Effectiveness of Intranasal Corticosteroids for Sleep Disturbances in Patients with Allergic Rhinitis: A Systematic Review and Meta-Analysis

Allergic rhinitis (AR) is a chronic condition that causes significant discomfort and health burden [1, 2]. This disorder is typically triggered by an immunoglobulin (Ig) E-mediated type 1 response to an exposure to allergens, such as pollen, pet dander, dust mites, and mold spores [1, 2]. Classic AR symptoms include sneezing, rhinorrhea, and nasal congestion, which can profoundly reduce quality of life [1]. Globally, AR affects an estimated 10%–40% of the population, and its prevalence is rising due to changes in lifestyle and the environment [3‒6]. The economic impact of AR is substantial, with direct medical costs amounting to USD 3.4 billion annually and indirect costs from lost productivity reaching as high as USD 5.2 billion annually [7].

However, beyond the well-known symptoms listed above lies another troubling consequence – sleep disturbances [8]. Adequate sleep is essential for maintaining mood, cognitive function, and the function of the endocrine and immune systems [9‒11]. In adolescents, sleep disturbances are associated with worsened symptom severity and the persistence of attention-deficit/hyperactivity disorder (ADHD) into adulthood [12]. Addressing sleep quality and duration issues is crucial for the prevention of numerous health conditions, as research indicates that poor sleep and sleep disorders can lead to serious health issues, such as obesity, diabetes, hypertension, and cardiovascular diseases, ultimately increasing the risk of mortality [13‒17]. The link between AR and sleep disturbances is well established, yet often overlooked in clinical discussions. A systematic review by Liu et al. [8] highlighted the adverse effects of AR on sleep, revealing that patients with AR often experience increased sleep latency, frequent awakenings, and reduced sleep efficiency. These sleep disturbances exacerbate the overall burden of AR, contributing to cognitive impairment, diminished daily functioning, and lower quality of life.

Intranasal corticosteroids have long been a cornerstone in the management of AR symptoms due to their potent anti-inflammatory effects, which help alleviate nasal congestion and other related symptoms [18‒20]. Their efficacy in managing daytime AR symptoms is well documented, and they are recommended as the first-line treatment [1, 21].

Despite the recognized importance of sleep quality in AR and inclusion of sleep disturbances as a significant issue in the latest International Consensus Statement on Allergy and Rhinology, no comprehensive systematic review and meta-analysis have specifically examined the effects of intranasal corticosteroids on sleep disturbances in AR patients [1]. Considering the critical role of sleep in maintaining health, it is essential to determine whether intranasal corticosteroids can also enhance sleep quality in AR patients. Therefore, this study aimed to bridge this knowledge gap by systematically reviewing the literature and performing a meta-analysis to evaluate the efficacy of intranasal corticosteroids for sleep quality among individuals with AR.

Our study protocol was registered with the International Prospective Register of Systematic Reviews under the number, CRD42023460698, before the study search process was initiated.

This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 2020 statement [22]. We performed electronic searches on the following databases: Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via PubMed, and Ichushi-Web, which is the search engine that collates the medical literature published in Japan. All databases were searched using terms related to AR and intranasal corticosteroids (online suppl. Table 1; for all online suppl. material, see https://doi.org/10.1159/000541389) on August 26, 2023.

Studies were included if they met all of the following criteria:

• 1.

  Study design: randomized controlled trials (RCTs) comparing intranasal corticosteroids with a placebo

• 2.

  Study participants: participants diagnosed with AR by a clinician, regardless of age, gender, race, or setting

• 3.

  Intervention: intervention group/s treated with intranasal corticosteroids at any dosage or per the regimen planned by the trialists of the respective trials

• 4.

  Comparator: participants in the comparison group/s treated with a placebo

• 5.

  Other drugs: other drugs, if any, used in the treatment group also used in the same way in the control group

• 6.

