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Obsessive compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models. Nine children and adolescents (mean age = $12.3$, SD = $2.6$) with mild to severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to eight weeks in their everyday lives and register OCD events, i.e., every time their OCD symptoms caused distress. We explored the relationships among the amount of physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A two-layer cross validation strategy with either random 10-folds, leave-one-person out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients. Eight of the nine participants collected biosensor signals totaling $2,045$ hours and registered $1,639$ OCD events. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70\% accuracy in random and temporal cross validation. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of youths using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes.

Behavioral observation is an important tool for research and practice in clinical, developmental, and social psychology. Traditional,expert-based methods for behavioral observation from videos are limited by the amount of time and resources it requires. Artificialintelligence tools are available that can automatically and reliably code specific emotions and behaviors. However, the generatedoutput is not specifically aligned to applications in psychology. We present methods for automating behavioral coding using opensource software and provide an overview of available sources and instructions on how they can be applied to generate codes fromwidely used behavioral coding manuals. Using two examples, we describe methods for applying open source computer visionand hearing tools to code behavior in two settings: a parent-child interaction and a psychotherapeutic exposure session. We use amodular approach, defining specific behaviors with features extracted with computer vision and hearing tools that can be combinedinto higher order constructs. Modules can be combined in different ways to allow a flexible application to a variety of codingmanuals or observational tasks. In addition, we propose ways of testing the validity of the automated codes. Some modalities arebetter suited than others for specific tasks. For example, the performance of computer vision tools is excellent for detecting emotion,whereas speech signal processing is less sensitive to detecting specific emotions. However, speech is better suited for detectingvocalization than computer vision. Issues of privacy and bias are discussed as well as methods for mitigating these risks. Weidentified behaviors that can be computationally coded. Our proposed automated methods will enable researchers to code more oftheir video and audio data than is possible with human coding for meaningful behavioral information and investigate mechanisms atplay in social interactions. Some of these methods can be directly applied by psychologists whereas others necessitate collaborationwith computer scientists.

