Original Article | Author(s)Tim Smith

Fact checked by: Chelsie Derman

In this analysis, investigators looked at the extent to which psoriasis severity correlates with patients’ risk of developing sleep disorders.

Mild psoriasis is significantly linked to insomnia, restless leg syndrome, and obstructive sleep apnea, new findings suggest, although moderate-to-severe psoriasis demonstrated greater magnitudes of association with such sleep disorders.¹

These findings and others resulted from a recent analysis that was conducted by investigators such as Michael J. Diaz, MD, from University of Florida, Gainesville’s College of Medicine. The study’s coauthors highlighted that severe psoriasis has been associated with increased sleep quality issues, especially among individuals living with intense inflammation and itch.

“Other factors such as race/ethnicity, socioeconomic status and comorbid conditions may further influence the presentation of [psoriasis] and its impact on sleep,” Diaz and colleagues wrote.¹ “Accounting for this diversity is essential for developing tailored interventions that address the distinct needs and health disparities within the [psoriasis] community.”

The investigators explored data from the All of Us version 7 database, which includes approximately 45% racial and ethnic minority participants. Their analysis was conducted to identify patients who had a diagnosis of psoriasis.

Diaz et al noted that individuals who did not meet certain treatment thresholds were classified as having mild disease. Moderate-to-severe psoriasis was defined by the investigative team as having a history of systemic therapy use, including such drugs as methotrexate, psoralen, cyclosporine, or acitretin. They also included patients in this category who had used biologics such as infliximab, adalimumab, etanercept, ustekinumab, iixekizumab, secukinumab, brodalumab, bimekizumab, tildrakizumab, guselkumab, or risankizumab, or used phototherapy.

The team assessed links between psoriasis and a set of 5 sleep-related conditions: insomnia, restless legs syndrome (RLS), obstructive sleep apnea (OSA), REM sleep behavior disorder (RBD), and narcolepsy. They also used a 1:4 nearest-neighbor propensity score matching approach with the goal of controlling for age, sex, and race/ethnicity.

There were 7,473 adults with psoriasis that Diaz and colleagues identified, with the group’s mean age being 62.6 years and 57.4% being female. Among these individuals, 1,935 (25%) were classified by the investigative team as having moderate-to-severe disease.

In their socioeconomic data, it was shown that 19.9% had annual incomes below $25,000, and 20.3% had never attended college. The findings suggest that racial distribution was similar between the study’s different disease severity cohorts, with approximately 71.3% of mild cases and 71.4% of moderate-to-severe cases identifying as White.

A higher average body mass index (BMI) was observed amonng those with psoriasis versus the matched control arm of the study (P < .001). BMI was shown to be slightly higher among those with moderate-to-severe disease compared to subjects with mild disease.

Significant elevation in rates of anxiety and type 2 diabetes mellitus (T2DM) were also noted by Diaz and coauthors versus the control group—anxiety: 48.5% versus 30.6% (P < .001); T2DM: 30.2% versus 19.7% (P < .001). In their multivariable regression analysis, results indicated that subjects living with mild psoriasis had increased odds of experiencing insomnia (OR: 1.48; CI: 1.37–1.60), RLS (OR: 1.20; 95% CI: 1.05–1.37), and OSA (OR: 1.38; CI: 1.28–1.49), even after adjusting for anxiety, various demographic variables,T2DM, BMI, smoking, and chronic pruritus.

Such associations were shown by the investigators to be even more pronounced for trial participants with moderate-to-severe disease—insomnia: OR 1.74 (CI: 1.53–1.97); RLS: OR 1.64 (CI: 1.33–2.02); OSA: OR 1.81 (CI: 1.60–2.06). Neither psoriasis severity cohort demonstrated a statistically significant link to narcolepsy or REM sleep behavior disorder.

Odds of sleep disorder risks were further shown by the investigative team as highest among non-White patients, after they conducted their race- and ethnicity-stratified analysis.

“In sum, this analysis provides additional support for the PsO-sleep disorder association in a diverse adult population with robust covariate control,” they wrote.¹ “Notably, none of the studies cited in our discussion stratified sleep outcomes by race/ethnicity as we have done. This stratification is crucial, as it reveals significant disparities in sleep disorder prevalence among PsO patients from different racial and ethnic backgrounds.”

