Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Jan 14.
Published in final edited form as: Behav Sleep Med. 2026 Jan 1;24(3):330–345. doi: 10.1080/15402002.2025.2610674

Estimating Public Knowledge About Cognitive Behavioral Therapy for Insomnia (CBT-I) and Alternative Treatments

Arash Assar a, Jamie Walker b, Mara Egeler a, Veronica Floyd b, Harrison Dickens b, Ivan Vargas a
PMCID: PMC12797138  NIHMSID: NIHMS2134572  PMID: 41479226

Abstract

Objectives:

This study assessed public knowledge about insomnia treatments, particularly Cognitive Behavioral Therapy for Insomnia (CBT-I), and examined whether familiarity varied by gender or race. The primary aim was to quantify what proportion of adults in the United States are familiar with and use CBT-I.

Methods:

A nationally representative sample of 3080 U.S. adults (Mage = 39.5 years, SDage = 12.9) was surveyed. Approximately 48.3% identified as women. Participants reported their familiarity with various insomnia treatments, including pharmacological and behavioral options, and whether they had used prescription medications, over-the-counter sleep aids, or CBT-I within the past year or at any point in their lifetime.

Results:

Participants were substantially more familiar with pharmacological treatments than behavioral therapies, with notably low recognition of CBT-I. Treatment utilization patterns supported that people tend to have a greater reliance on pharmacological interventions, particularly over-the-counter options, than CBT-I. Demographic differences emerged, with women and White participants reporting greater awareness of insomnia treatments than men and individuals from other racial groups. Age related differences were also observed, though, these varied by treatment approach.

Conclusions:

This study identified major gaps in public awareness of CBT-I and highlighted disparities in treatment knowledge. Addressing these gaps is critical for improving treatment access and promoting CBT-I as a first-line, evidence-based treatment for insomnia.

Chronic insomnia is one of the most common sleep disorders, with an estimated prevalence of 10%–15% (Morin & Jarrin, 2022; van Straten et al., 2025). However, an even larger subset of the population—approximately 20%–30%—experience subsyndromal insomnia (Morin et al., 2020). The daytime dysfunction associated with chronic insomnia has been linked to an increased risk of motor vehicle, work, and other accidents, absenteeism, and social isolation (Berkley et al., 2020; Ramakrishnan, 2007). Moreover, insomnia is linked to an increased risk for several chronic health conditions, including cardiovascular, metabolic, neurodegenerative, and psychological disorders (Bhaskar et al., 2016; Deschênes et al., 2020; Grandner et al., 2012). Taken together, the treatment of insomnia is a public health priority and efforts to identify barriers to treatment are essential.

Current evidence supports the efficacy of multiple pharmacological, psychological, and behavioral interventions for reducing insomnia symptoms (Mitchell et al., 2012; Paulos-Guarnieri et al., 2022; Smith et al., 2002). Of these options, pharmacological treatments—such as prescription medications for insomnia, over-the-counter(OTC) antihistamines, and melatonin supplements—are among the most widely used and discussed (Cheung et al., 2014; Rossman, 2019). While prescription medications (e.g., non-benzodiazepine hypnotics and dual-orexin receptor antagonists) are effective in treating insomnia symptoms in the short term, their long-term use is discouraged due to potential risks (Ohayon et al., 1999; Petrovic, 2023). Similarly, OTC antihistamines containing diphenhydramineand doxylamine produce relatively strong sedating effects (Roth et al., 1987); however, they also have anticholinergic properties. These effects can result in cognitive impairments, dizziness, and increased risk for falls, particularly in older adults (Glass et al., 2005). Melatonin has also been shown to reduce sleep onset problems in some people (Baglioni et al., 2020). That said, it does not reduce nighttime awakenings, lacks long-term safety and efficacy data, and is not regulated by the FDA (Baglioni et al., 2020; Morin & Benca, 2012).

Clinical guidelines increasingly favor behavioral treatments for chronic insomnia instead of pharmacological treatment options. Cognitive Behavioral Therapy for Insomnia (CBT-I) is the most established and empirically supported intervention for insomnia. CBT-I has been widely tested, with data from numerous randomized clinical trials demonstrating that it is safe, effective, and can be adapted across diverse contexts. It can be administered in various settings (e.g., outpatient clinics, primary care, workplaces; Davidson et al., 2019; Speed et al., 2022; Takano et al., 2023), among different groups (e.g., patients with psychiatric and medical comorbidities; Cunningham & Shapiro, 2018; Garland et al., 2014; Selvanathan et al., 2021), and through several delivery formats (e.g., group-based, individual, digital; Muench et al., 2022; Simon et al., 2023). Research has also consistently demonstrated that the treatment effects of CBT-I are durable over time (van der Zweerde et al., 2019). As a result of this overwhelming empirical support, multiple professional groups have recommended CBT-I as the first-line treatment for chronic insomnia (Qaseem et al., 2016; Riemann et al., 2017; Wilson et al., 2019).

Despite being the first-line treatment for insomnia, CBT-I remains severely underutilized across healthcare settings (e.g., Cheung et al., 2014; Rossman, 2019). This is particularly concerning given the high prevalence of insomnia symptoms in both general and clinical populations (Arroll et al., 2012; Maire et al., 2020). While the underutilization of CBT-I may be due to several factors (Vargas et al., 2023), one important consideration is the lack of knowledge (about CBT-I and insomnia) at the provider and public level. Because many people learn about insomnia treatment options during healthcare visits, gaps in provider training may directly limit public awareness and access. Primary care providers, for example, frequently report little to no training in sleep health, insomnia, or CBT-I, including information about the effectiveness of CBT-I (Koffel et al., 2020; Ulmer et al., 2017). In one survey of VA primary care providers, 82% reported some familiarity with CBT-I, but only 10% had a good understanding of it and incorporated it into their clinical practice (Ulmer et al., 2017). Without adequate training, many providers are not equipped with the knowledge to discuss or recommend CBT-I to patients. This is even more important considering that no public health policy or awareness campaigns currently exist to promote behavioral interventions for insomnia (such as CBT-I), placing the burden of knowledge acquisition on patients themselves. As a result, individuals may remain unaware of CBT-I unless they independently seek out this information (Cheung et al., 2014).

Despite the importance of public awareness in improving access to care, few studies, especially in the United States, have examined how familiar people are with CBT-I. For example, a survey conducted in the United Kingdom, found that 63.8% of individuals with insomnia disorder who were not seeking treatment, and 42.3% of those who were seeking treatment, believed that sleeping pills were the only available treatment option for insomnia (Stinson et al., 2006). Understanding this gap in awareness is particularly important given that most healthcare systems rely heavily on patient self-advocacy for accessing behavioral treatments, including CBT-I. Qualitative research suggests that patients often learn about CBT-I only after conducting extensive research on their own and commonly receive referrals for CBT-I only when they explicitly request them (Cheung et al., 2014). Overall, these findings highlight the need for empirical research estimating public knowledge about CBT-I and other treatments for insomnia.

