Sleeping without borders: Gaps in contextualizing sleep duration guidelines for adults in low-income countries

Abstract

Background

Empirical data on adult sleep health are scarce in many low-income countries, despite the significant health risks associated with inadequate sleep. To help address this gap in low-income countries, the World Sleep Society's Global Sleep Health Taskforce recommends applying evidence from high-income settings — such as guidance from the American Academy of Sleep Medicine/Sleep Research Society AASM/SRS) and the National Sleep Foundation (NSF) to inform the development of sleep health policies and programs.

Objective

To evaluate the appropriateness of adapting existing consensus recommendations on healthy sleep duration for adults from the AASM/SRS and the NSF for use in low-income countries by applying World Health Organization (WHO) contextualization benchmarks.

Methods

We reviewed AASM/SRS and NSF methods papers and assessed four WHO benchmarks: (1) consideration of low-income country priorities in question formulation and literature search; (2) inclusion of evidence from low-income countries; (3) involvement of stakeholders from low-income countries in consensus preparation; and (4) representation of low-income countries or relevant expertise in expert panels. Empirical studies cited in both statements were classified by country income level using World Bank 2025–2026 classifications. Chi-square tests compared distributions across income groups.

Results

Neither the AASM/SRS or NSF consensus process incorporated priorities, evidence, or representation from low-income countries. Of the empirical studies reviewed, 96% originated from high-income countries, 4% from upper-middle-income countries, and none from lower-middle-income countries (LMIC). No stakeholders or experts from low-income countries participated in guideline development. Both statements showed significant overrepresentation of high-income contexts (p < .001).

Conclusions

Current sleep duration recommendations lack contextualization for low-income settings and do not meet WHO standards in this regard. Future guideline development should broaden evidence sources, engage local stakeholders, and apply participatory frameworks to ensure cultural relevance, equity, and practical implementation.

Highlights

• •World Sleep Society advises projecting high-income sleep health data globally
• •Adult sleep guidelines do not meet WHO contextualization standards
• •Contextualization is essential to apply existing guidelines effectively
• •Applying WSS approach without adaptation risks poor public health outcomes
• •Guidelines must be modified to meet contextualization benchmarks for equity

1. Introduction

Sleep health is increasingly recognized as a critical determinant of overall well-being. Global perspectives on sleep health underscore the need to integrate sleep health into public health policies worldwide, particularly in low-income countries (LIC).

Recent calls for urgent action to integrate sleep health indicators into public health agendas in LIC recommend application of data from high-income countries (HIC) to assess the amount of sleep needed to promote healthy sleep in adults [1,2]. Consensus guidelines from HIC outline the health risks of chronic inadequate sleep and recommend achievable, evidence-based sleep duration targets that offer substantial health benefits in the population. Applying these guidelines—such as those from the American Academy of Sleep Medicine/Sleep Research Society (AASM/SRS) [3], or the National Sleep Foundation (NSF) [4]—to LIC is pragmatic and resource-efficient. However, contextualization is essential due to cultural, environmental, and social variations in sleep patterns and opportunities.

In the context of sleep health, a sleep guideline “target” or “benchmark” itself is inseparable from the environment in which the evidence was collected. Current research shows that the “7–9 h h” guideline recommendation reflects a biomedical, Western-centric framing that may not capture how sleep is structured, valued, or socially organized across societies. For example, a large cross-national study (20 countries; N = 4933) found that the sleep amount associated with the best health varies by country, and that people whose sleep duration is closer to their culture's perceived ideal report better health, independent of a universal 7–9-h target [5].

Adapting recommendations that are not grounded in local context can push public health systems to adopt practices that may worsen—not improve— health outcomes, set unrealistic expectations for what constitutes “healthy” sleep, and obscure the diverse sleep patterns that are both feasible and health-promoting across different cultural and environmental settings. For example, in settings with predictable work schedules, regularity is a core sleep health indicator. In contexts with irregular employment and long hours, flexible timing and “sleep banking” may be adaptive and healthier than rigid schedules. Guidelines emphasizing strict sleep regularity or limiting sleep to 7–9 h h risk pathologizing adaptive patterns and obscuring context-dependent pathways to healthy sleep, ultimately harming health outcomes.

Recent empirical work has demonstrated the efficacy of tailored approaches; for example, research on shift workers with irregular schedules has shown that adapting generic sleep hygiene advice to account for occupational realities leads to better engagement than standardized recommendations [6]. These findings underscore that even within high-income settings, failing to contextualize sleep advice can limit its public health utility. Extending this logic to LIC is essential, where the divergence between standard guidelines and socioeconomic realities is often much wider [6].

