Pharmacotherapy, acupoint stimulation, and psychotherapy for perimenopausal women with anxiety, depression, and panic disorder: a systematic review and network meta-analysis of randomized controlled trials

BackgroundPerimenopausal women frequently experience physiological and psychological symptoms, including anxiety, depression, and panic disorders, mainly due to declining ovarian function and hormonal changes. Current options include pharmacotherapy, acupoint stimulation (AcuStim), and psychotherapy (psych), but their comparative efficacy and safety remain controversial.ObjectiveThis network meta-analysis (NMA) systematically compared pharmacotherapy, AcuStim, and psychotherapy for perimenopausal anxiety, depression, and panic disorder, assessing clinical efficacy, adverse events (AEs), and changes in the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), Kupperman Index (KI), Self-rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), Pittsburgh Sleep Quality Index (PSQI), and serum hormone levels.MethodsWe searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and SinoMed from inception to June 14, 2026, for randomized controlled trials (RCTs). A Bayesian NMA was performed, and the Surface Under the Cumulative Ranking Curve (SUCRA) was calculated.ResultsThe study included 131 RCTs, encompassing 11457 perimenopausal women diagnosed with emotional disorders. These trials evaluated three distinct treatment strategies. The NMA showed that the highest SUCRA probabilities were observed for drug_psych across HAMD (SUCRA = 92.4%), KI (SUCRA = 97.9%), SDS (SUCRA = 94.5%), PSQI (SUCRA = 98.1%), and follicle-stimulating hormone (FSH) (SUCRA = 96.1%) reduction and estradiol (E2) (SUCRA = 0.1%) elevation; for AcuStim_psych (SUCRA = 93.7%) in HAMA reduction; for psych (SUCRA = 98.9%) in SAS reduction; for drug_AcuStim in clinical efficacy (SUCRA = 9.0%) and luteinizing hormone (LH) reduction (SUCRA = 100%); and for control (SUCRA = 65.5%) in safety outcomes. In pharmacotherapy subgroup analyses, antidepressants (ADs)_Traditional Chinese medicine (TCM) ranked highest for HAMD (SUCRA = 87.2%) and safety (SUCRA = 82%), ADs_antipsychotics (AP) (SUCRA = 97.5%) for HAMA, and ADs_hormone replacement therapy (HRT) (SUCRA = 10.2%) for clinical efficacy.ConclusionPharmacological, acupoint stimulation, and psychological interventions each demonstrated therapeutic benefits for perimenopausal women with emotional disorders. Combination therapies generally showed more favorable efficacy across multiple psychological and endocrine outcomes than single-modality interventions, while no single treatment strategy was consistently superior across all outcomes. These findings may provide evidence to support individualized treatment selection according to patients’ clinical characteristics and therapeutic goals.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261340530.

Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas

ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.

Perspectives on Remote Monitoring via Smartphones and Wearables Among Individuals With Lived Experience or at Risk of Eating Disorders (“This Could Go Very, Very Wrong”): Qualitative Interview Study