The current study

While there are several key issues and considerations when evaluating the dissemination and implementation of CBT-I, the present study focuses on one: public awareness and knowledge. In response to concerns about public under-recognition of behavioral therapies for insomnia, the current study systematically assessed public knowledge of insomnia and its treatment options (particularly CBT-I). Additionally, because insomnia prevalence and its related health consequences disproportionately affect certain demographic groups—such as women (Zeng et al., 2020), specific racial groups including Black or African American and Hispanic or Latinx individuals (Kalmbach et al., 2016; Kaufmann et al., 2016), and middle-aged and older adults (Morin & Jarrin, 2013)—this study also examined whether knowledge of insomnia treatments varied by gender, race, and age.

Methods

Participants and procedure

Participants were recruited through CloudResearch Connect, an online research platform commonly used for behavioral research (Chandler et al., 2019) and designed to recruit U.S. adults from diverse demographic backgrounds (Hartman et al., 2023). A total of 3080 adults across the United States completed the survey. The mean age of the sample was 39.5 years old (SD = 12.9; range = 18–85 years). Approximately half of the sample (48.3%) identified as women. The remaining participants identified as either men (48.3%), Transgender (0.7%), Nonbinary (1.6%) or did not specify a gender (1.2%). Most of the sample identified as White (65.3%), though 12.2% identified as Black or African American, 10.1% as Asian or Pacific Islander, 7.6% as Hispanic or Latinx, 2.1% as Native American, 0.5% as multi-racial, and 2.1% as other. Geographically, the sample represented all four U.S. Census regions, with 39.7% residing in the South, 21.8% in the West, 20.8% in the Midwest, and 17.7% in the Northeast (U.S. Census Bureau, 2021).

CloudResearch Connect uses an opt-in recruitment model, where participants choose to complete surveys based on their eligibility (Hartman et al., 2023). To enhance population representativeness and generalizability, researcher-defined demographic quotas based on U.S. Census Bureau benchmarks (i.e., American Community Survey estimates for age, race, ethnicity, and region) were used to recruit a more diverse sample (U.S. Census Bureau, 2021). To ensure data quality (i.e., removal of bots, duplicate entries, random responding), the platform uses built-in tools such as IP address checks and the Sentry system, which flags inattentive or potentially invalid responses (Hartman et al., 2023). Participants were given monetary compensation for completing the survey. The present analyses were not pre-registered; however, all data, code, and other materials are available at https://sleeplab.nd.edu/resources/research-resources/open-science/. The Institutional Review Board at the University of Arkansas approved the study and all participants provided their informed consent before completing the survey.

Measures

Insomnia symptom status

The seven-item Insomnia Severity Index (ISI; Morin et al., 2011) was used to assess the prevalence and severity of insomnia symptoms during the past two weeks. Items are rated on a five-point Likert scale, assessing the severity of insomnia symptoms, dissatisfaction with sleep, interference with daytime functioning, awareness of impairment, and concerns related to sleep disturbance. Total scores range from 0 to 28, with higher scores indicating greater insomnia severity. For classification purposes, individuals with subthreshold insomnia were identified as those with an ISI total score between 8 and 14, while those with clinically elevated insomnia had an ISI total score of 15 or greater (Morin et al., 2011).

Perceptions of good sleep

Participants were asked questions about their perceptions of sleep. Specifically, they were asked, “Would you consider yourself a ‘good’ sleeper?” This was answered as a yes/no question. They were also asked how important their sleep was to their physical or mental health, which they rated on a 7-point Likert scale ranging from 1 “not at all important” to 7 “extremely important.”

Knowledge of treatments for insomnia

Participants also indicated their familiarity with various treatments (i.e., “Have you ever heard of or are you familiar with any of the following treatments for insomnia (even if you have never used them)? Please check all that apply”). Response options included: prescription medications, over-the-counter medications (i.e., antihistamines), melatonin, CBT-I, sleep hygiene, sleep restriction therapy, stimulus control therapy, relaxation or mindfulness meditation therapy, and light therapy.

Prior treatment use

Additionally, all participants were asked two yes/no questions for each of three treatment types (CBT-I, prescription medications, and over-the-counter medications) to assess both recent and lifetime use. For CBT-I, participants were asked: “In the past 12 months, have you used cognitive behavioral therapy (CBT-I) to help you fall asleep or stay asleep?” and “Have you ever used cognitive behavioral therapy (CBT-I) to help you fall asleep or stay asleep?” The same question format was used for prescription medications (i.e., “In the past 12 months, have you taken any prescription medications to help you fall asleep or stay asleep [e.g., Ambien, Lunesta, trazodone]?” and “Have you ever taken any prescription medications to help you fall asleep or stay asleep [e.g., Ambien, Lunesta, trazodone]?”) and for over-the-counter medications (i.e., “In the past 12 months, have you taken any over-the-counter products to help you fall asleep or stay asleep [e.g., NyQuil, Unisom, melatonin]?” and “Have you ever taken any over-the-counter products to help you fall asleep or stay asleep [e.g., NyQuil, Unisom, melatonin]?”).

Data analysis

Analyses were conducted using SPSS Version 29.0. Descriptive statistics were used to assess sample characteristics. Chi-square tests of independence were conducted to examine associations among socio-demographic variables (gender, race, and age) and both familiarity with insomnia treatments and insomnia status. For significant chi-square results, post-hoc pairwise comparisons of column proportions were performed using z-tests with Bonferroni-adjusted p-values to identify which groups differed significantly from one another. Cramér’sV was used to measure effect size, with values interpreted as small (.10 to .29), medium (.30 to .49), and large (≥0.50; Cohen, 2013).

Results

Perceptions of good sleep

When asked to classify themselves as “good” sleepers or not, 52.2% of participants identified themselves as good sleepers. A large proportion of participants perceived sleep as at least highly important (rating ≥ 6) for both physical health (79%) and mental health (83%). Nearly half of the participants (49.2%) rated sleep as “extremely important” for physical health and 56.9% of participants endorsed sleep as “extremely important” for mental health.

Insomnia symptom status

Based on responses to the Insomnia Severity Index (ISI), 47.2% (n = 1455) of participants reported no clinically significant insomnia symptoms (ISI < 8), 35.0% (n = 1078) reported subthreshold insomnia symptoms (ISI 8–14), and 17.8% (n = 547) reported clinically elevated insomnia symptoms (ISI ≥15). Among those with at least subthreshold insomnia (ISI ≥8), 51.9% (n = 844) identified as women and 43.7% (n = 710) as men.

A chi-square test of independence indicated a significant association between gender and insomnia classification, χ2 (2, N = 2,975) = 29.66, p < .001, Cramér’s V = .10. Women (20.1%) were significantly more likely than men (14.2%) to meet criteria for clinically elevated insomnia symptoms, whereas men (52.3%) were more likely than women (43.3%) to fall in the no insomnia range. No gender differences were observed for subthreshold insomnia (33.6% vs. 36.6%). A similar analysis revealed no significant association between race and insomnia classification, χ2 (12, N = 3,080) = 18.90, p = .091, Cramér’s V = .06. Across racial and ethnic groups, the proportions of participants classified as having no insomnia, subthreshold insomnia, or clinically elevated insomnia were comparable. See Table 1 for a detailed demographic breakdown by insomnia classification.