Contextualization requires: (i) selecting guideline or recommendation questions based on, or in consideration of, local needs and stakeholder priorities; (ii) deriving evidence or consensus from populations with a similar risk level to the target population or to the population in which the original evidence was generated, while relying on local evidence; (iii) including regional or local representative panels in the guideline adaptation process to promote ownership and context-specific judgment; and (iv) engaging local stakeholders in the adaptation and implementation process to ensure acceptability [[6], [7], [8], [9]].

The World Health Organization (WHO) contextualization guide notes that “guidelines need to be suitable for the available resource and organizational contexts” [10]. Because resource availability, infrastructure, and system capacity differ substantially by country income level, income classification becomes a key contextual determinant.

In practice, income level operates as a central filtering criterion across multiple components of the guide, as outlined below:

Country Income as a “Feasibility” Filter:

Within the Evidence-to-Decision (EtD) framework, “Feasibility” is a mandatory criterion, and a country's income level directly shapes feasibility judgments. For example, in a low-income context, a recommendation such as “universal digital screening for adolescent insomnia” may be deemed not feasible when internet penetration or data affordability is insufficient. In contrast, the same recommendation may be rated highly feasible in high-income contexts with established electronic health records (EHRs) and widespread connectivity. To address such disparities, the WHO contextualization guide enables countries to “adolop” (adapt + develop) recommendations—such as modifying digital screening to teacher-led paper-based screening—to align with local system capacity.

Country Income and “Resource Use”:

Income also shapes assessments of “Resource Use,” which the WHO contextualization guide explicitly links to opportunity costs. In low-income settings, allocating resources to sleep health promotion may divert funds from foundational priorities such as immunization. Panels are therefore encouraged to review National Health Accounts and consider how income-driven constraints influence what constitutes a reasonable allocation. For example, in countries spending roughly $30 USD per capita on health, contextualization may require prioritizing low-cost, high-impact interventions (e.g., iron supplementation) over more cost-intensive options (e.g., routine MRIs for assessment of chronic pain).

Country Income in the 15-Step Adolopment Process:

The 15-step adolopment process described in the WHO contextualization guide is designed to prevent guideline waste in lower middle income countries (LMIC) Steps 5 through 9 focus specifically on contextual evidence retrieval, during which income-linked data—such as national expenditure profiles, workforce availability, essential service coverage, and infrastructure metrics—is prioritized to ensure that adapted guidelines reflect actual system capacity.

The GRADE-ADOLOPMENT process is a structured pathway for adopting, adapting, or creating contextualized recommendations from source guidelines and evidence syntheses produced in HIC to LIC [9]. This process ensures that recommendations remain relevant and applicable to local contexts, aligning with the WHO's emphasis on contextualization frameworks [7,8]. The GRADE-ADOLOPMENT process aligns with WHO's expectations of guideline development [11] as [1] it uses GRADE, which WHO recognizes as the gold standard for assessing certainty of evidence and formulating recommendations; and [2], it incorporates systematic assessment of evidence, transparent judgments, and explicit documentation, which are essential components of WHO benchmarks. Given its development through wide international participation, multidisciplinary authorship across income settings, and WHO-wide review [8], the GRADE-ADOLOPMENT contextualization approach is intentionally designed to be globally relevant, not the product of a narrow or high-income group.

The outcome of the ADOLOPMENT process may be adoption, adaptation, or de novo development [6]. Adoption involves using existing recommendations with minimal or no changes, as advocated in recent global efforts to promote sleep health [1,2]. Adaptation requires modifying recommendations for relevance and feasibility in the new context [[7], [8], [9], [11]]. De novo development is necessary when neither adoption nor adaptation is sufficient for the targeted country [[7], [8], [9], [11]].

Despite the World Sleep Society Global Sleep Health Taskforce's determination that sleep health data from high-income countries can be projected to assess the consequences of sleep deficiency in low-income countries [1], sleep health indicators from these settings—such as adult sleep duration consensus recommendations—have not been evaluated through the ADOLOPMENT process for contextual relevance.

