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

Abstract

Early-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.

Fig. 1. Variance marker of patient 1, calculated from a moving window of the activity data (see method section for details).

The left set of light-gray vertical lines indicated with an “A” represent the euthymic period. The right set of light-gray vertical lines indicates with a “B” represent the episode period. The y-axis shows the variance of the activity while the x-axis represents time in days.

Fig. 2. Frequency spectrum of patient 11 showing the dominant frequency (dark gray) and its two subsequent harmonic frequencies (light gray), one day before transitioning towards an atypical 12-h rhythm.

The y-axis shows the power spectrum density in U² while the x-axis shows the frequency in 1/min.

Fig. 3. Frequency spectrum of patient 11 showing the new dominant frequency (dark gray) after transitioning from a typical 24-h rhythm to an atypical 12-h rhythm.

The y-axis shows the power spectrum density in U² while the x-axis shows the frequency in 1/min.