Chronic insomnia affects 10%–15% of the population, with one-third of Western adults struggling with sleep initiation or maintenance. Binaural beats, which involve two audio frequencies, have shown the potential for enhancing sleep and mood. This study examined the efficacy of customized binaural audio tracks generated using facial analysis software to treat chronic insomnia. Methods: A 45-minute personalized binaural beat audio session was delivered using the Spatial app and headband (SoundHealth) to 20 participants with moderate-to-severe insomnia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and Insomnia Severity Index (ISI) criteria, over four weeks in California. Statistical analysis (paired t-test and linear mixed modeling) was used to compare baseline ISI scores to posttreatment scores, with p<0.05 indicating significance. The study assumed 80% power and aimed to achieve a 7-point ISI reduction. Results: All participants completed the study with no adverse events or full protocol adherence. The cohort was 60% White, with a 3:1 female-to-male ratio and an average age of 51.9 years. The baseline ISI was 19.8, dropping to 8.5 after four weeks, showing an 11.3-point reduction (95% confidence interval [CI]: -15 to -7.6, p<0.001). Mixed modeling indicated a similar ISI decrease of 11.28 points (95% CI: -14.98 to -7.57, p<0.001). The treatment response rate was 70%. Conclusions: Customized binaural beats show promise for insomnia treatment, with no adverse effects and high adherence. Most participants improved to no insomnia or subthreshold insomnia. Further research is needed to validate these results using larger samples and to assess long-term effects.

Chronic insomnia affects 10%–15% of the population, with one-third of Western adults struggling with sleep initiation or maintenance. Binaural beats, which involve two audio frequencies, have shown the potential for enhancing sleep and mood. This study examined the efficacy of customized binaural audio tracks generated using facial analysis software to treat chronic insomnia. Methods: A 45-minute personalized binaural beat audio session was delivered using the Spatial app and headband (SoundHealth) to 20 participants with moderate-to-severe insomnia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and Insomnia Severity Index (ISI) criteria, over four weeks in California. Statistical analysis (paired t-test and linear mixed modeling) was used to compare baseline ISI scores to posttreatment scores, with p<0.05 indicating significance. The study assumed 80% power and aimed to achieve a 7-point ISI reduction. Results: All participants completed the study with no adverse events or full protocol adherence. The cohort was 60% White, with a 3:1 female-to-male ratio and an average age of 51.9 years. The baseline ISI was 19.8, dropping to 8.5 after four weeks, showing an 11.3-point reduction (95% confidence interval [CI]: -15 to -7.6, p<0.001). Mixed modeling indicated a similar ISI decrease of 11.28 points (95% CI: -14.98 to -7.57, p<0.001). The treatment response rate was 70%. Conclusions: Customized binaural beats show promise for insomnia treatment, with no adverse effects and high adherence. Most participants improved to no insomnia or subthreshold insomnia. Further research is needed to validate these results using larger samples and to assess long-term effects.

Chronic insomnia affects 10%–15% of the population, with one-third of Western adults struggling with sleep initiation or maintenance. Binaural beats, which involve two audio frequencies, have shown the potential for enhancing sleep and mood. This study examined the efficacy of customized binaural audio tracks generated using facial analysis software to treat chronic insomnia. Methods: A 45-minute personalized binaural beat audio session was delivered using the Spatial app and headband (SoundHealth) to 20 participants with moderate-to-severe insomnia, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and Insomnia Severity Index (ISI) criteria, over four weeks in California. Statistical analysis (paired t-test and linear mixed modeling) was used to compare baseline ISI scores to posttreatment scores, with p<0.05 indicating significance. The study assumed 80% power and aimed to achieve a 7-point ISI reduction. Results: All participants completed the study with no adverse events or full protocol adherence. The cohort was 60% White, with a 3:1 female-to-male ratio and an average age of 51.9 years. The baseline ISI was 19.8, dropping to 8.5 after four weeks, showing an 11.3-point reduction (95% confidence interval [CI]: -15 to -7.6, p<0.001). Mixed modeling indicated a similar ISI decrease of 11.28 points (95% CI: -14.98 to -7.57, p<0.001). The treatment response rate was 70%. Conclusions: Customized binaural beats show promise for insomnia treatment, with no adverse effects and high adherence. Most participants improved to no insomnia or subthreshold insomnia. Further research is needed to validate these results using larger samples and to assess long-term effects.