  Outcome: improvement in sleep quality measured by standardized questionnaires

• 7.

  Language: articles published in English or Japanese.

• Studies were excluded if they met any of the following criteria:

• 1.

  Studies that included patients diagnosed with AR without a clinician’s diagnosis.

• 2.

  Studies that assessed the severity of AR, including sleep disturbance, without reporting changes in sleep-related outcomes.

Two of the four authors (K.T. and Y.S.) independently scanned the titles and abstracts of the articles obtained via the searches and identified full-text articles that appeared to meet the eligibility criteria or had insufficient information to assess eligibility. We independently assessed the eligibility of the trials and documented the reasons for exclusion. We resolved any disagreements between the two authors by discussion. Authors of articles were contacted if there was unclear or incomplete information. Any disagreements were resolved by consultation with the third reviewer (H.S.).

The following data relevant to the research purpose were extracted from each article: authors, year of publication, title, journal, study design, country in which the study was performed, inclusion/exclusion criteria, methods used for diagnosing AR, classification of AR into the perennial or seasonal type, allergen, comorbidity, intervention, participant characteristics (age, gender), methodology (number of participants randomized and analyzed, duration of follow-up), and outcome (the changes in sleep metrics measured by standardized questionnaires, such as the Epworth Sleepiness Scale [ESS], the Pittsburgh Sleep Quality Index [PSQI], the Rhinoconjunctivitis Quality of Life Questionnaire [RQLQ] sleep domain, the Nighttime Symptom Score (NSS), the Nocturnal Rhinoconjunctivitis Quality of Life Questionnaire [NRQLQ]) related to sleep disturbance. In addition, if any study defined the minimal clinically important difference (MCID) for sleep disturbance improvement in patients with AR, we assessed whether the symptom improvement in each included study exceeded the defined MCID. For example, the MCID for the NRQLQ score was set at 0.42 [23], for the RQLQ sleep domain at 0.5 [24], and for the NSS at 2.0 [25].

The data extraction process was collaboratively conducted by two authors (K.T. and Y.S.), who independently extracted and cross-checked data. If the extracted data differed between the two authors, the original studies were referred to, and a consensus was reached.

We assessed each study’s methodological quality and potential bias using RoB2 (Cochrane, London, United Kingdom), a revised tool for evaluating the risk of bias in randomized trials [26]. Two authors (K.T. and Y.S.) independently performed the quality assessment. Disagreements were discussed until a consensus was reached. Risk-of-bias plots were created using the robvis tool developed by McGuinness and Higgins [27], and a summary plot was constructed by equally weighting each study.

Data analysis and graph plotting were performed using The Comprehensive Meta-analysis software (CMA, version 4; Englewood, NJ, USA; www.meta-analysis.com). All statistical analyses were performed using random-effects modeling. A 95% confidence interval (CI) was used, with statistical significance defined as p < 0.05. The degree of statistical heterogeneity was evaluated using the I² statistic. The differences in the changes in sleep metrics between the treatment and placebo groups were examined through a meta-analysis. The standardized mean difference (SMD) in sleep metric scores, such as the RQLQ sleep domain, NRQLQ, and ESS scores, was used in this analysis. Meta-analyses were conducted for each sleep metric if there were two or more studies available. However, studies were not included in the meta-analysis if the number of studies was insufficient or if the necessary data for inclusion were not reported. These excluded studies are summarized in online supplementary Table 2. In the above meta-analysis, the following additional analyses were performed where possible:

• 1.

  Sensitivity analysis: A meta-analysis excluding studies with a high risk of bias according to RoB2 was conducted.

• 2.

  Subgroup analysis: Separate meta-analyses were conducted for patients with seasonal allergic rhinitis (SAR) and those with perennial allergic rhinitis (PAR).

The possibility of publication bias was assessed using a visual inspection of a funnel plot. Funnel plots were drawn if there were three or more studies available.

