Classification of psychiatric disorders is mainly based on clinical observations and scientific traditions. Such traditions have resulted in diagnostic systems where even conditions that appear to share many symptoms and risk factors are considered distinct categories. In the DynAMoND project we are comparing three such psychiatric diagnoses, i.e., ADHD, bipolar disorder, and borderline personality disorder. These conditions have traditionally been studied separately within the fields of child psychiatry, adult psychiatry, and personality disorders, respectively. All three disorders are characterized by excessive mood fluctuations that may last for hours, days or months. These mood swings, also termed emotional instability, are distressful for patients and may be difficult to treat. Still, we do not know whether the nature of these mood swings is similar across the disorders, or different. DynAMoND is a collaborative study performed in Germany, Spain, Switzerland, Norway and Italy that will explore emotional fluctuations in these disorders across traditional boundaries. We will include patients suffering from ADHD, bipolar disorder or borderline disorder as well as healthy controls (120 in each group, from 14 to 30 years of age). We will use data from smartphones and their inbuilt sensors, as well as data from online surveys, to collect information about mood, activity, and stress. Participants will also provide saliva samples for genetic studies. Data from questionnaires will be taken at the beginning of the study. At the end of the study, patients will receive a summary about findings, the main outcomes, and actionable recommendations. If successful, the project may lead to improved treatment, less burden of disease and improved quality of life for large groups of patients.
Attention deficit/hyperactivity disorder (ADHD) is the most frequent neuro-developmental disorder, with high persistence into adulthood, where it is often comorbid with bipolar disorder (BipD) and Borderline Personality Disorder (BPersD). Whether this is due to shared pathophysiological mechanisms is unclear. All three disorders are characterized by long- or short-term fluctuations in affective states; in BipD, these are low in frequency but high in amplitude, while in ADHD and BPersD, mood and emotional state are more dynamic. In this project we employ the DynAffect model which proposes the existence of an affective “attractor” (termed homebase) reflecting the organism’s main affective state. We extend this by proposing the existence of a second (or third, respectively) affective homebase during depressive (or manic) episodes; also, we hypothesize that the parameters attractor strength and variability differ between disorders. To test this, we will examine high- and low-frequency mood changes with ecological momentary assessment (EMA) in young patients suffering from either disorder, with respect to their pattern of affective dynamics and stressor exposure. This allows to model the fluctuation of affect according to the Modified DynAffect Model and the impact of stress and sleep thereon using granular real-world data. We will also test whether polygenic risk scores (PRS) for Depression, ADHD, BipD, BPersD and Resilience load onto critical parameters of the model. We will thus investigate shared and unique aspects of affect fluctuation across disorders. The results of this study can directly impact on patients’ lives, as they are empowered to link external exposures with their affective state. Also, our results will help in diagnostic assessment by validating an EMA platform, which could reduce misdiagnosis. Finally, the data might govern the perfect timing for therapeutic interventions as critical windows of mood fluctuation can be identified.