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      Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

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          Abstract

          Background

          In bipolar disorder treatment, accurate episode prediction 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 manic and depressive transitions in individual patients with bipolar disorder.

          Methods

          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). Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Positive and negative predictive values were calculated to determine clinical utility.

          Results

          Eleven patients reported 1–2 transitions. 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). 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 full of ideas, worry, and agitation. Large individual differences in the utility of EWS were found.

          Conclusions

          EWS 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.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s40345-022-00258-4.

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          Most cited references58

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Interrater reliability: the kappa statistic

            The kappa statistic is frequently used to test interrater reliability. The importance of rater reliability lies in the fact that it represents the extent to which the data collected in the study are correct representations of the variables measured. Measurement of the extent to which data collectors (raters) assign the same score to the same variable is called interrater reliability. While there have been a variety of methods to measure interrater reliability, traditionally it was measured as percent agreement, calculated as the number of agreement scores divided by the total number of scores. In 1960, Jacob Cohen critiqued use of percent agreement due to its inability to account for chance agreement. He introduced the Cohen’s kappa, developed to account for the possibility that raters actually guess on at least some variables due to uncertainty. Like most correlation statistics, the kappa can range from −1 to +1. While the kappa is one of the most commonly used statistics to test interrater reliability, it has limitations. Judgments about what level of kappa should be acceptable for health research are questioned. Cohen’s suggested interpretation may be too lenient for health related studies because it implies that a score as low as 0.41 might be acceptable. Kappa and percent agreement are compared, and levels for both kappa and percent agreement that should be demanded in healthcare studies are suggested.
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              A Coefficient of Agreement for Nominal Scales

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                Author and article information

                Contributors
                f.m.bos01@umcg.nl
                Journal
                Int J Bipolar Disord
                Int J Bipolar Disord
                International Journal of Bipolar Disorders
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2194-7511
                9 April 2022
                9 April 2022
                2022
                : 10
                : 12
                Affiliations
                [1 ]GRID grid.4494.d, ISNI 0000 0000 9558 4598, Department of Psychiatry, Rob Giel Research Center, , University of Groningen, University Medical Center Groningen, ; PO Box 30.001, 9700 RB Groningen, The Netherlands
                [2 ]GRID grid.4494.d, ISNI 0000 0000 9558 4598, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), , University of Groningen, University Medical Center Groningen, ; Groningen, The Netherlands
                [3 ]GRID grid.4830.f, ISNI 0000 0004 0407 1981, Lentis Research, , Lentis Psychiatric Institute, ; Groningen, The Netherlands
                [4 ]GRID grid.4830.f, ISNI 0000 0004 0407 1981, Department of Psychiatry, , University Medical Center Groningen, University of Groningen, ; Groningen, The Netherlands
                [5 ]GRID grid.83440.3b, ISNI 0000000121901201, Present Address: Department of Computer Science , , University College London , ; London, United Kingdom
                Author information
                http://orcid.org/0000-0002-9630-0440
                Article
                258
                10.1186/s40345-022-00258-4
                8994809
                35397076
                71d2796b-858e-4dc7-b417-8e659fcefd92
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 August 2021
                : 1 March 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, h2020 european research council;
                Award ID: 681466
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001826, zonmw;
                Award ID: 451001029
                Award Recipient :
                Funded by: rob giel research center
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                early warning signals,dynamical systems,critical transitions,bipolar disorder,ecological momentary assessment,experience sampling methodology,complexity,early detection,smartphone,mobile health,single-subject

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