Association between objective sleep structure and suicidal ideation in patients with depression: a study based on polysomnographic regression and cluster analysis

BackgroundDepression is a common mental disorder, and current suicidal ideation (SI) is a clinically important symptom. Sleep disturbance is common in depression, but the relationship between objective sleep architecture and current SI remains unclear. This study examined associations between objective sleep structure and current SI and explored whether sleep-clinical clustering could support exploratory SI stratification.Methods287 patients meeting DSM-5 criteria for depressive disorder underwent clinical assessment and overnight polysomnography (PSG). Current SI was assessed using item 3 of the HAMD-17. Binary logistic regression evaluated factors associated with current SI. K-Means clustering (K = 3) was performed using standardized HAMD score, PSQI score, AHI, total awakenings, N3%, R%, and sleep efficiency. Quantitative cluster validation, sensitivity analyses excluding HAMD from clustering, and ROC-based performance metrics were additionally conducted.ResultsCurrent SI was present in 50.52% (145/287) of patients. In the individual-variable regression model, only HAMD total score was independently associated with current SI (OR = 1.683, p < 0.001). Cluster analysis identified three subgroups with distinct current SI rates: Cluster 1 (deep-sleep dominant, 37.04%), Cluster 2 (high-arousal-apnea, 70.37%), and Cluster 3 (low-arousal-subjective insomnia, 60.80%). The K = 3 solution showed WSS = 1450.71, mean silhouette = 0.204, and Davies-Bouldin index = 1.679. In the demographic-adjusted model, membership in Clusters 2 + 3 was associated with current SI (OR = 3.152, 95% CI: 1.883-5.274, p < 0.001; AUC = 0.700, 95% CI: 0.639-0.760). When HAMD was excluded from clustering, the association remained (OR = 2.669, 95% CI: 1.598-4.458, p < 0.001), whereas additional adjustment for HAMD total score attenuated the primary cluster association (OR = 0.842, 95% CI: 0.411-1.725, p = 0.639).ConclusionsDepression severity was the primary factor associated with current SI. PSG-derived sleep-clinical clusters may help characterize heterogeneous presentations of current SI, but their incremental value beyond depressive severity should be interpreted as exploratory and validated in larger longitudinal samples.

Subjective sleepiness and objective sleep propensity in adults with attention-deficit/hyperactivity disorder referred for multiple sleep latency testing

IntroductionAdults with attention-deficit/hyperactivity disorder (ADHD) often report excessive daytime sleepiness, but the relationship between subjective sleepiness and objective sleep propensity remains unclear. We examined this relationship in adults referred for Multiple Sleep Latency Test (MSLT) evaluation, using a clinical comparison group with excessive daytime sleepiness (EDS) but without ADHD.MethodsIn this retrospective cross-sectional study, we analyzed medical records of 130 adults aged 18 years or older who underwent MSLT between January and December 2021, including 68 adults in the ADHD group and 62 in the EDS-only group. Subjective sleepiness was assessed by the Epworth Sleepiness Scale (ESS) and objective sleep propensity by mean MSLT sleep latency, with MSLT positivity defined as mean sleep latency ≤ 480 s. Associations between ESS scores and mean sleep latency were assessed within each group, and correlation coefficients were compared between groups using Fisher’s r-to-z transformation.ResultsESS scores did not differ significantly between groups, with median scores of 14.0 in the ADHD group and 13.0 in the EDS-only group. In contrast, objective sleep propensity differed significantly: median mean sleep latency was longer in the ADHD group than in the EDS-only group (432.0 s vs 322.0 s, p = 0.008), and MSLT positivity was less frequent in the ADHD group (61.8% vs 87.1%, p = 0.001). Within the ADHD group, ESS scores were not significantly correlated with mean sleep latency, including among MSLT-positive cases. A significant inverse correlation was observed in the MSLT-positive EDS-only subgroup, although formal comparison of correlation coefficients did not demonstrate a statistically significant between-group difference in the ESS–MSLT relationship. SOREMP frequencies were numerically higher in the EDS-only group but did not differ significantly between groups.DiscussionThese findings suggest that subjective sleepiness complaints and objective sleep propensity may not closely align in adults with ADHD referred for sleep evaluation, and support the need for integrated psychiatric and sleep-medicine assessment when such patients present with excessive daytime sleepiness.

Heat waves mess with your brain. Scientists are trying to figure out why.

It’s been hot in London this week. Really hot. A dangerous heat wave has hit Western Europe. Yesterday, the UK recorded its highest ever June temperature at 36.1 °C (about 97 °F). But as the weather app on my phone confirmed, it felt like 39 °C.