Table 1.

Descriptive statistics of sample based on insomnia classification.

No Insomnia (n = 1455; 47.2%) Subthreshold Insomnia (n = 1078; 35%) Clinically Elevated Insomnia (n = 547; 17.8%)
Gender
 Women, n (%) 644 (43.3) 545 (36.6) 299 (20.1)
 Men, n (%) 777 (52.3) 499 (33.6) 211 (14.2)
Race
 White, n (%) 972 (48.3) 700 (34.8) 340 (16.9)
 Black or African American, n (%) 184 (48.8) 122 (32.4) 71 (18.8)
 Asian or Pacific Islander, n (%) 147 (47.3) 116 (37.3) 48 (15.4)
 Hispanic or Latinx, n (%) 95 (40.6) 85 (36.3) 54 (23.1)
 Native American, n (%) 27 (40.9) 22 (33.3) 17 (25.8)
 Multi-Racial, n (%) 3 (20.0) 7 (46.7) 5 (33.3)
 Other-Not Specified, n (%) 27 (41.5) 26 (40.0) 12 (18.5)
Open in a new tab

Note. Bolded values indicate the group with a significantly higher proportion within each insomnia category based on post hoc tests of column proportions at (p < .05).

Public knowledge of insomnia treatments

When asked how familiar participants were with various insomnia treatments, 80.5% of participants reported they were familiar with melatonin, 69.4% with over-the-counter (OTC) medications (e.g., antihistamines), 65.6% with prescription medications (e.g., Ambien, trazodone), 47% with relaxation or mindfulness meditation therapy, 26.4% with light therapy, 26% with sleep hygiene, 15.1% with CBT-I, 5% with stimulus control therapy, and 4.4% with sleep restriction therapy (Figure 1).

Figure 1.

Figure 1.

Open in a new tab

Public knowledge of insomnia treatments. Note. Percentages represent self-reported familiarity with various insomnia treatments by overall sample (N = 3080).

Gender and knowledge of insomnia treatments

A series of chi-square tests of independence examined the association between gender and familiarity with various insomnia treatments. As seen in Figure 2 and Table 2, women were significantly more likely than men to report awareness of prescription medications, OTC antihistamines, melatonin, sleep hygiene practices, relaxation or mindfulness meditation therapies, and light therapy. In contrast, no significant gender differences were observed for CBT-I, sleep restriction therapy, or stimulus control therapy. Overall, findings indicate that women generally exhibit greater awareness of most insomnia treatments, whereas both men and women remain comparatively unfamiliar with CBT-I and its core behavioral components.

Figure 2.

Figure 2.

Open in a new tab

Awareness of insomnia treatments by gender. Note. Percentages reflect self-reported familiarity with various insomnia treatments by gender. Asterisks at the end of a bar denote statistically significant gender differences in familiarity: p < .05 (*), p < .001 (**).

Table 2.

Gender differences in awareness of insomnia treatments.

Treatment Type Female % Male % χ2 Cramer’sV Significant group differences
Melatonin 85.4 75.6 45.8** .12(S) Female > Male
Over-the-Counter Meds 74.6 63.8 41.0** .12 (S) Female > Male
Prescription Meds 70.7 60.5 32.1** .11 (S) Female > Male
Relaxation/Mindfulness 52.6 41.0 39.7** .12 (S) Female > Male
Sleep Hygiene 28.9 22.3 16.9** .08 (VS) Female > Male
Light Therapy 28.0 24.2 5.6* .04 (VS) Female > Male
CBT-I 15.7 13.7 2.4 .03 (VS) n.s.
Sleep Restriction Therapy 3.8 4.5 0.9 .02 (VS) n.s.
Stimulus Control Therapy 4.6 5.3 0.9 .02 (VS) n.s.
Open in a new tab

Note. p < .05*, p < .001** The sample size for these analyses excluded other gender categories due to small cell sizes; N = 2975. Cramer’s V values indicate effect size (S = small, VS = very small).

Race and knowledge of insomnia treatments

A series of chi-square tests of independence examined the association between race and familiarity with various insomnia treatments. Significant racial differences emerged in awareness of prescription medications, OTC medications, melatonin, and light therapy. In contrast, no significant differences were observed for CBT-I, relaxation or mindfulness meditation therapy, sleep hygiene, sleep restriction therapy, or stimulus control therapy. Post-hoc tests of column proportions indicated that participants identifying as White reported significantly greater familiarity with prescription medications, OTC medications, and light therapy compared with several other racial groups. Although awareness of melatonin was high across all racial and ethnic groups, it was significantly lower among Black or African American participants compared with Asian, Hispanic, and White respondents. Overall, Black or African American participants tended to report the lowest familiarity, followed by somewhat lower awareness among Asian and Hispanic groups, whereas White participants demonstrated the highest familiarity across most treatment categories. See Table 3 for descriptive statistics and post hoc comparisons of racial differences in awareness of insomnia treatments.

Table 3.

Racial differences in awareness of insomnia treatments.

Treatment Type Total Asian or Pacific Islander Black or African American White Hispanic or Latinx Native American Multi-Racial Other-Not Specified χ2 (df), Cramer’s V Significant Group Differences (p < .05)
Prescription Medications (%) 65.6% 47.3% 54.9% 71.5% 58.5% 66.7% 60.0% 56.9% 104.2 (6) **, 0.18 (S-M) White > Asian, Black, Hispanic
OTC Medications (%) 69.4% 62.4% 61.5% 73.3% 62.4% 59.1% 60.0% 64.6% 42.3 (6) **, 0.12 (S) White> Asian, Black, Hispanic
Melatonin (%) 80.5% 81.4% 67.6% 82.8% 84.2% 75.8% 86.7% 69.2% 55.2 (6) **, 0.13 (S) Asian, Hispanic, White > Black
Relaxation/Mindfulness Meditation (%) 47.0% 42.4% 41.1% 49.2% 42.3% 51.5% 46.7% 47.7% 14.4 (6)*, 0.07 (VS) No significant group differences
Light Therapy (%) 26.4% 26.4% 19.4% 28.0% 25.6% 30.3% 6.7% 23.1% 16.1 (6)*, 0.07 (VS) White> Black
Sleep Hygiene (%) 26.0% 26.4% 22.5% 27.2% 24.4% 21.2% 13.3% 21.5% 6.9 (6), 0.05(VS) No significant group differences
CBT-I (%) 15.1% 10.6% 14.1% 15.7% 14.1% 19.7% 20% 21.5% 9.4 (6), 0.06 (VS) No significant group differences
Sleep Restriction Therapy (%) 4.4% 2.9% 5.8% 4.1% 5.1% 6.1% 0% 9.2% 9.1 (6) 0.05 (VS) No significant group differences
Stimulus Control Therapy (%) 5.0% 5.1% 3.7% 5.2% 5.1% 7.6% 0% 1.5% 4.9 (6), 0.04 (VS) No significant group differences
Open in a new tab

Note. Percentages reflect the proportion of respondents within each racial and ethnic category who reported familiarity with each insomnia treatment. Post hoc column proportions tests identified pairwise group differences at p< .05. Significance levels are denoted as p < .01*, p < .001**. Cramer’s V values indicate effect size (M = medium, S = small, VS = very small).

