Mental health, substance use, and a composite of sleep health in adults, 2018 Ohio behavioral risk factor surveillance system
Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, USA
Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, USA
Abstract
Objectives:
Various factors impact sleep health including mental health and substance use. Mental health issues and substance use continue to rise in the United States. Yet, the association between mental health, substance use and sleep health in adults remains unclear.
Methods:
We used multivariable linear regression models to examine the associations between mental health (poor mental health days in the past 30 days) and substance use (marijuana, tobacco, alcohol) with sleep health (individual dimensions of sleep: alertness, sleep efficiency, duration, and sleep health composite score) in 4333 participants from the 2018 Ohio Behavioral Risk Factor Surveillance System Survey.
Results:
Better mental health was associated with higher alertness, higher sleep efficiency, longer sleep duration and a higher sleep health composite score even after controlling for covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) (all p < .001). Higher marijuana and tobacco use were associated with lower individual sleep health dimensions (marijuana with sleep efficiency and duration and tobacco use with lower efficiency) and a lower sleep health composite score even after controlling for covariates for tobacco use (p < .001). Contrary to the hypothesis, higher alcohol use was associated with higher alertness and a higher sleep health composite score (p < .001), however after adjusting for covariates these associations were no longer significant.
Conclusions:
The implications of these trends on sleep health are important to address as mental health and substance use are modifiable targets to consider when addressing sleep health.
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Keywords: Sleep health, Sleep dimensions, Mental health, Substance use
Article notes
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Issue date 2024 Dec.
1.Introduction
Sleep health is an underrecognized and widespread public health issue [1]. Sleep health varies across individuals and can be characterized across multiple dimensions including, regularity, satisfaction, alertness, timing, efficiency, and duration (known by the acronym RU-SATED) [2–4]. Regularity refers to the consistency of an individual’s sleep-wake timing [5]. Satisfaction is a self-assessment of sleep; alertness, the ability to maintain attentive daytime wakefulness; timing, the placement of sleep during a 24-h time period; efficiency, the ease of falling and maintaining sleep; and duration, the duration of sleep time per 24 h [3]. Lower achievement of individual sleep health, in particular shorter sleep duration and lower sleep efficiency, are linked to increased morbidity and mortality among adults [3,6]. Short sleep duration is defined by attaining less than the National Sleep Foundation recommended 7–9 h of sleep per night [7]. The age adjusted prevalence of short sleep duration among adults in the United States ranges by state from 29.3 % in Colorado to 42.8 % in West Virginia and is 38.2 % in Ohio [8]. Many factors can influence one’s sleep health including the interaction between societal, social, and individual level factors [6]. Guided by the Social Ecological Model of Sleep Health which describes how sleep health is impacted by constructs at the societal, social, and individual levels [6], we examined the relationship between mental health (individual level), substance use (i.e., marijuana, tobacco, and alcohol) (individual level), and sleep health using the 2018 Behavioral Risk Factor Surveillance System (BRFSS) Ohio data.
In the United States, $280 billion has been expended to address mental health and substance use disorders among a growing number of individuals affected by one or both conditions resulting in an escalating need for services and a significant burden on society [9]. Nearly 1 in 3 adults had either any mental illness (AMI) defined as a mental, behavioral, or emotional disorder or a substance use disorder in the past year, with the highest prevalence occurring in young adults aged 18–25 years (46 % of all adults) according to the National Survey on Drug Use and Health [10]. The state of Ohio had the largest increase in AMI among adults from 2021 to 2022 (2.24 %) compared to any other state [11]. Sleep and mental health have a bidirectional relationship [12]. For instance, mental distress at night is associated with an inability to fall and stay asleep [13], and on the other hand inadequate quantity or quality of sleep leads to an imbalance between emotional regulation and mood hormones (i.e., dopamine, serotonin) [14]. Several modifiable risks underlie the mechanisms between sleep health and internalized problems such as stress arousal and emotion-processing [15]. Substance use is associated with lower overall sleep health (lower satisfaction, lower alertness, less stable timing, lower efficiency, and shorter duration) [16,17]. Therefore, examining the role of mental health or substance use in sleep health can inform future longitudinal studies and cognitive behavioral therapy interventions focused on improving sleep behavior.