It’s frightening that we are seeing such temperatures in the UK in June. According to the Met Office, the country’s national weather and climate service, June temperatures peaked at an average 19 °C (66 °F) in England between 1991 and 2020. Across Europe, the heat wave is likely to cause thousands of deaths. There will be other awful consequences for agriculture, infrastructure, and the health system.

But this week I want to look at what the heat does to our minds and brains. Personally, I’ve found it almost impossible to think straight. The heat is distracting and my mind is foggy. I dread to think about the conditions of people who work outdoors, in even hotter regions.

It’s not just exhaustion and confusion. The effects of heat on the brain can be deadly. And researchers are still trying to figure out why.

Studies have confirmed that as temperatures rise, people seem to get more irritable and more violent. Most of these studies are based on associations, though. It’s difficult to directly study how a heat wave might affect our thinking, says Catherine Thompson, a cognitive psychologist at Liverpool Hope University. 

She has been studying the effects of extreme heat on firefighters instead. It’s easier to measure people’s cognitive skills before and after they undergo scheduled training that involves entering a burning building.  

It’s early days, but the team found that firefighters found it harder to focus and control their attention immediately after heat exposure—something people in heat waves can empathize with, I’m sure. 

The firefighters’ skills returned to normal after 20 minutes or so of cooling down. But they’d experienced just 15 minutes of intense heat exposure. Thompson doesn’t know what the effects of living through a days-long heat wave might be—or how long they’ll last. Figuring that out might involve shipping cognitive test kits to thousands of people during the few days’ notice of an impending heat wave. “My guess [is] that no one’s done it because it’s just so difficult to do,” says Thompson. 

Still, researchers can learn about some of the impacts of heat waves through studies after the fact. And those studies suggest that the heat seems to have more disastrous outcomes for people with mental-health disorders. 

Those outcomes become apparent when temperatures rise above what is considered typical for a given region. “There seems to be a correlation where the hotter it gets, especially during the hottest times of the year, the worse the mental-health outcomes,” says Joshua Wortzel, who directs the Heat-Mind Lab at Hartford HealthCare in Connecticut.

In a study published in 2023, Emma Lawrence at the University of Oxford, who studies the effect of climate change on mental health, and her colleagues reviewed the evidence linking mental-health outcomes to ambient outdoor temperatures. They found that during heat waves, there was a 9.7% increase in the rate of hospital admissions for people with such conditions. 

“People who live with mental-health conditions are among the most susceptible to the physical impacts of heat,” says Lawrence. People with schizophrenia were found to have been three times more likely to die during the record-breaking heat wave that affected Canada in 2021, for example.

In order to protect people, we need a better understanding of the mechanisms underlying these effects. After all, a lot of things change when it’s very, very hot. Some people may end up stuck indoors, avoiding outdoor play and exercise, and it can be difficult to get a good night of sleep, for example. Sleep, socializing, and exercise are all really important for our mental health. 

But whether unusual heat does something specific to our brains is, as Wortzel puts it, “the million-dollar question.”

Research in lab animals suggests that excessive heat can alter the way chemical signals work in our brain. The levels of neurotransmitters like serotonin, for example, seem to increase when rats and mice are exposed to high temperatures, according to multiple studies. The heat may also interfere with the way networks in our brains communicate with each other. It might affect the way oxygen reaches our brain cells.

“There are so many biological reasons why brains may be negatively affected by heat,” says Wortzel.

Emerging research suggests that for whatever reason, children and young people are among the most vulnerable. In research published earlier this week, Wortzel and his colleagues saw a 2.97% increase in the suicide rate among people in the US aged 15 to 24 for every 1 °C increase in average monthly temperature. That’s more than double the increase seen in people over the age of 24 (which is concerning in its own right).

Other work hints that heat exposure might have long-term consequences for children’s brain development. Babies who were exposed to either extreme heat or cold appeared to have altered white matter by the time they were nine to 12 years old—although it’s not clear how these impacts might affect an individual child.

“It seems that extreme temperature exposure for very young children may affect their brain development,” says Lawrence, who spoke to me from Oxford. She was meant to be in London for Climate Action Week, but her event, which focused on extreme heat, ended up being canceled … owing to the extreme heat.

