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BACKGROUND: The objectives of this study were to examine the association of psychiatric comorbidities and patient characteristics with treatment change and response as well as to assess the association between treatment change and healthcare resource utilization (HCRU) among adult patients with attention-deficit/hyperactivity disorder (ADHD) and psychiatric comorbidities. METHODS: De-identified electronic health records from the NeuroBlu Database (2002-2021) were used to select patients ≥ 18 years with ADHD who were prescribed ADHD-specific medication. The index date was set as the first prescription of ADHD medication. The outcomes were treatment change (discontinuation, switch, add-on, or drop) and HCRU (inpatient, outpatient, composite) within 12 months of follow-up. Cox proportional-hazard model was used to assess the association between clinical and demographic patient characteristics and treatment change, while generalized linear model with negative binomial distribution and log link function was used to assess the association between key risk factors linked to treatment change and HCRU rates. RESULTS: A total of 3,387 patients with ADHD were included (ADHD only: 1,261; ADHD + major depressive disorder (MDD): 755; ADHD + anxiety disorder: 467; ADHD + mood disorder: 164). Nearly half (44.8%) of the study cohort experienced a treatment change within the 12-month follow-up period. Treatment switch and add-on were more common in patients with ADHD and comorbid MDD and anxiety disorder (switch: 18.9%; add-on: 20.5%) compared to other cohorts (range for switch: 8.5-13.6%; range for add-on: 8.9-12.1%) Survival analysis demonstrated that the probability of treatment change within 12 months from treatment initiation in the study cohort was estimated to be 42.4%. Outpatient visit rates statistically significantly increased from baseline (mean [SD] 1.03 [1.84] visits/month) to 3 months post-index (mean [SD] 1.62 [1.91] visits/month; p < 0.001), followed by a gradual decline up to 12 months post-index. Being prescribed both a stimulant and a non-stimulant at index date was statistically significantly associated with increased risk of treatment change (adjusted hazard ratio: 1.64; 95% CI: 1.13, 2.38; p = 0.01). CONCLUSIONS: This real-world study found that treatment change was common among patients with ADHD and psychiatric comorbidities. These findings support the need for future studies to examine the unmet medical and treatment needs of this complex patient population.

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BACKGROUND: Autism commonly co-occurs with attention-deficit/hyperactivity disorder (ADHD), but less is known regarding how ADHD symptoms impact the early presentation of autism. This study examined early behavioral characteristics of a community sample of toddlers later identified with autism diagnosis, ADHD symptoms, combined autism and ADHD symptoms, or neither condition. METHODS: Participants were 506 toddlers who were part of a longitudinal study of children's behavioral development. Parents completed questionnaires about their children's behavior at two time points. Four groups were identified based on study measures or medical record: autism diagnosis (n = 45), elevated ADHD symptoms (n = 70), autism and ADHD symptoms (n = 30), or neurotypical development (n = 361). Relationships between early parent report of autism- and ADHD-related behaviors, social-emotional and behavioral functioning, and caregiver experience and subsequent group designation were evaluated with adjusted linear regression models controlling for sex. RESULTS: Significant group differences were found in measures of autism-related behaviors, ADHD-related behaviors, externalizing and internalizing behaviors, and parent support needs (p < .0001). Pairwise comparisons indicated toddlers later identified with combined autism diagnosis and ADHD symptoms had higher levels of autism-related behaviors, externalizing and internalizing behaviors, and autism-related parent support needs compared to the other groups. Toddlers with subsequent elevated ADHD symptoms or combined autism diagnosis and ADHD symptoms exhibited similar levels of ADHD-related behaviors, while both groups displayed more ADHD-related behaviors than toddlers subsequently identified with autism or those with neither condition. CONCLUSIONS: In this community sample, toddlers for whom combined autism diagnosis and ADHD symptoms were subsequently identified showed a distinct presentation characterized by higher early autism-related behaviors, broader behavioral concerns, and higher parent support needs. Presence of ADHD symptoms (alone or in combination with autism) was associated with higher parent-reported ADHD-related behaviors during toddlerhood. Results indicate that ADHD-related behaviors are manifest by toddlerhood, supporting screening for both autism and ADHD during early childhood.