Age and knowledge of insomnia treatments

We also examined whether familiarity with insomnia treatments varied as a function of age (18–24, 25–34, 35–44, 45–54, 55–64, 65+ years). Significant age differences emerged for several treatments (see Table 4). Familiarity with prescription, over-the-counter medications, melatonin, and relaxation/mindfulness therapies generally increased with age. Familiarity with sleep hygiene and sleep restriction therapy declined modestly among older adults. No significant age differences were observed for CBT-I, stimulus control therapy, or light therapy.

Table 4.

Familiarity with insomnia treatments by age category.

Treatment Type Total 18–24 25–34 35–44 45–54 55–64 65+ χ2 (df), Cramer’s V Significant Age Differences (p< .05)
Prescription Medications (%) 65.7% 40.8% 60.6% 69.7% 73.0% 80.3% 76.4% 148.09 (5)**, 0.22 (M) Increases with age; 45–85 > 18–34
OTC Medications (%) 69.5% 58.5% 64.9% 71.3% 76.7% 74.6% 79.3% 50.31 (5)**, 0.13 (S) Increases with age; ≥ 35 > 18–24
Melatonin (%) 80.7% 76.3% 76.2% 81.8% 84.9% 88.1% 84.3% 35.17 (5)**, 0.11 (S) Modest rise; ≥ 45 > 18–34
Relaxation/Mindfulness Meditation (%) 47.2% 37.8% 44.2% 46.1% 53.5% 54.2% 57.1% 34.07 (5)**, 0.11 (S) Increases with age; ≥ 45 > 18–34
Light Therapy (%) 26.5% 22.4% 27.6% 27.4% 26.4% 28.2% 18.6% 6.4 (5), 0.05 (VS) No significant age differences
CBT-I (%) 15.0% 14.0% 14.8% 14.9% 16.1% 15.7% 13.6% 1.08 (5), 0.02 (VS) No significant age differences
Sleep Hygiene (%) 26.0% 27.1% 28.9% 25.7% 26.6% 21.3% 13.6% 19.58 (5)**, 0.08 (VS–S) Declines with age; 18–34 > 65+
Sleep Restriction Therapy (%) 4.3% 7.4% 5.9% 3.1% 1.9% 3.8% 2.9% 23.56 (5)**, 0.09 (S) Decreases with age; 18–24 > ≥ 45
Stimulus Control Therapy (%) 5.0% 4.3% 5.1% 6.1% 4.0% 5.3% 2.1% 5.83 (5), 0.04 (VS) No significant group differences
Open in a new tab

Note. Percentages reflect the proportion of respondents within each age category who reported familiarity with each insomnia treatment. Post hoc column proportions tests identified pairwise group differences at p < .05. Significance levels are denoted as **p < .001. Cramer’s V values represent effect sizes, with interpretative abbreviations as follows: VS = very smally, S = small, M = moderate. Reported significant age differences (right most column) indicate which age groups had higher familiarity when overall effects were statistically significant.

Treatment utilization among those with insomnia

Participants reported the use of prescription medications (e.g., Ambien, Lunesta, trazodone), over-the-counter sleep aids (e.g., NyQuil, Unisom, melatonin), and cognitive behavioral therapy (CBT-I). Across the full sample, regardless of insomnia severity, 9.1% of participants reported using prescription medications within the past year and 18.4% reported lifetime use. Additionally, 39.2% of participants reported using OTC sleep aids in the past year and 56.9% lifetime use. In contrast, only 2.6% of participants reported utilizing CBT-I in the past year and 3.5% lifetime use.

Treatment utilization was then examined separately across three groups categorized by Insomnia Severity Index (ISI) scores: clinically elevated insomnia, subthreshold insomnia, and no insomnia. Among participants with clinically elevated insomnia (ISI ≥15; n = 547; 17.8%), 21.4% reported using prescription medications to aid sleep in the past 12 months, while 35.9% reported having used prescription medication at any point in their lifetime. Additionally, 59.4% reported using OTC sleep aids in the past year, and 76.9% reported using OTC sleep aids in their lifetime. Only 6.2% reported using CBT-I in the past year, and 7.5% indicated ever using CBT-I.

Among participants with subthreshold insomnia (ISI = 8–14; n = 1,075; 34.9%), 10% reported using prescription medications to aid sleep in the past 12 months, while 19.9% reported having used prescription medication at any point in their lifetime. Additionally, 44.4% reported using OTC sleep aids in the past year, and 62.1% reported using OTC sleep aids in their lifetime. Only 2.3% reported using CBT-I in the past year, and 3.7% indicated ever using CBT-I.

Among participants with no insomnia (ISI = 0–7; n = 1,455; 47.2%), 3.8% reported using prescription medications to aid sleep in the past 12 months, while 10.7% reported having used prescription medication at any point in their lifetime. Additionally, 27.9% reported using OTC sleep aids in the past year, and 45.8% reported using OTC sleep aids in their lifetime. Only 1.5% reported using CBT-I in the past year, and 1.9% indicated ever using CBT-I (Figure 3).

Figure 3.

Figure 3.

Open in a new tab

Prior treatment utilization by insomnia status. Note. Treatment utilization data represents the percentage of participants with no insomnia (ISI = 0–7; n = 1455), subthreshold insomnia (ISI = 8–14; n = 1075) and clinically elevated insomnia (ISI ≥ 15; n = 547) who reported using specific treatments in the past 12 months or at any point in their lifetime.

Discussion

The current study builds on the existing knowledge base regarding the different factors that contribute to the underutilization of CBT-I (Vargas et al., 2023). Here, the focus was on public awareness and knowledge of insomnia treatments, including CBT-I. Findings indicated a stark contrast between public familiarity with pharmacological treatments versus behavioral options, with notably low recognition of CBT-I and its components. Additionally, treatment utilization patterns indicate a greater reliance on pharmacological interventions (primarily over-the-counter options), both in the past year and across their lifetime, compared to CBT-I. Significant demographic differences also emerged, with respondents who identified as women and White reporting greater awareness of insomnia treatment options than men and individuals from other racial groups, respectively. Familiarity also tended to increase with age. Older adults, for example, reported greater awareness of pharmacological sleep interventions compared to younger adults.

Public knowledge about insomnia treatments

Our findings suggest that respondents were substantially more familiar with pharmacological treatments than behavioral interventions. Specifically, most participants reported familiarity with melatonin, prescription medications, and OTC medications (all > 65%). In contrast, familiarity with behavioral options was substantially lower, with 47% of respondents reporting familiarity with relaxation or mindfulness therapy and 26% reporting familiarity with sleep hygiene. Most notably, only about 15% of the entire sample were familiar with CBT-I. These results are consistent with prior research indicating that public awareness of insomnia treatments are typically limited to pharmacological options (Stinson et al., 2006). The current study builds on this work by quantifying and directly comparing public awareness of both pharmacological and behavioral treatments in a large, nationally representative sample in the United States.