The purpose of this study was to explore the relationship between mental health (i.e., number of poor mental health days in the past 30 days), substance use (i.e., marijuana, tobacco, and alcohol), and sleep health using the 2018 BRFSS Ohio data. The Ohio data serves as an appropriate proxy for the United States of America as the AMI data reflects national trends and the prevalence of short sleep is in the middle range compared to other states [8,18]. Socioeconomic deprivation measured by the social deprivation index was tabulated from zip code tabulation area (ZCTA) level data from Ohio residents from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) survey. Each state owns the zip code tabulation area (ZCTA) level data and access to zip code requires a data use agreement with the BRFSS state coordinator. We specifically examined if mental health and substance use were independently associated with individual sleep health dimensions (i.e., alertness, efficiency, and duration). We hypothesized better mental health (i.e., fewer poor mental health days in the past 30 days) and lower substance use were associated with better sleep health.
2.Participants and methods
2.1.Data and study participants
The 2018 BRFSS Ohio data were used for this analysis and this data is representative of the 14 geographic regions in Ohio. The 2018 BRFSS dataset was chosen due to the availability of questions tapping into multiple dimensions of sleep health, the datasets after 2018 contained limited sleep questions. The total 2018 BRFSS Ohio sample was 12,763. The BRFSS supported by the Centers for Disease Control and Prevention (CDC) through technical and methodological assistance is a cross-sectional, ongoing, random digit dialed telephone survey with oral consent conducted by states in the United States regarding chronic conditions, health-related risk behaviors and use of preventive services from individuals aged 18 years and older. Ohio BRFSS data are weighted to known proportions of age race, ethnicity, gender, and geographic region in Ohio to ensure it is representative of the Ohio adult population. Data are aggregated for each state and weighted by the CDC. Data were collected by a random sample design. Case Western Reserve University approved the study (IRB #STUDY20221217).
2.2.Measures
Sample demographics:
Sample characteristics were obtained from the 2018 BRFSS dataset. Sample characteristics included marital status, employment status, income, as well as marijuana, tobacco, and alcohol use.
Covariates:
Individual covariates adjusted for included: sex at birth, age, race, body mass index (BMI), education level (never attended or only kindergarten, elementary (grades 1–8), some high school (grades 9–11), high school graduate, some college (1–3 years), college graduate (4 or more years), as well as sleep disordered breathing and the area-level covariate socioeconomic deprivation. Covariates were selected based on a priori knowledge and the extant literature [6]. Sleep disordered breathing was defined as self-report derived observed cessation of breathing during one’s sleep. Socioeconomic deprivation was assessed by the social deprivation index (SDI), an area-level composite measure of seven socioeconomic characteristics determinants including: demographics, education (percent of individuals with less than 12 years of education), income (percent of individuals living in poverty), employment (percent non-employed adults under 65 years of age), transportation (percent of households without a car), housing (percent living in rented housing and percent living in overcrowded housing) and household characteristics (percent single parent households with dependents <18 years old) at the zip code tabulation area (ZCTA) level [19]. As severity of socioeconomic deprivation increases, the SDI score increases [19].
Derivation of the Sleep Health Composite:
We based the derivation of the Sleep Health Composite on several considerations. The following dimensions of sleep health based on available BRFSS data were analyzed: alertness, efficiency, and duration. We converted the original (raw) score for each dimension into a standard score (z-score) and summed the scores to create the sleep health composite. This approach allowed for each dimension to receive equal weight in the composite and higher sleep health composite scores indicated better sleep health.
2.3.Sleep health dimensions
Alertness was derived from a self-reported single-item measure. Participants were asked “Over the last 2 weeks, how many days did you unintentionally fall asleep during the day?”. Responses ranged from 0 to 14 days. We reverse coded the alertness variable to facilitate interpretation [4,20].
Sleep efficiency is the percentage of time spent asleep in bed. As sleep efficiency was derived for this study from a self-reported single-item measure, it is important to note that this measure is an indicator of sleep continuity and sleep efficiency can be a component of sleep continuity. Participants were asked “Over the last 2 weeks, how many days have you had trouble falling asleep or staying asleep or sleeping too much?”. Responses ranged from 0 to 14 days. We reverse coded the sleep efficiency variable to facilitate interpretation [4,20].
Sleep duration was derived from a self-reported single-item measure. Participants were asked “On average, how many hours of sleep do you get in a 24-h period?”. Responses ranged from 1 to 24 h. We used hours in the linear regression models with sleep duration as an outcome.