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AKL-T01 is a digital therapeutic (DTx) that targets attention by generating conflict at dynamically updated difficulty levels during a multitasking game. Clinical trials support AKL-T01's efficacy in attention-deficit/hyperactivity disorder (ADHD), but there is a need to understand how in-game data can be used to monitor patient changes in cognition. We aimed to derive a real-time measure of attention from AKL-T01 gameplay data and validate it against clinical outcomes. Trials of AKL-T01 included: STARS-ADHD-Adult (NCT05183919), a 6-week trial in adults 18 and older (n = 221; M age = 39.9; 70% female); STARS-ADHD-Adolescent (NCT04897074), a 4-week trial in adolescents ages 13-17 (n = 162; M age = 14.4; 41% female); and STARS-ADHD (NCT02674633), a 4-week trial in children ages 8-12 (n = 180; M age = 9.7; 31% female). A cognitive metric was derived from targeting response speed, targeting sensitivity (d-prime), and navigation skill level. Using multiple linear regression models, we analyzed relationships between cognitive metric change and changes in the Test of Variables of Attention (TOVA)-Attention Comparison Score (ACS), controlling for TOVA-ACS baseline, cognitive metric baseline, age, and sex. We explored associations with ADHD symptoms and quality of life. Increases in the cognitive metric significantly predicted increases in TOVA-ACS in the adult (ß = 0.16, p < 0.001), adolescent (ß = .09, p = 0.007), and pediatric (ß = 0.06, p = 0.014) trials. Cognitive metric changes additionally related to self-reported quality of life in adults and clinician-rated ADHD symptoms in adolescents. Findings support the clinical validity of a real-time measure of attention derived from AKL-T01 patient-device interactions.

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PURPOSE: Early detection and intervention are associated with improved outcomes for autistic children. Thus, it is important to understand factors influencing early screening tools designed to detect autism. This study examined the relationship between caregiver-reported emotional and behavioral symptoms and children's scores on a commonly used autism screening questionnaire, the Modified Checklist for Autism in Toddlers-Revised with Follow-Up (M-CHAT-R/F). METHODS: Toddlers were recruited from four primary care clinics between 2018 and 2021. Their caregivers completed the M-CHAT-R/F as well as the Child Behavior Checklist (CBCL), a well-validated, normed measure of emotional and behavioral functioning. Correlational and group analyses were evaluated to examine relationships between CBCL scales and M-CHAT-R/F scores. RESULTS: 1765 toddlers were recruited for the study. CBCL scores for the internalizing, externalizing, autism, ADHD, and anxiety scales were all modestly positively correlated with M-CHAT-R/F scores. Compared to toddlers with elevated autism scale scores only, toddlers with elevations in both autism and ADHD/externalizing scales had higher M-CHAT-R/F scores. In contrast, no significant difference in scores were found between toddlers with elevated autism scale scores only compared to those with elevated scores on both autism and internalizing scales. CONCLUSION: Findings suggest that, for children with elevated autism behaviors, the presence of externalizing symptoms, including ADHD-related concerns, is associated with elevated scores on the M-CHAT-R/F. In contrast, internalizing symptoms did not show an association with elevated M-CHAT-R/F scores among toddlers with elevated autism-related behaviors. Interpretation of the M-CHAT-R/F should include consideration of co-occurring psychiatric conditions, especially externalizing conditions such as ADHD.

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Inattention symptoms represent a key driver of functional impairment in ADHD and often persist into adolescence and adulthood, underscoring a need for novel treatments targeting attentional control. We evaluated AKL-T01-a digital therapeutic that is FDA-cleared for children 8-12 y with ADHD-in adolescents and adults with ADHD in two independent single-arm trials: STARS-ADHD-Adolescent, a 4-week trial in adolescents 13-17 y (n = 162 enrolled), and STARS-ADHD-Adult, a 6-week trial in adults 18 and older (n = 221 enrolled). AKL-T01 was linked with improvements on the Test of Variables of Attention (TOVA®) Attention Comparison Score (ACS) of 2.6 (95% CI: 2.02, 3.26; p < 0.0001) in adolescents and 6.5 in adults (95% CI: 5.35, 7.57; p < 0.0001), along with improvements in secondary endpoints. 15 participants reported adverse device effects, all mild or moderate. Though limited by a single-arm design, results provide preliminary support for the safety and efficacy of AKL-T01 for adolescents and adults with ADHD.

