Journal of Sleep Sciences

A Comprehensive Classification of Snoring Appliances: A Systematic Review

Maryam Jahangiri, Sayed Shojaedin Shayegh — Sun, 26 Jul 2026 13:46:24 +0430

Background and Objective: Obstructive sleep apnea (OSA) and snoring are common conditions that adversely affect sleep quality. Snoring has several etiologies; however, difficulty in airflow through the upper airways is considered a principal mechanism. If snoring persists despite addressing the underlying causes, appliances can help reduce snoring. A variety of appliances with different mechanisms have been proposed. This review aimed to classify appliances, their mechanisms of action, and methods of fabrication.

Materials and Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was performed in PubMed, Scopus, and Google Scholar for studies published between 1995 and 2024 using the following keywords: mandibular advance-ment devices, obstructive sleep apnea, snoring, continuous positive airway pressure, and dentistry.

Results: Of the 200 initially identified articles, 25 relevant abstracts were selected for full-text review, and 13 studies were ultimately included in the review. This study investigated the fabrication methods and characteristics of snoring appliances. Based on the reviewed literature, appliances used for snoring and OSA management were categorized into five groups: custom-made mandibular advancement splints (MASs), prefabricated mandibular advancement devices (MADs), modified complete dentures, bite-raising appliances (BRAs), and tongue-retaining devices (TRDs).

Conclusion: A wide variety of appliances have been proposed for snoring. MASs are the most frequently reported appliances, followed by TRDs. Current evidence suggests that these appliances are primarily effective in mild and mod-erate cases.

Prevalence and Risk Factors of Sleep Quality among Older Adults in Varamin, Iran

Sun, 26 Jul 2026 13:38:13 +0430

Background and Objective: The global aging population is increasing. Aging is associated with a gradual decrease in physiological activities and an increased risk of chronic diseases and sleep disorders. This study aimed to determine the prevalence and risk factors of sleep quality among elderly people.

Materials and Methods: This cross-sectional study was conducted on 894 elderly individuals aged 60 years or older, selected using a multistage sampling method in Varamin City, Tehran Province, Iran. Data were collected using the Pittsburgh Sleep Quality Index (PSQI). Mean and frequency were used for descriptive analysis, and multivariable logistic regression was used to identify risk factors for poor sleep quality. P-value < 0.05 was considered statistically significant, and Stata software was used for data analysis.

Results: Of the participants, 509 (56.5%) were women, and the mean age was 69.1 ± 7.5 years. The overall prevalence of poor sleep quality was 39.8%. Multivariable logistic regression analysis showed that poor sleep quality was signifi-cantly associated with female sex [male odds ratio (OR)=0.5, 95% confidence interval (CI): 0.4-0.7], coronary heart disease (CHD) (OR=1.9, 95% CI: 1.5-3.0), gastrointestinal (GI) disease (OR=1.8, 95% CI: 1.5-3.0), and depression (OR=3.6, 95% CI: 2.1-6.2). There was no relationship between age, marital status, occupation, education, financial sta-tus, and other diseases with sleep quality (P > 0.05).

Conclusion: Poor sleep quality among elderly individuals was relatively common and more prevalent among women. Moreover, comorbidities such as depression, CHD, and GI diseases were identified as factors affecting the quality of sleep in elderly people.

Investigating the Effect of Enhancing Understanding of Embodied Metaphors on Sleep Status in Children with Autism and Sleep Problems

Mahdi Madanifard, Sahar Bahrami-Khorshid, Hayat Ameri, Soghra Akbari Chermahini — Sun, 26 Jul 2026 13:14:35 +0430

Background and Objective: Children with autism spectrum disorder (ASD) often suffer from language disorders; however, those who experience sleep problems usually have more severe impairments in various domains of language and communication. Given the essential role of language in communication, this study aimed to investigate the effect of enhancing understanding of embodied conceptual metaphors on the sleep status of children with autism experiencing sleep problems.

Materials and Methods: This study employed a within-subjects design. The study population consisted of boys with autism aged 7-12 years. A total of 34 children with sleep disorders were selected using purposive sampling. Pre-test data were obtained using the Children's Sleep Habits Questionnaire (CSHQ), which includes items related to the cognitive, emotional, and communication difficulties in children with autism, such as sleep anxiety, sleep onset delay, bed-time resistance, and daytime sleepiness. Participants then underwent 20 sessions designed to enhance their understanding of conceptual metaphors. The CSHQ was readministered as a post-test and follow-up to evaluate the effect of language-based intervention on sleep problems.

Results: Repeated-measures analysis of variance (ANOVA) demonstrated a significant positive effect of enhancing understanding of embodied conceptual metaphors on sleep improvement in children with autism experiencing sleep problems (P < 0.0001).

Conclusion: Enhancing understanding of embodied conceptual metaphors may be effective in improving sleep prob-lems in children with autism, potentially through reducing sleep anxiety and increasing communication abilities.

Comparison of Sleep Health, Psychological Hardiness, and Social Responsibility in Community-Dwelling Older Adults and Nursing Home Residents

Maryam Shahyad, Nooshin Pordelan, Mahdi Khasmohammadi — Sun, 26 Jul 2026 13:01:18 +0430

Background and Objective: With the rapid global growth of the elderly population, identifying factors associated with their psychological and social well-being has become increasingly important. This study aimed to compare sleep health, psychological hardiness, and social responsibility between community-dwelling older adults and nursing home residents.

Materials and Methods: This causal-comparative (ex post facto) study examined differences between two naturally existing groups of older adults. A total of 120 participants (50 nursing home residents and 70 community-dwelling older adults) were recruited through convenience sampling. Data were collected using the Personal-Social Responsibility Questionnaire, the Sleep Health Scale (SHS) by Brandolim Becker et al., and the Kobasa Hardiness Scale. Data were ana-lyzed using SPSS software through descriptive statistics and independent samples t-tests with a significance level of 0.05.

Results: The mean age of participants was 66 years. Sleep health scores were significantly higher among community-dwelling older adults (19.31 ± 3.65) than nursing home residents (15.72 ± 3.11) [t(118) = -5.65, P = 0.001]. Similarly, social responsibility was significantly higher in community-dwelling participants (36.33 ± 6.13) compared with nursing home residents (24.74 ± 5.14) [t(118) = -10.91, P = 0.001]. No significant difference was observed in psychological hardiness between groups (P = 0.41).

Conclusion: The living environment appears to be associated with the sleep health and social responsibility of older adults, whereas psychological hardiness remains relatively stable across living conditions. These findings provide valu-able insights for policymakers and healthcare professionals in designing programs to improve the quality of life (QOL) in the older population.

Artificial Intelligence and Sleep Medicine

Morteza Zangeneh Soroush — Sat, 25 Jul 2026 14:38:25 +0430

Given the rapid advancement of artificial intelli-gence (AI), I aim to emphasize the emerging po-tential of AI in sleep medicine while also address-ing the challenges related to its adoption and im-plementation in clinical settings. AI has signifi-cantly enhanced our understanding of sleep pat-terns, enabling more precise diagnosis and per-sonalized treatment plans. Beyond sleep tracking, AI contributes to telemedicine, smartwatches, and more.