Mental health was derived from a self-reported single-item measure. Participants were asked “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”. For the linear regression models, we used poor mental health days from 0 to 30. This approach for analyzing poor mental health has been used in previous research [21,22].
Substance use:
Marijuana, tobacco, and alcohol use were obtained from a single-item measure per substance. For marijuana, participants were asked “During the past 30 days, on how many days did you use marijuana or cannabis?”. We used days of marijuana use in the linear regression models [23]. For tobacco use participants were asked “Over your lifetime, how many years have you smoked tobacco products?”. We used years of tobacco use in the linear regression models. For alcohol use participants were asked “During the past 30 days, how many days per week or per month did you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage or liquor?”. Responses with days per week were converted to days per month. We used days per month of alcohol use in the linear regression models [24,25].
2.4.Statistical analyses
Statistical analyses were performed in IBM SPSS Statistics (Version 28). Demographic and characteristics of the participants were summarized as sample size and percentages or mean and standard deviations. We examined the bivariate relationships between mental health (i.e., poor mental health days in the past 30 days) and substance use (i.e., marijuana, tobacco, and alcohol) with sleep health dimensions (i.e., alertness, efficiency, duration, and the sleep health composite score). We used days in the linear regression models for alertness and sleep efficiency. Implausible values for sleep duration (1< or >18 h) were excluded [26]. Upon removal of implausible values, the significance was not altered.
In the first set of models, the goal was to evaluate the explanatory contributions of mental health to sleep health as well as the contributions of substance use to sleep health. A series of multivariable linear models were conducted to examine the relationships between: 1) mental health and sleep health (individual dimensions and composite); along with 2) substance use (marijuana, tobacco, and alcohol) and sleep health (individual dimensions and composite). Statistical significance was set at p < .05.
3.Results
A total of 4333 participants completed the telephone surveys including questions regarding sleep. The mean age of the participants was 54.9 (±17.4) years of age, 81.9 % were White (n = 3548), and 55.5 % were female (n = 2400). Mean sleep duration in this population was 6.91 (±1.6) hours. Mean number of days in the past 14 days that participant’s had issues with sleep efficiency was 3.37 (±5.1) days. Participants reported unintentionally falling asleep during the day over the past 14 days on average 1.15 (±2.9) days. Mean number of days in the past 30 days that participant’s experienced poor mental health was 13.33 (±11.1) days. Use of marijuana or cannabis in the past 30 days was 1.09 (±5.2), tobacco use (years smoked over a lifetime) was 10.31 (±14.9), and alcohol use (days per month with at least one drink of alcohol) was 16.74 (±35.6). Demographic characteristics of the sample are presented in Table 1.
| Variable | N | Number (%) or Mean (SD)* |
|---|---|---|
| Age (years) | 4333 | 54.9 (17.4) * |
| Sex at Birth | 4325^ | |
| Female | 2400 (55.5) | |
| Male | 1925 (45.5) | |
| Race/Ethnicity | 4333 | |
| White | 3548 (81.9) | |
| African American/Black | 498 (11.5) | |
| Asian | 28 (.6) | |
| American Indian/Alaskan Native | 47 (1.1) | |
| Other | 132 (3.0) | |
| Hispanic | 80 (1.8) | |
| Marital Status | 4316^ | |
| Married | 2176 (50.1) | |
| Divorced/Widowed/Separated/Never Married/Unmarried | 2140 (49.9) | |
| Education Level | 4333 | |
