Reduced heart rate variability is associated with higher illness burden in bipolar disorder

Ortiz, Abigail; Brádler, Kamil; Moorti, Pooja; MacLean, Stephane; Husain, Muhammad Ishrat; Sanches, Marcos; Goldstein, Benjamin I.; Alda, Martin; Mulsant, Benoit H.

doi:10.1016/j.jpsychores.2021.110478

Cited by 25 publications

(22 citation statements)

References 70 publications

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“…The major strengths of our study include its foundation on a solid work of an interdisciplinary team on clinical trajectories in BD; nonlinear properties of mood regulation in healthy controls, BD patients, and their unaffected first-degree relatives [10,30]; feasibility of using passive sensing in adults with BD during different types of episodes [13]; and a simulation using machine learning for outcome prediction [31]. By conceptualizing mood as a dynamic property of biological systems, we plan to demonstrate the feasibility of incorporating individual variability in a model informing clinical trajectories and predicting relapse in BD.…”

Section: Discussionmentioning

confidence: 99%

“…This baseline assessment includes six domains: socio-demographic, diagnosis, clinical course, cardiovascular screening, chronotype determination, and pharmacotherapy. Collecting this information will allow us to characterize the sample and assess variables that will be included in the predictive model, including Illness Burden Index [13]. Participants will receive standard ("as usual") medical treatment.…”

Section: Baseline Assessment and Clinical Follow-upmentioning

confidence: 99%

“…Chronotype will be assessed with the 19-item Morningness-Eveningness Questionnaire (MEQ) [19], the responses of which are combined into a composite score that indicates participant's chronotype (morning, intermediate or evening). Finally, we will classify medications (and changes in medications), as we have done in previous studies, based on class, dosage, and duration of treatment [13].…”

Section: Baseline Assessment and Clinical Follow-upmentioning

confidence: 99%

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Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study

et al. 2022

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Background Predictive models for mental disorders or behaviors (e.g., suicide) have been successfully developed at the level of populations, yet current demographic and clinical variables are neither sensitive nor specific enough for making individual clinical predictions. Forecasting episodes of illness is particularly relevant in bipolar disorder (BD), a mood disorder with high recurrence, disability, and suicide rates. Thus, to understand the dynamic changes involved in episode generation in BD, we propose to extract and interpret individual illness trajectories and patterns suggestive of relapse using passive sensing, nonlinear techniques, and deep anomaly detection. Here we describe the study we have designed to test this hypothesis and the rationale for its design. Method This is a protocol for a contactless cohort study in 200 adult BD patients. Participants will be followed for up to 2 years during which they will be monitored continuously using passive sensing, a wearable that collects multimodal physiological (heart rate variability) and objective (sleep, activity) data. Participants will complete (i) a comprehensive baseline assessment; (ii) weekly assessments; (iii) daily assessments using electronic rating scales. Data will be analyzed using nonlinear techniques and deep anomaly detection to forecast episodes of illness. Discussion This proposed contactless, large cohort study aims to obtain and combine high-dimensional, multimodal physiological, objective, and subjective data. Our work, by conceptualizing mood as a dynamic property of biological systems, will demonstrate the feasibility of incorporating individual variability in a model informing clinical trajectories and predicting relapse in BD.

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Section: Discussionmentioning

confidence: 99%

Section: Baseline Assessment and Clinical Follow-upmentioning

confidence: 99%

Section: Baseline Assessment and Clinical Follow-upmentioning

confidence: 99%

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Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study

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“…BD is characterized by changes in activation, and correspondingly, ANS activity and function has been found to be altered and dysregulated in BD [10, 14-19]. A 2017 review article and meta-analysis reported reduced HRV in BD compared to healthy controls [10].…”

Section: Introductionmentioning

confidence: 99%

“…A 2017 review article and meta-analysis reported reduced HRV in BD compared to healthy controls [10]. Moreover, two recent studies report an inverse relationship between BD illness severity and HRV [19, 20]. This is of interest considering diagnostic tool development, general understanding of the disorder, and as a candidate biomarker of cardiovascular risk [21].…”

Section: Introductionmentioning

confidence: 99%

Heart rate variability as biomarker for bipolar disorder

Stautland

Jakobsen

Fasmer

et al. 2022

Preprint

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Bipolar disorder (BD) is characterized by alterations in mood, energy levels and the ability to function. Accordingly, it is also associated with dysfunction of the autonomic nervous system (ANS), indexed by heart rate variability (HRV). Literature concerning differences in ANS functioning between mood states is still sparse. The main aim of the study was to investigate within-individual changes in HRV from manic to euthymic states in bipolar disorder (BD). This is the first study to do so using wrist-worn sensors. Seventeen patients with BD were equipped with photoplethysmography (PPG) sensor wristbands and provided 24-hour recordings both during a manic state and a euthymic state. We calculated mean heart rate and the commonly used HRV measures SDNN, RMSSD, HF, LF and Sample Entropy in 5-minute segments during rest at night. We compared HRV by mood state within individuals using paired t-tests and linear regression to control for age and sex. Recordings from 15 BD patients were analyzed. There were statistically significant increases in HRV measures SDNN, RMSSD, LF and Sample Entropy from mania to euthymia. Effect sizes were predominately large. Our findings reveal lower HRV in the manic state compared to the euthymic state. This indicates that HRV collected by wrist-worn PPG sensors is a possible biomarker for bipolar mood states. Movement artifacts were problematic and sampling during rest or in combination with actigraphy is recommended. Our findings can be further implemented to develop a monitoring device for bipolar patients.

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Development and calibration data for the Illness Burden item bank: a new computer adaptive test for persons with type 2 diabetes mellitus

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Reduced heart rate variability is associated with higher illness burden in bipolar disorder

Cited by 25 publications

References 70 publications

Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study

Identifying patient-specific behaviors to understand illness trajectories and predict relapses in bipolar disorder using passive sensing and deep anomaly detection: protocol for a contactless cohort study

Heart rate variability as biomarker for bipolar disorder

Development and calibration data for the Illness Burden item bank: a new computer adaptive test for persons with type 2 diabetes mellitus

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