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BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) remains underdiagnosed and undertreated in girls. Inattentive symptoms, often predominant in girls with ADHD, represent a key driver of impairment and often persist into adulthood. AKL-T01 is a regulated digital therapeutic targeting inattention. We examined potential sex differences in the efficacy of AKL-T01 in three separate trials for 1) children, 2) adolescents, and 3) adults. METHODS: We conducted secondary analyses of clinical outcomes by sex in three AKL-T01 randomized clinical trials in ADHD (n1 = 180 children 30.6% female, M(SD) age = 9.71 (1.32); n2 = 146 adolescents; 41.1% female, M(SD) age = 14.34 (1.26); n3 = 153 adults; 69.9% female, M(SD) age = 39.86 (12.84)). Active treatment participants used AKL-T01 for 25 min/day over 4-6 weeks. Primary outcomes included change in attention on the Test of Variables of Attention (TOVA) and symptom change on the clinician-rated ADHD Rating Scale (ADHD-RS). To evaluate study hypotheses, we conducted a series of robust linear regressions of TOVA and ADHD-RS change scores by sex, adjusting for baseline scores. RESULTS: In children, girls demonstrated greater improvement in objective attention relative to boys following AKL-T01 (TOVA Attentional Composite Score; Cohen's d = .36 and Reaction Time Mean Half; Cohen's d = .54), but no significant sex differences in ADHD rating scale change. We did not observe significant sex differences in outcomes in the adolescent or adult trials. Limitations include binary sex categorization and slight study design variation across the three samples. CONCLUSION: AKL-T01 might notably improve attentional functioning in girls with ADHD relative to boys. Objective attention measures may be particularly important in the assessment of attentional improvement in childhood, given known gender biases in ADHD symptom reporting. We emphasize the importance of considering sex and gender-specific factors in ADHD treatment evaluation. TRIAL REGISTRATIONS: STARS ADHD CHILD: ClinicalTrials.gov ID NCT03649074; STARS ADHD ADOLESCENT: ClinicalTrials.gov ID NCT04897074; STARS ADHD ADULT: ClinicalTrials.gov ID NCT05183919.

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Objective: To evaluate treatment responder rate using the Attention-Deficit/Hyperactivity Disorder Rating Scale-5 (ADHD-RS-5) score based on optimized dose level of serdexmethylphenidate/dexmethylphenidate (SDX/d-MPH) and changes in ADHD severity in children (aged 6-12 years) with ADHD. Methods: During a 21-day dose-optimization phase, 155 patients initiated treatment with 39.2/7.8 mg SDX/d-MPH in the first week and then were titrated to an optimum dose; 5 patients were downtitrated to 26.1/5.2 mg, 76 were uptitrated to 52.3/10.4 mg, and 69 remained at 39.2/7.8 mg during the following 2 weeks. Responder threshold values were 30% and 50% based on the percent change from baseline (day 0) to days 7, 14, and 21 in the ADHD-RS-5 score. The Conners 3rd Edition-Parent score was used to assess weekly changes in ADHD severity during the dose-optimization and treatment phases. Results: Of the 5 subjects whose dose was optimized at 26.1/5.2 mg, ≥80% across all days had ≥50% responder rate. Of the 69 subjects whose dose was optimized at 39.2/7.8 mg, 81.2% had ≥50% responder rate by day 21. Of the 76 subjects whose dose was optimized to 52.3/10.4 mg, 72.4% had ≥50% responder rate by day 21. Changes in ADHD severity, based on mean Conners 3rd Edition-Parent scores, improved from baseline at each visit during dose optimization for each subscale. At the dose-optimization phase, Conners 3rd Edition-Parent scores improved from baseline for SDX/d-MPH in all subscales. Conclusion: A high percentage of subjects were responders upon reaching their final optimized dose. SDX/d-MPH demonstrated significant reductions in ADHD severity in children based on the Conners 3rd Edition-Parent scores. Determining the optimal dosage of SDX/d-MPH and its effect on ADHD severity could enable the development of a more clinically relevant treatment regimen in children with ADHD.