| Never Attended or Only Kindergarten | 1 (0) | |
| Elementary (Grades 1–8) | 109 (2.5) | |
| Some Highschool (Grades 9–11) | 315 (7.3) | |
| High School Graduate | 1349 (31.1) | |
| Some College (1–3 years) | 1134 (26.2) | |
| College Graduate (4 or more years) | 1413 (32.6) | |
| Employment | 4301^ | |
| Employed | 2030 (46.8) | |
| Out of work | 162 (3.8) | |
| Homemaker/Student/Unable to work | 913 (21.1) | |
| Retired | 592 (27.6) | |
| Annual Household Income | 3697^ | |
| Less than $10,000 | 237 (5.5) | |
| Less than $25,000 | 976 (22.6) | |
| Less than $50,000 | 945 (21.8) | |
| Less than $75,000 | 532 (12.5) | |
| $75,000 or more | 1007 (23.2) | |
| Marijuana or Cannabis Use in the Past 30 days | 4302^ | 1.09 (5.2) * |
| Reason for Use | 286^ | |
| Medical | 81 (28.3) | |
| Non-medical | 114 (39.9) | |
| Both | 91 (31.8) | |
| Alcoholic Beverage in Past 30 days | 4288^ | 16.74 (35.6) * |
| Tobacco Use (years smoked tobacco) | 4138^ | 10.31 (14.9) * |
| Sleep Health Composite Score | 4314^ | 1.84 (.97)* |
| Sleep Duration (average hours of sleep, 24-h period) | 4246^ | 6.91 (1.6) * |
| Sleep Efficiency (in past 14 days) (falling, staying, too much) | 4240^ | 3.37 (5.1) * |
| Alertness (unintentionally fall asleep during the day in past 14 days) | 4236^ | 1.15 (2.9) * |
| Snore Loudly | 4301^ | 1935 (44.7) |
| Stop Breathing During Sleep | 4297^ | 781 (18.0) |
4.Sleep and mental health
We examined the unadjusted and adjusted associations between mental health and sleep health dimensions (i.e., alertness, efficiency, duration, and the sleep health composite score) through linear regression models (Table 2). The unadjusted association between mental health with each sleep dimension (i.e., alertness, efficiency, and duration) and the sleep health composite score were statistically significant (all p < .001). More days of self-reported poor mental health is associated with lower alertness and sleep efficiency, shorter sleep duration, and a lower sleep health composite score. The associations between mental health and sleep health dimensions (i.e., alertness, efficiency, duration, and the sleep health composite score) remained statistically significant after adjusting for covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) (all p < .001).
| Model | Independent Variable/s | Dependent Variable/s | B | SE | β | R2 | P value |
|---|---|---|---|---|---|---|---|
| Model 1 (unadjusted) | Mental Health | Alertness | .047 | .008 | .159 | .025 | <.001 |
| Efficiency | .184 | .012 | .361 | .131 | <.001 | ||
| Duration | .039 | .005 | .218 | .047 | <.001 | ||
| Composite | .060 | .004 | .327 | .107 | <.001 | ||
| Model 2 (adjusted) | Mental Health | Alertness | .034 | .008 | .115 | .058 | <.001 |
| Efficiency | .170 | .013 | .333 | .143 | <.001 | ||
| Duration | .031 | .005 | .169 | .071 | <.001 | ||
| Composite | .050 | .005 | .269 | .148 | <.001 |
5.Sleep health and substance use
We examined the unadjusted and adjusted associations between marijuana use and sleep health dimensions (i.e., alertness, efficiency, duration, and the sleep health composite score) through linear regression models (Table 3). The unadjusted association between marijuana use and sleep efficiency, duration, and the composite sleep dimension score was statistically significant (all p < .05), except for alertness (p = .764). Lower marijuana use was associated with higher sleep efficiency, longer sleep duration and a higher sleep health composite score (p < .001). The associations between marijuana use and sleep efficiency and duration remained statistically significant after adjusting for covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) (all p < .05). The association between marijuana use and the sleep health composite score was no longer significant after adjusting for covariates.
