Evidence for Mood Instability in Patients with Bipolar Disorder: Applying Multilevel Hidden Markov Modelling

Steffen Salvatore^*
*Correspondence: Steffen Salvatore, Department of Clinical Assessment, University of Aberdeen, London, UK, Email:

Introduction

Mood instability in Bipolar Disorder (BD) is a significant clinical feature characterized by fluctuating emotional states that can affect various aspects of an individual’s life. Understanding and quantifying mood instability is crucial for effective treatment and management. Recent advances in statistical modelling, particularly multilevel hidden Markov models, offer a sophisticated approach to analysing intensive longitudinal ecological momentary assessment data. This approach provides valuable insights into the dynamics of mood instability in BD. Multilevel hidden Markov models are an extension of traditional hidden Markov models that account for hierarchical structures in data. In the context of BD, these models are used to analyse time-series data collected through EMA, which involves frequent and repeated assessments of mood and related symptoms. EMA allows for the capture of real-time fluctuations in mood and provides a rich dataset for exploring the underlying patterns of mood instability [1].

The core idea behind HMMs is that the observed data are generated by a system that transitions between different latent states over time. Each latent state corresponds to a specific mood state in BD, which may include states such as manic, hypomanic, depressive, or euthymic. The transitions between these states are probabilistic and can be influenced by various factors such as daily stressors, medication adherence, or life events. MHMMs extend this concept by incorporating multiple levels of variation, such as individual differences in mood instability and variability across different time scales [2].

Description

One of the key advantages of using MHMMs in BD research is their ability to model both the within-person and between-person variability in mood states. Within-person variability refers to the fluctuations in mood experienced by an individual over time, while between-person variability captures the differences in mood instability across individuals. By accounting for these multiple levels of variability, MHMMs provide a more nuanced understanding of mood instability and its underlying mechanisms. Recent studies applying MHMMs to EMA data from BD patients have revealed several important findings. First, these models have identified distinct latent mood states that correspond to different phases of BD. For example, research has shown that patients can transition between manic, hypomanic, and depressive states with varying frequencies and durations. These transitions are not random but are influenced by factors such as recent mood states, daily stressors, and treatment adherence [3].

Second, MHMMs have highlighted the role of mood instability as a predictor of functional outcomes. For instance, increased mood instability has been associated with poorer social and occupational functioning, higher levels of interpersonal conflict, and greater difficulty in maintaining stable relationships. By quantifying mood instability and its impact on daily functioning, MHMMs provide valuable information for tailoring individualized treatment plans and interventions. Furthermore, MHMMs have been used to investigate the effectiveness of different treatment strategies in managing mood instability. For example, researchers have explored how medication changes, psychotherapy, or lifestyle interventions affect the transitions between mood states. These studies have shown that certain treatments can reduce the frequency and severity of mood fluctuations, thereby improving overall stability and quality of life for BD patients [4]. In addition to their clinical applications, MHMMs offer insights into the underlying mechanisms of mood instability in BD. By analysing the patterns of transitions between latent mood states, researchers can identify potential triggers or exacerbating factors for mood episodes. This information can inform the development of preventive strategies and early interventions to mitigate the risk of mood episodes. However, there are also challenges associated with applying MHMMs to BD research. One challenge is the need for large and high-quality EMA datasets to accurately estimate the model parameters and capture the complexities of mood instability. Additionally, the interpretation of MHMM results requires careful consideration of the clinical context and individual differences among patients [5].

Conclusion

Multilevel hidden Markov modelling represents a powerful tool for studying mood instability in bipolar disorder. By leveraging intensive longitudinal ecological momentary assessment data, MHMMs offer detailed insights into the dynamics of mood fluctuations, their impact on functioning, and the effectiveness of various treatments. As research in this area continues to evolve, MHMMs will likely play a crucial role in advancing our understanding of bipolar disorder and improving patient outcomes through more personalized and effective interventions.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Mildiner Moraga, Sebastian, Fionneke M. Bos, Bennard Doornbos and Richard Bruggeman, et al. "Evidence for mood instability in patients with bipolar disorder: Applying multilevel hidden Markov modelling to intensive longitudinal ecological momentary assessment data."J psy path clin scie(2024).

   Google Scholar, Crossref, Indexed at

2. Crayen, Claudia, Michael Eid, Tanja Lischetzke and Delphine S. Courvoisier, et al. "Exploring dynamics in mood regulation—Mixture latent Markov modeling of ambulatory assessment data."Psycho Med74 (2012): 366-376.

   Google Scholar, Crossref, Indexed at

3. Sperry, Sarah H., Molly A. Walsh and Thomas R. Kwapil. "Emotion dynamics concurrently and prospectively predict mood psychopathology."J Affec Disor261 (2020): 67-75.

   Google Scholar, Crossref, Indexed at

4. Bolton, Thomas AW, Elenor Morgenroth, Maria Giulia Preti and Dimitri Van De Ville. "Tapping into multi-faceted human behavior and psychopathology using fMRI brain dynamics."Trends Neuroscie43 (2020): 667-680.

   Google Scholar, Crossref, Indexed at

5. Becker, Dennis, Ward van Breda, Burkhardt Funk and Mark Hoogendoorn, et al. "Predictive modeling in e-mental health: A common language framework."Inter Interv12 (2018): 57-67.

   Google Scholar, Crossref, Indexed at