Salivary oxytocin in children with and without OCD: A statistical analysis plan of a cross- sectional study Anna-Rosa Cecilie Mora-Jensen1,2, Line Katrine Harder Clemmensen3, Eli R. Lebowitz4, Daniel S. Quintana5,6,7,8, Niklas Rye Jørgensen9, Anne Katrine Pagsberg1,2, Nicole Nadine Lønfeldt1,2 1Child and Adolescent Mental Health Center, Copenhagen University Hospital – Mental Health Services CPH, Copenhagen, Denmark 2 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 3Technical University of Denmark, DTU Compute, Kgs. Lyngby, Denmark 4Child Study Center, Yale University, New Haven, CT, USA 5Department of Psychology, University of Oslo, Oslo, Norway 6Norwegian Centre for Mental Disorders Research (NORMENT), University of Oslo, Oslo, Norway 7K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway 8NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway 9Department of Clinical Biochemistry, Rigshospitalet, Glostrup Background The association between endogenous oxytocin activity and obsessive-compulsive disorder (OCD) in youth is a sparsely researched area. Based on animal studies and a few smaller studies in adults, oxytocin concentrations have been hypothesized to be increased in patients with OCD[1]. However, oxytocin has also been associated with anxiolytic effects and negative correlations between oxytocin and ratings of anxiety in children have been described in separation anxiety[2]. Endogenous oxytocin in humans shows large variability, making it difficult to understand findings between groups and over time[3]. Therefore, understanding and including relevant covariates in the populations being studied is important. Objective The primary objective with this study is to investigate if salivary oxytocin concentration levels are associated with OCD status in children. The secondary objective is to investigate if salivary oxytocin is associated with age, pubertal stage, and sex. For the primary objective we hypothesize that salivary oxytocin levels are different in children with OCD compared to healthy controls. We have no preconceived hypothesis on the direction of a difference given the limited number of previous studies and possible theoretical explanations in both directions. Methods The study will include data on 130 youth with OCD and 90 sex- and aged matched healthy controls and their parents from the TECTO trial (Treatment Effects of Family Based Cognitive Therapy in Children and Adolescents with Obsessive Compulsive Disorder)[4]. We will examine salivary oxytocin collecting saliva with Salivettes (Sarstedt, Rommelsdorf, Germany) and analyzing with immunoassay using Enzo® (NY, USA) oxytocin kits. Statistical analysis Power The sample size of this study is based on the sample size calculation of the primary outcome of the TECTO trial. In this sub study of oxytocin assuming n=220, α=0.05 (2-sided) and power (80%) we will be able to reliably detect effect sizes of at least a Cohen's d of 0.4. There is a large difference in reported standard deviations between endogenous oxytocin studies. In addition, differences in means of oxytocin levels between patients with OCD and healthy controls have only been described in cerebrospinal fluid and plasma. We have therefore chosen to describe the effect size (described with Cohen's-D) we can detect, but not describe the expected difference in mean and SD. Outcome The primary outcome is child salivary oxytocin concentration. This will be included as a continuous variable. In an exploratory model we will also include parent salivary oxytocin as an outcome. Covariates In the primary statistical model, we will investigate the following covariates: OCD status, age, pubertal stage and sex. OCD status and sex will be included as dichotomous variables. Age will be included as continuous variables. Pubertal stage measured with the Tanner Staging[5,6] will be included as an ordinal variable. In an exploratory analysis we will also include a family variable (family variable implying participants related as a family). Family will be included as a categorical variable. In the supplementary analyses we will investigate the variables “time of sample across the day”, self-reported menarche, functional level measured with the Children's Global Assessment Scale (CGAS)[7],IQ total score measured with the WISC[8] or WAIS[9] test, social responsiveness scale total score [10] and OCD severity measured as total score from a CY-BOCS interviews [11]. The use of these variables will be specified further below. General analysis principles Frequentist statistical analyses will be performed using R (R Core Team, Vienna, Austria). Bayesian hypothesis testing will be performed using JASP. Data Exclusion Outliers will be defined as values in salivary oxytocin above mean + 3SD. Analysis will be performed both with and without outliers and difference between the two will be reported. Results without outliers will be presented in the main results section. Result without outliers will be provided in supplementary section. Data transformation We will systematically assess the statistical assumptions underlying each statistical model. If the distributional assumptions do not hold, methods like logarithmic transformation will be used, which has also been used in previous studies of salivary oxytocin. In the section below we have described which parametric test will be used. If the assumptions are not met, we will use the corresponding non-parametric tests. Correction for multiplicity We will not adjust for multiple comparisons, but report results of all comparisons made and be aware of the choice of no mathematical correction in our interpretation. Principle for statistical analysis Analyses will be performed with both frequentist and Bayesian statistics. Bayesian statistics will be performed using a Cauchy distribution prior centered around zero with a scale parameter of r=0.5. We will conduct three primary and one exploratory analysis. In addition, we will conduct exploratory analyses on variables that are not part of our primary focus e.g., time of sample across the day and menarche. These results will be presented in a supplement. If we find associations between variables in primary analysis, we will include them in the analyses of the exploratory analyses in the supplement. If we find associations between oxytocin and variables in the supplementary exploratory analyses, we will show results of analyses with and without the variables included in the primary models. These will be described in the supplement. Primary statistical models Model 1: Difference in child salivary oxytocin between children with OCD and healthy controls (OCD status) will be tested using Welch’s t-test. Model 2: Association between salivary oxytocin in all children and the variables OCD status, age and sex will be tested with an analysis of covariance. Analysis of a possible interaction between age and sex will be included in the model. Model 3: To investigate if pubertal stage is associated with salivary oxytocin, we will repeat Model 1 with Tanner stage instead of age. We expect age and Tanner stage to be highly correlated and thus we will not include the two predictors in the same model. Exploratory analyses Model 4: To further investigate the importance of age and sex we will include all participants (children, mothers, and fathers) in an ANCOVA with age and sex and a possible interaction between age and sex. Since previous studies have shown a correlation between mothers' and fathers' salivary oxytocin[12] and children's and parents' oxytocin[13], a family variable will be included as a covariate. This analysis will allow us to increase our sample size from Model 1 in the analysis of age and sex but without OCD-status as we do not have information on parent diagnostic status. Supplementary analysis We will analyze the variables: - Time of sample. This will show possible associations between oxytocin and diurnal variation. We will include all participants and the family variable if this shows significant association in Model 4. Time of sample will be included as a categorical variable with four categories (8am-10am, 10am-12am, 2pm-4pm). - Menarche. This will be analyzed in all female children and included as a dichotomous variable. - Menstrual cycle. We will investigate a difference in oxytocin levels between the menstrual phase and ovulation. Phase of menstrual cycle will be calculated from self-reported first date of last menstruation. We will include participant in menstrual phase or ovulation only and analyze this as a dichotomous variable. We will show results from all female participants together but also show results after stratification between children (

AIM The objective of this study was to identify possible pre- and postnatal factors influencing neurodevelopment of the young child. METHODS We used data from the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010 ) mother-child cohort, but excluded those with a neurological diagnosis, born 0.05 in all tests). Early age at milestone achievement was associated with male sex (p = 0.05), lower maternal age (p = 0.02), higher gestational age (p