References

1. Diaz MJ, Haq Z, Tran JT, et al. (2025). The Association of Psoriasis With Sleep Disorders in a Diverse National Cohort. JEADV Clinical Practice. https://doi.org/10.1002/jvc2.70022.
2. M Abrouk, K Lee, M Brodsky, et al. “Ethnicity Affects the Presenting Severity of Psoriasis,” Journal of the American Academy of Dermatology 77, no. 1 (2017): 180–182, https://doi.org/10.1016/j.jaad.2017.02.042.
3. E Mahé, A Beauchet, Z Reguiai, et al. “Socioeconomic Inequalities and Severity of Plaque Psoriasis at a First Consultation in Dermatology Centers,” Acta Dermato Venereologica 97, no. 5 (2017): 632–638, https://doi.org/10.2340/00015555-2625.

Summary: Researchers from the University of Cambridge have developed “smart pajamas” with printed fabric sensors and a lightweight AI model that can accurately monitor sleep disorders at home by detecting different sleep states and wirelessly transmitting data for potential long-term health monitoring.

Key Takeaways: 

• The SleepNet AI model processes sensor data in real time, identifying sleep conditions like snoring, teeth grinding, central sleep apnea, and obstructive sleep apnea with minimal computational power.
• The pajamas can wirelessly transmit data to a smartphone or computer and operate with low energy consumption, making them practical for long-term use without requiring a hospital visit.
• Researchers aim to adapt the technology for other health monitoring uses, including baby monitoring and tracking additional respiratory or neurological conditions.

Researchers have developed comfortable, washable “smart pajamas” that can monitor sleep disorders such as sleep apnea at home.

The team, led by the University of Cambridge, developed printed fabric sensors that can monitor breathing by detecting tiny movements in the skin, even when the pajamas are worn loosely around the neck and chest.

According to results reported in the Proceedings of the National Academy of Sciences, the smart pajamas can identify six different sleep states with 98.6% accuracy, while ignoring regular sleep movements such as tossing and turning. The energy-efficient sensors only require a handful of examples of sleep patterns to identify the difference between regular and disordered sleep.

“Poor sleep has huge effects on our physical and mental health, which is why proper sleep monitoring is vital,” says Luigi Occhipinti, CEng, PhD, SMIEEE, from the Cambridge Graphene Centre, who led the research, in a release. “However, the current gold standard for sleep monitoring, polysomnography or PSG, is expensive, complicated, and isn’t suitable for long-term use at home.”

“We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality.”

To develop the smart pajamas, Occhipinti and his colleagues built on their earlier work on a smart choker for people with speech impairments. The team re-designed the graphene-based sensors for breath analysis during sleep and made several design improvements to increase sensitivity.

“Thanks to the design changes we made, the sensors are able to detect different sleep states, while ignoring regular tossing and turning,” says Occhinpinti. “The improved sensitivity also means that the smart garment does not need to be worn tightly around the neck, which many people would find uncomfortable. As long as the sensors are in contact with the skin, they provide highly accurate readings.”

The researchers designed a machine learning model, called SleepNet, that uses the signals captured by the sensors to identify sleep states including nasal breathing, mouth breathing, snoring, teeth grinding, central sleep apnea, and obstructive sleep apnea. 

SleepNet is a “lightweight” artificial intelligence (AI) network that reduces computational complexity to the point where it can be run on portable devices, without the need to connect to computers or servers. “We pruned the AI model to the point where we could get the lowest computational cost with the highest degree of accuracy,” says Occhinpinti. “This way we are able to embed the main data processors in the sensors directly.”

The smart pajamas were tested on healthy patients and those with sleep apnea, and were able to detect a range of sleep states with an accuracy of 98.6%. By treating the smart pajamas with a special starching step, they improved the durability of the sensors so they can be run through a regular washing machine.

The most recent version of the smart pajamas can wirelessly transfer data, meaning the sleep data can be securely transferred to a smartphone or computer.

“Sleep is so important to health, and reliable sleep monitoring can be key in preventative care,” says Occhipinti. “Since this garment can be used at home, rather than in a hospital or clinic, it can alert users to changes in their sleep that they can then discuss with their doctor. Sleep behaviors such as nasal versus mouth breathing are not typically picked up in an NHS sleep analysis, but it can be an indicator of disordered sleep.”

The researchers are hoping to adapt the sensors for a range of health conditions or home uses, such as baby monitoring, and have been in discussions with different patient groups. They are also working to improve the durability of the sensors for long-term use.