Treatment utilization patterns

We also examined patterns of treatment utilization in this sample. Results revealed a substantial discrepancy between the use of pharmacological and behavioral interventions despite current insomnia symptom severity. Overall, participants were more likely to report using prescription or over-the-counter sleep aids than CBT-I. This pattern of reliance on pharmacological treatments is consistent with prior research suggesting that sedative-hypnotics remain the most commonly used treatment for insomnia in primary care settings (Hughes et al., 2016). Furthermore, the low utilization of CBT-I observed in the present study underscores the notion that public knowledge gaps serve as a critical barrier to the intervention’s uptake. Given that only 15.1% of all respondents recognized CBT-I, it is not surprising that less than 10% of the sample had ever used CBT-I. As mentioned previously, limited public awareness is an important issue considering that patient-driven advocacy is often the primary means to learning about CBT-I (Cheung et al., 2014). These data confirm that additional efforts are needed to more broadly inform the public about the benefits of CBT-I.

Sociodemographic factors that are related to treatment awareness

Considering previous findings that women are nearly twice as likely as men to develop acute or chronic insomnia (Zeng et al., 2020), we also examined whether familiarity with insomnia treatments differed by gender. Results showed that women were significantly more likely than men to report awareness of multiple treatments for insomnia (though notably, not CBT-I). These findings align with previous research suggesting that women generally exhibit greater health awareness and are more likely to seek health-related information compared to men (Bidmon & Terlutter, 2015). Additionally, the higher prevalence of insomnia among women may further contribute to their increased familiarity with treatment options and stronger motivation to seek treatment-related information (Suh et al., 2018). We also examined whether knowledge of insomnia treatments varied by race. Overall, individuals who identified as White tended to report the highest levels of familiarity with insomnia treatments relative to other racial and ethnic groups, particularly Black or African Americans. These disparities highlight critical gaps in public knowledge, suggesting that individuals from historically underserved groups may be less informed about available insomnia treatments despite potentially greater clinical need. Future work should systematically explore whether there are systemic barriers to getting sleep health-related information. Beyond gender and race, age differences also emerged, with familiarity tending to increase across older age groups for several common insomnia treatments. This suggests that younger adults may represent a key target for sleep health outreach efforts, particularly through social media platforms that shape much of their health information exposure (Lim et al., 2022). In contrast, older adults may benefit from clinical or community-based initiatives that emphasize recent advances in behavioral sleep medicine and evidenced-based treatments for insomnia.

Interestingly, awareness of CBT-I did not significantly differ based on gender, race, or age. This suggests that its low recognition persists across all groups. That is, because the base rate of CBT-I knowledge was low, sociodemographic differences (if they exist) were not able to be detected in the present sample. Future research should examine whether differences emerge as CBT-I awareness increases at the population level.

Strengths and limitations

Existing literature on knowledge gaps regarding insomnia treatment options has primarily focused on clinical populations (Cheung et al., 2014; Stinson et al., 2006) or healthcare providers (Conroy & Ebben, 2015; Ulmer et al., 2017). To our knowledge, this is the first study to systematically assess public knowledge about insomnia and its treatments—particularly CBT-I—by directly comparing public awareness of both pharmacological and behavioral treatments and examining group disparities in familiarity. A key strength of the current study is its use of a large-scale, nationally representative sample, enhancing the generalizability of findings. To further improve population representativeness, researcher-defined demographic quotas based on U.S. Census benchmarks for age, race, ethnicity, and region were used to recruit a more diverse sample. By doing so, we were able to examine disparities across gender, racial, and age groups, as well as differences among individuals with varying levels of insomnia severity, offering valuable insights into potential barriers to the utilization of evidence-based treatments for insomnia. Importantly, this study represents an important first step in understanding the scope of the issue. By quantifying public awareness and identifying disparities in knowledge, these findings provide a foundation for future research to explore contributing factors, which could inform targeted educational initiatives and public-health efforts to improve awareness and access to CBT-I.

Several limitations should be acknowledged. First, as this study was survey-based, the findings do not provide detailed insight into respondents’ depth of familiarity with various treatment options. For instance, while 15% of respondents reported awareness of CBT-I, far fewer recognized its core components, such as sleep restriction therapy (4.4%) and stimulus control therapy (5%). This discrepancy raises questions about the depth and accuracy of public familiarity with CBT-I, indicating that even among those who have heard of it, detailed understanding may be limited. Future research should examine the nature and accuracy of this familiarity, including potential misconceptions about CBT-I. Relatedly, little is known about how people educate themselves about insomnia treatments, whether through healthcare providers, social media, online resources, or personal networks. Identifying these knowledge sources could help uncover informational gaps that may contribute to the widespread lack of knowledge and underutilization of CBT-I. Second, we did not measure socioeconomic status (SES), which may have influenced individuals’ exposure to insomnia treatments and contributed to the racial disparities observed in treatment awareness. Research has shown that lower SES, particularly reduced educational attainment, is the most significant factor influencing health literacy, which in turn mediates the relationship between SES and health outcomes and disparities (Stormacq et al., 2019). As health literacy is a modifiable risk factor, it plays a critical role in shaping individuals’ understanding of evidence-based health interventions, including insomnia treatments. Incorporating SES and health literacy measures in future research could provide valuable insights into the mechanisms driving disparities in treatment knowledge. Third, although we used demographic quotas to match U.S. Census benchmarks for age, race, ethnicity, and region to improve the representativeness of our sample, some degree of non-response bias may still be present. Non-response bias consists of two parts: (1) survey response rate, and (2) whether people who choose to participate differ in meaningful ways from those who do not. Regarding the first component, CloudResearch Connect operates as an opt-in system where individuals self-select into surveys rather than being directly invited. While this makes response rate difficult to calculate, evidence suggests that the association between response rate and non-response bias is minimal: studies with higher response rates have similar levels of bias as those with much lower response rates (Hendra & Hill, 2019). Regarding the second component, respondents who participate in surveys such as this one often have higher levels of education and socioeconomic status than the general population (Demarest et al., 2013; Reinikainen et al., 2018). This may potentially lead to inflated estimates of public health knowledge. Importantly, this means that any bias in our sample would likely result in an overestimation of public awareness. As such, one of our main findings—that only 15% of respondents had heard of CBT-I—may actually represent an upper bound of population-level awareness, with the true prevalence of familiarity likely even lower. Although attention-check items were not manually embedded in the survey, CloudResearch Connect employs built-in data-quality safeguards (e.g., IP verification and the Sentry system) that automatically flag inattentive or invalid responses. While researchers do not have direct access to these screening data, these automated procedures help ensure that the final dataset reflects valid and attentive participation.

