Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data

Moraga, Sebastian Mildiner; Aarts, Emmeke

doi:10.1080/00273171.2023.2205392

Cited by 2 publications

(1 citation statement)

References 88 publications

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“…Importantly, the total sample size (Number of Participants × Number of Assessments) is comparable with previous studies that explore switches in latent mood states of patients with BD (e.g., Cochran et al, 2016; Prisciandaro et al, 2019; Yee et al, 2021). Indeed, previous research in MHMMs has demonstrated that parameters are accurately estimated with smaller samples when the number of assessments is sufficiently large (McClintock, 2021; Mildiner Moraga & Aarts, 2024). However, clinically, the sample was small and quite heterogeneous (e.g., both BD Type I and II, males and females, with different comorbidities and medication types), and participants were selected based on a clinical history of frequent manic or depressive episodes.…”

Section: Discussionmentioning

confidence: 99%

Evidence for mood instability in patients with bipolar disorder: Applying multilevel hidden Markov modeling to intensive longitudinal ecological momentary assessment data.

Mildiner Moraga,

Bos,

Doornbos

et al. 2024

Journal of Psychopathology and Clinical Science

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Bipolar disorder (BD) is a chronic psychiatric condition characterized by large episodic changes in mood and energy. Recently, BD has been proposed to be conceptualized as chronic cyclical mood instability, as opposed to the traditional view of alternating discrete episodes with stable periods in-between. Recognizing this mood instability may improve care and call for high-frequency measures coupled with advanced statistical models. To uncover empirically derived mood states, a multilevel hidden Markov model (HMM) was applied to 4-month ecological momentary assessment data in 20 patients with BD, yielding ∼9,820 assessments in total. Ecological momentary assessment data comprised self-report questionnaires (5 × daily) measuring manic and depressive constructs. Manic and depressive symptoms were also assessed weekly using the Altman Self-Rating Mania Scale and the Quick Inventory for Depressive Symptomatology Self-Report. Alignment between HMM-uncovered momentary mood states and weekly questionnaires was assessed with a multilevel linear model. HMM uncovered four mood states: neutral, elevated, mixed, and lowered, which aligned with weekly symptom scores. On average, patients remained <25 hr in one state. In almost half of the patients, mood instability was observed. Switching between mood states, three patterns were identified: patients switching predominantly between (a) neutral and lowered states, (b) neutral and elevated states, and (c) mixed, elevated, and lowered states. In all, elevated and lowered mood states were interspersed by mixed states. The results indicate that chronic mood instability is a key feature of BD, even in “relatively” euthymic periods. This should be considered in theoretical and clinical conceptualizations of the disorder.

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Section: Discussionmentioning

confidence: 99%

Evidence for mood instability in patients with bipolar disorder: Applying multilevel hidden Markov modeling to intensive longitudinal ecological momentary assessment data.

Mildiner Moraga,

Bos,

Doornbos

et al. 2024

Journal of Psychopathology and Clinical Science

Self Cite

View full text Add to dashboard Cite

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Hidden Markov model detection of interpersonal interaction dynamics in predicting patient depression improvement in psychotherapy: Proof-of-concept study

Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data

Cited by 2 publications

References 88 publications

Evidence for mood instability in patients with bipolar disorder: Applying multilevel hidden Markov modeling to intensive longitudinal ecological momentary assessment data.

Evidence for mood instability in patients with bipolar disorder: Applying multilevel hidden Markov modeling to intensive longitudinal ecological momentary assessment data.

Hidden Markov model detection of interpersonal interaction dynamics in predicting patient depression improvement in psychotherapy: Proof-of-concept study

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