Scaling-up Behavioral Observation with Computational Behavior Recognition

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. Artificial intelligence tools are available that can automatically and reliably code specific emotions and behaviors. However, the generated output is not specifically aligned to applications in psychology. We present methods for automating behavioral coding using open source software and provide an overview of available sources and instructions on how they can be applied to generate codes from widely used behavioral coding manuals. Using two examples, we describe methods for applying open source computer vision and hearing tools to code behavior in two settings: a parent-child interaction and a psychotherapeutic exposure session. We use a modular approach, defining specific behaviors with features extracted with computer vision and hearing tools that can be combined into higher order constructs. Modules can be combined in different ways to allow a flexible application to a variety of coding manuals or observational tasks. In addition, we propose ways of testing the validity of the automated codes. Some modalities are better 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 detecting vocalization than computer vision. Issues of privacy and bias are discussed as well as methods for mitigating these risks. We identified behaviors that can be computationally coded. Our proposed automated methods will enable researchers to code more of their video and audio data than is possible with human coding for meaningful behavioral information and investigate mechanisms at play in social interactions. Some of these methods can be directly applied by psychologists whereas others necessitate collaboration with computer scientists

Incidence and Determinants of Ventilation Tubes in Denmark

<div><p>Background and objectives</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Conclusion</p><p>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.</p></div

Middle ear effusion, ventilation tubes and neurological development in childhood.

BackgroundOtitis media with middle ear effusion (MEE) can be treated with ventilation tubes (VT) insertion, and it has been speculated that prolonged MEE in childhood can affect neurological development, which in turn may be important for later academic achievements.ObjectiveTo investigate the association between middle ear effusion (MEE), treatment with ventilation tubes (VT) and childhood neurological development.Study designWe examined 663 children from the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC2010) unselected mother-child cohort study. Children were followed by study pediatricians with regular visits from pregnancy until 3 years of age. MEE was diagnosed using tympanometry at age 1, 2 and 3 years. Information regarding VT from age 0-3 years was obtained from national registries. We assessed age at achievement of gross motor milestones from birth, language scores at 1 and 2 years, cognitive score at 2.5 years and general development score at age 3 years using standardized quantitative tests.ResultsChildren with MEE had a lower 1-year word production vs. children with no disease: (median 2, IQR [0-6] vs. 4, IQR [1-7]; p = 0.017), and a lower 1-year word comprehension (median 36; IQR [21-63] vs. 47, IQR [27-84]; p = 0.03). Children with VT had a lower 2-5-year cognitive score vs. children with no disease; estimate -2.34; 95% CI [-4.56;-0.12]; p = 0.039. No differences were found between children with vs. without middle ear disease regarding age at achievement of gross motor milestones, word production at 2 years or the general developmental score at 3 years.ConclusionOur study supports the previous findings of an association between MEE and concurrent early language development, but not later neurological endpoints up to the age of 3. As VT can be a treatment of those with symptoms of delayed development, we cannot conclude whether treatment with VT had positive or negative effects on neurodevelopment

Determinants of ventilation tubes.

<p>Determinants of ventilation tubes.</p

Age distribution of children 0–15 years who received ventilation tubes between 1997–2011 in Denmark.

<p>Age distribution of children 0–15 years who received ventilation tubes between 1997–2011 in Denmark.</p

Incidence of ventilation tubes in Denmark.

<p>The incidence (ventilation tubes/1000 children 0–3 years of age) from 1997–2011 in Denmark.</p

Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents:Protocol for a Statistical and Machine Learning Analysis

Artificial intelligence tools have the potential to objectively identify youth in need of mental health care. Speech signals have shown promise as a source for predicting various psychiatric conditions and transdiagnostic symptoms. We designed a study testing the association between obsessive-compulsive disorder (OCD) diagnosis and symptom severity on vocal features in children and adolescents. Here, we present an analysis plan and statistical report for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. Audio recordings of clinical interviews of 47 children and adolescents with OCD and 17 children and adolescents without a psychiatric diagnosis will be analyzed. Youths were between 8 and 17 years old. We will test the effect of OCD diagnosis on computationally derived scores of vocal activation using ANOVA. To test the effect of OCD severity classifications on the same computationally derived vocal scores, we will perform a logistic regression. Finally, we will attempt to create an improved indicator of OCD severity by refining the model with more relevant labels. Models will be adjusted for age and gender. Model validation strategies are outlined. Simulated results are presented. The actual results using real data will be presented in future publications. A major strength of this study is that we will include age and gender in our models to increase classification accuracy. A major challenge is the suboptimal quality of the audio recordings, which are representative of in-the-wild data and a large body of recordings collected during other clinical trials. This preregistered analysis plan and statistical report will increase the validity of the interpretations of the upcoming results. DERR1-10.2196/39613