Implications

Improving public and professional awareness of insomnia treatments is a critical next step toward reducing the widespread underutilization of CBT-I. The present findings highlight several opportunities for intervention across individual, provider, and system levels. At the individual level, public awareness of CBT-I was strikingly low, underscoring the need for targeted educational and public-health initiatives to improve sleep health literacy and knowledge of evidence-based treatments for insomnia. Campaigns modeled after successful health initiatives (e.g., those promoting physical activity or mental-health awareness) could leverage social media, workplace/school initiatives, and healthcare settings to disseminate accurate information about sleep and insomnia (den Braver et al., 2022; Hanisch et al., 2016). However, increasing awareness alone is insufficient without an adequately trained workforce to meet the resulting demand. At the provider and professional level, translating awareness into access will require expanding training opportunities and improving coordination across health disciplines. Primary-care providers are often the first point of contact for patients with insomnia, yet most receive minimal formal training in sleep health or behavioral-sleep medicine (Koffel et al., 2020; Mindell et al., 2011; Ulmer et al., 2017). Integrating standardized sleep-health modules into medical and psychology curricula and expanding continuing-education offerings through organizations such as the American Academy of Sleep Medicine and the Society of Behavioral Sleep Medicine could strengthen referral pathways and clinical competence, ensuring that clinicians can recognize insomnia, discuss behavioral options confidently, and connect patients to appropriate care. Psychologists are particularly well positioned to lead these efforts—not only by providing CBT-I directly but also by training supervisees, consulting in integrated-care settings, and contributing to implementation research aimed at scaling CBT-I delivery and improving integration into healthcare systems. As training capacity expands, a broader question emerges within the field—Who is best positioned to deliver CBT-I? Recent discourse highlights two perspectives: one emphasizing broader dissemination through stepped-care systems to improve access by training a wider range of healthcare professionals (Espie et al., 2025) and the other underscoring the importance of delivery by formally trained and credentialed behavioral-sleep specialists to preserve treatment fidelity (McCrae et al., 2025). Although this discussion continues to evolve, both perspectives share common priorities—expanding workforce capacity, clarifying referral pathways, and ensuring treatment fidelity through high-quality training and supervision to meet patient needs. Moving forward, advancing these shared priorities will require evaluating scalable implementation strategies, such as stepped-care delivery models and expanded provider-training pipelines, to increase access to CBT-I.

Conclusion

This study highlights critical gaps in public awareness of insomnia treatments, particularly CBT-I, and underscores the impact of these knowledge gaps on treatment utilization. Women and White individuals reported greater awareness of insomnia treatments, whereas men and racial minority groups exhibited lower recognition across multiple treatment categories. Gaining insight into public awareness and utilization of CBT-I compared to other treatment options and identifying gaps in knowledge, particularly across demographic groups, is a key step toward developing effective educational and public-health campaigns. Moving forward, greater emphasis must be placed on prioritizing education and awareness initiatives within the public to close these gaps, support the development of effective strategies, and promote more equitable access to information about CBT-I as a viable, evidence-based first-line treatment for insomnia and sleep disturbances.

Acknowledgments

The authors would like to thank the participants who took the time to complete our survey.

Funding

This project was partially supported by the National Institutes of Health via a K23HL141581 awarded to IV.

Footnotes

Disclosure statement

No potential conflict of interest was reported by the author(s).