The objective of this paper is to assess the suitability of adapting the AASM/SRS and NSF consensus recommendations for adult sleep duration for application in LIC. The AASM/SRS and NSF used the RAND-UCLA Appropriateness Method (RAM) to develop these recommendations [12]. The RAM combines synthesized information extracted from scientific literature with expert judgment through a structured process. A core panel reviews evidence and creates scenarios, which a multidisciplinary expert panel (seven to fifteen members) rates on a 1–9 scale: 1–3 “inappropriate,” 4–6 “uncertain,” and 7–9 “appropriate.” Consensus is based on median scores and absence of significant disagreement, and results inform the final wording of the guidelines. The guidelines are subsequently reviewed and approved by the governing board of the professional organization, followed by dissemination.

To assess the suitability of adapting the AASM/SRS or NSF consensus recommendations for LIC, we evaluated these statements against key WHO contextualization benchmarks [9,11] using the following questions:

• I.Was the selection of consensus statement questions based on, or did it take into account, the needs and priorities of stakeholders from low-income countries?
• II.Did the literature reviewed and synthesized by the core panel include evidence from studies conducted in LIC?
• III.Were stakeholders from LIC involved in the core panel's preparation for consensus voting?
• IV.Did the multidisciplinary expert panel responsible for voting on the appropriateness of scenarios include representatives from LIC or individuals with expertise in sleep or health in LIC?

2. Methods

Questions I, III, and IV were evaluated through a detailed review of the methods papers published by the AASM/SRS Consensus Conference Panel [3] and the NSF [4].

Question II was assessed as follows: We reviewed all included studies (Appendix A), identified the empirical studies cited in the synthesized literature reviewed and prepared by the core panels of the AASM/SRS Consensus Conference Panel [3] and NSF [4], and classified each study according to the income status of the country where the research was conducted, using the World Bank's 2025–2026 income classifications: high-income (HIC), upper-middle-income (UMIC), lower-middle-income (LMIC), and low-income (LIC) [13]. When country names had changed over time, studies were reassigned based on current country names before applying income-level classifications. We calculated the proportion of studies originating from each income-level country (see Table 1).

Table 1.

Percentage of countries of studies included in the AASM/SRS and NSF adult sleep duration consensus statement by world bank FY2025–2026 income classification.

Country	Income Level (WB FY26)	AASM (%)	NSF (%)
Australia	High income	2.30	5.45
Austria	High income	NA	0.50
Belgium	High income	1.8	0.50
Brazil	Upper middle income	2.30	0.99
Canada	High income	1.80	1.97
China	Upper middle income	2.30	2.97
Denmark	High income	0.50	NA
Ethiopia	Undetermined	NA	0.50
Finland	High income	2.30	2.48
France	High income	1.80	1.49
Germany	High income	4.10	1.98
Ireland	High income	0.50	NA
India	Lower middle income	NA	0.50
Israel	High income	NA	0.50
Iran	Upper middle income	1	0.50
Italy	High income	0.90	1.19
Japan	High income	7.20	7.41
Korea, Rep.	High income	0.90	3.7
Netherlands	High income	1.80	1.49
New Zealand	High income	NA	0.99
Norway	High income	0.5	1.49
Poland	High income	NA	0.50
Portugal	High income	NA	0.50
Saudi Arabia	High income	NA	0.99
Singapore	High income	0.90	0.50
South Korea	High income	NA	4.95
Spain	High income	1.80	3.47
Sweden	High income	4.00	2.48
Switzerland	High income	0.90	NA
Taiwan	High income	0.50	2.48
Thailand	Upper-middle income	0.5	NA
Turkey	Upper middle income	NA	0.50
United Kingdom	High income	4.50	3.96
United States	High income	51.3	43.07
Multi-country	High income	3.60	0.20

Note. AASM/SRS American Academy of Sleep Medicine/Sleep Research Society; NSF= National Sleep Foundation; Income Level (WB FY26) — Low/Lower-middle/Upper-middle/High/Undetermined income classification for FY26 (July 1, 2025–June 30, 2026); NA=Not Available [14].

The table layout displayed in this section is not how it will appear in the final version. The representation below is solely purposed for providing corrections to the table. To view the actual presentation of the table, please click on the located at the top of the page.

Percentage of countries of studies included in the AASM/SRS and NSF adult sleep duration consensus statement by world bank FY2025–2026 income classification.

For the AASM/SRS statement, which specifically focused on adults’ sleep duration, the only inclusion criterion was empirical studies incorporated into the literature synthesis by the core panel. Exclusion criteria included non-empirical studies such as reviews, systematic reviews, and meta-analyses.

