Forecasting the Course of Bipolar Disorder Using Rest-Activity Rhythms: Protocol for a Multi-Study Modelling Project [version 2; Peer Review: 1 Approved, 2 Approved with Reservations]

Background Risk of new episodes is a constant concern for people living with bipolar disorder (BD). Research into sleep and circadian rhythm disruption (SCRD) suggests that relapse risk may be measurable in 24-hour rest-activity rhythms (RAR) via actigraphy. The primary aim of the Tipping Point project is to develop an algorithm forecasting relapse based on RAR. A secondary aim is to explore SCRD mechanisms linking RAR to mood dynamics in BD. The mechanistic investigation involves analysis of biological correlates of RAR, and exploration of system instability as a predictor of two types of state transition (relapse and recovery). Method Three prospective studies will enrol adults living with BD I or II. Study 1 (Australia) recruits N = 100 individuals with interepisode BD who will undergo three contiguous 26-week monitoring epochs. Each epoch commences with 14 days of actigraphy, followed by retrospective interviews at 13 and 26 weeks. RAR predictors of time to first relapse will be explored using three computational frameworks - statistical risk, probabilistic network graphs and non-linear dynamic models. Study 2 (India) replicates one of the 26-week epochs of Study 1, recruiting 25 individuals with BD and 25 matched healthy controls. Study 2 investigates the replicability of models identified in Study 1 and tests the mechanistic association between amplitude of the RAR and amplitude of clock gene expression. Study 3 (New Zealand) explores system instability as a mechanism, by investigating whether complex system indicators of relapse risk (from Study 1) also forecast recovery from acute episodes. In Study 3, actigraphy data will be collected from people experiencing a severe episode of BD depression (N = 30) or mania (N = 15). Discussion The Tipping Point project uses a multi-study design to develop a mechanistically-informed predictive algorithm for relapse risk in BD.