ObjectiveNon-suicidal self-injury (NSSI) is a clinically significant concern among adolescents, and its accurate yet efficient assessment is essential for research, early identification, and intervention. Existing Chinese instruments—most notably the 19-item Adolescent Self-Harm Questionnaire (ASHS)—are relatively lengthy and ill-suited to rapid, large-scale screening. The present study had two aims: (a) to develop a short form of the ASHS, the Adolescent Non-Suicidal Self-Injury Scale–Short Form (ANSSI-SF), by integrating classical test theory (CTT) item analysis with genetic algorithm (GA) cross-validation, and (b) to evaluate its reliability, validity, and screening utility relative to the parent scale.MethodsUsing a convenience sample of 1,090 primary and secondary school students, seven items—one for each of seven behavioral categories derived from a functional–typological framework of self-harm—were selected on the basis of CTT item statistics and GA cross-validation. The scale was evaluated through item analysis, internal-consistency and split-half reliability, exploratory and confirmatory factor analysis (EFA/CFA), correlations with theoretically related constructs, known-groups comparisons (gender; smartphone addiction), and consistency and screening-accuracy analyses against the full 19-item ASHS.ResultsIt indicated that all items demonstrated strong correlations with the total score, and both internal consistency and split-half reliability reached satisfactory levels.A unidimensional factor structure also exhibited good model fit. Additionally, scale scores were significantly positively correlated with related psychological indicators such as depression, anxiety, and sleep problems, and effectively differentiated differences in self-injurious behavior across gender and smartphone addiction groups.GA cross-validation confirmed that the selected items balanced parsimony with full coverage of all seven behavioral categories.DiscussionThese findings indicate that the ANSSI-SF is a brief, reliable, and valid instrument that preserves the content breadth of the parent ASHS while substantially reducing administration burden. It is well suited to rapid, large-scale screening and early identification of at-risk adolescents, facilitating timely referral and prevention. Limitations include convenience sampling from a single region and the absence of test–retest reliability; future research should validate the scale in clinical and diverse cultural samples and examine its predictive validity.
Methodology of Super-Early Training of Developer Engineers and Leading Analysts With a Diagnosis of ADHD/ASD for Ensuring the Engineering Sovereignty of the State: Time Horizon: 2026-2038-2050
Conditions: Attention Deficit Hyperactivity Disorder (ADHD)
Interventions: Device: Voice of Sleep “MamaBee” training modules (Fairy Tales)
Sponsors: Pisareva Citadel Group LLC
Active, not recruiting
Interventions: Device: Voice of Sleep “MamaBee” training modules (Fairy Tales)
Sponsors: Pisareva Citadel Group LLC
Active, not recruiting
Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review
Background: Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented. Objective: This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor. Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension). Results: The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an -score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain. Conclusions: Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.
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Non-motor correlates of anxiety in de novo Parkinson’s disease: a cross-sectional study with exploratory subgroup analyses
ObjectiveAnxiety is a prevalent early non-motor symptom of Parkinson’s disease (PD), but its clinical correlates remain poorly defined in de novo, drug-naïve patients. This retrospective cross-sectional study enrolled 284 medication-naïve de novo PD patients and 147 age- and sex-matched healthy controls to identify non-motor correlates of anxiety after adjustment for demographic and motor covariates, and explore potential subgroup heterogeneity.MethodsAnxiety was assessed with the Hamilton Anxiety Rating Scale; multivariate logistic regression and prespecified stratified analyses by age, sex, disease duration, Hoehn-Yahr stage and motor severity were performed.ResultsClinically significant anxiety was detected in 44.0% of PD patients versus 8.1% of controls (p < 0.001), with psychic anxiety as the dominant subtype. After multivariate adjustment, higher depressive symptom scores were positively associated with anxiety (OR = 1.386), whereas better sleep quality (OR = 0.965) and intact olfactory identification (OR = 0.815) were negatively associated with anxiety; motor severity showed no significant association after multivariable adjustment. Exploratory subgroup analyses revealed heterogeneous patterns of sleep and olfactory correlates across clinical strata. The three-factor predictive model yielded an overall classification accuracy of 84.81%.ConclusionThese findings indicate that non-motor abnormalities, rather than motor impairment, are associated with anxiety in early PD after adjustment for demographic and motor covariates. The distinct correlative patterns across subgroups provide preliminary clinical clues for the heterogeneous neurodegenerative processes underlying PD-related anxiety, and support routine screening of depressive, sleep and olfactory symptoms to facilitate anxiety risk stratification at initial diagnosis.