We are living through the effects of climate change. And that brings a new urgency to the question of how heat affects our brains. Children born in 2020 are predicted to experience around seven times the number of heat waves their grandparents did, says Lawrance. “[We] need to be serious about adapting to a warming world.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Cognitive-attitudinal factors predict CBT-I enrollment willingness in Chinese sleep clinic patients: a knowledge-attitudes-practices survey

BackgroundDespite strong evidence for cognitive behavioral therapy for insomnia (CBT-I), uptake remains constrained by poorly understood cognitive, attitudinal, and practical barriers. This study examined determinants of willingness to enroll in sleep improvement programs among adults at risk of sleep disorders—including insomnia, obstructive sleep apnea, and comorbid psychological distress—attending a tertiary sleep clinic in China.MethodsA cross-sectional knowledge-attitudes-practices survey was conducted among 2,661 adults attending the sleep and behavioral medicine outpatient clinic at Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, Jiangxi, China, between February 2022 and June 2025. Willingness to enroll in a structured sleep improvement program was assessed alongside sleep health knowledge, perceived need, CBT-I versus medication effectiveness beliefs, telehealth acceptability, clinical severity (Insomnia Severity Index, Epworth Sleepiness Scale, STOP-Bang), psychological symptoms (PHQ-2, GAD-2), perceived barriers, and sociodemographic characteristics. Multivariable logistic regression identified independent predictors of willingness to enrollment, with secondary analyses evaluating model discrimination and testing prespecified interactions.ResultsAmong 2,661 participants (median age 45 years, 56.5% female, median ISI = 13), 1,386 (52.1%) expressed willingness to enroll. Univariable comparisons showed no significant differences between willing and not-willing groups across demographics, clinical characteristics, or barriers (all p>0.05). However, multivariable modeling revealed that when considered simultaneously, cognitive-attitudinal factors emerged as significant independent predictors, suggesting complex interactions rather than simple bivariate associations. In multivariable models, perceived need (OR = 1.20, 95% CI: 1.16–1.25, p<0.001), beliefs that CBT-I is more effective and durable than sleep medication (OR = 1.12, 95% CI: 1.08–1.16, p<0.001), sleep health and treatment knowledge (assessed by a six-item knowledge score) (OR = 1.09, 95% CI: 1.05–1.13, p<0.001), and anxiety symptoms (OR = 1.07, p=0.005) positively predicted willingness. Paradoxically, depression symptoms (OR = 0.94, p<0.001) and insomnia severity (OR = 0.93, p<0.001) inversely predicted willingness. Model discrimination was modest (AUC = 0.543, 95% CI: 0.504–0.590). Time (mean 3.54) and cost (3.44) were most severe barriers but showed no independent association with willingness (p>0.05).ConclusionCognitive-attitudinal factors (perceived need, CBT-I beliefs, knowledge) independently predicted enrollment willingness, whereas demographics and practical barriers did not. Depression and insomnia severity paradoxically reduced willingness, creating an inverse care law. However, poor model discrimination and measurement of stated willingness rather than actual enrollment limit conclusions. Prospective validation and motivational enhancement strategies for patients with depression are needed.

SleepPathfinder: A Socratic Questioning and Self-Decision–Based Chatbot to Support User Engagement in Digital CBT-I: Usability and Feasibility Study

Background: Chronic insomnia is a highly prevalent sleep disorder that adversely affects quality of life and mental health. Cognitive behavioral therapy for insomnia (CBT-I) is internationally recommended as the first-line treatment, and digital CBT-I (dCBT-I) has been developed to improve accessibility and scalability. While existing dCBT-I systems effectively support structured behavioral training through standardized protocols, they provide relatively limited support for users’ cognitive exploration and meaning-making processes, particularly in helping users reflect on and internalize the rationale behind CBT-I practices in daily life. These limitations may contribute to challenges in sustained engagement and long-term adherence. Objective: This study aimed to examine the usability and feasibility of SleepPathfinder, a conversational CBT-I support chatbot that integrates Socratic questioning and a self-decision mechanism to support users’ understanding of and engagement with CBT-I practices. Methods: SleepPathfinder was designed around a 4-stage conversational flow: education on CBT-I techniques, Socratic cognitive exploration, self-decision, and advice provision. We conducted (1) a single-session pilot usability study (n=45) to assess system stability and user experience and (2) a 5-day condition-based comparative experiment (n=30) consisting of daily sessions, comparing an exploratory dialogue condition with a directive, protocol-guided dialogue condition. Quantitative measures assessed usability, cognitive appraisals related to sleep problems, autonomy-related experiences, and behavioral readiness, while qualitative feedback and conversational log analyses were used to examine interaction patterns and engagement characteristics. Results: In the comparative experiment, the exploratory dialogue condition showed a tendency toward reduced perceived threat and severity appraisal of sleep problems compared with the directive condition, accompanied by moderate effect sizes in cognitive perception measures. Autonomy-related experiences, including perceived choice and engagement, demonstrated suggestive upward trends in the exploratory condition. Behavioral intention changes were comparable across conditions, while overall readiness for change increased across participants. Conversational log analyses indicated that greater depth and volume of user self-narrative were associated with larger shifts in cognitive appraisals, whereas the frequency of chatbot questions alone was not. The pilot usability study indicated generally positive evaluations of system usability and content credibility, while identifying areas for improvement in emotional responsiveness and conversational naturalness. Conclusions: These findings suggest that a Socratic questioning–based and self-decision–based conversational structure is usable and feasible as a supportive interaction layer within dCBT-I systems. Rather than altering the directive behavioral structure of CBT-I, such an approach may complement existing protocols by facilitating cognitive exploration and supporting user-perceived autonomy. This study provides design-oriented evidence to inform the refinement of dialogue-supported digital CBT-I systems aimed at enhancing user engagement with CBT-I practices.
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A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations

ObjectiveThis study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts.MethodsWe retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score.ResultsMultivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients.ConclusionA hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards.

[Comment] From policy to practice: implementing China’s measures to strengthen student mental health

In October 2025, China’s Ministry of Education issued ten national measures to strengthen mental health work in primary and secondary schools.1 These measures target major school-linked stressors such as academic pressure, physical activity, sleep, and internet use, and they call for whole-staff responsibility and cross-department collaboration. The policy signals a shift from episodic crisis response towards a public mental health agenda spanning prevention, early identification, supportive school environments, and referral pathways.

Wireless Stress Detector Offers Multiple Medical Uses

A next-generation device that detects signs of stress could have wide-ranging applications, from investigating sleep disorders to detecting signs of sepsis.

The polygraph detector, described in Science Advances, is worn on the chest and can even sense when a person is lying.

It allows psychophysiological states to be continuously monitored through a combination of multimodal sensing and wireless data transmission.

The gadget offers an alternative to current approaches such as such as polygraphy and polysomnography (PSG), which involve cumbersome wired sensors that limit their practicality.

“By uncovering mechanistic links between autonomic imbalance, stress reactivity, and health outcomes, these devices have the potential to transform diagnostic workflows, optimize educational programs, and enable personalized therapeutic monitoring across stress medicine, pediatrics, and behavioral health,” reported Sun Hong Kim, PhD, from the University of Seoul in South Korea, and co-workers.

Subtle physiological variations in cardiac, respiratory, electrodermal, and thermal activity often serve as indicators of compromised health or heightened stress responses.

These can be reflected in many scenarios, from pediatric sleep disorders that disrupt neurodevelopment to the psychological strain experienced in high-stakes clinical settings or during polygraph examinations.

Accurate monitoring of psychophysiological states is therefore essential for understanding how stress and autonomic dysfunction manifest across a wide spectrum of medical conditions.

However, most existing devices monitor only one or two parameters or rely on electrochemical sensors that detect sweat biomarkers, thereby failing to reflect the complex and dynamic interplay between multiple physiological systems.

Wearable polygraph device in the palm of a hand for scale. [John A. Rogers/Northwestern University]

Kim and co-workers therefore designed a single platform to enable comprehensive assessment of autonomic and stress-related physiology in real time.

The device continuously measures changes in heartbeat, skin temperature, and breathing, which are then converted using machine learning into measures of psychological strain.

The device had high fidelity with gold standard systems in quantifying the complex psychological stress induced by polygraph interviews and complex cognitive load tasks as well as the physical stress caused by repeatedly putting a hand in an iced water.

During overnight monitoring of children, it reliably identified arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures among infants with Down syndrome.

Real-world deployment during emergency simulation training showed that multimodal stress signatures correlate inversely with performance, reflecting its value for medical education.

Machine learning analyses across all studies confirmed that multimodal features outperformed single-signal approaches in detecting stress and clinical events with high sensitivity and specificity.

“A particularly notable contribution lies in pediatric sleep medicine,” the authors noted.

“Simultaneous comparison with PSG confirms the ability to detect arousals, hypopnea, and apnea while also providing mechanistic insights into autonomic regulation.

“In infants with Down syndrome, multimodal analysis reveals attenuated sympathetic responsiveness and parasympathetic dominance, consistent with known vulnerabilities in airway patency and autonomic control.

“Such disease-specific autonomic signatures may serve as valuable biomarkers for risk stratification, early diagnosis, and targeted intervention in neurodevelopmental disorders.”

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