The quality of evidence obtained from the review was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) framework [28]. A summary of the findings, along with their corresponding GRADE ratings, is provided in a summary of findings table created with GRADEpro GDT [29].

We identified 1,284 potentially relevant publications from PubMed, 1,165 from Cochrane, and 770 from Ichushi. Endnote was used to eliminate duplicate publications, resulting in 2,798 records for screening. After excluding publications that did not meet the inclusion criteria, we included 18 studies for systematic review and meta-analysis. A flow diagram illustrating the exclusion of articles with specific reasons is shown in Figure 1.

Table 1 summarizes the included studies from which data, such as the study population, investigated measures, and individual parameter values, were extracted. A total of 18 studies, comprising 18 RCTs, were included in this systematic review. It should be noted that one study by Andrews et al. [23] reported two RCTs; additionally, two studies, one by Berger et al. [30] and another by Mohar et al. [31], reported on the same RCT (National Clinical Trial number, #NCT00953147) but as separate studies because the outcomes were measured at different timepoints. Each of the following four studies involved one RCT with three groups – one placebo group and two groups receiving different dosages of the active drug – resulting in two comparisons each: Berger et al. [30], Mohar et al. [31], Ratner et al. [32], and Ratner et al. [33]. Furthermore, the study by Ratner et al. [34] involved one RCT with four groups, leading to two comparisons. Consequently, a total of 24 comparisons were derived from the 18 included studies.

The included studies involved a total of 6,019 unique participants. Among these, the sex of 605 participants was not reported in three studies [30, 31, 44], and the age of 581 participants was not reported in two studies [30, 31]. Of the 5,414 participants whose sex was reported, 2,050 (37.9%) were male. The mean age of the 5,438 participants whose age was reported was 38.1 years old. Six studies [37‒39, 43, 44, 46] included adult participants, while the other 12 studies [23, 30‒36, 40, 42, 45] included participants aged ≥12 years, encompassing both children and adults.

Twelve studies [23, 30‒36, 40‒42, 45] were conducted as multicenter trials. Two studies [43, 46] were performed with a crossover design. Fifteen studies were conducted in North America or Europe [23, 30‒37, 39‒42, 44, 45], two were conducted in Asia [38, 46], and one was conducted in Oceania [43]. Ten studies [23, 32‒34, 36‒38, 42, 44, 45] enrolled patients with a documented clinical history of SAR for at least 2 years. Five studies [30, 31, 35, 40, 41] comprised patients who had a minimum 2-year history of symptoms indicative of PAR. One study [39] enrolled patients with PAR with or without SAR. One study [43] lacked a description of the type of AR. All included studies except one [46] used the skin prick test to diagnose AR. The study [46] that did not use the skin prick test used alternative criteria, including the detection of specific IgE responses to Dermatophagoides pteronyssinus or Dermatophagoides farinae in the blood and a positive nasal challenge test with a house-dust-coated disk.

All studies excluded patients with nasal disorders that could interfere with the efficacy evaluation, such as chronic rhinosinusitis, nasal polyps, nasal surgery, biopsy history, or trauma. Most studies excluded patients with varying severity of asthma. However, the six studies [30, 32‒34, 36, 46] did not explicitly mention asthma in their exclusion criteria, nor did they provide specific details about the participants’ asthma status. The one study [43] specifically included patients with coexisting asthma and AR. Only one study [39] included specific information regarding participants’ body mass index, employing an exclusion criterion of body mass index ≥40. Additionally, three studies [23, 35, 41] excluded smokers. No study reported participants’ smoking or drinking habits, except for the study by Rimmer et al. [43], which indicated the smoking rate among participants. With the exception of seven studies [30, 32‒34, 43, 44, 46], all studies listed the recent use of systemic or intranasal steroids as an exclusion criterion. The exclusion periods varied from 10 days to 4 weeks, with 4 weeks being the most common duration. Additionally, one study [43] excluded participants using high doses of oral and inhaled steroids but not those using lower doses, without specific information about participants’ inhaled steroid use.