A double-blind randomized controlled trial of n-3 long-chain polyunsaturated fatty acid (n-3 LCPUFA) supplementation or matching placebo during third trimester of pregnancy was conducted within the COPSAC2010 mother-child cohort consisting of 736 women and their children. The objective was to determine if maternal n-3 LCPUFA pregnancy supplementation affects offspring neurodevelopment until 6 years. Neurodevelopment was evaluated in 654 children assessing age of motor milestone achievement, language development, cognitive development, general neurodevelopment, and emotional and behavioral problems. Maternal n-3 LCPUFA supplementation during pregnancy improved early language development and reduced the impact of emotional and behavioral problems. The n-3 LCPUFA supplementation was in boys associated with the earlier achievement of gross motor milestones, improved cognitive development, and a reduced impact of emotional and behavioral problems.

The objective of this study was to identify possible pre- and postnatal factors influencing neurodevelopment of the young child.We used data from the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSACNeurodevelopmental scores were not associated with breastfeeding, persistent wheeze, eczema and number of sick days (p 0.05 in all tests). Early age at milestone achievement was associated with male sex (p = 0.05), lower maternal age (p = 0.02), higher gestational age (p 0.001) and paternity leave (p = 0.01). A higher 1-year language score was associated with female sex (p = 0.02) and maternal smoking during pregnancy (p = 0.01) and a higher 2-year language score with female sex (p 0.001) and being first born (p = 0.01). A higher cognitive score was associated with female sex (p = 0.02).Neurodevelopmental scores were unrelated to breastfeeding, persistent wheeze, eczema and number of sick days. Neurodevelopment in early childhood was mostly associated with gender.

Objectives To study the association between antibiotic intake in pregnancy and the development of otitis media and placement of ventilation tubes (VTs) in the offspring under the hypothesis that antibiotics in pregnancy may alter the offspring's propensity for disease. Study design Data from the 700 children in the Copenhagen Prospective Studies on Asthma in Childhood 2010 unselected birth cohort study were used. Information on maternal antibiotic use and other exposures during pregnancy was collected prospectively from interviews and validated in national registries. Otitis media episodes were registered in a prospective diary for 3 years. Information regarding children's VTs was obtained from national registries. Results There were 514 children who had diary information and were included in the analysis regarding otitis media episodes. For VTs analysis, 699 children were included. Thirty-seven percent of the mothers received antibiotics during pregnancy, and this was associated with increased risk of otitis media (adjusted hazard ratio 1.30; 95% CI 1.04-1.63; P = .02). The risk of receiving VTs was especially associated with third trimester antibiotics (adjusted hazard ratio 1.60; 95% CI 1.08-2.36, P = .02). The risk of otitis media increased with increasing number of treatments (per-level adjusted hazard ratio 1.20; 95% CI 1.04-1.40; P = .02), but for VTs this association was not significant after adjustment. Conclusion Maternal use of antibiotics during pregnancy is associated with an increased risk of otitis media and VT insertions in the offspring. Antibiotics late in pregnancy mainly contributed to these effects, pointing toward potential transmission of an unfavorable microbiome from mother to child.

Background and objectives: Many children are treated for recurrent acute otitis media and middle ear effusion with ventilation tubes (VT). The objectives are to describe the incidence of VT in Denmark during 1997-2011 from national register data, furthermore, to analyze the determinants for VT in the Copenhagen Prospective Studies on Asthma in Childhood2010 (COPSAC2010) birth cohort. Methods: The incidence of VT in all children under 16 years from 1997-2011 were calculated in the Danish national registries. Determinants of VT were studied in the COPSAC2010 birth cohort of 700 children. Results: Nationwide the prevalence of VT was 24% in children aged 0 to 3 three years, with a significant increase over the study period. For all children 0-15 years, the incidence of VT was 35/1,000. In the VT population, 57% was male and 43% females. In the COPSAC2010 birth cohort, the prevalence of VT during the first 3 years of life was 29%. Determinants of VT were: maternal history of middle ear disease; aHR 2.07, 95% CI [1.45-2.96] and siblings history of middle ear disease; aHR 3.02, [2.11-4.32]. Paternal history of middle ear disease, presence of older siblings in the home and diagnosis of persistent wheeze were significant in the univariate analysis but the association did not persist after adjustment. Conclusion: The incidence of VT is still increasing in the youngest age group in Denmark, demonstrating the highest incidence recorded in the world. Family history of middle ear disease and older siblings are the main determinants for VT.