Background: Remote measurement technology (RMT) is increasingly used in health research to collect real-world data relevant to clinical states (eg, sleep, activity, and stress). Concerns exist about the impact of remote tracking via personal devices and wearables on individuals with or at risk of eating disorders (EDs) by promoting a focus on exercise, diet, and appearance. There is a lack of research applying RMT to EDs. Objective: This study aimed to explore how smartphone- and wearable-based RMTs influence eating-, exercise-, and weight-related experiences among individuals with a history of or at risk of EDs and to identify perceived benefits, harms, and recommendations for their use in this population. Methods: In total, 14 semistructured interviews were conducted with former participants of Remote Assessment of Disease and Relapse: Major Depressive Disorder, a 2-year digital health study tracking depression outcomes via RMTs. Participants were included in this follow-up if they had disclosed a history of a comorbid ED or were within the at-risk age range (18-30 years) for EDs during Remote Assessment of Disease and Relapse: Major Depressive Disorder and displayed subclinical ED symptoms (Eating Disorder Diagnostic Scale). Interviews explored the impact of app engagement and wearables (Fitbits) on food, activity, and weight-related behaviors and attitudes. Template analysis was adopted to capture themes guided by the focus on ED-relevant domains. Results: In total, 6 themes captured participants’ experiences with RMTs across clinical status and presentation. Participants broadly appreciated the convenience and reflective potential, while some described emotional strain linked to constant self-tracking. Health data impacted participants’ eating and exercise habits through a dynamic process from awareness to cognition to action, fostering healthy routines or obsessive patterns, depending on emotional state, ED presentation, and recovery stage. Self-tracking appeared to mirror illness stage, supporting ED recovery among those with greater distance from illness, but risking reinforcement of compulsive patterns among those with residual or emerging symptoms. Participants’ recommendations for future studies in EDs stressed balancing autonomy with safeguards for vulnerable individuals. Conclusions: These exploratory findings, drawn from individuals with lived ED experience and young people at subclinical risk, suggest that RMT use was shaped by recovery stage and contextual factors, rather than being inherently beneficial or harmful. While findings should not be interpreted as evidence of RMT safety or acceptability in ED cohorts broadly, they raise important questions about ethical RMT design, including the selection of wearables, access to data, and researcher communication with participants.
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Maintaining Emotional Boundaries as a Parent

Parents often emphasize to children the importance of respecting others’ boundaries — don’t tickle another kid if they say they don’t like it, for example. Don’t kiss someone unless they welcome it. But within the family, we can fail to notice our own difficulty setting and respecting boundaries with our children, especially emotional boundaries.

What are emotional boundaries

Emotional boundaries refer to an individual’s sense of autonomy and ability to control how they think, feel, and engage with others. Those boundaries are crossed either when you try to control someone else’s thoughts or feelings or they try to control yours.   

Difficulty setting your own and respecting others’ emotional boundaries often comes from a place of care and concern. Parents are usually concerned that their child is not going to make safe choices or are trying to protect their child from distressing emotions. But the intrusion can have a negative impact on children’s emotional development as well as parents’ mental health, especially when it’s done repeatedly. Everyone has boundaries, even parents, and it’s important for kids to learn that.  

How parents overstep their child’s emotional boundaries

Parents can inadvertently encroach on their child’s right to their own thoughts and feelings through:

  • Over-involvement in children’s academic and social life: Your child has a whole life outside the home, and it can be anxiety-provoking to trust them to manage their homework, advocate for themselves with teachers, and navigate conflicts with peers. It can alleviate your anxiety to get involved, but that can feel highly intrusive to your child. For example, if your kid feels snubbed by a friend, you might feel the urge to contact that friend’s parent to try to smooth things over. However, over-involvement in these tasks can prevent your kid from developing the skills they need to manage challenging situations successfully and can impact their confidence in their ability to cope independently.
  • Overconcern to protect your child’s safety: Fears that something negative will happen to your child are understandable, but aggressive monitoring can backfire. This can include strict control over the ingredients in your teen’s food, the information they consume online, or what activities they do. This may temporarily alleviate anxiety about the child’s health and safety but it can lead to resentment and rebellion. 
  • Ignoring or rejecting children’s requests for privacy: It is normal for children and adolescents to want more privacy as they mature, from showering alone to keeping a private diary.Unless your child engages in an activity that suggests they are being unsafe, it is important to trust your child and let them decide what to share with you.
  • Sharing private information without the child’s permission: Parents telling anecdotes about their children, much to their kids’ embarrassment, is hardly new. However, that sharing now includes photographs and videos posted on social media that is broadcast far beyond close friends.It is important to include your child in decisions about what information is shared with others (excluding, of course, medical professionals). When in doubt, consider what information you would feel comfortable with them sharing about you.
  • Telling children what is or isn’t acceptable to value, think, or feel: Many parents, in an effort to help their child feel better, say things like, “Don’t worry about that” or “You can’t think that way.” These seemingly innocuous phrases can come across as attempts to control how the child feels. We can forget that kids are real people just like adults. And if they’re mad or upset about something, they want to be able to feel those feelings, not be told that that their feelings are wrong.