The interventions used in the included studies are summarized in Table 1. Two comparisons [40, 45] involved beclomethasone dipropionate hydrofluoroalkane nasal aerosol 320 μg; three comparisons [30, 31, 33], ciclesonide hydrofluoroalkane (CIC-HFA) 148 μg; three comparisons [30, 31, 33], CIC-HFA 74 μg; one comparison [32], CIC-HFA 80 μg; five comparisons based on four studies [23, 35, 36, 41], fluticasone furoate nasal spray 110 μg; one comparison [37], fluticasone propionate nasal spray (FPNS) 100 μg; and two comparisons [34, 43], FPNS 200 μg. In Ratner’s study [34], two treatment comparisons were conducted: one comparison of FPNS 200 μg versus placebo and the other comparison of FPNS 200 μg + loratadine 10 mg versus placebo + loratadine 10 mg. One comparison [44] involved mometasone furoate nasal spray (MFNS) twice daily during the first week and once daily during the second week (dosage not specified), one [38] involved MFNS 100 μg, and three [39, 42, 46] involved MFNS 200 μg. The duration of the study periods varied across the included studies, ranging from 2 to 26 weeks. Most studies had a study period of 2–6 weeks, while a few extended up to 26 weeks.

The RQLQ sleep domain was assessed in 14 comparisons, based on 13 studies, with the following outcome measurement timings: eight comparisons, based on seven studies, measured outcomes at 2 weeks or almost 2 weeks [32‒34, 36, 42, 44, 45]; three comparisons at 4 weeks [35, 37, 41]; two comparisons at 6 weeks [31, 40]; and one comparison at 26 weeks [30]. The NRQLQ score was evaluated in two comparisons by Andrews et al. [23] in both their first and second RCTs. The ESS was utilized in four comparisons [38, 39, 44, 46], the NSS was measured in a single comparison [39], and the PSQI was assessed in one comparison [43]. The specific measurement timings for these outcomes are detailed in Table 1.

The risk of bias for each included study was evaluated using the RoB2 tool [26]. The results were summarized using a traffic light plot and a summary bar plot (shown in Fig. 2). In the domain of bias arising from the randomization process (domain [D]1), most studies did not clearly describe the methods used for random sequence generation or whether allocation concealment was implemented, resulting in an unclear risk of bias assessment for this domain. Bias due to deviations from intended interventions (D2), bias due to missing outcome data (D3), and bias in the measurement of the outcome (D4) were generally assessed as low across the studies. Only one study [30] raised concerns regarding bias due to missing outcome data (D3), with 13.1% of participants discontinuing before study completion. There was great variability in bias in the selection of the reported result (D5). Two studies [30, 31] analyzed only patients with severe disease at baseline for the outcome assessment, which was not specified in the protocol, leading to a high risk of bias due to selective reporting. Overall, two studies [30, 31] were assessed as having a high risk of bias, five studies [23, 32, 36, 41, 43] were considered to have a low risk of bias, and the remaining studies were evaluated as having some concerns of bias.

The meta-analysis included several comparisons, as illustrated in the following figures: Figure 3, RQLQ sleep domain; Figure 4, NRQLQ; Figure 5, ESS; Figure 6, sensitivity analysis (RQLQ sleep domain); and Figure 7, subgroup analysis. The SMD results were generally favorable for the treatment group, with the exception of the ESS, which did not show significant results.

In the RQLQ sleep domain (shown in Fig. 3), the analysis included 12 comparisons, based on eight studies, yielding an SMD of 0.292 (95% CI: 0.235–0.350, p = 0.000, I² = 0.0%, moderate certainty of evidence; Table 2) [30‒33, 35, 36, 40, 45]. Of these, seven comparisons from five studies reported results that exceeded the MCID of 0.5 points [32, 33, 35, 36, 40].