Original Article MedScape | Edited by Anushree Chaphalkar

TOPLINE:

A new study found that pre-sleep screen time usage, more weekend screen time, using a phone as an alarm, and video game addiction were common factors associated with poor sleep patterns, poor sleep regulation, and an increased risk for obesity and adiposity in adolescents aged 11-14 years. Quality of life (QOL) partially mediated most of these associations.

METHODOLOGY:

• Researchers conducted a cross-sectional quantitative study (TSWS) including 62 school-going children of age 11-14 years (mean age, 12.2 years; 53.2% girls) from North-East Fife, Scotland.
• Participant demographics were self-reported. Chronotype, insomnia symptoms, sleep habits, and QOL were assessed using validated questionnaires, and sleep duration and sleep onset variability were measured using actigraphy.
• Adiposity (body fat percentage) was assessed using bioelectrical impedance, and obesity was assessed using body mass index (BMI) percentiles.
• The timing, quantity, location, and addiction of screen use were assessed using validated questionnaires.
• The potential role of QOL was investigated in the association between screen time and sleep and obesity.

TAKEAWAY:

• Frequent pre-sleep screen time usage (regression coefficient [β], 2.86; 95% CI, 1.39-4.34), frequent phone use in bed (β, 9.45; 95% CI, 3.64-15.26), videogaming addiction (β, 0.33; 95% CI, 0.06-0.61), and social media addiction (β, 0.37; 95% CI, 0.05-0.70) were significantly associated with a higher body fat percentage. Videogaming addiction (β, 1.03; 95% CI, 0.22-2.28) and social media addiction (β, 1.58; 95% CI, 0.11-3.04) were associated with higher BMI percentiles.
• Frequent post-sleep screen time usage (β, 1.64; 95% CI, 0.47-2.82), frequent pre-sleep screen time usage (β, 1.63; 95% CI, 0.45-2.82), frequent use of a phone as an alarm (compared with not; β, 8.14; 95% CI, 3.86-12.41), videogaming addiction (β, 0.43; 95% CI, 0.23-0.63), and social media addiction (β, 0.27; 95% CI, 0.04-0.51) were significantly associated with more severe insomnia symptoms in adolescents.
• Frequent pre-sleep screen time usage (β, 867.77; 95% CI, 313.04-1422.49) was significantly associated with a larger sleep onset variability in adolescents. More screen time on weekends (β, 1.59; 95% CI, 0.17-3.01) and keeping the phone in the bedroom overnight (β, −10.94; 95% CI, −19.89 to −1.98) were associated with poorer sleep habits.
• QOL partially mediated 51.4% of the association between weekend screen time and insomnia symptoms and 38.0% of the association between weekend screen time and body fat percentage.

IN PRACTICE:

“Our findings suggest that screen exposure is one of multiple contributing factors to poor sleep and increased adiposity rather than an isolated driver. Future research should examine whether a holistic approach — modifying pre-sleep screen habits alongside strategies to enhance wellbeing, increase physical activity, and improve sleep hygiene — offers a more effective multi-component solution to improving adolescent health,” the authors wrote.

SOURCE:

This study was led by Emma Louise Gale, University of St Andrews, Fife, Scotland. It was published online on May 07, 2025, in BMC Global and Public Health.

LIMITATIONS:

This study was limited by its cross-sectional design, which prevented conclusions about causality or directionality. Participants were recruited from a single county in Scotland, limiting generalisability in terms of ethnicity and socioeconomic status. Additionally, variations in weather and daylight hours during data collection may have affected activity levels and mood.

DISCLOSURES:

This study was funded by the University of St Andrews. The authors reported having no conflicts of interest.

Original Article | UChicago Medicine | by Esra Tesali, MD

From counting sheep to white noise and weighted blankets, people have tried innumerable ways to get a good night’s sleep. Sleep disruptions can have far-reaching negative consequences, impacting cardiovascular and metabolic health, memory, learning, productivity, mood regulation, interpersonal relationships and more.

It turns out that an important tool for improving sleep quality may have been hiding in plain sight…in the produce aisle. A new study led by researchers at the University of Chicago Medicine and Columbia University found that eating more fruits and vegetables during the day was associated with sleeping more soundly later that same night.

“Dietary modifications could be a new, natural and cost-effective approach to achieve better sleep,” said co-senior author Esra Tasali, MD, director of the UChicago Sleep Center. “The temporal associations and objectively-measured outcomes in this study represent crucial steps toward filling a gap in important public health knowledge.”

Uncovering the connection between diet and sleep

Studies have shown that inadequate sleep can cause people to gravitate towards unhealthier diets higher in fat and sugar. However, despite the far-reaching effects of sleep on public health and even economic productivity, doctors and scientists know far less about how diet can affect sleep patterns.