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OBJECTIVE: Few studies have characterized the nature of sleep problems among adolescents with attention-deficit/hyperactivity disorder (ADHD) using polysomnography (PSG). Additionally, although adolescents with ADHD and adolescents with sleep disturbances display similar neurocognitive deficits, the role of sleep in contributing to neurocognitive impairment in adolescent ADHD is unknown. This study investigated differences in PSG-measured sleep among adolescents with ADHD compared with non-psychiatric controls and associations with neurocognition. METHOD: Medication-free adolescents aged 13 to 17 (N = 62, n = 31 with ADHD; mean age = 15.3 years; 50% female) completed a diagnostic evaluation, 3 nights of ambulatory PSG, the Cambridge Neuropsychological Test Automated Battery, and subjective reports of sleep and executive functioning. Linear regressions covarying for age, sex, and pubertal status examined group differences in sleep indices, and partial Pearson correlations assessed relations between sleep and neurocognition. RESULTS: Although adolescents with ADHD did not exhibit differences in PSG-measured sleep duration, awakenings, or latency (ps > .05) compared with non-psychiatric controls, they displayed lower slow wave sleep percentage (ß = -.40) and non-rapid eye movement (NREM) electroencephalogram (EEG) delta power (ß = -.29). They also exhibited greater stage 2 percentage (ß = .41), NREM EEG sigma power (ß = .41), and elevated self-reported sleep disturbances (ps < .05). Lower NREM EEG delta power, increased high-frequency power, and slower decline in NREM EEG delta power overnight were associated with poorer neurocognition among adolescents with ADHD. CONCLUSIONS: Adolescents with ADHD reported more sleep disturbances than non-psychiatric controls and exhibited differences in sleep stage distribution and NREM sleep EEG frequency. Sleep-EEG spectral indices were associated with impaired neurocognition, suggesting that physiological sleep processes may underlie neurocognitive deficits in ADHD. Future studies may clarify whether sleep plays a causal role in neurocognitive impairments in adolescent ADHD and whether interventions normalizing sleep improve neurocognition. CLINICAL TRIAL REGISTRATION INFORMATION: Sleep Dysfunction and Neurocognitive Outcomes in Adolescent ADHD; https://clinicaltrials.gov/; NCT02897362. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list.

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BACKGROUND AND HYPOTHESIS: Schizophrenia and comorbid substance use disorders (SUDs) are associated with poor treatment outcomes but differences between the associations of different SUDs with clinical outcomes are poorly characterized. This study examines the associations of comorbid SUDs with clinical outcomes in schizophrenia using a largescale electronic health record (EHR) database. DESIGN: Real-world data (RWD) analysis using the NeuroBlu database; de-identified EHR data were analysed. Multivariable logistic regression, Poisson and CoxPH models were used to compare the associations of specific comorbid SUDs with outcome variables. RESULTS: Comorbid SUD was significantly different on all outcome measures compared to no SUD (U = 1.44e7-1.81e7, all ps < .001), except number of unique antipsychotics (U = 1.61e7, p = .43). Cannabis (OR = 1.58, p < .001) and polysubstance (OR = 1.22, p = .007) use disorders were associated with greater CGI-S. Cannabis (IRR = 1.13, p = .003) and polysubstance (IRR = 1.08, p = .003) use disorders were associated with greater number of unique antipsychotics prescribed, while cocaine (HR = 1.87, p < .001), stimulants (HR = 1.64, p = .024), and polysubstance (HR = 1.46, p < .001) use disorders were associated with a shorter time to antipsychotic discontinuation. Conversely, alcohol use (IRR = 0.83, p < .001), cocaine use (IRR = 0.61, p < .001), opioid use (IRR = 0.61, p < .001), stimulant use (IRR = 0.57, p < .001) and polysubstance use (IRR = 0.87, p < .001) disorders were associated fewer inpatient days. CONCLUSION: Comorbid SUDs were generally associated with greater CGI-S and poorer clinical outcomes in patients with schizophrenia. Treatment strategies should target not only schizophrenia symptoms but also comorbid SUD to improve management of both conditions.

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BACKGROUND: Digitization (using novel digital tools and strategies) and consumerism (taking a consumer-oriented approach) are increasingly commonplace in clinical trials, but the implications of these changes are not well described. METHODS: We assembled a group of trial experts from academia, industry, non-profit, and government to discuss implications of this changing trial landscape and provide guidance. RESULTS: Digitization and consumerism can increase the volume and diversity of trial participants and expedite recruitment. However, downstream bottlenecks, challenges with retention, and serious issues with equity, ethics, and security can result. A "click and mortar" approach, combining approaches from novel and traditional trials with the thoughtful use of technology, may optimally balance opportunities and challenges facing many trials. CONCLUSION: We offer expert guidance and three "click and mortar" approaches to digital, consumer-oriented trials. More guidance and research are needed to navigate the associated opportunities and challenges.