This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity, the total sleep time (TST) standard deviation (SD) and sleep onset (SO) SD were included as parameters. Inferential analyses were performed on additional sleep parameters, including physiological (heart rate and respiratory rate) and sleep architecture parameters (TST, sleep efficiency, and wake after SO), in typically regular (average TST SD <60 minutes) vs. irregular (≥60 minutes) users. Results: 4,175,260 recorded nights were analyzed. The overall estimated TST SD (SD) across users mean was 66.1 (18.7) minutes, and the SO SD (SD) was 55.6 (20.5) minutes. The population was divided into 6 groups according to age: Groups 1 (20–29), 2 (30–39), 3 (40–49), 4 (50–59), 5 (60–69), and 6 (70–79). The estimated TST SD were: 70.7 (20.0), 67.2 (18.0), 66.8 (18.5), 66.1 (18.4), 63.4 (18.2), and 60.5 (18.9) minutes, respectively. The estimated SO SD were: 62.1 (21.1), 57.4 (19.4), 57.0 (20.2), 55.7 (20.0), 51.6 (20.3), and 46.7 (20.8) minutes, respectively. When participants were divided into those with regular and irregular sleep schedules, 58.4% were found to have an irregular sleep-wake schedule. Conclusions: Irregular sleep-wake schedules were prevalent across all age categories in this population. Even though this does not implicate causality, sleep habits represent a potentially modifiable behavior with health implications.

This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity, the total sleep time (TST) standard deviation (SD) and sleep onset (SO) SD were included as parameters. Inferential analyses were performed on additional sleep parameters, including physiological (heart rate and respiratory rate) and sleep architecture parameters (TST, sleep efficiency, and wake after SO), in typically regular (average TST SD <60 minutes) vs. irregular (≥60 minutes) users. Results: 4,175,260 recorded nights were analyzed. The overall estimated TST SD (SD) across users mean was 66.1 (18.7) minutes, and the SO SD (SD) was 55.6 (20.5) minutes. The population was divided into 6 groups according to age: Groups 1 (20–29), 2 (30–39), 3 (40–49), 4 (50–59), 5 (60–69), and 6 (70–79). The estimated TST SD were: 70.7 (20.0), 67.2 (18.0), 66.8 (18.5), 66.1 (18.4), 63.4 (18.2), and 60.5 (18.9) minutes, respectively. The estimated SO SD were: 62.1 (21.1), 57.4 (19.4), 57.0 (20.2), 55.7 (20.0), 51.6 (20.3), and 46.7 (20.8) minutes, respectively. When participants were divided into those with regular and irregular sleep schedules, 58.4% were found to have an irregular sleep-wake schedule. Conclusions: Irregular sleep-wake schedules were prevalent across all age categories in this population. Even though this does not implicate causality, sleep habits represent a potentially modifiable behavior with health implications.

This study aimed to characterize sleep-wake schedules in a large U.S. sample. Methods: Descriptive analyses were performed on data from 12,507 users (46.9% female; mean age, 48.5±13.4 years) of a home-based sleep-monitoring device. To understand sleep-wake schedules and sleep regularity, the total sleep time (TST) standard deviation (SD) and sleep onset (SO) SD were included as parameters. Inferential analyses were performed on additional sleep parameters, including physiological (heart rate and respiratory rate) and sleep architecture parameters (TST, sleep efficiency, and wake after SO), in typically regular (average TST SD <60 minutes) vs. irregular (≥60 minutes) users. Results: 4,175,260 recorded nights were analyzed. The overall estimated TST SD (SD) across users mean was 66.1 (18.7) minutes, and the SO SD (SD) was 55.6 (20.5) minutes. The population was divided into 6 groups according to age: Groups 1 (20–29), 2 (30–39), 3 (40–49), 4 (50–59), 5 (60–69), and 6 (70–79). The estimated TST SD were: 70.7 (20.0), 67.2 (18.0), 66.8 (18.5), 66.1 (18.4), 63.4 (18.2), and 60.5 (18.9) minutes, respectively. The estimated SO SD were: 62.1 (21.1), 57.4 (19.4), 57.0 (20.2), 55.7 (20.0), 51.6 (20.3), and 46.7 (20.8) minutes, respectively. When participants were divided into those with regular and irregular sleep schedules, 58.4% were found to have an irregular sleep-wake schedule. Conclusions: Irregular sleep-wake schedules were prevalent across all age categories in this population. Even though this does not implicate causality, sleep habits represent a potentially modifiable behavior with health implications.

Obstructive sleep apnoea (OSA) and Type 2 diabetes mellitus (T2DM) are common diseases. The global prevalence of OSA is between 2% and 7% in general population cohorts. The worldwide prevalence of T2DM among adults (aged 20-79 years) was estimated to be 6.4%. The concurrent presence of OSA and T2DM can be expected in the same patient, given their high prevalence and similar predisposition. We reviewed the overlapping pathophysiology of OSA and T2DM in this article.
Adult ; Aged ; Continuous Positive Airway Pressure ; Diabetes Mellitus, Type 2 ; complications ; epidemiology ; physiopathology ; Female ; Humans ; Male ; Middle Aged ; Sleep Apnea, Obstructive ; complications ; epidemiology ; physiopathology ; therapy ; Young Adult