| Model | Independent Variable/s | Dependent Variable/s | B | SE | β | R2 | P value |
|---|---|---|---|---|---|---|---|
| Model 1 (unadjusted) | Marijuana | Alertness | .003 | .009 | .005 | .000 | .764 |
| Efficiency | .081 | .015 | .084 | .007 | <.001 | ||
| Duration | −.019 | .005 | −.060 | .004 | <.001 | ||
| Composite | −.011 | .006 | −.031 | .001 | .042 | ||
| Model 2 (adjusted) | Marijuana | Alertness | .000 | .009 | .000 | .046 | .982 |
| Efficiency | .083 | .015 | .088 | .057 | <.001 | ||
| Duration | −.015 | .005 | −.051 | .012 | .002 | ||
| Composite | −.008 | .062 | −.022 | .029 | .176 | ||
| Model 1 (unadjusted) | Tobacco | Alertness | .018 | .003 | .095 | .009 | <.001 |
| Efficiency | .041 | .005 | .121 | .015 | <.001 | ||
| Duration | −.002 | .002 | −.022 | .000 | .163 | ||
| Composite | −.018 | .002 | −.149 | .011 | <.001 | ||
| Model 2 (adjusted) | Tobacco | Alertness | .012 | .003 | .061 | .049 | <.001 |
| Efficiency | .032 | .006 | .095 | .060 | <.001 | ||
| Duration | −.002 | .002 | −.015 | .011 | .373 | ||
| Composite | −.011 | .002 | −.097 | .077 | <.001 | ||
| Model 1 (unadjusted) | Alcohol Use | Alertness | −.004 | .001 | −.053 | .003 | <.001 |
| Efficiency | −.004 | .002 | −.028 | .001 | .071 | ||
| Duration | .000 | .001 | −.006 | .000 | .680 | ||
| Composite | .003 | .001 | .051 | .003 | <.001 | ||
| Model 2 (adjusted) | Alcohol Use | Alertness | −.002 | .001 | −.026 | .047 | .100 |
| Efficiency | .001 | .002 | .004 | .051 | .734 | ||
| Duration | −.001 | .001 | −.018 | .010 | .266 | ||
| Composite | .002 | .001 | .015 | .068 | .338 | ||
Next, we examined the unadjusted and adjusted associations between tobacco use and sleep health dimensions (i.e., alertness, efficiency, duration, and the sleep health composite score) through linear regression models (Table 3). The unadjusted association between tobacco use with alertness, sleep efficiency, and the sleep health composite sleep score was statistically significant (all p < .001), but not with duration (p = .163). Lower tobacco use was associated with higher alertness and sleep efficiency as well as a higher sleep health composite score (p < .001). The associations between tobacco use and alertness, sleep efficiency, and the sleep health composite score remained statistically significant after adjusting for covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) (all p < .001).
Last, we examined the unadjusted and adjusted associations between alcohol use and sleep health dimensions (i.e., alertness, efficiency, duration, and the sleep health composite score) through linear regression models (Table 3). The unadjusted association between alcohol use with alertness and the sleep health composite sleep score was statistically significant (all p < .001), but not with sleep efficiency (p = .071) nor duration (p = .680). Lower alcohol use was associated with lower alertness and sleep health composite score (p < .001). The associations between alcohol use with alertness and the sleep health composite score were no longer statistically significant after adjusting for covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) (all p > .005).
6.Discussion
In a large representative sample of Ohio residents, we investigated concurrent and individual associations among mental health and substance use with sleep health. We found that fewer reported days of poor mental health and lower substance use were associated with better sleep health across sleep dimensions (individual sleep health dimensions and a composite measure of sleep health dimensions). This is the first study to our knowledge where the contributions of mental health and substance use were analyzed in relation to a composite measure of sleep health in a large sample of individuals living in Ohio.
In support of our hypothesis, better perceived mental health in the past 30 days was concurrently associated with better sleep health (individual dimensions of higher alertness and sleep efficiency, longer duration, and a higher sleep health composite score) even after considering covariates (individual: sex at birth, age, body mass index, race, education, sleep disordered breathing, and area-level: socioeconomic deprivation) in the current study. Our findings regarding the concurrent and individual relationship between mental health and sleep health are consistent with the literature. For instance, better emotional health was concurrently associated with higher sleep health composite in a sample of 176 adolescents (mean age 14.77 years) [4]. In a National Health and Nutrition Examination Survey (NHANES) study of 25,962 Americans (mean age = 48.1 years), those with short sleep duration (<7 h) and long sleep duration (>9 h) had higher odds of depression symptoms [27]. Further, Dickinson et al. [28] found daytime sleepiness (decreased alertness) mediated the relationship between short sleep and depression as well as anxiety risk in 2218 young adult university students [28].
The link between individual dimensions of sleep efficiency and sleep duration with depression symptoms is also consistent with other studies [29–31]. For instance, lower sleep efficiency was associated with a higher incidence of depression symptoms in the Sleep Heart Health Study of 3375 men and women [31]. The association between poorer mental health and shorter sleep duration is consistent with one study of 20,822 young adults living in Australia and another study of 273,695 United States adults [29,30]. However, sleep duration and sleep efficiency dimensions were considered individually in these studies. There is a growing body of evidence on the role of mental health problems in the disruption of sleep satisfaction, duration, onset, and the recurrence of sleep disorders [32,33]. Also, there are several mental health interventions designed to improve sleep health [32,33].