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BACKGROUND: Identifying patients most at risk of psychiatric hospitalisation is crucial to improving service provision and patient outcomes. Existing predictors focus on specific clinical scenarios and are not validated with real-world data, limiting their translational potential. This study aimed to determine whether early trajectories of Clinical Global Impression Severity are predictors of 6 month risk of hospitalisation. METHODS: This retrospective cohort study used data from the NeuroBlu database, an electronic health records network from 25 US mental health-care providers. Patients with an ICD-9 or ICD-10 code of major depressive disorder, bipolar disorder, generalised anxiety disorder, post-traumatic stress disorder, schizophrenia or schizoaffective disorder, ADHD, or personality disorder were included. Using this cohort, we assessed whether clinical severity and instability (operationalised using Clinical Global Impression Severity measurements) during a 2-month period were predictors of psychiatric hospitalisation within the next 6 months. FINDINGS: 36 914 patients were included (mean age 29·7 years [SD 17·5]; 21 156 [57·3%] female, 15 748 [42·7%] male; 20 559 [55·7%] White, 4842 [13·1%] Black or African American, 286 [0·8%] Native Hawaiian or other Pacific Islander, 300 [0·8%] Asian, 139 [0·4%] American Indian or Alaska Native, 524 (1·4%) other or mixed race, and 10 264 [27·8%] of unknown race). Clinical severity and instability were independent predictors of risk of hospitalisation (adjusted hazard ratio [HR] 1·09, 95% CI 1·07-1·10 for every SD increase in instability; 1·11, 1·09-1·12 for every SD increase in severity; p<0·0001 for both). These associations were consistent across all diagnoses, age groups, and in both males and females, as well as in several robustness analyses, including when clinical severity and clinical instability were based on the Patient Health Questionnaire-9 rather than Clinical Global Impression Severity measurements. Patients in the top half of the cohort for both clinical severity and instability were at an increased risk of hospitalisation compared with those in the bottom half along both dimensions (HR 1·45, 95% CI 1·39-1·52; p<0·0001). INTERPRETATION: Clinical instability and severity are independent predictors of future risk of hospitalisation, across diagnoses, age groups, and in both males and females. These findings could help clinicians make prognoses and screen patients who are most likely to benefit from intensive interventions, as well as help health-care providers plan service provisions by adding additional detail to risk prediction tools that incorporate other risk factors. FUNDING: National Institute for Health and Care Research, National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Medical Research Council, Academy of Medical Sciences, and Holmusk.

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Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants: This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures: Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results: Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance: In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.

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OBJECTIVE: To assess the relationships of prenatal and childhood smoke exposure with specific neurodevelopmental and behavioral problems during early childhood. STUDY DESIGN: A subsample (n = 386) of mother-child dyads from the Newborn Epigenetic Study (NEST) prebirth cohort participated in the study. Cotinine concentrations were used to objectively measure prenatal and childhood smoke exposure when youth were aged 3-13 years. Multivariable regression models were used to estimate associations of prenatal and childhood cotinine concentrations with performance on the National Institutes of Health (NIH) Toolbox and attention-deficit/hyperactivity disorder and behavioral symptoms, measured using the Behavior Assessment System for Children, 2nd edition (BASC-2). RESULTS: After adjusting for confounders, childhood cotinine concentrations were associated with poorer cognitive performance on tasks measuring cognitive flexibility (B = -1.29; P = .03), episodic memory (B = -0.97; P = .02), receptive language development (B = -0.58; P = .01), and inhibitory control and attention (B = -1.59; P = .006). Although childhood cotinine concentration was associated with higher levels of attention problems (B = 0.83; P = .004) on the BASC-2, after adjustment for confounders, the association is nonsignificant. Although associations for maternal cotinine concentrations were null, an interaction was detected between prenatal and childhood cotinine concentrations on the NIH Toolbox Picture Vocabulary Task (P = .02). CONCLUSIONS: Our findings suggest that childhood tobacco smoke exposure may lead to poorer attention regulation and language acquisition, complex visual processing ability, and attention problems.