How parents fail to set their own emotional boundaries

Letting kids change your own values, thoughts, and feelings can also be unhealthy. Here are some common ways in which parents fail to set their own boundaries:

  • Allowing your child’s thoughts and feelings to influence your own too much:  Your child may act like it will be the end of the world if they don’t get into the right college. If their anxiety becomes your anxiety, then it’s going to be very hard for you to encourage your kid to have fun on the weekend or to go to bed with work left undone. What they need you to do is validate their feelings but challenge those worry thoughts and help them to relax.
  • Implying that your child is responsible for how you feel:  As a parent, your child’s well-being is your priority, and your emotional state is affected by your child’s behavior. But phrases like, “You are making me crazy” or “I cannot cope with one more word from you” unintentionally suggest that the child has control over your feelings. It’s not healthy for children to feel that they are responsible for your well-being.
  • Depending on your child for emotional support: If you’re a parent under stress and you’re not getting support elsewhere, it makes sense that you’re going to vent to a child. It’s not necessarily meant to burden a kid with financial stressors or relationship drama, and the child might be a sympathetic ear. But it blurs the boundaries between the parent role and the kid role, and that often causes difficulties for the kid in accepting parental authority in other domains. If they see themselves as equals in terms of emotional support, then they might think, why can you tell me what to do?
  • Sharing age-inappropriate information: Many children want to be treated as older than they are (at least in certain ways). So they might requestinformation about finances, romantic relationships, or family stressors that are inappropriate for their age. Although it can be tempting to share, it is not helpful long-term. It may alleviate the child’s current anxiety (and stop the nagging), but it will impair their ability to respect boundaries as well as interfere with them just being a kid.
  • Difficulty saying no: If you’re exhausted, the last thing you want is an argument. One way to avoid an argument is to say yes to your kid’s requests to buy a toy, stay up 15 more minutes, or have a different dinner. If you have boundaries around what you will spend, how much sleep you need, or what you will cook, you are still a good parent. Sticking to your limits teaches your child to accept other people’s boundaries without whining or threats. 
  • Allowing your child to treat you unkindly: Many parents allow their children to treat them in ways they would never tolerate from another person. This includes calling the parent mean or profane names, hitting them, or disregarding their needs (for money, sleep, leisure time, etc.).  Allowing this kind of behavior prevents the child from learning how to respect boundaries and tolerate the emotions they experience when they face them.

Factors that contribute to boundary concerns

There are specific circumstances that can make it difficult for a parent to know where the appropriate boundaries are. They include a child’s late development, psychiatric challenges, and history of unsafe choices. For example, a child may have delays in language, executive functioning, or social or emotional skills. These things can make it challenging to determine how involved you need to be in your child’s daily life and how much independence they can handle.

  • Poor risk assessment and impulsivity: Many disorders can impact children’s ability to think clearly, regulate emotionally, and act safely. For example, a teen experiencing a manic episode may overestimate their abilities, underestimate risk, and act impulsively. Or a child with ADHD might hard to control on crowded city streets or in restaurants, so you avoid taking them out or letting them do activities on their own with friends.  
  • Lack of confidence: Anxious children may underestimate their abilities and request continued support past when they are capable of independence. For example, a socially anxious child may ask their parent to order for them at a restaurant or keep track of their homework assignments.  When a parent accommodates these requests, it confirms their belief that they still need help.
  • Executive functioning deficits: Children who struggle with executive functioning may need more scaffolding to complete daily self-care tasks than other kids their age. This can look like parents providing frequent reminders of assignments, events, or even hygiene tasks — as well as cleaning their room for them long past when an child with ADHD should be doing it themselves. Consider how you can scaffold the skills (packing their bookbag!) without doing tasks for them, and gradually remove the supports over time.
  • History of not successfully navigating tasks: A child’s history of poorly handling a responsibility (safe use of technology, completion of homework, brushing their teeth) often reduces parents’ confidence in the child’s abilities and increases their inclination to step in. Although extra supervision and support may be needed initially, it is important to reassess your child’s abilities over time as they can learn and grow if you let them!