In the case of the NRQLQ (shown in Fig. 4), two comparisons, based on one study, were included, with an SMD of 0.284 (95% CI: 0.164–0.404, p = 0.000, I² not applicable, high certainty of evidence; Table 2) [23]. Both comparisons exceeded the MCID of 0.42. For the ESS (shown in Fig. 5), two studies were analyzed, resulting in an SMD of 0.027 (95% CI: −0.429 to 0.483, p = 0.907, I² not applicable, moderate certainty of evidence; Table 2) [38, 44].

The sensitivity analysis for the RQLQ sleep domain (shown in Fig. 6) included eight comparisons, based on six studies, showing an SMD of 0.304 (95% CI: 0.235–0.373, p = 0.000, I² = 0.0%, high certainty of evidence; Table 2) [32, 33, 35, 36, 40, 45]. Excluding the study by van Bavel et al. [45], other five studies reported results that exceeded the MCID [32, 33, 35, 36, 40].

The subgroup analysis was divided into two parts. One subgroup included patients with SAR (shown in Fig. 7a), involving six comparisons, based on four studies, with an SMD of 0.329 (95% CI: 0.250–0.408, p = 0.000, I² = 0.0%) [32, 33, 36, 45]. Excluding the study by van Bavel et al. [45], other three studies reported results that exceeded the MCID [32, 33, 36]. The other subgroup included patients with PAR (shown in Fig. 7b), involving six comparisons, based on four studies, showing an SMD of 0.252 (95% CI: 0.168–0.336, p = 0.000, I² = 0.0%) [30, 31, 35, 40]. Of these, two comparisons from two studies reported results that exceeded the MCID [35, 40]. The certainty of evidence for patients with SAR is considered high (Table 2). However, the certainty of evidence for patients with PAR was downgraded because, in the studies by Berger et al. [30] and Mohar et al. [31], changes in the RQLQ sleep domain were reported only for patients with a baseline RQLQ total score of ≥3.0, indicating indirectness.

Notably, the studies by Berger et al. [30] and Mohar et al. [31] reported on the same RCT, with Berger et al. covering a 26-week observation period and Mohar et al. [31], a 6-week period, sharing both the treatment and control groups [30]. To confirm the robustness of the previous analyses, additional analyses were conducted by excluding either the Berger et al. [30] study or the Mohar et al. [31] study. In both the overall RQLQ sleep domain analysis and PAR subgroup RQLQ sleep analysis, nasal steroid sprays significantly improved the RQLQ sleep domain scores, even when excluding either the Berger et al. [30] study or the Mohar et al. [31] study (shown in online suppl. Fig. 1, 2). These results indicate that nasal steroid sprays generally have a favorable effect on sleep metrics in patients with AR, with significant improvements observed in most measures except for the ESS score.

Online supplementary Table 2 provides a detailed summary of the eight comparisons, based on seven studies, which were not included in the meta-analysis [34, 37, 39, 41‒43, 46]. The table outlines the changes in sleep metrics before and after the intervention in both the treatment and placebo groups and describes the differences between these groups. In the study by Jen et al. [37], there was no significant improvement in the RQLQ sleep domain. Conversely, the study by Ratner et al. [34] showed a significant improvement in the RQLQ sleep domain. In the study by Meltzer et al. [39], while the NSS did not significantly improve, the ESS score did. Although some studies did not report on statistical analyses, multiple studies indicate that intranasal corticosteroid sprays may improve sleep metrics.

The funnel plots for all the analyses are presented in online supplementary Figure 3. In the funnel plots for the studies analyzed in Figures 3, 6, and 7, visual inspection did not suggest any specific bias.

The summary of findings from this review, along with the GRADE framework, is presented in Table 2.

