Mapping the time course of overt emotion dysregulation, self-injurious behavior, and aggression in psychiatrically hospitalized autistic youth: A naturalistic study

A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study

BACKGROUND

Accurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders.

METHODS

Cox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots.

RESULTS

Research findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86-0.98), single (HR = 0.11, 95% CI: 0.02-0.68), having children (one child, HR = 0.07, 95%CI: 0.01-0.87; more than one child, HR = 0.33, 95%CI: 0.04-2.48), living in dormitory (HR = 0.25, 95%CI: 0.08-0.77), non-student (employee, HR = 0.24, 95% CI: 0.07-0.88; non-employee, HR = 0.09, 95% CI: 0.02-0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82-0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23-22.48), living alone (HR = 4.37, 95% CI: 1.60-11.94), having suicide history (HR = 2.51, 95% CI: 1.06-5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00-1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00-1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit.

CONCLUSIONS

A novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures.

Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life

BACKGROUND

Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings.

METHODS

From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature).

RESULTS

Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.

CONCLUSIONS

This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.

Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data

Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.

Associations between executive functioning, challenging behavior, and quality of life in children and adolescents with and without neurodevelopmental conditions

The present study sought to clarify the impact of executive and social functioning on challenging behavior and the downstream influence of challenging behavior on quality of life and functioning in a large transdiagnostic sample. Understanding these relationships is crucial for developing and designing tailored intervention strategies. In a cross-sectional study, parent informants of 2,004 children completed measures of executive and social functioning, challenging behavior, child and family quality of life, and reported on functional impacts of challenging behavior. Using structural (path) modeling, analyses evaluated the associations between executive and social functioning, including emotion regulation and risk avoidance, with overall and specific types of challenging behavior. Structural models also examined the influence of challenging behavior on child and family quality of life, including measures of the immediate and extended environment, and functional impacts on the parent/child as well as interactions with the medical/legal systems. Finally, mediational models explored the direct and indirect effects of executive and social functioning on quality of life and impact measures via challenging behavior. Results indicated that executive functioning accounts for substantial variance (R² = 0.47) in challenging behavior. In turn, challenging behavior accounts for substantial variance in child and family quality of life (R² = 0.36) and parent/child impacts (R² = 0.31). Exploratory mediational models identified direct effects from executive and social functioning measures on quality of life and functional impacts and indirect effects for executive functioning via challenging behavior. These findings support the development of new intervention strategies and suggest the need to measure executive functioning when assessing and tailoring the treatment of challenging behavior in clinical practice.

Insights on dental care management and prevention in children with autism spectrum disorder (ASD). What is new?

Autistic subjects represent a severe concern to dentistry due to the considerable difficulty in managing their oral health, maintaining routine toothbrushing, and preventing dental and periodontal problems. The social and economic burden of managing dental care in autism spectrum disorder (ASD) children is particularly cumbersome for families and public and private health expenditure, especially when children reach the dentist following a late diagnosis with evident oral health problems. An early diagnosis of ASD helps dentists better address these children's oral health. Unfortunately, insufficient attention is paid to the training and education of general pediatricians, dentists, and dental hygienists, allowing them to get to approach the different clinical aspects of ASD. Usually, children diagnosed with ASD are scheduled for dental appointments like their neurotypical peers, whereas their needs are typically complex and personalized. Scant attention is also devoted to these patients by commercial manufacturers of dental products and devices for oral hygiene and prevention of caries and periodontal diseases, leaving parents without the support and often failing when they address the oral health of autistic children. The difficulties of oral care do not derive simply from the behavior of ASD patients, as is commonly assumed, and therefore cannot be overcome solely by the patience and attention of parents and dentists. Genetics, dietary habits, sensory impairments, and cognition disorders are other causes contributing in various degrees to the impact on the mood and psychological reactions of autistic children towards dentists. How can we prevent teeth caries, periodontal disorders, and other oral health impairments by properly managing ASD children? This manuscript gives an up-to-date overview of these problems and helps to provide good remarks.