How to get better at boundaries

Once you have recognized the challenges in respecting your child’s boundaries and protecting your own, the next step is to figure out what those boundaries are.

  • Identify your boundaries: What things are most important to support your child’s growing independence and sense of autonomy? What boundaries do you need to set to protect your own mental health? Consider what level of involvement you want to have in their academics, friendships, emotion regulation, and appearance and what you want to disclose to them about your own relationship, emotions, or work.  
  • Practice setting these boundaries: It is much easier to set a boundary when you are not forced to make the choice with a child’s puppy dog eyes looking at you. Rehearsing how you will say no, decline to share certain information, or respond to an anxiety-provoking situation can prepare you to respond more effectively and in line with your values in a moment of conflict.
  • Share your reasons for boundaries: Children can be quick to interpret lack of boundaries as “more caring,” but being consistent in language around why boundaries are being set can help prevent this. When setting a boundary, it is helpful to couch it in care. For example, “I care about you enjoying your childhood, so I do not feel comfortable sharing with you about our family’s finances.”

When kids want more independence than you are sure they are ready to handle, identifying steps toward their goal can be effective. Giving them opportunities to show maturity, with success at one step leading to more responsibility, can help you trust your child with greater independence. What can your kids show you that will help you feel confident in their ability to manage their emotions themselves or make well thought-out decisions?

Kids also need to recognize that they sometimes overestimate their own abilities, that there are times they have not assessed risk accurately and still need their parents. It is important to teach your child that you should be alerted if they are experiencing something that is unsafe or concerning (such as a friend talking about suicide or sharing an inappropriate photo). Discussions with your kid can sort out how to work toward new milestones and help everybody feel confident that they have the skills to do it.

Modeling a healthy respect for boundaries will set your child up to establish their own and respect others’ boundaries throughout their life.

Frequently Asked Questions

What are emotional boundaries between parents and children?

Emotional boundaries are the limits that protect each person’s right to their own thoughts, feelings, values, and decisions. In families, healthy boundaries allow children to develop independence while helping parents avoid taking responsibility for emotions or choices that belong to their child.

Why are emotional boundaries important in parenting?

Healthy emotional boundaries support children’s confidence, autonomy, and ability to solve problems on their own. They also protect parents’ well-being by preventing them from becoming overly responsible for their child’s feelings, worries, or decisions.

What are signs a parent is overstepping a child’s emotional boundaries?

Common signs include getting overly involved in a child’s friendships or school life, refusing age-appropriate privacy, sharing personal information without permission, or telling a child what they should think or feel. While these behaviors often come from a place of love and concern, they can undermine a child’s confidence and independence.

How can parents determine appropriate emotional boundaries?

Parents can start by considering where their child is developmentally and what level of support versus independence is appropriate. A good guideline is to provide enough structure to keep children safe while gradually giving them more responsibility and privacy as they demonstrate readiness.

The post Maintaining Emotional Boundaries as a Parent appeared first on Child Mind Institute.

Evaluating Wearable Devices for Remote Monitoring in Psychosis: Pilot Study Nested Within the CONNECT Cohort Study