Case Report: Rapid resolution of psychogenic cough in an adolescent following bromazepam treatment
BackgroundSomatic cough syndrome, formerly termed psychogenic cough or habit cough, is an under-recognized cause of chronic cough in pediatric populations, characterized by repetitive, dry, and honking cough that is absent during sleep. Behavioral interventions are first-line treatment with high success rates of 80%–90%. However, when access to behavioral therapy is unavailable, alternative pharmacological interventions, particularly benzodiazepines, have received minimal investigation in this population.Case presentationI report a 13-year-old previously healthy male patient with abrupt-onset, near-continuous dry cough occurring every 2 seconds during waking hours. The cough had a distinctive honking quality and disappeared completely during sleep. The patient presented with severe impairment in function, including inability to eat for 3 days, multiple emergency presentations requiring intravenous hydration, and complete school absence. Medical evaluation, including physical examination, laboratory investigations, spirometry, and chest X-ray, was unremarkable. Empirical trials of inhaled bronchodilators and nasal corticosteroids yielded no improvement. Psychiatric assessment excluded psychotic features, depression, obsessive-compulsive disorder, and tic disorder but noted family stressors, including high expressed emotion and overprotective parenting behavior. Based on the sign of sleep-related cough cessation and exclusion of organic pathology, somatic cough syndrome was diagnosed. Given the severity of impairment and family circumstances precluding immediate behavioral therapy, bromazepam 3 mg orally was initiated. Within 24–36 hours, the cough resolved completely. At 3-month follow-up, the patient remained symptom-free without medication. He had returned to school and demonstrated normal eating patterns.ConclusionThis case demonstrates rapid and sustained resolution at 3-month follow-up of severe psychogenic cough following short-term bromazepam treatment in an adolescent. While behavioral therapy is still an evidence-based first-line treatment, this report suggests using benzodiazepines as a pharmacological intervention alternative when behavioral therapy is unavailable. The rapid response and sustained remission at 3-month follow-up are consistent with the hypothesis that benzodiazepines may interrupt the self-perpetuating cycle of anxiety and cough in somatic cough syndrome. Further research is needed for this case in pediatric populations to establish efficacy, safety, and optimal treatment duration.
Relationship between loneliness and self-efficacy: the mediating role of internet addiction and the moderating role of sleep quality
IntroductionThis study aimed to examine the association between loneliness and self-efficacy among adolescents and to investigate whether internet addiction mediated this association and whether sleep quality moderated the direct and indirect pathways. Although previous research has demonstrated significant associations between loneliness and both general and social self-efficacy, the behavioral mechanisms underlying these associations and the conditions under which their strength may vary remain insufficiently understood.MethodsA cross-sectional survey was conducted among 1,323 students in Grades 7–9 from two schools in Baotou, Inner Mongolia, China. Participants completed the UCLA Loneliness Scale, the General Self-Efficacy Scale, the Internet Addiction Diagnostic Questionnaire, and the Athens Insomnia Scale. A moderated mediation model was used to examine the mediating role of internet addiction and the moderating role of sleep quality in the association between loneliness and self-efficacy.ResultsLoneliness was negatively associated with self-efficacy. Internet addiction partially mediated the association between loneliness and self-efficacy. In addition, sleep quality moderated the direct association between loneliness and self-efficacy and the first stage of the indirect pathway from loneliness to internet addiction. Specifically, the associations of loneliness with self-efficacy and internet addiction varied across different levels of sleep quality.DiscussionThese findings provide a more comprehensive understanding of the mechanisms linking loneliness to adolescent self-efficacy by identifying internet addiction as an important behavioral mediator and sleep quality as a moderating factor. The findings highlight the importance of considering problematic Internet use and sleep quality when developing interventions aimed at promoting adolescents’ psychological adjustment, self-efficacy, and well-being.
STAT+: Pharmalittle: We’re reading about Novo plans to expand its pipeline, early Alkermes ADHD data, and more
Hello there and welcome to a new week. STAT reporter Andrew Joseph here in London filling in for Mr. Pharmalot for the day. Before we get to the headlines, an extra dose of encouragement to check out all the great pieces that STAT has to offer today, from Reed Jobs opining on the NIH budget to our new trust-in-science reporter Nick Florko outlining his own experience grappling with, as he puts it, “how frustrating it is to realize that modern medicine does not have all the answers.” Now to those headlines. …
Novo Nordisk is seeking to assure investors that it can regain its momentum in the coming years, outlining plans to expand its pipeline and find new products that it could sell more like consumer goods than traditional medicines, building off what’s occurring with its obesity treatments, STAT shares. At the company’s capital markets day in London, CEO Mike Doustdar said Novo would launch at least five multi-blockbusters by 2030 and deliver revenue growth on par with other major pharma firms. He also presented a strategy that involved investing more in disease areas outside diabetes and obesity and said the company “will be more active within business development,” an acknowledgment of investors’ concerns that Novo was too reliant on its landmark GLP-1 drugs and needed to develop other products.
Drugs called orexin agonists have been hailed for their ability to treat rare sleep disorders, but with new data, Alkermes is showing for the first time that the new class of therapies may benefit people with ADHD, STAT writes. In a randomized Phase 1 study, the company’s new drug, called ALKS 7290, was well tolerated and showed a signal of efficacy. Participants in the study started with a median score of 39 on a diagnostic known as the Adult ADHD Investigator Symptom Rating Scale, indicating that they had moderate to severe symptoms. After two weeks, those taking a high dose of 50 milligrams of ALKS 7290 experienced a 19-point reduction on the scale, indicating that their symptoms had become mild.
STAT+: Alkermes’ orexin agonist shows potential to treat ADHD in early-stage trial
Drugs called orexin agonists have been hailed for their ability to treat rare sleep disorders, but with new data, Alkermes is showing for the first time that the new class of therapies may benefit people with ADHD.
In a randomized Phase 1 study, the company’s new drug, called ALKS 7290, was well tolerated and showed a signal of efficacy, Alkermes said Monday.
Participants in the study started with a median score of 39 on a diagnostic known as the Adult ADHD Investigator Symptom Rating Scale, indicating that they had moderate to severe symptoms. After two weeks, those taking a high dose of 50 milligrams of ALKS 7290 experienced a 19-point reduction on the scale, indicating that their symptoms had become mild.