In previous observational studies, high fruit and vegetable intakes were associated with better self-reported overall sleep quality, but this new study was the first to draw a temporal connection between a given day’s dietary choices and objectively-measured sleep quality that same night.

Healthy young adults who participated in the study reported their food consumption each day with an app and wore a wrist monitor that allowed the researchers to objectively measure their sleep patterns. The researchers specifically looked at “sleep fragmentation,” an index that reflects how often someone awakens or shifts from deep to light sleep throughout the night.

Promising findings support dietary guidelines

The researchers found that each day’s diet was correlated with meaningful differences in the subsequent night’s sleep. Participants who ate more fruits and vegetables during the day tended to have deeper, more uninterrupted sleep that same night, as did those who consumed more healthy carbohydrates like whole grains.

Based on their findings and statistical modeling, the researchers estimate that people who eat the CDC-recommended five cups of fruits and veggies per day could experience a 16 percent improvement in sleep quality compared to people who consume no fruits or vegetables.

“16 percent is a highly significant difference,” Tasali said. “It’s remarkable that such a meaningful change could be observed within less than 24 hours.”

Future studies will help establish causation, broaden the findings across diverse populations, and examine the underlying mechanisms of digestion, neurology, and metabolism that could explain the positive impact of fruits and vegetables on sleep quality. But based on current data, the experts confidently advise that regularly eating a diet rich in complex carbohydrates, fruits, and vegetables is best for long-term sleep health.

“People are always asking me if there are things they can eat that will help them sleep better,” said co-senior author Marie-Pierre St-Onge, PhD, director of the Center of Excellence for Sleep & Circadian Research at Columbia. “Small changes can impact sleep. That is empowering — better rest is within your control.”

“Higher daytime intake of fruits and vegetables predicts less disrupted nighttime sleep in younger adults” was published in Sleep Health: The Journal of the National Sleep Foundation in June 2025. Co-authors include Hedda L. Boege (Columbia), Katherine D. Wilson (University of California San Diego), Jennifer M. Kilkus (UChicago), Waveley Qiu, (Columbia), Bin Cheng (Columbia), Kristen E. Wroblewski (UChicago), Becky Tucker (UChicago), Esra Tasali, (UChicago), and Marie-Pierre St-Onge (Columbia). The work was supported by grants from the National Institutes of Health (R01HL142648, R35HL155670, UL1TR001873, CTSA-UL1TR0002389, UL1TR002389, R01DK136214, T32HL007605), and the Diabetes Research and Training Center at the University of Chicago.

Untreated sleep apnea during pregnancy is associated with several increased health risks for both the mother and the baby. Sleep apnea is characterized by repeated interruptions in breathing during sleep, leading to poor oxygenation and fragmented sleep. Below is a summary of the risks:

Maternal Risks

1. Preeclampsia: Sleep apnea is linked to a higher risk of preeclampsia, a condition marked by high blood pressure and damage to organs such as the liver and kidneys.

2. Gestational Hypertension: Increased risk of elevated blood pressure during pregnancy.

3. Gestational Diabetes: Sleep apnea may impair glucose metabolism, leading to a higher incidence of gestational diabetes.

4. Cardiovascular Strain: Recurrent oxygen deprivation can stress the cardiovascular system, potentially leading to arrhythmias or heart issues.

5. Fatigue and Depression: Chronic sleep disruption can exacerbate pregnancy-related fatigue and increase the risk of depression or mood disorders.

6. AND, weight gain, hormonal changes and fluid retention can place you at a higher risk of having sleep apnea when you are pregnant.

Fetal Risks

1. Low Birth Weight: Poor oxygenation in the mother can restrict fetal growth.

2. Preterm Birth: Sleep apnea may increase the likelihood of delivering before 37 weeks of gestation.

3. Fetal Hypoxia: Reduced oxygen levels in the mother can lead to inadequate oxygen delivery to the fetus, affecting development.

4. Neonatal Complications: Babies born to mothers with untreated sleep apnea may face a higher risk of requiring neonatal intensive care.

Long-Term Considerations

Both the mother and the child may face long-term health implications if sleep apnea remains untreated. Early intervention, including the use of an oral appliance or continuous positive airway pressure (CPAP) therapy, weight management, and proper prenatal care, can mitigate many of these risks.

If you suspect sleep apnea during pregnancy, it is crucial to consult a healthcare provider for evaluation and management.