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OBJECTIVE: Few studies of prescription stimulant non-oral, non-medical use (NMU) (defined by use not as prescribed) have been conducted in adults beyond the college population. The purpose of this study was to characterize prescription stimulant non-oral use, specifically intranasal (IN) use (snorting) in young adults. METHOD: Amazon's MTurk platform was used to recruit participants for an online survey. Data were collected from March to April 2020. RESULTS: Thirty-two percent (n = 157) of survey respondents (N = 975), aged 18 to 30, reported IN prescription stimulant use (average of 32.1 episodes of lifetime IN use). Adderall was the most-reported prescription stimulant used intranasally (89.2%). Most IN users (82%; n = 68) reported spending no more than 5 minutes tampering with prescription stimulants. Intranasal users said they would take the medication orally if unable to tamper or manipulate medication for IN use. CONCLUSION: These data help quantify a complex public health issue of ongoing IN use of prescription stimulants and suggest a potential role for manipulation-deterrent medications.

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Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.

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Attention-deficit/hyperactivity disorder (ADHD) symptoms affect 40-60% of autistic children and have been linked to differences in adaptive behavior. It is unclear whether adaptive behavior in autistic youth is directly impacted by co-occurring ADHD symptoms or by another associated feature of both autism and ADHD, such as increased irritability. The current study examined relationships between irritability, ADHD symptoms, and adaptive behavior in 3- to 7-year-old autistic children. Results suggest that, after adjusting for co-occurring ADHD symptoms, higher levels of irritability are associated with differences in social adaptive behavior specifically. Understanding relationships between irritability, ADHD, and adaptive behavior in autistic children is critical because measures of adaptive behavior, such as the Vineland Scales of Adaptive Functioning, are often used as a proxy for global functioning, as well as for developing intervention plans and measuring outcomes as primary endpoints in clinical trials.

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BACKGROUND/OBJECTIVES: Sleep measures, such as duration and onset timing, are associated with adiposity outcomes among children. Recent research among adults has considered variability in sleep and wake onset times, with the Sleep Regularity Index (SRI) as a comprehensive metric to measure shifts in sleep and wake onset times between days. However, little research has examined regularity and adiposity outcomes among children. This study examined the associations of three sleep measures (i.e., sleep duration, sleep onset time, and SRI) with three measures of adiposity (i.e., body mass index [BMI], waist circumference, and waist-to-height ratio [WHtR]) in a pediatric sample. SUBJECTS/METHODS: Children (ages 4-13 years) who were part of the U.S. Newborn Epigenetic STudy (NEST) participated. Children (N = 144) wore an ActiGraph for 1 week. Sleep measures were estimated from actigraphy data. Weight, height, and waist circumference were measured by trained researchers. BMI and WHtR was calculated with the objectively measured waist and height values. Multiple linear regression models examined associations between child sleep and adiposity outcomes, controlling for race/ethnicity, child sex, age, mothers' BMI and sleep duration. RESULTS: When considering sleep onset timing and duration, along with demographic covariates, sleep onset timing was not significantly associated with any of the three adiposity measures, but a longer duration was significantly associated with a lower BMI Z-score (ß = -0.29, p < 0.001), waist circumference (ß = -0.31, p < 0.001), and WHtR (ß = -0.38, p < 0.001). When considering SRI and duration, duration remained significantly associated with the adiposity measures. The SRI and adiposity associations were in the expected direction, but were non-significant, except the SRI and WHtR association (ß = -0.16, p = 0.077) was marginally non-significant. CONCLUSIONS: Sleep duration was consistently associated with adiposity measures in children 4-13 years of age. Pediatric sleep interventions should focus first on elongating nighttime sleep duration, and examine if this improves child adiposity outcomes.

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PURPOSE: NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health. PARTICIPANTS: As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months). FINDINGS TO DATE: The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data. FUTURE PLANS: The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

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As children age, they can learn increasingly complex features of environmental structure-a key prerequisite for adaptive decision-making. Yet when we tested children (N = 304, 4-13 years old) in the Children's Gambling Task, an age-appropriate variant of the Iowa Gambling Task, we found that age was negatively associated with performance. However, this paradoxical effect of age was found only in children who exhibited a maladaptive deplete-replenish bias, a tendency to shift choices after positive outcomes and repeat choices after negative outcomes. We found that this bias results from sensitivity to incidental nonrandom structure in the canonical, deterministic forms of these tasks-and that it would actually lead to optimal outcomes if the tasks were not deterministic. Our results illustrate that changes in decision-making across early childhood reflect, in part, increasing sensitivity to environmental structure.

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