Principles derived from complex dynamical systems theory may provide such techniques. 2019), the field is in need of tools to utilize these data to anticipate future increases in psychopathological symptoms. ![]() Now that intensive longitudinal monitoring through smartphones has become increasingly feasible (Vachon et al. Such early identification of episodes might be especially relevant for patients with bipolar disorder (BD), who experience frequent and disruptive depressive and manic episodes, and whose treatment is strongly focused on episode recognition (Michalak et al. However, rapid technological advances have enabled patients to easily monitor their mood and symptoms in real-time, opening the door to prospective and personalized anticipation of clinically relevant symptom changes in the near future (Dunster and Swendsen 2020). 2016), which unfortunately say little about which individual patient will relapse when. Until now, research has mostly focused on group-level retrospective risk factors (Meter et al. ConclusionsĮWS show theoretical promise in anticipating manic and depressive transitions in bipolar disorder, but the level of false positives and negatives, as well as the heterogeneity within and between individuals and preprocessing methods currently limit clinical utility.Ī major challenge in psychiatry is to timely identify impending psychopathological episodes for individual patients. Large individual differences in the utility of EWS were found. The momentary states that indicated nearby transitions most accurately (predictive values: 65–100%) were full of ideas, worry, and agitation. However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The presence of EWS increased the probability of impending depressive and manic transitions from 32-36% to 46–48% (autocorrelation) and 29–41% (standard deviation). ResultsĮleven patients reported 1–2 transitions. Positive and negative predictive values were calculated to determine clinical utility. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. ![]() Twenty bipolar type I/II patients (with ≥ 2 episodes in the previous year) participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months ( Mean = 491 observations per person). The present study investigated whether EWS can anticipate manic and depressive transitions in individual patients with bipolar disorder. EWS could thus form personalized alerts in clinical care. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult.
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