This systematic review and meta-analysis included 18 RCTs involving 6,019 unique participants. The findings indicate that intranasal corticosteroids significantly improve sleep quality in patients with AR, including both SAR and PAR. The improvement in sleep quality was consistently observed across studies that used various validated tools, including the RQLQ sleep domain [47, 48] and NRQLQ [49] with moderate and high quality of evidence, respectively. Notably, all of the NRQLQ results and the majority of the RQLQ sleep domain results demonstrated clinically meaningful improvements, with most comparisons and sensitivity analyses exceeding the established MCID. Based on these findings, this study recommends the use of intranasal corticosteroids for patients with AR who experience sleep disturbances.

All the included studies used the skin prick test or IgE test, ensuring reliable patient selection. This consistent and accurate identification of patients with AR enhanced the reliability of the findings. Furthermore, the exclusion of patients with nasal disorders that could interfere with the efficacy evaluation. This careful patient selection process in the included studies ensured that the observed effects were due to the intervention.

The results of this systematic review and meta-analysis demonstrate robustness due to several factors. First, the use of multiple outcome measures related to sleep disturbances, including the RQLQ sleep domain, NRQLQ, and ESS scores, allowed for a nuanced assessment of the effects of the intervention on sleep. Despite the inclusion of studies with a high risk of bias according to the RoB2 assessment, sensitivity analyses confirmed the consistency of the findings, underscoring the robustness of the results. Additionally, the low heterogeneity observed in the meta-analyses further supports the consistency and robustness of the results.

Clinically, these findings reinforce current guidelines that recommend intranasal corticosteroids as the first-line treatment for AR, particularly highlighting their efficacy across different age groups, including both adults and children aged ≥12 years [1, 21]. Although the positive effect of intranasal corticosteroids on sleep quality has been suggested previously, this study is the first systematic review and meta-analysis to confirm these effects comprehensively. The alignment with existing guidelines not only validates the current practice but also provides stronger evidence for the use of these agents, potentially influencing future clinical decision-making and patient management strategies.

The improvement in sleep quality observed with intranasal corticosteroid is thought to be attributed to several mechanisms as suggested by prior research [8]. First, intranasal corticosteroids effectively reduce nasal inflammation and congestion, which are common symptoms of AR that can disrupt sleep [50]. By alleviating these symptoms, patients may experience fewer disturbances during the night, leading to improved sleep continuity and quality. Additionally, previous studies have indicated that intranasal corticosteroids may reduce the severity of nocturnal symptoms, such as nasal obstruction, sneezing, and itching, which further contributes to better sleep [51]. In this systematic review and meta-analysis, we focused primarily on changes in sleep symptoms as our main outcome measure. Although we classified AR into subtypes such as SAR and PAR, we did not classify specific AR symptoms like the degree of nasal blockage. Examining the relationship between sleep improvement and other clinical symptoms of AR, such as nasal obstruction or running nose, is important and should be the focus of future research. While this aspect was beyond the scope of our current study, further investigation into additional clinical outcomes could provide a more comprehensive understanding of the effects of intranasal corticosteroids.

This review had several limitations. First, in the risk of bias assessment, many studies did not clearly describe the methods for random sequence generation and allocation concealment, leading to an unclear risk of bias in the randomization process. Second, the variability in the duration of the studies, ranging from 2 to 26 weeks, might influence the comparability of the results, as longer studies may capture different aspects of treatment efficacy. Third, while most studies excluded the recent use of steroids, the variation in the exclusion periods (10 days–4 weeks) could introduce inconsistencies in baseline conditions among participants. Fourth, 12 of 18 included studies were conducted in the USA or Canada, indicating a potential geographic bias. This is particularly relevant as the allergens causing AR, such as pollen and dust mites, can vary significantly across different regions of the world, thereby introducing heterogeneity. Fifth, the lack of detailed reporting on participant characteristics, such as age and sex, in some studies limited the ability to perform more granular subgroup analyses. Sixth, although visual inspection of the funnel plots did not suggest any specific bias, the potential for publication bias cannot be entirely ruled out.

Seventh, while some studies included in our review accounted for lifestyle factors impacting both AR symptoms and sleep quality, such as smoking, alcohol consumption, and weight, comprehensive consideration was lacking. Additionally, the consideration of asthma as a potential complication was not consistent across the studies. Both lifestyle factors and the presence of asthma can influence the efficacy of intranasal corticosteroids in treating AR and associated sleep disturbances. However, despite these inconsistencies, the RCT design employed in the studies – with comparisons between nasal corticosteroids and placebo – demonstrated generally favorable outcomes across the board. This suggests that nasal corticosteroids were effective in improving sleep disturbances beyond the potential impacts of both lifestyle factors and asthma. Future research should systematically record and analyze these factors to provide a more holistic understanding of managing AR and its related symptoms.

Eighth, in the included studies, the assessment of sleep symptoms was based on questionnaires. While significant changes in questionnaire scores were observed in the active treatment groups in the RCTs, the physiological changes related to sleep symptoms remain unclear. To better evaluate sleep-related conditions, combining objective tests with subjective questionnaires may be beneficial. Polysomnography, for instance, can assess nasal airflow, snoring, and the degree of arousal during sleep, making it suitable for evaluating sleep disturbances associated with AR. Although a previous study suggested that the prevalence of obstructive sleep apnea in patients with PAR does not differ from that in patients without the condition, detailed examinations of physiological changes in this population are lacking [52]. Recently, the use of wearable devices has been proposed as a tool to better reflect actual sleep conditions in daily life, and this could also be a valuable addition [53].

Ninth, we performed separate meta-analyses for each sleep quality questionnaire used in the included studies. While this approach allows for a more accurate analysis within each scale, the use of different questionnaires introduces variability that may affect the comparability of the results across studies. This variability should be considered when interpreting the overall findings.

Finally, while the studies by Andrews et al. [23] and Yamada et al. [46] focused on sleep disturbances related to AR, the other studies primarily aimed at improving rhinitis symptoms. Consequently, the observation of changes in sleep symptoms was secondary in the study design. Future studies specifically focused on sleep disturbances in AR could better capture the effects of intranasal corticosteroid interventions on sleep quality.

In conclusion, this systematic review and meta-analysis highlight the beneficial effects of intranasal corticosteroids on sleep quality in patients with AR. These findings support their continued use as a first-line treatment and suggest directions for future research to further elucidate the effects of AR on sleep. Future studies should focus on exploring sleep quality changes as a primary outcome in intervention studies and on incorporating both subjective and objective measures to enhance our understanding of the relationship between sleep and AR symptoms.

We would like to thank librarians from the information service section of Shiga University of Medical Science Library, for their support in providing advice on search methods for the systematic review, creating search strategies, and conducting the searches. This study was supported by the Shizuoka Graduate University of Public Health and encouraged by Dr. Mikio Hoshino at National Center of Neurology and Psychiatry (NCNP).

Since the online databases used in our analysis are publicly accessible, this study did not require review by an Institutional Review Board.

The authors have no conflicts of interest to declare.

This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant No. 24K10731). The funder had no role in the design, data collection, data analysis, and reporting of this study.

Kenshiro Tabata and Yukiyoshi Sumi: conceived the systematic review, conducted the searches, assessed inclusion and extracted data, assessed risk of bias, performed the meta-analyses, wrote the review, and edited the manuscript. Hatoko Sasaki and Noriko Kojimahara: supervised the entire project. All authors: critically reviewed the manuscript for content, approved the final manuscript as submitted, and provided consent for publication of this article.

Kenshiro Tabata and Yukiyoshi Sumi contributed equally to this work.Edited by: D.Y. Wang, Singapore.

The data that support the findings of this study are publicly available. These data were extracted from previously published articles, which are listed in the References section. All data generated or analyzed during this study are included in this article and its supplementary material files. Further inquiries can be directed to the corresponding author.