A comparative study of traditional Chinese mind-body therapies on sleep quality, mental health, and quality of life in patients with sleep disorders: a network meta-analysis based on randomized controlled trials
BackgroundSleep disorders are a common clinical condition characterized primarily by difficulty falling asleep, trouble maintaining sleep, and impaired daytime functioning, with a high prevalence among adults. This condition can lead to a persistent decline in sleep quality, trigger mental health issues such as anxiety and depression, and ultimately reduce overall quality of life. Traditional Chinese mind-body exercise therapies are considered potentially effective non-pharmacological interventions due to their low risk, high accessibility, and dual mind-body regulation effects. However, the relative efficacy of different exercise therapies on sleep quality, mental health, and quality of life in patients with sleep disorders remains unclear. This study employed a web-based meta-analysis to systematically evaluate the effects of three interventions—Tai Chi, Baduanjin, and Tai Chi combined with transcranial magnetic stimulation—on three outcome measures and to rank their efficacy, aiming to provide evidence-based support for clinical decision-making.MethodsThis study conducted a systematic search of the PubMed, Embase, Web of Science, Cochrane, EBSCO, and CNKI databases and included 18 randomized controlled trials that met the inclusion criteria. Outcome measures included sleep quality, mental health, and quality of life. A network meta-analysis using a random-effects model was performed to calculate standardized mean differences and 95% confidence intervals, and the cumulative ranking probability curve area was used to rank the efficacy of the interventions.ResultThis study included a total of 18 randomized controlled trials. Regarding sleep quality, TCCWTMS yielded the best results (SMD = −1.15, 95% CI [−2.07, −0.22]). Regarding mental health, only the Tai Chi intervention achieved statistical significance (SMD = 0.64, 95% CI [0.07, 1.21], P = 0.03); although Baduanjin ranked first in the SUCRA ranking (92.6%), pairwise comparisons did not reach statistical significance (P = 0.07). Regarding quality of life, both Tai Chi (SMD = -0.47, 95% CI [-0.71, -0.24], P < 0.0001) and Baduanjin (SMD = -0.57, 95% CI [-0.91, -0.23], P = 0.001) were significantly effective. There were significant heterogeneous differences in the efficacy of the three interventions across different outcome measures.ConclusionThis study indicates that the TCCWTMS is the most effective intervention for improving sleep quality in patients with sleep disorders. Regarding quality of life, both Baduanjin and Tai Chi demonstrated significant effects, with Baduanjin showing superior efficacy. Regarding mental health, the current evidence is insufficient to determine the optimal intervention strategy. There are significant differences in the efficacy of the three interventions across various outcome measures; clinical decisions can be made by selecting the appropriate intervention based on the patient’s score needs.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420261415202, identifier CRD420261415202.
Predicting multiple mental health outcomes in adolescents using explainable machine learning models
IntroductionAdolescent mental health problems, including depression, anxiety, and stress, are an increasing public health concern, yet the factors associated with different mental health outcomes may vary across domains. This study investigated shared and outcome-specific predictive features of depression, anxiety, perceived stress, and psychological well-being using explainable machine learning models.MethodsA cross-sectional study was conducted among 1,088 adolescents aged 13–18 years recruited from secondary schools in Wuhan, China, using multistage cluster sampling. Participants completed validated measures of emotional dysregulation, loneliness, social media addiction, self-esteem, sleep quality, academic stress, family support, physical activity, and mental health outcomes. Four algorithms—linear regression, support vector regression, Random Forest, and XGBoost—were trained using an 80/20 train-test split with five-fold cross-validation, and model performance was evaluated using test-set R², RMSE, and MAE. SHapley Additive exPlanations (SHAP) were used to examine feature contributions. To minimize target leakage, outcome-specific feature sets were used, with PSQI and RSES excluded from the depression model because of direct or substantial conceptual overlap with PHQ-9 content, and PSQI excluded from the well-being model because of overlap with WHO-5 content.ResultsXGBoost showed the strongest out-of-sample predictive performance across all four outcomes, explaining 49% of the variance in depression (R² = 0.49, 95% CI: 0.44–0.53; RMSE = 3.52; MAE = 2.81), 55% in anxiety (R² = 0.55, 95% CI: 0.50–0.59; RMSE = 3.04; MAE = 2.47), 60% in perceived stress (R² = 0.60, 95% CI: 0.56–0.64; RMSE = 3.71; MAE = 2.87), and 50% in psychological well-being (R² = 0.50, 95% CI: 0.45–0.54; RMSE = 3.38; MAE = 2.68).DiscussionEmotional dysregulation and loneliness were consistently among the most influential features, while academic stress, family support, social media addiction, self-esteem, and sleep quality showed outcome-specific contributions. SHAP rankings for the depression model were stable across five-fold cross-validation, with emotional dysregulation and loneliness consistently occupying the highest ranks.