References

  1. Arroll B, Fernando A, Falloon K, Goodyear-Smith F, Samaranayake C, & Warman G (2012). Prevalence of causes of insomnia in primary care: A cross-sectional study. British Journal of General Practice, 62(595), e99–e103. 10.3399/bjgp12X625157 [DOI] [Google Scholar]
  2. Baglioni C, Bostanova Z, Bacaro V, Benz F, Hertenstein E, Spiegelhalder K, Rücker G, Frase L, Riemann D, & Feige B (2020). A systematic review and network meta-analysis of randomized controlled trials evaluating the evidence base of melatonin, light exposure, exercise, and complementary and alternative medicine for patients with insomnia disorder. Journal of Clinical Medicine, 9(6), 1949. 10.3390/jcm9061949 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Berkley AS, Carter PA, Yoder LH, Acton G, & Holahan CK (2020). The effects of insomnia on older adults’ quality of life and daily functioning: A mixed-methods study. Geriatric Nursing, 41(6), 832–838. 10.1016/j.gerinurse.2020.05.008 [DOI] [PubMed] [Google Scholar]
  4. Bhaskar S, Hemavathy D, & Prasad S (2016). Prevalence of chronic insomnia in adult patients and its correlation with medical comorbidities. Journal of Family Medicine and Primary Care, 5(4), 780–784. 10.4103/2249-4863.201153 [DOI] [Google Scholar]
  5. Bidmon S, & Terlutter R (2015). Gender differences in searching for health information on the internet and the virtual patient-physician relationship in Germany: Exploratory results on how men and women differ and why. Journal of Medical Internet Research, 17(6), e156. 10.2196/jmir.4127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chandler J, Rosenzweig C, Moss AJ, Robinson J, & Litman L (2019). Online panels in social science research: Expanding sampling methods beyond Mechanical Turk. Behavior Research Methods, 51(5), 2022–2038. 10.3758/s13428-019-01273-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cheung JM, Bartlett DJ, Armour CL, Glozier N, & Saini B (2014). Insomnia patients’ help-seeking experiences. Behavioral Sleep Medicine, 12(2), 106–122. 10.1080/15402002.2013.764529 [DOI] [PubMed] [Google Scholar]
  8. Cohen J (2013). Statistical power analysis for the behavioral sciences. Routledge. [Google Scholar]
  9. Conroy DA, & Ebben MR (2015). Referral practices for cognitive behavioral therapy for insomnia: A survey study. Behavioural Neurology, 1, 819402. 10.1155/2015/819402 [DOI] [Google Scholar]
  10. Cunningham JE, & Shapiro CM (2018). Cognitive behavioural therapy for insomnia (CBT-I) to treat depression: A systematic review. Journal of Psychosomatic Research, 106, 1–12. 10.1016/j.jpsychores.2017.12.012 [DOI] [PubMed] [Google Scholar]
  11. Davidson JR, Dickson C, & Han H (2019). Cognitive behavioural treatment for insomnia in primary care: A systematic review of sleep outcomes. British Journal of General Practice, 69(686), e657–e664. 10.3399/bjgp19X705065 [DOI] [Google Scholar]
  12. Demarest S, Van der Heyden J, Charafeddine R, Tafforeau J, Van Oyen H, & Van Hal G (2013). Socio-economic differences in participation of households in a Belgian national health survey. The European Journal of Public Health, 23(6), 981–985. 10.1093/eurpub/cks158 [DOI] [PubMed] [Google Scholar]
  13. den Braver NR, Garcia Bengoechea E, Messing S, Kelly L, Schoonmade LJ, Volf K, Lakerveld J, Gelius P, Forberger S, Woods CB, & Lakerveld J (2022). The impact of mass-media campaigns on physical activity: A review of reviews through a policy lens. European Journal of Public Health, 32(Supplement_4), iv71–iv83. 10.1093/eurpub/ckac085 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Deschênes SS, Burns RJ, Graham E, & Schmitz N (2020). Depressive symptoms and sleep problems as risk factors for heart disease: A prospective community study. Epidemiology and Psychiatric Sciences, 29, e50. 10.1017/S2045796019000441 [DOI] [Google Scholar]
  15. Espie CA, Grandner MA, & Drake CL (2025). Pro: CBT-I for insomnia treatment can be delivered by a range of healthcare providers. Sleep, 48(10), zsaf124. 10.1093/sleep/zsaf124 [DOI] [Google Scholar]
  16. Garland SN, Johnson JA, Savard J, Gehrman P, Perlis M, Carlson L, & Campbell T (2014). Sleeping well with cancer: A systematic review of cognitive behavioral therapy for insomnia in cancer patients. Neuropsychiatric Disease and Treatment, 10, 1113–1124. 10.2147/NDT.S47790 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Glass J, Lanctôt KL, Herrmann N, Sproule BA, & Busto UE (2005). Sedative hypnotics in older people with insomnia: Meta-analysis of risks and benefits. BMJ, 331(7526), 1169. 10.1136/BMJ.38623.768588.47 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Grandner MA, Jackson NJ, Pak VM, & Gehrman PR (2012). Sleep disturbance is associated with cardiovascular and metabolic disorders. Journal of Sleep Research, 21(4), 427–433. 10.1111/j.1365-2869.2011.00990.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hanisch SE, Twomey CD, Szeto AC, Birner UW, Nowak D, & Sabariego C (2016). The effectiveness of interventions targeting the stigma of mental illness at the workplace: A systematic review. BMC Psychiatry, 16(1), 1. 10.1186/s12888-015-0706-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hartman R, Moss AJ, Jaffe SN, Rosenzweig C, Litman L, & Robinson J (2023). Introducing connect by CloudResearch: Advancing online participant recruitment in the digital age. 10.31234/osf.io/ksgyr [DOI] [Google Scholar]
  21. Hendra R, & Hill A (2019). Rethinking response rates: New evidence of little relationship between survey response rates and nonresponse bias. Evaluation Review, 43(5), 307–330. 10.1177/0193841X18807719 [DOI] [PubMed] [Google Scholar]
  22. Hughes LD, Raitt N, Riaz MA, Baldwin SJ, Erskine K, & Graham G (2016). Primary care hypnotic and anxiolytic prescription: Reviewing prescribing practice over 8 years. Journal of Family Medicine and Primary Care, 5 (3), 652–657. 10.4103/2249-4863.197312 [DOI] [Google Scholar]
  23. Kalmbach DA, Pillai V, Arnedt JT, & Drake CL (2016). Dsm-5 insomnia and short sleep: Comorbidity landscape and racial disparities. Sleep, 39(12), 2101–2111. 10.5665/sleep.6306 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kaufmann CN, Mojtabai R, Hock RS, Thorpe RJ Jr., Canham SL, Chen LY, Spira AP, Chen-Edinboro LP, & Spira AP (2016). Racial/ethnic differences in insomnia trajectories among US older adults. The American Journal of Geriatric Psychiatry, 24(7), 575–584. 10.1016/j.jagp.2016.02.049 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Koffel E, Amundson E, Polusny G, & Wisdom JP (2020). “You’re missing out on something great”: Patient and provider perspectives on increasing the use of cognitive behavioral therapy for insomnia. Behavioral Sleep Medicine, 18(3), 358–371. 10.1080/15402002.2019.1591958 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lim MS, Molenaar A, Brennan L, Reid M, & McCaffrey T (2022). Young adults’ use of different social media platforms for health information: Insights from web-based conversations. Journal of Medical Internet Research, 24(1), e23656. 10.2196/23656 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Maire M, Linder S, Dvořák C, Merlo C, Essig S, Tal K, Del Giovane C, Syrogiannouli L, Duss SB, Heinzer R, Nissen C, Bassetti CLA, & Auer R (2020). Prevalence and management of chronic insomnia in Swiss primary care: Cross-sectional data from the “Sentinella” practice-based research network. Journal of Sleep Research, 29(5), e13121. 10.1111/jsr.13121 [DOI] [PubMed] [Google Scholar]
  28. McCrae CS, Curtis AF, & Stearns MA (2025). Con: CBT-I for insomnia treatment can be delivered by a range of healthcare providers. Sleep, 48(10), zsaf123. 10.1093/sleep/zsaf123 [DOI] [Google Scholar]
  29. Mindell JA, Bartle A, Abd Wahab N, Ahn Y, Ramamurthy MB, Huong HTD, Goh DY, Ruangdaraganon N, Sekartini R, Teng A, & Goh DYT (2011). Sleep education in medical school curriculum: A glimpse across countries. Sleep Medicine, 12(9), 928–931. 10.1016/j.sleep.2011.07.001 [DOI] [PubMed] [Google Scholar]
  30. Mitchell MD, Gehrman P, Perlis M, & Umscheid CA (2012). Comparative effectiveness of cognitive behavioral therapy for insomnia: A systematic review. BMC Family Practice, 13(1), 1–11. 10.1186/1471-2296-13-40 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Morin CM, Belleville G, Bélanger L, & Ivers H (2011). The insomnia severity index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep, 34(5), 601–608. 10.1093/sleep/34.5.601 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Morin CM, & Benca R (2012). Chronic insomnia. The Lancet, 379(9821), 1129–1141. 10.1016/S0140-6736(11)60750-2 [DOI] [Google Scholar]