Our hypothesis of higher substance use and poorer sleep health was supported in the current study for marijuana and tobacco. Specifically, higher marijuana use was associated with poorer sleep health (efficiency, duration, sleep health composite score) even after considering covariates. However, research regarding the relationship between marijuana use and sleep health has yielded mixed results [34]. For instance, in a community sample of 409 young adults reporting simultaneous use of alcohol and marijuana, better sleep health on marijuana-only days and mixed results on alcohol and marijuana use days (higher efficiency but poorer next-day alertness [35]. Our hypothesis on the association between higher alcohol use, higher alertness, and a higher sleep health composite score was not supported. However, after adjusting for covariates these associations were no longer significant.
We found higher tobacco use was associated with poorer sleep health (efficiency, duration, sleep health composite score) even after considering covariates; and this finding is supported by the literature [36–38].
Higher tobacco use was associated with shorter sleep duration and poorer sleep quality in two studies, one of 1071 smokers in a German multicenter study and the other in 32 adult cigarette smokers in the United States [36,37]. Additionally, tobacco smoke exposure was associated with higher odds of short or long sleep duration, lower sleep efficiency, and lower sleep satisfaction, but was not significantly associated with alertness in the 10,806 adults from the Canadian Health Measures Study (2007–2013) [38].
The current study has some limitations that should be considered when interpreting the findings. First, not all sleep health dimensions could be examined including regularity, satisfaction, and timing. As such, inclusion of these dimensions may provide more insight into the concurrent associations between mental health or substance use and sleep health. Second, self-report sleep data were used. In some studies, it has been reported that there is poor agreement between self-report sleep duration data and objectively measured sleep duration data [39–41]. However, objective measures are unavailable for large, representative samples. Further large epidemiological studies have demonstrated that self-report sleep data is reliable [42] and self-report sleep duration data reliably predicts prospective mortality and other health outcomes [43]. Third, sleep health dimensions were derived from single items and were equally weighted. However, different dimensions may warrant different weights. Fourth, the sleep health composite score comprised of only three of the six sleep dimensions. In turn, inclusion of all six sleep dimensions into the sleep health composite score may provide greater understanding into the concurrent associations between mental health or substance use and sleep health. Fifth, mental health was derived from a single item response. In turn, caution should be taken to not over interpret this as a true marker of mental health. Sixth, the cross-sectional data limited our ability to assess causality. Despite these limitations, we believe that our findings contribute to the literature regarding the associations between mental health, substance use, and sleep health.
This study is unique as based on several factors. First, we consider the contributions of mental health and substance use, highly prevalent concerns among adults living in the United States, to sleep health more comprehensively when compared to most of the prior work. In previous studies, single sleep health dimensions are primarily examined, predominantly sleep duration [44] followed by sleep efficiency [45]. A study with a larger sample across the United States and other countries with a comprehensive assessment of sleep health dimensions (both self-reported and objective measures) is needed to psychometrically evaluate the reliability and validity indices of the sleep health composite score, though Ohio does serve as a robust exemplar for the United States as Ohio AMI data reflects national trends and the prevalence of short sleep is in the middle range compared to other states. Nevertheless, self-reported sleep health data is the gold standard for measuring perceived sleep [46,47], therefore deriving a sleep health composite from self-reported sleep health dimension data is more deployable in clinical settings.
7.Conclusions
These results support the notion that both mental health and substance use play an important role in sleep health. Currently, in the United States mental health is declining and substance use is on the rise. The ramifications of these trends on sleep health are of critical importance. Considering the role of risk factors in disrupting the equilibrium of sleep health, mental health and substance use tendencies may provide additional insight to the findings in the present study. In the future researchers should aim to address mental health and substance use as intervention strategies to improve sleep health in adults in the United States.
Acknowledgements
This work was supported by the National Institute of Nursing Research under Grants [K23NR019744] (CHD), [R00NR018886] (SG) and the National Institute of Diabetes and Digestive and Kidney Diseases under Grant [R01DK136604] (SG). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH.Ohio BRFSS dataset 2018.
Footnotes
Footnote Group
Data statement
The Ohio BRFSS dataset 2018 is a publicly available dataset.
References
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Associated Data
Data Availability Statement
The Ohio BRFSS dataset 2018 is a publicly available dataset.