The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity
Open accessAbstract
BACKGROUND: Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS: Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS: Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS: Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.
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Siddi et al. found that during resting periods, decreases in heart rate variation during the day were related to increased severity of depression, and heart rate at night was higher in participants with more severe depressive symptoms.
“During resting periods: decreases in HR variation during the day were related with an increased severity of depression.An HR at night was higher in participants with more severe depressive symptoms”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [29]
The association between higher depression severity and lower resting heart rate variation remains significant after accounting for individual characteristics.
“The findings showed that individuals with higher depression severity tended to have a lower resting HR variation throughout the day, and this association remained significant even after accounting for individual characteristics.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [29]
Digital biomarkers have a potential role in predicting individual depression severity, once confirmed and replicated in larger population studies.
“digital biomarkers potential role in predicting individual depression severity may be a groundbreaking advancement, once confirmed and replicated in larger populations studies, providing clinicians with even more personalized assessment instruments (29, 30).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [29]
Passive behaviors indicative of depression are more common in individuals with greater depression severity, particularly during the nighttime when heart rate may be elevated due to sleep problems often seen in MDD.
“The research indicates that passive behaviors, which are indicative of depression, are more common in individuals with greater depression severity, particularly during the nighttime when HR may be elevated”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [29]
Individuals with higher depression severity tend to have a lower resting heart rate variation throughout the day.
“The findings showed that individuals with higher depression severity tended to have a lower resting HR variation throughout the day, and this association remained significant even after accounting for individual characteristics.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [29]
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- Authors
- Siddi, S., Bailon, R., Giné-Vázquez, I., Matcham, F., Lamers, F., Kontaxis, S., Laporta, E., Garcia, E., Lombardini, F., Annas, P., Hotopf, M., Penninx, B. W. J. H., Ivan, A., White, K. M., Difrancesco, S., Locatelli, P., Aguiló, J., Peñarrubia-Maria, M. T., Narayan, V. A., Folarin, A., Leightley, D., Cummins, N., Vairavan, S., Ranjan, Y., Rintala, A., de Girolamo, G., Simblett, S. K., Wykes, T., members, P., Myin-Germeys, I., Dobson, R., & Haro, J. M.
- Affiliations
- Centro de Investigación Biomédica en Red de Salud Mental (ES)
- University of Sussex (GB)
- Amsterdam Public Health (NL)
- Universidad de Zaragoza (ES)
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES)
- Universitat Autònoma de Barcelona (ES)
- Lundbeck (Denmark) (DK)
- King's College London (GB)
- Amsterdam University Medical Centers (NL)
- University of Bergamo (IT)
- Janssen Research & Development (United States) (US)
- LAB University of Applied Sciences (FI)
- KU Leuven (BE)
- Corresponding author
- Siddi, S.
- ORCID
- Siddi, S.
- Bailon, R.
- Giné-Vázquez, I.
- Matcham, F.
- Lamers, F.
- Kontaxis, S.
- Laporta, E.
- Lombardini, F.
- Annas, P.
- Hotopf, M.
- Penninx, B. W. J. H.
- Ivan, A.
- White, K. M.
- Difrancesco, S.
- Locatelli, P.
- Aguiló, J.
- Narayan, V. A.
- Folarin, A.
- Leightley, D.
- Cummins, N.
- Vairavan, S.
- Ranjan, Y.
- Rintala, A.
- Simblett, S. K.
- Wykes, T.
- Dobson, R.
- Haro, J. M.
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2023-05-15
- Volume
- 53
- Issue
- 8
- Pages
- 3249-3260
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- OpenAlex
- W4376616421
- Cited by
- 31
- References
- 77
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 11 · 2023: 3
- FWCI
- 6.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Psychological Medicine (publishedVersion)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- UCL Discovery (University College London) (acceptedVersion)
- Pure Amsterdam UMC (acceptedVersion, cc-by)
- Zaguan (Universidad de Zaragoza) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by-nc-nd)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Lirias (submittedVersion)
- Funders
- NHS Blood and Transplant
- South London and Maudsley NHS Foundation Trust
- European Federation of Pharmaceutical Industries and Associations
- Alzheimer's Society
- National Institute for Health and Care Research (NF-SI-0514-10028)
- Department of Health and Social Care
- King's College London
- European Commission (115902, HORIZON2020)
- ZonMw
- King's Health Partners
- Innovative Medicines Initiative (115902)
- Institut Català de la Salut
- GIESKES-STRIJBIS FONDS
- NIHR BioResource
- NIHR Maudsley Biomedical Research Centre
- Medical Research Council (MC_PC_17214)
- Health Data Research UK
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- heart rate dynamics, digital biomarkers, depression severity, major depressive disorder, resting heart rate, nocturnal heart rate, within-person variability, wearable devices, Fitbit
- MeSH terms
- Antidepressive Agents, Depression, Major Depressive Disorder, Heart Rate, Humans, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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