Background: Digital remote monitoring technologies, including smartphones and wearables, offer promising avenues for early detection of psychosis relapse. However, selecting devices that are acceptable to participants and produce high-quality data remains challenging. Objective: The aim of this nested pilot study was to assess the acceptability and data quality of 3 commercially available wearable devices in people with psychosis recruited to the CONNECT cohort study. Methods: Participants recruited to the CONNECT study before July 31, 2024, were included in the pilot study and selected 1 of 3 wearable devices: a Fitbit Charge 5, Samsung Galaxy Watch 5, or Apple Watch SE. Baseline demographics were compared between device groups. Acceptability of devices to participants was assessed through a Wearable Device Satisfaction Questionnaire after 3 months of use, with the proportion of positive responses to each question calculated and compared. Data completeness was also assessed by calculating the number (and percentage) of valid days of step count, heart rate, and sleep data, and comparing between groups. Data quality was assessed through summarizing the amount of troubleshooting required, additional metrics available from the wearables, and continuity of data completeness by calculating the proportion of participants with at least 3 days of heart rate data per week for the first 20 weeks of follow-up. Predefined criteria were used to determine the next steps for the wider CONNECT study: if one device was superior, this would be selected; if none were found to be superior and the Fitbit was found to be noninferior, then Fitbit would be retained. Results: Of the first 107 participants recruited to CONNECT, 105 were included in the pilot study evaluation. The Samsung Galaxy Watch was selected most frequently by participants (46/105, 43.8%), followed by the Apple Watch (27/105, 25.7%), and Fitbit Charge (23/105, 21.9%). Differences in participant demographics were observed across device groups. Self-reported acceptability after use did not differ substantially between devices. However, in terms of data completeness, the median proportion of valid heart rate data days was significantly lower for Samsung Galaxy (median 31.2%, IQR 8.5%-46.0%) compared to Fitbit (median 80.1%, IQR 26.7%-95.0%; =.003) and Apple Watch (median 49.3%, IQR 21.5%-86.0%; =.02). There was no significant difference between Fitbit and Apple Watch. Similar patterns were observed for step count and sleep data. The Samsung Galaxy Watch required more frequent troubleshooting for data flow issues and lacked additional physiological metrics, available from the other devices. Conclusions: Due to comparatively lower data quality and technical performance, the Samsung Galaxy Watch was discontinued for use in the subsequent phase of the CONNECT study. The study highlights the importance of incorporating nested evaluations of devices in long-term research.
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Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing

Background: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed intervention. Early identification of individuals at risk of developing elevated depressive symptoms is therefore critical, but traditional approaches show limited predictive accuracy. To date, no study has applied machine learning (ML) models to predict clinically significant depressive symptoms at 2-year follow-up in older adults in the United Kingdom using data from the English Longitudinal Study of Ageing (ELSA). Moreover, the impact of encoding strategies for categorical health care variables has not been examined. Objective: This study aimed to develop and evaluate ML models to predict the clinically significant depressive symptoms at 2-year follow-up in older adults using ELSA data. We further compared ordinal and one-hot encoding strategies across different ML architectures and identified key predictors of depressive symptoms at follow-up. Methods: Data were drawn from 4 consecutive waves of ELSA, including participants aged ≥50 years without significant depressive symptoms at the baseline wave (waves 6‐9). Clinically significant depressive symptoms were defined as 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8) scores of ≥4 at the subsequent wave (waves 7‐10). Over 120 features spanning sociodemographic, psychological, and health-related domains were analyzed. Eight ML models were applied, including tree-based ensembles, deep learning architectures for tabular data, distance-based methods, probabilistic methods, and linear methods. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and -score. Model interpretability was examined using Shapley additive explanations (SHAP). Sensitivity analyses assessed the robustness of results across alternative CES-D 8 thresholds (≥3, ≥4, and ≥5) and encoding strategies. Results: Across waves, the best-performing models achieved mean AUROC scores of 0.72‐0.73, with a peak of 0.75 in the highest-performing wave. Ordinal encoding consistently outperformed one-hot encoding across all ML models, yielding improvements in AUROCs and -scores, with the greatest increase in tree-based methods. SHAP consistently identified loneliness, sleep disturbances, and low social engagement as strong predictors of elevated depressive symptoms at follow-up. Sensitivity analyses across CES-D 8 thresholds demonstrated robust feature importance, with AUROCs ranging from 0.67 to 0.82. Traditional ML models (random forest, extreme gradient boosting, and support vector machines) generally achieved higher performance than the deep learning models for this task. Conclusions: Our findings demonstrate the feasibility of predicting clinically significant depressive symptoms at 2-year follow-up in UK older adults, with moderate accuracy. Ordinal encoding demonstrates superior performance for health care datasets with inherently ordered categorical features. The identification of consistent risk factors highlights opportunities for developing targeted clinical screening tools and preventive interventions. This study provides new evidence on depressive symptom prediction in the UK context, leveraging longitudinal data from ELSA, and contributes to advancing digital mental health research for aging populations.
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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.