Literature Collection

Youth with epilepsy (YWE) are at elevated risk for anxiety, yet anxiety is often undetected and understudied in this population. Most research on anxiety in YWE is based on parent proxy-report and broad-band measures with limited sensitivity. The aim of the current study was to: 1) examine rates of anxiety symptoms in YWE using a diagnosis-specific, self-report measure of anxiety symptoms, 2) assess differences in anxiety symptoms by sociodemographic and medical variables, and 3) evaluate changes in anxiety symptoms following a brief behavioral health intervention delivered within an interdisciplinary epilepsy clinic visit. As part of routine clinical care, 317 YWE [M(age)=13.4+2.5 years (range 7-19 years); 54% female; 84% White: Non-Hispanic] completed the Multidimensional Anxiety Scale for Children, self-report (MASC-10), with a subset completing the MASC-10 at a second timepoint (n=139). A retrospective chart review was completed and sociodemographic, medical variables and behavioral health interventions were collected. Thirty percent of YWE endorsed elevated anxiety symptoms, with higher rates in those who were younger. YWE who received a behavioral health intervention for anxiety (n=21) demonstrated greater decreases in anxiety symptoms from Time 1 to Time 2 compared to those who did not receive a behavioral intervention (n=108). The integration of psychologists into pediatric epilepsy clinics may have allowed for early identification of anxiety symptoms, as well behavioral interventions to address these symptoms, which has the potential to decrease the need for more intensive services.

PURPOSE: Timely identification of comorbidities is critical in sleep medicine, where large language models (LLMs) like ChatGPT are currently emerging as transformative tools. Here, we investigate whether the novel LLM ChatGPT o1 preview can identify individual health risks or potentially existing comorbidities from the medical data of fictitious sleep medicine patients. METHODS: We conducted a simulation-based study using 30 fictitious patients, designed to represent realistic variations in demographic and clinical parameters commonly seen in sleep medicine. Each profile included personal data (eg, body mass index, smoking status, drinking habits), blood pressure, and routine blood test results, along with a predefined sleep medicine diagnosis. Each patient profile was evaluated independently by the LLM and a sleep medicine specialist (SMS) for identification of potential comorbidities or individual health risks. Their recommendations were compared for concordance across lifestyle changes and further medical measures. RESULTS: The LLM achieved high concordance with the SMS for lifestyle modification recommendations, including 100% concordance on smoking cessation (κ = 1; p < 0.001), 97% on alcohol reduction (κ = 0.92; p < 0.001) and endocrinological examination (κ = 0.92; p < 0.001) or 93% on weight loss (κ = 0.86; p < 0.001). However, it exhibited a tendency to over-recommend further medical measures (particularly 57% concordance for cardiological examination (κ = 0.08; p = 0.28) and 33% for gastrointestinal examination (κ = 0.1; p = 0.22)) compared to the SMS. CONCLUSION: Despite the obvious limitation of using fictitious data, the findings suggest that LLMs like ChatGPT have the potential to complement clinical workflows in sleep medicine by identifying individual health risks and comorbidities. As LLMs continue to evolve, their integration into healthcare could redefine the approach to patient evaluation and risk stratification. Future research should contextualize the findings within broader clinical applications ideally testing locally run LLMs meeting data protection requirements.

OBJECTIVE: Sleep disturbance is an important feature of fetal alcohol spectrum disorder (FASD). We sought to describe sleep patterns in school-aged children with FASD, in comparison with a typically developing community group, and investigate the relationship between sleep and neurodevelopmental profiles. METHOD: The FASD cohort (N = 36) was recruited from a tertiary Australian FASD diagnostic center, and the typically developing group (N = 36) was previously recruited as a control cohort for a separate study. Sleep disturbance was assessed with the caregiver-completed Sleep Disturbance Scale for Children (SDSC) questionnaire. Neurodevelopmental assessment results for the 10 domains impaired in FASD were used for correlations with sleep disturbance. RESULTS: In the FASD group, 80% of children scored above the SDSC cutoff, compared with 22% of the control group ( p < 0.001). Statistically significant group differences were seen for all 6 subscales of the SDSC ( p < 0.05). The most frequently affected domains in the FASD group related to difficulties with initiating and maintaining sleep (58%), sleep-wake transition disorders (44%), and disorders of arousal (42%). A statistically significant relationship was not found between sleep and the severity of neurodevelopmental impairment or impairment of a particular domain, acknowledging the limitations of our small sample size. Half of the FASD sample (52%) were taking a pharmaceutical agent to support sleep, which was not associated with lower SDSC scores. CONCLUSION: In this small study, sleep disturbances were frequently reported by carers of children with FASD, independent of the severity of their neurodevelopmental impairments. Persistent sleep disturbance despite the use of sleep medications highlights the need for prospective studies exploring sleep interventions in this population. Integration of behavioral sleep medicine into management is recommended for all children with FASD.

Sleep insufficiency, insomnia, and related sleep disorders are concerns that affect millions of US adults. The disorders also contribute to significant cognitive, emotional, and physical health challenges. Insomnia affects approximately 30% of the US population. It is characterized by difficulty falling asleep, difficulty staying asleep, and early-morning waking and is linked to daytime distress and impairment. Common sleep disruptors include environmental factors such as noise, light, and air pollution and also personal habits such as excessive screen use before bedtime. Evaluating patients with insomnia in primary care requires integrating sleep health assessments into routine visits and use of insomnia screening tools and sleep diaries for accurate diagnosis. Nonpharmacologic therapies such as sleep hygiene and cognitive behavior therapy for insomnia are the preferred treatments. Pharmacotherapy or combination therapy (with cognitive behavior therapy for insomnia and pharmacotherapy) may be considered when these interventions are ineffective. Family physicians should weigh the risks and benefits of insomnia medication use carefully for all patients but especially for older adults because of potential adverse effects. Managing insomnia effectively in primary care involves a comprehensive approach, prioritizing nonpharmacologic strategies, regular monitoring, and patient-centered care.