scholarly journals Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoram K. Kunkels ◽  
Harriëtte Riese ◽  
Stefan E. Knapen ◽  
Rixt F. Riemersma - van der Lek ◽  
Sandip V. George ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Early Warning ◽  
Type I ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Research Fields ◽  
Mood Episode ◽  
Final Sample ◽  
Bipolar Patients ◽  
Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

scholarly journals Anticipating manic and depressive shifts in patients with bipolar disorder using early warning signals

2021 ◽  
Author(s):  
Fionneke Bos ◽  
Marieke Schreuder ◽  
Sandip Varkey George ◽  
Bennard Doornbos ◽  
Richard Bruggeman ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Standard Deviation ◽  
Early Warning ◽  
Self Report ◽  
Type I ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Predictive Values ◽  
Idiographic Approach ◽  
Depressive Episodes

Background. In bipolar disorder treatment, accurate prediction of manic and depressive episodes is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate mood shifts in individual patients with bipolar disorder. Methods. Twenty bipolar type I/II patients participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean=491 observations per person). Weekly completed symptom questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms were used to determine transitions. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA items. Positive and negative predictive values were calculated to determine clinical utility. Results. Eleven (of the twenty) patients reported 1-2 manic or depressive transitions. The presence of EWS increased the probability of impending transitions towards depression and mania from 32-36% to 46-48% (autocorrelation) and 29-41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65-100%) were cheerfulness, focusing ability, full of ideas, worry, racing thoughts, agitation, energy, and tiredness. Large individual differences in the utility of EWS were found.Conclusions. EWS may improve anticipating manic and depressive transitions in bipolar disorder, but await further empirical testing.

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

2015 ◽  
Vol 47 (43) ◽  
pp. 4630-4652 ◽  
Author(s):  
Chia-Chien Chang ◽  
Te-Chung Hu ◽  
Chiu-Fen Kao ◽  
Ya-Chi Chang
Keyword(s):  
Early Warning ◽  
Stock Index ◽  
Garch Models ◽  
Infinitely Divisible ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Early warning signals in plant disease outbreaks

2019 ◽  
Vol 393 ◽  
pp. 12-19 ◽  
Author(s):  
S. Orozco-Fuentes ◽  
G. Griffiths ◽  
M.J. Holmes ◽  
R. Ettelaie ◽  
J. Smith ◽  
...  
Keyword(s):  
Early Warning ◽  
Plant Disease ◽  
Disease Outbreaks ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

2015 ◽  
Vol 112 (32) ◽  
pp. 10056-10061 ◽  
Author(s):  
Lei Dai ◽  
Kirill S. Korolev ◽  
Jeff Gore
Keyword(s):  
Early Warning ◽  
Alternative Stable States ◽  
Basin Of Attraction ◽  
Environmental Drivers ◽  
Ecological Communities ◽  
Stable States ◽  
Warning Signals ◽  
Early Warning Signals ◽  
The Stability ◽  
Stability And Resilience

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.

scholarly journals Variance and Rate-of-Change as Early Warning Signals for a Critical Transition in an Aquatic Ecosystem State: A Test Case From Tasmania, Australia

2018 ◽  
Vol 123 (2) ◽  
pp. 495-508 ◽  
Author(s):  
Kristen K. Beck ◽  
Michael-Shawn Fletcher ◽  
Patricia S. Gadd ◽  
Henk Heijnis ◽  
Krystyna M. Saunders ◽  
...  
Keyword(s):  
Early Warning ◽  
Aquatic Ecosystem ◽  
Rate Of Change ◽  
Test Case ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Ecosystem State
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