For the NSF statement, inclusion criteria were empirical studies included in the literature synthesis of evidence for young adults, adults, and older adults. Exclusion criteria included studies on newborns, infants, toddlers, preschoolers, school-aged children, and teenagers, as well as non-empirical studies (e.g., review papers).

Chi-square tests of independence were used to compare (i) whether the distribution of studies across income levels differed between the AASM/SRS and NSF consensus statements, and (ii) whether it differed within each statement. Pearson chi-square and likelihood ratio statistics were calculated, and assumptions regarding expected cell counts were evaluated. Significance was set at p < .05.

3. Results

3.1. Benchmark 1: consideration of LIC priorities in literature search strategies and question formulation

Literature Search Strategies. Both the AASM/SRS and NSF consensus statements described systematic reviews focused on health outcomes associated with sleep duration, using predefined categories and inclusion criteria. Searches were structured around medical subject headings, health domains, and sleep-related outcomes, and articles were selected based on study design, quality, and relevance to these domains. Neither group reported incorporating geographic context, country income classification, or stakeholder priorities from low-income settings in the search strategy or during data extraction.

Assessment of Guiding Questions. AASM/SRS aligned its questions with objectives from the National Healthy Sleep Awareness Project and the Centers for Disease Control and Prevention, while NSF emphasized developing scientifically sound recommendations for the U.S. population. Neither group reported incorporating LIC priorities in question formulation.

3.2. Benchmark 2: inclusion of evidence from LIC in synthesized literature

Table 1 presents the distribution of countries by income level for each consensus statement. Table 1 presents distribution of studies by income level within the NSF and AASM consensus statements.

A total of 433 empirical studies were reviewed: AASM/SRS included 221 studies, and NSF included 212 studies. A total of 69 studies appeared in both statements but were counted separately to address the question of whether data from low-income countries were included in each consensus statement. Across both consensus statements, 92.17% of the empirical studies were from high-income countries (HIC), 7.33% from upper-middle-income countries (UMIC), 0.25% from lower-middle-income countries (LMIC), and 0.25% from a country with an undetermined income level (Ethiopia) [13]. A chi-square test of independence comparing the distribution of studies by income level across the consensus statements revealed no significant differences, χ2(1, N = 434) = 2.6, p > .05. There were no significant differences between AASM/SRS and NSF in the inclusion of studies from low-income countries. Both statements showed significant differences in the distribution of studies across income groups, indicating uneven representation concentrated in high-income countries.

AASM consensus statement. A chi-square test of independence examining the association between income level and country distribution for studies included in the AASM/SRS statement revealed significantly higher representation from high-income countries, χ2(28, N = 221) = 221, p < .001. Among the included studies, 90.3% originated from high-income countries, 9.7% from upper-middle-income countries, and none from lower-middle-income or low-income countries.

NSF consensus statement. A chi-square test of independence examining the association between income level and country distribution for studies included in the NSF statement revealed significantly higher representation from HIC, χ2(60, N = 214) = 424, p < .001. Among the included studies, 94.54% originated from HIC, 4.46% from UMIC, 0.5% from LMIC, and none from LIC and 0.5% from a country with an undetermined income level (Ethiopia) [13].

3.3. Benchmark 3: were stakeholders from low-income countries involved in the core panel's preparation for consensus voting?

For the AASM/SRS statement, the core panel consisted of 15 members, including 14 men and 1 woman. All members were affiliated with institutions in the United States, a HIC. No representation from LIC, LMIC, or UMIC was identified.

For the NSF statement, the core panel consisted of 18 members, including sleep researchers, physicians, and experts in medicine, physiology, and related sciences. Twelve representatives were selected by stakeholder organizations, and six sleep experts were appointed by the NSF. Stakeholder organizations included the American Academy of Pediatrics, American Association of Anatomists, American College of Chest Physicians, American Congress of Obstetricians and Gynecologists, American Geriatrics Society, American Neurological Association, American Physiological Society, American Psychiatric Association, American Thoracic Society, Gerontological Society of America, Human Anatomy and Physiology Society, and the Society for Research in Human Development. All panel members were affiliated with institutions in HIC, primarily the United States and Canada, with one member from Italy. No representation from LIC, LMIC, or UMICcountries was identified.

3.4. Benchmark 4: did the multidisciplinary expert panel responsible for voting on the appropriateness of scenarios include representatives from low-income countries or individuals with expertise in sleep or health in low-income countries?

The AASM/SRS multidisciplinary expert panel responsible for voting on the appropriateness of scenarios included experts with specialties in obstructive sleep apnea, sleep-disordered breathing, narcolepsy, insomnia, parasomnias, behavioral sleep disorders, psychiatry, pulmonary health, and neurological disorders. No representatives from LIC were included on the panel, and no listed expertise specifically referenced sleep or health in LIC contexts.

The NSF multidisciplinary expert panel comprised individuals with clinical expertise in internal medicine, geriatric medicine, sleep medicine, polysomnography, pediatric neurology, insomnia, circadian rhythm disorders, sleep apnea, nightmares, sleepwalking, narcolepsy, respiratory disease, sleep-disordered breathing, pediatric obstructive sleep apnea, pulmonology, critical care, epidemiology, public health, dementia, geriatrics, clinical psychology, and neurology. No representatives from LIC were included on the panel, and no listed expertise specifically referenced sleep or health in LIC contexts.

4. Discussion

The World Sleep Society Global Sleep Health Taskforce determined that sleep health data from high-income countries could be projected to assess the health consequences of sleep deficiency in LIC [1]. The objective of this paper was to examine the potential suitability of adapting the AASM/SRS [3] and NSF [4] consensus recommendations on sleep duration for use in LIC. This assessment applied four WHO contextualization benchmarks: consideration of LIC priorities in the formulation of research questions and literature search strategies; inclusion of evidence from studies conducted in LIC in the synthesized literature; involvement of stakeholders from LIC in the core panel's preparation for consensus voting; and, representation of LIC or relevant expertise in the multidisciplinary expert panel responsible for voting on the appropriateness of scenarios.

Across all four benchmarks, neither consensus process incorporated priorities, evidence, or representation from LIC. Therefore, caution is needed when applying sleep duration recommendations developed in HIC to LIC settings. Because recommendations developed in HIC may not reflect the cultural, environmental, and socioeconomic realities of LIC, applying them can lead to inappropriate or misleading public health guidance.

Our review indicates that almost all evidence shaping current sleep duration recommendations comes from HIC, with no studies from LIC or LMIC. Furthermore, current indicators of healthy sleep duration are based on evidence that is not only concentrated in high-income settings but also largely derived from studies conducted more than a decade ago. This is inconsistent with the WHO ADOLOPMENT guideline [11], which requires recent evidence and permits either empirical data or, when such data are unavailable, consensus opinions and knowledge-based input from local stakeholders.

These findings confirm the urgent need to collect more globally representative sleep data. However, expanding data collection without considering cultural, environmental, or local priorities hinders progress and contradicts the WHO's contextualization principle [[7], [8], [9], [11]].

Approaches to Data Collection. Updating adult sleep duration recommendations should go beyond refreshing data; the process should integrate contemporary research practices that involve stakeholders throughout the research process. Recently, implementation science has emphasized approaches such as the Knowledge-to-Action (KTA) framework, which supports co-creation by defining desired changes, assessing contextual needs, and addressing barriers before selecting strategies [[15], [16], [17], [18], [19]]. Evidence shows that engaging communities as partners—through co-definition of indicators, adaptation of tools, and shared decision-making—produces data that reflect real-world conditions and makes resulting recommendations more feasible and acceptable for implementation [20]. Participatory processes not only generate context-specific data but also increase engagement [21] and relevance, ensuring that resulting recommendations are practical, equitable, and grounded in lived experience rather than imported standards [22].

Facilitating Relevant Data Production. The absence of stakeholders from LIC in consensus panels highlights another critical gap in guideline development. To ensure cultural relevance and practical implementation, future processes should include local experts, clinicians, and public health officials, alongside community stakeholders who can provide lived experience and local priorities [20,21]. Building capacity for local researchers to participate in evidence generation and guideline formulation is essential for ownership and sustainability [23]. It reduces reliance on artificial assumptions of similarity and avoids the lengthy, resource-intensive process of convening international experts to produce global guidelines based on outdated evidence, which often lags behind current realities. When stakeholder opinion is incorporated, it is imperative to establish a clear and comprehensive definition of roles and employ a standardized approach to distinguish evidence from opinion. These measures should be systematically applied to address evidence gaps and interpret ambiguous findings, thereby ensuring methodological rigor and transparency.

Country-Level Contextualization: Rejecting One-Size-Fits-All. The notion that “one size fits all” in health policy is increasingly being challenged. Personalizing public health interventions can enhance outcomes, improve adherence, and promote equity [23].

The process of developing consensus-based recommendations can be applied to create locally relevant guidelines in specific countries where, despite shared characteristics such as low income, substantial differences in cultural, social, and health system realities may limit the effectiveness of global guidelines designed for broad contexts.

Recognition of the need to contextualize public health recommendations at the country level parallels calls to personalize interventions for individuals, both rejecting the “one-size-fits-all” approach. Just as individuals differ in their DNA variants, influencing how they respond to treatments, countries differ in their structural and contextual makeup—such as cultural norms, health system capacity, and socioeconomic status.

Sleep health prioritization. Although sleep health is recognized as a global priority by the World Sleep Society and other sleep-advocacy organizations, it may not yet be a public-health priority in many LIC. Within the Adolopment process, recommendations are ranked within national health plans through structured Evidence-to-Decision (EtD) deliberations. These deliberations consider local disease burden, feasibility, acceptability, equity, costs, and health-system capacity before determining whether to adopt, adapt, or develop recommendations de novo. Therefore, when sleep is not prioritized, this reflects context-specific trade-offs rather than neglect.

Imposing an external agenda—for example, assuming that sleep must be a universal priority—risks poor uptake, low sustainability, and unintended opportunity costs. Recent work has demonstrated context-appropriate strategies for elevating the role of sleep health within public-health agendas in LIC [[24], [25], [26]], as well as advancing population sleep health more broadly in these settings. These efforts highlight the importance of tailoring advocacy, capacity building, and programmatic approaches to local sociocultural contexts, and system-level realities in order to effectively promote population sleep health in resource-constrained settings [[24], [25], [26]].

Future Directions. Strengthening future guideline development requires several actions. First, expand evidence searches beyond high-income sources by incorporating regional databases and gray literature, foster partnerships with local institutions to generate empirical research data and capture unpublished findings [24]. Second, prioritize recent, context-specific evidence through participatory research approaches, and where empirical data are limited, incorporate locally informed consensus. Third, ensure inclusive governance by forming multidisciplinary panels with regional experts, clinicians, public health officials, and community representatives to guarantee cultural relevance and ownership [[22], [23], [24]]. When incorporating stakeholder input, clearly define roles and use a standardized framework to separate evidence from opinion. Fourth, improve implementation feasibility by applying frameworks such as Knowledge-to-Action (KTA) to co-create strategies with end-users, assess barriers early, and adapt recommendations to local systems [20]. Fifth, because such criteria do not currently exist, establish clear standards for determining when locations share sufficiently similar populations, challenges, and contextual realities to strengthen assessments of generalizability and support appropriate global application.

Accomplishing “Sleeping Without Borders” requires overcoming not only concrete barriers such as access and implementability, but also the boundaries inherent in our professional paradigms. We need to broaden our toolbox to include approaches that combine scientific rigor with real-world relevance, while ensuring they can determine how to avoid being overly localized or excessively generalized—a balance that requires careful fine-tuning. While technological capabilities have progressed, corresponding evolution in conceptual frameworks, methodological standards, and indicators of scholarly merit is imperative to ensure meaningful impact across diverse national settings and local realities. Together, these steps are expected to align rigor with inclusivity, making guidelines both current and implementable.

5. Conclusion

The evidentiary foundations of widely cited adult sleep duration recommendations are overwhelmingly rooted in high-income settings, with virtually no representation from LIC and no formal involvement of low-income stakeholders in guideline development. When judged against WHO contextualization benchmarks and GRADE-ADOLOPMENT principles, these recommendations are not suitable for simple adoption in LIC and would require substantial contextualization or de novo development.

HIC-based guidelines can inform the starting point, but adoption requires a structured GRADE-ADOLOPMENT process. However, advancing “Sleeping Without Borders” will require guideline processes that deliberately broaden evidence sources beyond high-income databases, co-produce recommendations with researchers and communities in low-income settings, and embed sleep health within broader public health and social policy agendas while balancing the risks of either excessive localization or overgeneralization.

CRediT authorship contribution statement

Reut Gruber: Writing – review & editing, Data curation, Conceptualization. Jean-Philippe Chaput: Writing – review & editing. Oliviero Bruni: Writing – review & editing. Rosalie Barbeau: Resources.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Reut Gruber reports was provided by McGill University. Reut Gruber reports a relationship with Canadian Institutes of Health Research that includes: funding grants.

Reut Gruber has patent NA pending to NA.

If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article: