity are also increasingly significant (Wang, McPherson, Marsh, Gortmaker, & Brown, 2011). The degree to which comorbid chronic conditions such as depression and anxiety contribute to sleeping problems and vice versa is difficult to decipher but a recent systematic review suggests a bidirectional relationship (Alvaro, Roberts, & Harris, 2013). Some studies have investigated characteristics of adults with sleeping problems but the impact of concurrent chronic health conditions on the results has been uncertain, with no indication of which participants with sleeping problems also have a major chronic condition.

Despite the subsequent rising societal and economic pressures relating to sleeping problems, a clear picture of whom sleeping problems affect and why remains elusive (Ohayon, 2002, 2008). Both North American and broader international research suggest being female and older are two of the main predictors of sleeping problems (Byles, Mishra, & Harris, 2005; Morin, LeBlanc, Daley, Gregoire, & Merette, 2006). As Leger et al. show in their 2010 review of societal costs of insomnia (Léger & Bayon, 2010), studies tend to conclude that sleeping problems rise exponentially with age. However, many studies have looked at age groups of over 65 and other studies have investigated women under 45 but data on characteristics for women with sleeping problems for the older mid-age group of 50 to 65, is scarcely reported (Meredith, Frawley, Adams, & Sibbritt, 2017). This is pertinent as recent studies examining sleeping problems among mid-age and older women, including women in their 50s, have reported that being aged 50 to 59 was strongly associated with sleeping problems (Morin et al., 2006; Simpson, Allegra, Ezeamama, Elkins, & Miles, 2014).

Because being female (Byles et al., 2005; Morin et al., 2006), the existence of chronic conditions (Smagula, Stone, Fabio, & Cauley, 2015), and being over 50 (Morin et al., 2006) are three of the main risk factors for sleeping problems, other factors that may contribute to sleeping problems for older women with a chronic condition are not discussed at length in the literature. This is despite the World Health Organization (WHO) warning that issues such as reduced levels of exercise and socioeconomic struggle are amplified hazards to health for certain populations such as older women and people with chronic disability or illnesses (Marmot, Allen, Bell, Bloomer, & Goldblatt, 2012) and despite being associated with sleeping problems (Arber, Bote, & Meadows, 2009; de Castro Toledo Guimaraes, de Carvalho, Yanaguibashi, & do Prado, 2008; Paine, Gander, Harris, & Reid, 2004). This article, therefore, aims to examine risk factors for sleeping problems in women over 50 with a prominent chronic health condition (Hoy, 2016) to better address underlying causes for sleeping problems among mid-age and older women.

Method

Sample

The study sample was acquired via the 45 and Up Study (Banks et al., 2008). The study is the largest ongoing study of healthy aging in the Southern Hemisphere with baseline data collected from 267,153 women and men. The study assesses various aspects of health and quality of life of adults over the age of 45 in the state of New South Wales (NSW), Australia.

Prospective participants were randomly sampled from the Department of Human Services (formerly Medicare Australia) enrolment database, which provides near complete coverage of the Australian population. Recruitment took place between February 2006 and April 2009 and 18% of those invited participated, representing 11% of the NSW population aged 45 years and over. Participants gave signed consent for follow-up and linkage of their information to routine health databases. A more detailed methodology has been published elsewhere (Banks et al., 2008).

The research reported here is based on a substudy survey of 1,925 women who had identified in the baseline 45 and Up Study survey as having one or more of the following chronic conditions: diabetes (n = 392, 20%), asthma (n = 375, 20%), osteoarthritis (n = 404, 21%), osteoporosis (n = 393, 20%), or depression (n = 361, 19%). These five conditions were originally chosen to represent common chronic health conditions among Australian women over 45 and to provide a broad spread of chronic health conditions that includes respiratory, cardiovascular, musculoskeletal, and psychological conditions. These five chronic health conditions are included by the Australian Government as areas of national public health priority (Caughey et al., 2010; Hoy, 2016). Such priority conditions are thought to cost 42% of Australia’s designated health expenses alone and account for 60% of Australia’s burden of injury and disease (Caughey et al., 2010). On an international stage, the prevalence of such chronic conditions is rising rapidly in the developing world in tandem with aging populations (Briggs et al., 2016; Caughey et al., 2010; Shaw, Sicree, & Zimmet, 2010). The WHO has emphasized that musculoskeletal conditions such as osteoarthritis and osteoporosis—known to affect older women disproportionately—are global threats to healthy aging (Briggs et al., 2016). In addition, depression (Beekman et al., 2002), asthma (Gibson, McDonald, & Marks, 2010), and diabetes (Caughey et al., 2010) have been shown to have a substantial impact on the social and economic well-being of older adults.

Women who answered affirmatively to the question “has a doctor ever told you that you have . . .” with regard to at least one of the five aforementioned chronic conditions were invited to participate in the substudy. Data collection took place between August and November 2016. Participants in the substudy were then asked if they had been diagnosed or treated by a doctor for diabetes, asthma, osteoarthritis, osteoporosis, or depression in the previous 12 months. This article examines health service use, self-care, and demographic factors associated with sleeping problems among older women with chronic health conditions.

Measure of Health Status

A validated sleep scale (Smith & Wegener, 2003; Viala-Danten, Martin, Guillemin, & Hays, 2008; Zagalaz-Anula, Hita-Contreras, Martínez-Amat, Cruz-Díaz, & Lomas-Vega, 2017), the Medical Outcomes Study Sleep Scale (MOS-SS) Sleep Problems Index II, was employed to determine the presence of sleep problems (Spritzer & Hays, 2003; Ware & Sherbourne, 1992). The MOS-SS Sleep Problems Index II has been shown to have good psychometric properties among the general population and populations with chronic conditions (Allen, Kosinski, Hill-Zabala, & Calloway, 2009; Cappelleri et al., 2009; Hays, Martin, Sesti, & Spritzer, 2005). For example, the reliability and validity of the MOS-SS Sleep Problems Index II has been tested in a large nationally representative sample of American adults (Hays et al., 2005) and was found to have acceptable reliability (Hays et al., 2005). Where Cronbach’s alpha coefficient recommends internal reliability scores be over .70 for scales to be deemed sufficiently reliable (Cronbach, 1951), the Sleep Problems Index II internal reliability estimates were α = .83 in the general population (Hays et al., 2005).

The MOS-SS, including the long form Sleep Problems Index II, has shown good psychometric properties among studies of mid-age and older women in previous large studies (Zagalaz-Anula et al., 2017). The MOS-SS has also been validated in studies of large cohorts of older adults (Haut, Katz, Masur, & Lipton, 2009; Zimmerman, Bigal, Katz, Derby, & Lipton, 2013), mid-age and older women (Kline et al., 2012; Zagalaz-Anula et al., 2017), and international populations with chronic conditions (Coyne et al., 2013; Leonavicius, 2015; Schaefer et al., 2016; Schofield & Khan, 2014; Smith & Wegener, 2003; Viala-Danten et al., 2008; Wolfe, Michaud, & Li, 2006; Zimmerman et al., 2013) including in Australia (Viala-Danten et al., 2008) and specifically among populations with conditions with include arthritis, depression, diabetes, and asthma (Smith & Wegener, 2003).

The questions from the MOS-SS Sleep Problems Index II were, how often during the past 4 weeks did you: feel that your sleep was not quiet, get enough sleep to feel rested upon waking in the morning, awaken short of breath or with a headache, feel drowsy or sleepy during the day, have trouble falling asleep, awaken during your sleep time and have trouble falling asleep again, have trouble staying awake during the day, get the amount of sleep you needed, and, how long did it usually take for you to fall asleep during the last 4 weeks? As the scale uses a continuous variable, a cut-off score of higher than 33.33 was used to indicate sleeping problems (Zagalaz-Anula et al., 2017). This score has been shown to be successful in distinguishing between good and poor sleepers and yields a robust correlation with the Pittsburgh Sleep Quality Index total score (Zagalaz-Anula et al., 2017).

Participant Demographic Characteristics

Women in this study were asked about the following characteristics: age, area of residence, marital status, education attainment, income, private health insurance, physical activity (PA), Body Mass Index (BMI), smoking, and alcohol consumption.

Use of Health Services and Self-Care

Women were asked whether they used prescription medications and/or consulted a conventional health practitioner such as a general practitioner (GP), medical specialist, hospital doctor, nurse, pharmacist, counselor, psychologist, dietician, physiotherapist, or occupational therapist for their chronic health condition(s). Participants were also asked about their use of self-care products and practices such as aromatherapy, herbal medicines, homeopathic medicines, meditation (without instructor), yoga (without instructor), physical activities/exercises, multivitamins, or supplements specifically for the treatment of their chronic condition.

Statistical Analysis

Initially, bivariate analysis testing the association between sleeping problems and health service use, self-care and demographic characteristics of women, were undertaken using chi-square analysis. All variables identified as having a bivariate association (p < .30; Hosmer, Lemeshow, & Sturdivant, 2013) with sleeping problems were then analyzed using logistic regression modeling. These variables were entered into the logistic regression model and a stepwise backward elimination process was then used to find the most significant predictors of sleeping problems. The model was adjusted for depression due to the multiple ways that depression and depression medications lessen sleep quality (Wichniak, Wierzbicka, & Jernajczyk, 2013). Due to the large sample size, a p < .005 was used for removal of variables from the model. Statistical analyses were conducted using statistical software program STATA 14.1 (StataCorp LP, College Station, TX, USA).

Results

Of the 1,925 women participating in this 45 and Up Study substudy, 43% (n = 835) indicated they had a sleeping problem (Table 1). Of those with a sleeping problem, 14% indicated that they had depression, 11%. diabetes, 19% osteoarthritis, 12% asthma, and 10%, osteoporosis. The majority of women were either married or living in de facto relationships (61%) with 31% widowed, divorced, or separated, and 8% single. A total of 51% of participants lived in either inner regional or outer regional Australia, 48% of participants lived in a major city, and just 1% lived remotely. Private health insurance was held by 69% of the women. Some participants reported financial difficulties with 11% of women reporting that they struggled on their current income and 22% reported some difficulties. A total of 67% of women claimed to have no or little difficulty getting by on their income. Furthermore, 29% of the cohort was university educated, 30% had attained a trade/apprenticeship or diploma, 33% left school with intermediate or higher/leaving certificates, and 8% indicated they had received no school certificate or other qualification.

Table 1.

Characteristics of Older Women With Sleeping Problems.

Characteristic	Total	Sleeping problems?		p value
	Total	No	Yes
	n = 1,925	n = 1,090 (57%)	n = 835 (43%)
	%	%	%
Use self-care (for chronic condition?)
Yes	47	48	46	.435
No	53	52	54	.435
Use of prescription meds (for condition)
Yes	80	79	80	.464
No	20	21	20	.464
Consult with health practitioner (conventional)
Yes	69	66	73	<.0001
No	31	34	27	<.0001
Income:
Struggles	11	6	17	<.001
Some difficulties	22	19	26
No or little difficulties	67	75	57
Private health insurance
Yes	69	73	63	<.0001
No	31	27	37
Education
No formal school certificate	8	6	10
School certificate only	33	32	34
Trade/apprenticeship/diploma	30	29	32	<.0001
University/higher degree or higher	29	33	25
Marital status
Single	8	7	9
Married	56	58	54
De facto	5	4	5	.035
Widowed	17	18	16
Divorced	12	11	13
Separated	2	2	3
Physical activity category
Sedentary (<150 min. per week)	31	27	37
Moderately active (150-300 min. per week)	17	15	17	<.0001
Highly active (>300 min. per week)	53	58	46
Smoking
Have never smoked	87	88	84
Not smoking but have	8	7	9	.080
Sometimes	1	1	1
Regularly	5	4	6
Alcohol drinking risk
No alcohol	43	39	47
Low risk	51	55	47	.006
Risky	5	5	5
High risk	1	1	1
Age group
50-59	17	13	20
60-69	39	40	39	<.0001
70-79	30	31	28
80-89	12	14	11
90+	2	2	2
BMI group
Underweight	2	1	2
Normal weight	31	35	27	<.0001
Overweight	30	32	27
Obese	37	32	44
Area
Major cities	48	48	49
Inner regional Australia	40	40	39	.442
Outer regional Australia	11	11	11
Remote	1	1	1

Note. BMI = Body Mass Index.

Table 1 shows the distribution of women across various demographic and health characteristics, and sleeping problems. With regard to health-seeking characteristics, women with sleeping problems were more likely to consult with a conventional health care provider for their chronic health condition than women without sleeping problems (73% vs. 66%; p<.001). Demographically, women with sleeping problems were more likely than those without sleeping problems to struggle or have some difficulties with their available income (43% vs. 25%), be physically sedentary (37% vs. 27%), be aged 50 to 59 (20% vs. 13%), and to be obese (44% vs. 32%, according to the WHO definition of a BMI of > 30; all p < .001). Women with sleeping problems were less likely than women without sleeping problems to have private health insurance (63% vs. 73%) or to be educated to degree level (25% vs. 33%; all p < .001).

The result of the logistic regression modeling is presented in Table 2. The statistically significant factors associated with sleeping problems were income and PA. Specifically, women with sleeping problems were more likely than women without sleeping problems to have some difficulties with available income, odds ratio (OR) = 1.61; 95% confidence interval (CI): [1.27, 2.04]; p<.005, or to be struggling with available income (OR = 2.84; 95% CI: [2.04, 3.96]; p < .005). Women were less likely to have sleeping problems if they were highly active (completing over 300 min of moderate PA per week; OR = .63; 95% CI: [0.51, 0.79]; p < .005).

Table 2.

Characteristics of Older Women With Sleeping Problems.

Characteristic	Odds ratio	95% confidence interval	p value
Income
No difficulties	1.00
Some difficulties	1.61	[1.27, 2.04]	<.0001
Struggles with income	2.84	[2.04, 3.96]	<.0001
Physical activity category
Inactive/sedentary (<150 min)	1.00
Moderately active (150-300 min)	0.88	[0.66, 1.19]	.415
Highly active (>300 min)	0.63	[0.51, 0.79]	<.0001

Discussion

This study found a significant number of older women with chronic health conditions have sleeping problems, which concurs with previous research (Byles et al., 2005; Leigh, Hudson, & Byles, 2015; Meredith et al., 2017). This article reports analysis from one of the few studies to explore the characteristics associated with comorbid sleeping problems in mid-age and older women living with chronic health conditions such as depression, osteoporosis, osteoarthritis, diabetes, and asthma. Our research identified two main findings: mid-age and older women living with a chronic health condition who also have a comorbid sleep problem are less likely to be financially secure, and lower levels of intense PA are associated with sleeping problems in women with chronic health conditions. These findings are explored in more detail below.

Sleeping Problems and Income

A recent international systematic review of prospective studies for risk factors for sleeping problems found only one article out of 21 reported a significant predictor of sleeping problems in older adults to be lower income (Smagula et al., 2015). However, unlike our study, none of this previous research investigated women with chronic conditions specifically, thus the most commonly reported risk factors for sleeping problems reported according to the 2015 review were chronic mental and physical conditions and being female. Our study, using a large population exclusively of women over 50 living with chronic health conditions, found women with sleeping problems were significantly more likely to struggle with their available income. This is unsurprising in the context of international literature which finds lower socioeconomic status to be associated with an increased likelihood of developing many chronic health conditions (Arber et al., 2009; Marmot et al., 2012), leading to a complex interplay between income, health, and sleep. Research from a large international cross-sectional study (n = 27,103) across 23 European countries found that, compared to adults with no worries about income, respondents who had income worries had a 7% increase in disturbed sleep from “working age” (41-65) to “retirement age” (66 years and over; Dregan & Armstrong, 2011). There is also an increased risk for income worries among women: the WHO has reported gender (being female) and lower economic status as significant social determinants of health requiring urgent attention to improve public health and economic wealth, internationally (Marmot et al., 2012).

The highlighting of reduced financial security as a significant risk factor in this study may also be partly due to study methodology and perception: while other studies have focused on socioeconomic status patterning (Arber et al., 2009), participants in our study indicated their perceived ability to cope on their current income. It is possible that a subjective concern of “struggling” on income—found to be a risk factor for sleeping problems in our study—may result in worry. As worry has been linked to sleeping problems (Brosschot, Van Dijk, & Thayer, 2007; McGowan, Behar, & Luhmann, 2016), further research investigating the impact of worry about finances for sleep is warranted. Furthermore, given that the high prevalence of sleeping problems among older women with chronic conditions found in this research is also in accordance with international literature which finds being female is a risk factor for sleeping problems (Byles et al., 2005; Morin et al., 2006), disparities in socioeconomic circumstances between men and women and related stress may be targeted by policy makers to help alleviate sleeping problems.

Public health researchers and organizations may find it fruitful to further investigate the relationship between economic comfort and quality of sleep for mid-age and older women. Enquiry by GPs and other health professionals into the general concerns and worries of individuals beyond their immediate chronic health condition(s) could help highlight stressors that may be contributing to sleeping problems and/or the chronic condition(s) themselves.

Sleeping Problems and PA

Current evidence supports a modest improvement in sleeping problems for adults by increasing daily PA (Dobrosielski, Patil, Schwartz, Bandeen-Roche, & Stewart, 2015; Kredlow, Capozzoli, Hearon, Calkins, & Otto, 2015; Reid et al., 2010; Simpson et al., 2014; Yang, Ho, Chen, & Chien, 2012) but research is limited regarding correlations between comorbid sleeping problems and PA specifically in older women with chronic conditions.

Our research found that older women with chronic health conditions and comorbid sleeping problems were more likely to be sedentary and less likely to be highly active than women without sleeping problems. It is notable that, after logistic regression modeling, there is no significant correlation between moderate PA and an absence of sleeping problems among mid-age and older women. While both American and Australian PA guidelines recommend over 150 min of moderate PA or more for adults up to 64 years of age—and for those over 65 where possible—a week (U.S. Department of Health; Australian Department of Health), there is little research available on the health consequences of increased levels of PA (over 300 min of moderate PA per week). More research is warranted to investigate whether higher levels of activity (over 300 min per week) reduce the risk of sleeping problems for mid-age and older women with chronic health conditions. In addition, although women with sleeping problems were more likely to be obese than women without sleeping problems in our study, after logistic regression modeling, this was not a significant risk factor for sleeping problems while PA levels were. As such, an emphasis by educators and physicians on PA rather than specifically focusing on BMI or weight loss, may be constructive for mid-age and older women.

As our research shows that older women with chronic conditions have a reduced risk for sleeping problems if they are highly active and because both aging and the presence of chronic conditions may encumber an ability to engage in PA—particularly vigorous activity—further research and planning is required to facilitate feasible opportunities for older women with chronic health conditions and comorbid sleeping problems, to engage in PA. This may help improve sleep and prevent or alleviate chronic conditions for mid-age and older women. Furthermore, appropriate activities may need to be tailored to individuals depending on their specific health condition(s). For example, while many studies show improvements in sleep quality for older people with various forms of arthritis engaged in increased PA (Durcan, Wilson, & Cunnane, 2014; McManus, Visker, & Cox, 2015), there are also limitations and dangers to some forms of PA imposed on individuals by the chronic condition itself such as the danger of vigorous PA for lumbar zygapophyseal (facet) joint osteoarthritis (Suri et al., 2015).

Engagement in a patient-centered approach—which involves a collaborative approach to treatment and respectful and open communication—may be helpful (Epstein & Street, 2011) to ascertain PA preferences. Understanding patients PA preferences, in conjunction with a medical professional’s understanding of limitations or dangers of PA related to individual chronic conditions, may help inform a realistic treatment plan for older women with chronic conditions and comorbid sleeping problems. This approach to communication between medical professionals and patients with regard to sleeping problems is relevant in light of evidence which suggests adults with sleeping problems are often reluctant to follow their medical professional’s advice for fear it may result in the prescription of inappropriate long-term treatments such as benzodiazepines (Venn & Arber, 2012). Encouragingly, much of the literature also suggests women with sleeping problems are motivated to use self-care strategies (Henry, Rosenthal, Dedrick, & Taylor, 2013; Homsey & O’Connell, 2012; Morin et al., 2006; Sánchez-Ortuño, Bélanger, Ivers, LeBlanc, & Morin, 2009), such as PA (Henry et al., 2013; Suen, Ellis Hon, & Tam, 2008), to treat sleeping problems and chronic health conditions more generally (Mun et al., 2016). In accordance with our findings that being sedentary is a risk factor for sleeping problems among older women with chronic conditions, medical professionals may find working with mid-age and older female patients to determine appropriate PA routines may help either alleviate or prevent sleeping problems.

It should also be noted that while the results of our study show that being inactive and sedentary is a risk factor for sleeping problems among older women with chronic health conditions, the link between vigorous PA and an absence of sleeping problems does not necessarily mean that vigorous PA will prevent sleeping problems. It is possible that older women struggling with sleeping problems are less likely to engage in vigorous activity due to lack of energy or tiredness or physical limitations. Nonetheless, given research suggests better overall health outcomes, including improved sleep, are associated with higher levels of activity (Freburger, Callahan, Shreffler, & Mielenz, 2010; Kredlow et al., 2015), older women with chronic conditions should be supported to achieve national PA guidelines to improve health and to alleviate sleeping problems.

Limitations

There are limitations to our study that require consideration. First, our results are based on self-report and so there remains potential for recall bias. Second, the usefulness of information on health service, prescription medicine, and self-care use was limited as the 45 and Up Study substudy questioned participants relating to their health service use for one of five specified chronic conditions, rather than sleeping problems. Third, the cut-off score from the MOS-SS Sleep Problems Index II used to identify sleeping problems and utilized in this research was based on a study of healthy older women which may not accurately reflect sleeping problems in women with chronic health conditions. Fourth, as this is a cross-sectional study, we are unable to make statements regarding causation. Notwithstanding these limitations, our study provides an excellent opportunity to present findings about risk factors for sleeping problems using a large number of older adults spanning a breadth of ages of older women with chronic conditions.

Conclusion

Public health experts may wish to investigate and address the interrelation of the predictors for sleeping problems in women with chronic conditions highlighted in this study—that is, struggling on income and not participating in vigorous PA—to help reduce the potential impediments to good sleep for both mid-age and older women. Medical professionals may be able to further aid patients with chronic health conditions by being aware of the elevated risk of sleeping problems for certain women over 50. Levels of PA and socioeconomic concerns may be discussed with patients to determine potential causes for sleeping problems before deciding successful long-term treatment options. A patient-centered approach may also help to overcome complex barriers to vigorous PA for older women with chronic health conditions and comorbid sleep problems. Public health experts and government health officials may wish to further examine the link between the financial resources of older women with chronic health conditions and sleeping problems to help reduce impediments to sleep.

Supplemental Material

Outline_of_revisions_Risk_factors_for_developing_co-morbid_sleeping_problems – Supplemental material for Risk Factors for Developing Comorbid Sleeping Problems: Results of a Survey of 1,925 Women Over 50 With a Chronic Health Condition

Supplemental material, Outline_of_revisions_Risk_factors_for_developing_co-morbid_sleeping_problems for Risk Factors for Developing Comorbid Sleeping Problems: Results of a Survey of 1,925 Women Over 50 With a Chronic Health Condition by Sophie Meredith, Jane Frawley, Jon Adams and David Sibbritt in Journal of Aging and Health

Footnotes

Acknowledgements

We thank the many thousands of people participating in the 45 and Up Study. We also thank the Australian Research Council (ARC) for the Discovery Grant that has funded this project (ARC DP140100238), and supported the work of Distinguished Professor Jon Adams for this manuscript through an ARC Professorial Future Fellowship (FT140100195) and an ARC-funded project nested PhD scholarship for the lead author of this research, Sophie L. Meredith—whose research is also supported by an Australian Government Research Training Program Scholarship.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is based on a substudy of the 45 and Up Study survey () developed by researchers at the University of Technology, Sydney (UTS). The 45 and Up Study is managed by the Sax Institute in collaboration with major partner Cancer Council NSW, and partners: the National Heart Foundation of Australia (NSW Division); NSW Ministry of Health; NSW Government Family & Community Services—Aging, Carers and the Disability Council NSW; and the Australian Red Cross Blood Service.

Ethical Approval

The conduct of the 45 and Up Study was approved by the University of New South Wales Human Research Ethics Committee (HREC). Ethics approval for this present 45 and Up Study substudy was gained from the relevant institutional review board at the University of Technology, Sydney (approval number ETH16-0470).

ORCID iD

Sophie Meredith

Supplemental Material

Supplemental material for this article is available online.

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