  33. Morin CM, & Jarrin DC (2013). Epidemiology of insomnia: Prevalence, course, risk factors, and public health burden. Sleep Medicine Clinics, 8(3), 281–297. 10.1016/j.jsmc.2013.05.002 [DOI] [Google Scholar]
  34. Morin CM, & Jarrin DC (2022). Epidemiology of insomnia: Prevalence, course, risk factors, and public health burden. Sleep Medicine Clinics, 17(2), 173–191. 10.1016/j.jsmc.2022.03.003 [DOI] [PubMed] [Google Scholar]
  35. Morin CM, Jarrin DC, Ivers H, Mérette C, LeBlanc M, & Savard J (2020). Incidence, persistence, and remission rates of insomnia over 5 years. JAMA Network Open, 3(11), e2018782–e2018782. 10.1001/jamanetworkopen.2020.18782 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Muench A, Vargas I, Grandner MA, Ellis JG, Posner D, Bastien CH, Drummond SP, & Perlis ML (2022). We know CBT-I works, now what? Faculty Reviews, 11, 4. 10.12703/r/11-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Ohayon MM, Caulet M, Arbus L, Billard M, Coquerel A, Guieu JD, Vespignani H, Laffont F, Lemoine P, Paty J, Pechadre JC, Vecchierini M-F, & Vespignani H (1999). Are prescribed medications effective in the treatment of insomnia complaints? Journal of Psychosomatic Research, 47(4), 359–368. 10.1016/S0022-3999(98)00079-8 [DOI] [PubMed] [Google Scholar]
  38. Paulos-Guarnieri L, Linares IMP, & El Rafihi-Ferreira R (2022). Evidence and characteristics of acceptance and commitment therapy (ACT)-based interventions for insomnia: A systematic review of randomized and non-randomized trials. Journal of Contextual Behavioral Science, 23, 1–14. 10.1016/j.jcbs.2021.11.001 [DOI] [Google Scholar]
  39. Petrovic M (2023). Pharmacotherapy of insomnia in older adults. In Cherubini A, Mangoni A, O’Mahony D, & Petrovic M (Eds.), Optimizing pharmacotherapy in older patients: An interdisciplinary approach (pp. 391–404). Springer International Publishing. 10.1007/978-3-031-28061-0_27 [DOI] [Google Scholar]
  40. Qaseem A, Kansagara D, Forciea MA, Cooke M, Denberg TD, & Clinical Guidelines Committee of the American College of Physicians*. (2016). Management of chronic insomnia disorder in adults: A clinical practice guideline from the American College of Physicians. Annals of Internal Medicine, 165(2), 125–133. 10.7326/M15-2175 [DOI] [PubMed] [Google Scholar]
  41. Ramakrishnan K (2007). Treatment options for insomnia. South African Family Practice, 49(8), 34–41. [Google Scholar]
  42. Reinikainen J, Tolonen H, Borodulin K, Härkänen T, Jousilahti P, Karvanen J, Koskinen S, Kuulasmaa K, Männistö S, Rissanen H, & Vartiainen E (2018). Participation rates by educational levels have diverged during 25 years in Finnish health examination surveys. The European Journal of Public Health, 28(2), 237–243. 10.1093/eurpub/ckx151 [DOI] [PubMed] [Google Scholar]
  43. Riemann D, Baglioni C, Bassetti C, Bjorvatn B, Dolenc Groselj L, Ellis JG, Spiegelhalder K, Garcia-Borreguero D, Gjerstad M, Gonçalves M, Hertenstein E, Jansson-Fröjmark M, Jennum PJ, Leger D, Nissen C, Parrino L, Paunio T, Pevernagie D Verbraecken J… Zoetmulder M (2017). European guideline for the diagnosis and treatment of insomnia. Journal of Sleep Research, 26(6), 675–700. 10.1111/jsr.12594 [DOI] [PubMed] [Google Scholar]
  44. Rossman J (2019). Cognitive-behavioral therapy for insomnia: An effective and underutilized treatment for insomnia. American Journal of Lifestyle Medicine, 13(6), 544–547. 10.1177/1559827619867677 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Roth T, Roehrs T, Koshorek G, Sicklesteel J, & Zorick F (1987). Sedative effects of antihistamines. Journal of Allergy and Clinical Immunology, 80(1), 94–98. 10.1016/S0091-6749(87)80197-5 [DOI] [PubMed] [Google Scholar]
  46. Selvanathan J, Pham C, Nagappa M, Peng PW, Englesakis M, Espie CA, Chung F, & Chung F (2021). Cognitive behavioral therapy for insomnia in patients with chronic pain-a systematic review and meta-analysis of randomized controlled trials. Sleep Medicine Reviews, 60, 101460. 10.1016/j.smrv.2021.101460 [DOI] [PubMed] [Google Scholar]
  47. Simon L, Steinmetz L, Feige B, Benz F, Spiegelhalder K, & Baumeister H (2023). Comparative efficacy of onsite, digital, and other settings for cognitive behavioral therapy for insomnia: A systematic review and network meta-analysis. Scientific Reports, 13(1). 10.1038/s41598-023-28853-0 [DOI] [Google Scholar]
  48. Smith MT, Perlis ML, Park A, Smith MS, Pennington J, Giles DE, & Buysse DJ (2002). Comparative meta-analysis of pharmacotherapy and behavior therapy for persistent insomnia. American Journal of Psychiatry, 159(1), 5–11. 10.1176/appi.ajp.159.1.5 [DOI] [PubMed] [Google Scholar]
  49. Speed TJ, Hanks L, Turner G, Gurule E, Kearson A, Buenaver L, Smith MT, & Antoine D (2022). A comparison of cognitive behavioral therapy for insomnia to standard of care in an outpatient substance use disorder clinic embedded within a therapeutic community: A RE-AIM framework evaluation. Trials, 23(1), 965. 10.1186/s13063-022-06885-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Stinson K, Tang NKY, & Harvey AG (2006). Barriers to treatment seeking in primary insomnia in the United Kingdom: A cross-sectional perspective. Sleep, 29(12), 1643–1646. 10.1093/sleep/29.12.1643 [DOI] [PubMed] [Google Scholar]
  51. Stormacq C, Van den Broucke S, & Wosinski J (2019). Does health literacy mediate the relationship between socioeconomic status and health disparities? Integrative review. Health Promotion International, 34(5), e1–e17. 10.1093/heapro/day062 [DOI] [PubMed] [Google Scholar]
  52. Suh S, Cho N, & Zhang J (2018). Sex differences in insomnia: From epidemiology and etiology to intervention. Current Psychiatry Reports, 20(9), 1–12. 10.1007/s11920-018-0940-9 [DOI] [PubMed] [Google Scholar]
  53. Takano Y, Ibata R, Machida N, Ubara A, & Okajima I (2023). Effect of cognitive behavioral therapy for insomnia in workers: A systematic review and meta-analysis of randomized controlled trials. Sleep Medicine Reviews, 71, 101839. 10.1016/j.smrv.2023.101839 [DOI] [PubMed] [Google Scholar]
  54. Ulmer CS, Bosworth HB, Beckham JC, Germain A, Jeffreys AS, Edelman D, Macy S, Kirby A, & Voils CI (2017). Veterans affairs primary care provider perceptions of insomnia treatment. Journal of Clinical Sleep Medicine, 13(8), 991–999. 10.5664/jcsm.6702 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. U.S. Census Bureau. (2021). American community survey 1-year estimates. U.S. Department of Commerce. https://www.census.gov/programs-surveys/acs [Google Scholar]
  56. van der Zweerde T, Bisdounis L, Kyle SD, Lancee J, & van Straten A (2019). Cognitive behavioral therapy for insomnia: A meta-analysis of long-term effects in controlled studies. Sleep Medicine Reviews, 48, 101208. 10.1016/j.smrv.2019.08.002 [DOI] [PubMed] [Google Scholar]
  57. van Straten A, Weinreich KJ, Fábián B, Reesen J, Grigori S, Luik AI, Harrer M, & Lancee J (2025). The prevalence of insomnia disorder in the general population: A meta-analysis. Journal of Sleep Research, 34(5), e70089. 10.1111/jsr.70089 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Vargas I, Egeler M, Walker J, & Benitez DD (2023). Examining the barriers and recommendations for integrating more equitable insomnia treatment options in primary care. Frontiers in Sleep, 2, 1279903. 10.3389/frsle.2023.1279903 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Wilson S, Anderson K, Baldwin D, Dijk DJ, Espie A, Espie C, Gringras P, Krystal A, Nutt D, Selsick H, & Sharpley A (2019). British Association for Psychopharmacology consensus statement on evidence-based treatment of insomnia, parasomnias and circadian rhythm disorders: An update. Journal of Psychopharmacology, 33(8), 923–947. 10.1177/0269881119855343 [DOI] [PubMed] [Google Scholar]
  60. Zeng LN, Zong QQ, Yang Y, Zhang L, Xiang YF, Ng CH, Xiang YT, & Xiang Y-T (2020). Gender difference in the prevalence of insomnia: A meta-analysis of observational studies. Frontiers in Psychiatry, 11, 577429. 10.3389/fpsyt.2020.577429 [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES