3 New Updates on Pediatric Bipolar Disorder

Early signs of bipolar disorder fall into 2 patterns: a relatively characteristic “homotypic” pattern, consisting mainly of symptoms or other features associated with mood disorders, and a “heterotypic” pattern of other symptoms, including anxiety and disruptive behavior. A homotypic pattern consisted of affective or mood-associated symptoms that are related to, but fall short of, standard diagnostic criteria for bipolar disorder, including mood swings, relatively mild symptoms of excitement, or major depression, sometimes severe and with psychotic symptoms. Homotypic symptoms have low sensitivity and moderate to high specificity. The heterotypic pattern consisted of other types of prodromal symptoms, such as early anxiety and disorders of attention or behavior. This pattern had both low sensitivity and low specificity. Other factors lead to increased risk of developing bipolar disorder, including preterm birth, head injury, drug exposures (especially cocaine), physical or sexual abuse, and other forms of stress. Most of these risk factors have low sensitivity and specificity.

In a prospect cohort study, the Avon Longitudinal Study of Parents and Children, nearly 3600 children completed a detailed questionnaire on sibling bullying at age 12, and then subsequently filled out a standardized clinical examination assessing psychotic symptoms when they were 18. A total of 664 children were victims of sibling bullying; 486 children were "pure" bullies to their siblings and 771 children were bully-victims at age 12; a psychotic disorder had developed in 55 children by aged 18 years. Those involved in bullying several times a week were two to three times more likely to meet criteria for a psychotic disorder. Involvement in both sibling and peer bullying had more than four times the odds for a psychotic disorder.

BiAffect monitors keyboard dynamics metadata, such as typing speed and rhythm, mistakes in texts, and the use of backspace and auto-correct. The metadata (not the content of the text) is analyzed using an artificial intelligence-based machine learning approach to identify digital biomarkers of bipolar episodes. To download the app, users first opt into a study to help researchers continue to search for digital biomarkers of bipolar disorder and to further refine and improve the app.