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.
Sleep Quality and Biomarkers in Adolescent Girls With Anorexia Nervosa
Conditions: Anorexia Nervosa
Interventions: Procedure: Longitudinal Fasting Blood Sampling for Biomarker Assessment; Other: Multimodal Psychiatric Treatment Program; Device: Home Polysomnography – NOX A1
Sponsors: University of Split, School of Medicine; University Hospital of Split
Not yet recruiting
Interventions: Procedure: Longitudinal Fasting Blood Sampling for Biomarker Assessment; Other: Multimodal Psychiatric Treatment Program; Device: Home Polysomnography – NOX A1
Sponsors: University of Split, School of Medicine; University Hospital of Split
Not yet recruiting
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.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/53b6f3cffbd734291f43739e09584341" />
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.
Retail-Grade Weighted Blanket Effectiveness on ADHD-related Sleep Disturbances (BEAD)
Conditions: ADHD – Attention Deficit Disorder With Hyperactivity
Interventions: Device: Sleep Intervention with weighted device
Sponsors: University Hospital Bispebjerg and Frederiksberg
Not yet recruiting
Interventions: Device: Sleep Intervention with weighted device
Sponsors: University Hospital Bispebjerg and Frederiksberg
Not yet recruiting
[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.
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.”
The post Wireless Stress Detector Offers Multiple Medical Uses appeared first on Inside Precision Medicine.
The Child Mind Institute Hosts 2026 Spring Luncheon “Future-Proofing Your Kids: Empowered Parenting in the Digital Age”
New York Times bestselling author Lisa Damour, PhD, led a thoughtful discussion to honor Mental Health Awareness Month
New York, NY – The Child Mind Institute, the leading independent nonprofit dedicated to transforming the lives of children struggling with mental health and learning disorders, hosted its 2026 Spring Luncheon on Monday, May 11. The event featured a dynamic discussion between Lisa Damour, PhD, a three-time New York Times bestselling author and host of the podcast, Ask Lisa: The Psychology of Raising Tweens & Teens, and Dave Anderson, PhD, Vice President of Public Engagement and Education and a senior psychologist at the Child Mind Institute. Their conversation was moderated by Ali Wentworth, an actress, comedian, author, and host of the television show, The Parent Test.
The event brought together advocates and distinguished individuals dedicated to equipping children and families with the skills they need to thrive in today’s rapidly evolving online and social environments. Attendees included Carson and Siri Daly, Jeannie Gaffigan, Kyle MacLachlan, Zibby Owens, and Alysia Reiner.
“We are raising children in a world fundamentally different from any generation before them…a world where childhood unfolds not just in homes and schools but online,” said Harold S. Koplewicz, MD, founding president and medical director of the Child Mind Institute. “Technology brings creativity and connection but also real risks: constant comparison, disrupted sleep, compulsive engagement, and exposure to harmful content. Our job is to help kids build the skills to navigate this world with resilience, confidence, and balance.”
The discussion centered on kids and families and how they can build healthy habits and resilience as they face the demands and distractions of a world increasingly reliant upon and centered around digital technology.
“My umbrella concern is what the conversation about technology is doing to the relationship between adults and kids. The single most powerful force for youth mental health is strong relationships with caring adults,” said Dr. Damour.
“If we focus on driving causal factors — such as family relationships, academic success, in-person friendships, sleep, and movement — we end up promoting a child’s wellness far more than by taking technology away,” said Dr. Anderson.
The luncheon raised over $260,000 to support the Child Mind Institute’s mission to change the lives of children with mental health and learning disorders in the United States and around the world.
The luncheon was co-chaired by Chris Mack, Lisa and Guy Metcalfe, Zibby Owens, and Jil Schaps. The host committee included Robyn and Paul Goldschmid, Desiree Gruber, Molly Jong-Fast, Breanna and John Khoury, Isabelle Krishana, Arielle Tepper, and Sarah J. Wetenhall.
Photos from the luncheon can be found here.
This special event is part of the Child Mind Institute’s programming during Mental Health Awareness Month. The Child Mind Institute recently launched its latest campaign, Mental Health Fitness. Physical fitness doesn’t just happen — it takes skills, regular practice, and a supportive environment. The same is true for mental health. Alongside relatable content from influencers and world-renowned athletes, the Mental Health Fitness resources from the Child Mind Institute provide kids and families with five core mental health skills they can practice every day.
About the Child Mind Institute
The Child Mind Institute is dedicated to transforming the lives of children and families struggling with mental health and learning disorders by giving them the help they need. We’ve become the leading independent nonprofit in children’s mental health by providing gold-standard, evidence-based care, delivering educational resources to millions of families each year, training educators in underserved communities, and developing tomorrow’s breakthrough treatments.
Visit Child Mind Institute on social media: Instagram, Facebook, X, LinkedIn
For press questions, contact our press team at childmindinstitute@ssmandl.com or our media officer at mediaoffice@childmind.org.
The post The Child Mind Institute Hosts 2026 Spring Luncheon “Future-Proofing Your Kids: Empowered Parenting in the Digital Age” appeared first on Child Mind Institute.
Phenotype, Genetics, and Interpretation: Further Considerations on Atypical Depression
We read with great interest the recent article by Shin et al. (1). The authors leveraged the substantial Australian Genetics of Depression Study (AGDS) cohort to provide compelling evidence for the clinical and biological validity of the atypical depression subtype. Their integrative analysis of clinical features, polygenic scores (PGSs) for mental, metabolic, and circadian traits, and self-reported treatment outcomes is a significant contribution to the field. The particularly robust finding of an association between a genetic predisposition for eveningness (lower-chronotype PGS) and atypical depression, which persisted after adjustment for body mass index (BMI), is noteworthy and points to a potentially core, BMI-independent pathway.
Excessive Internet use and depressive symptom levels in adolescents with depressive disorders: chain mediation of social anxiety and sleep quality
BackgroundAdolescents with depressive disorders are at elevated risk for adverse mental health outcomes, and excessive Internet use has been increasingly linked to greater symptom severity. Therefore, this study aimed to examine the chain mediating roles of social anxiety and sleep quality in the association between excessive Internet use and depressive symptoms among adolescents with depressive disorders.MethodsA cross-sectional design was used. A total of 266 Chinese adolescents with clinically diagnosed depressive disorders (M = 15.79 years, SD = 1.85; 71.4% female) were assessed using the Internet Addiction Test, Zung Self-Rating Depression Scale, Social Anxiety Scale for Children, and Pittsburgh Sleep Quality Index. Correlation analyses and bootstrapping methods were conducted using SPSS and the PROCESS macro to examine the chain mediating effects of social anxiety and sleep quality.ResultsThe total indirect effect of excessive Internet use on depressive symptoms accounted for 65.66% of the total effect. Specifically, the indirect effects via social anxiety and sleep quality accounted for 24.10% and 26.51% of the total effect, respectively. In addition, the chain mediating effect of social anxiety and sleep quality was significant, accounting for 14.76% of the total effect.ConclusionExcessive Internet use was positively associated with more severe depressive symptoms among adolescents with depressive disorders, both directly and indirectly through the chain mediating effects of social anxiety and sleep quality. These findings highlight potential targets for preventing and intervening in excessive Internet use among this population.