Journal of Psychology & Psychotherapy
Open Access

Using Early Warning Signals to Predict Manic and Depression Transitions in Bipolar Disorder Patients

Amnie Kammoun^{* *}Correspondence: Amnie Kammoun, Department of Psychology, Alexandria University, Alexandria, Egypt, Email:

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Description

A bipolar disorder is associated with episodes of mood swings ranging from depressive lows to manic highs. The exact cause of bipolar disorder is not known, but a combination of genetics, environment and altered brain structure and chemistry may play a role in bipolar disorder.

The ability to predict future psychopathological crises in individuals is a major difficulty in psychiatry. Rapid technology advancements, on the other hand, have made it possible for patients to track their mood and symptoms in real time, allowing for the prospective and individualized prediction of clinically significant symptom changes in the near future.

Patients with Bipolar Disorder (BD), who have numerous disruptive depressive and manic episodes, and whose treatment is heavily focused on episode recognition, may benefit from such early episode detection. Now comprehensive longitudinal monitoring via cell phones is becoming more practical, systems to use these data to predict future rises in psychopathological symptoms are needed. A prediction tool based on smartphone monitoring could revolutionize therapy for individuals with Bipolar Disorder (BD) which is one of the greatest promises of mental health, according to patients and experts. In this approach, Ecological Momentary Assessment (EMA) might serve a dual purpose. Monitoring itself could raise awareness of mood swings, while the collected data could be evaluated to offer a personalized Early Warning Signals (EWS) based alarm system.

As a result, this empirical and exploratory investigation is the first to see if Early Warning Signals (EWS) may predict mood swings in Bipolar Disorder (BD) patients and if they can be used clinically. This study is based on the motion that transitions between depressed and manic states mimic crucial transitions in a bistable system due to fold bifurcation. Exploratory repeated single subject design is used in bipolar disorder.

Sociodemographic characteristics have not consistently been linked to bipolar disorder. Bipolar I disorder affects both men and women equally; however bipolar II disorder is more common in women. There is no evident link between race/ ethnicity, socioeconomic level, and home location (e.g., rural vs. urban). Unmarried people have higher prevalence of bipolar disorder.

Direct treatment costs, while indirect costs resulting from morbidity and lost output are typically included in economic assessments. This is the model for bipolar disorder and other chronic or life-long illnesses. Exorbitant costs and mistreatment result from misdiagnosis. Costs are highly influenced by late presentation, insufficient diagnosis, and under treatment.

Bipolar I disorder, bipolar II disorder, cyclothymic disorder, and bipolar disorder not otherwise specified are all included in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders Text Revision (DSM-IV-TR). Mania, hypomania, depressive, and mixed episodes are the four types of episodes. Patients with bipolar I disorder have at least one episode of mania by definition. Bipolar II patients have experienced depressed and hypomanic episodes. Although many therapists use the phrase to describe mood swings day to day, rapid cycling formally refers to four or more episodes per year. Secondary mania is defined as mania that occurs as a result of medicine, chemicals, or medical disease and is classified independently.