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Psychological Medicine
cambridge.org/psm
Editorial
*Patient Advisory Board.
Cite this article: Siddi S et al (2023). The
usability of daytime and night-time heart rate
dynamics as digital biomarkers of depression
severity. Psychological Medicine 1–12. https://
doi.org/10.1017/S0033291723001034
Received: 9 February 2022
Revised: 20 November 2022
Accepted: 29 March 2023
Keywords:
Depression; mobile health (mHealth); real-
world monitoring; resting heart rate
Abbreviations:
ANS: Autonomous nervous system; aRMT:
Active remote measurement technology;
CIBER: Centro de Investigación Biomédica en
Red; HR: Heart rate; HRV: Hear rate variability;
KCL: King’s College London; LED: light-
emitting diode; MDD: Major depressive
disorder; mHR: mean HR; PPG:
photoplethysmography; pRMT: passive remote
measurement technology; PHQ-8: 8-item
patient health questionnaire; RADAR-CNS:
Remote assessment of disease and relapse –
central nervous system; RADAR-MDD: Remote
assessment of disease and relapse –major
depressive disorder; RMT: remote monitoring
technology; stdHR: Standard deviation Heart
Rate; VUmc: Vrije Universiteit Medisch
Centrum
Corresponding author:
S. Siddi; Email: sara.siddi@sjd.es
© The Author(s), 2023. Published by
Cambridge University Press
The usability of daytime and night-time heart
rate dynamics as digital biomarkers of
depression severity
S. Siddi1, R. Bailon2,3 , I. Giné-Vázquez1, F. Matcham4,5 , F. Lamers6,7 ,
S. Kontaxis2,3 , E. Laporta3, E. Garcia3,8, F. Lombardini1, P. Annas9,
M. Hotopf4,10 , B. W. J. H. Penninx6,7 , A. Ivan4, K. M. White4,
S. Difrancesco6, P. Locatelli11, J. Aguiló3,8, M. T. Peñarrubia-Maria12,
V. A. Narayan13, A. Folarin4, D. Leightley4, N. Cummins4, S. Vairavan13,
Y. Ranjan4, A. Rintala14,15, G. de Girolamo16, S. K. Simblett4,T.Wykes
4,10,
PAB members*, I. Myin-Germeys14, R. Dobson4, J. M. Haro1and on behalf of
the RADAR-CNS consortium17
1
Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona,
Spain;
2
Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain;
3
Centros de
investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid,
Spain;
4
King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK;
5
School of
Psychology, University of Sussex, Falmer, UK;
6
Department of Psychiatry, Amsterdam UMC, Vrije Universiteit,
Amsterdam, the Netherlands;
7
Amsterdam Public Health Research Institute, Amsterdam, the Netherlands;
8
Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain;
9
H. Lundbeck A/S, Valby, Denmark;
10
South London and Maudsley NHS Foundation Trust, London, UK;
11
Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy;
12
Catalan Institute of
Health, Primary Care Research Institute (IDIAP Jordi Gol), CIBERESP, Barcelona, Spain;
13
Research and
Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA;
14
Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven,
Belgium;
15
Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland;
16
IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy and
17
https://radar-cns.org/
Abstract
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 per-
iods 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 wor-
sening depression symptoms which could allow clinicians to take responsive treatment mea-
sures promptly.
Introduction
Major depressive disorder (MDD) is a highly common mental disorder, globally affecting
approximately 265 million people of all ages (James et al., 2018). MDD is often associated
with poor health outcomes (Penninx, Milaneschi, Lamers, & Vogelzangs, 2013) and non-
adherence to medications and treatments (DiMatteo, Lepper, & Croghan, 2000). Further,
MDD is often comorbid with medical conditions such as cardiovascular diseases (CD)
(Correll et al., 2017; Lett et al., 2004; Penninx, 2017; Penninx et al., 2001). Numerous studies
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
have shown that both MDD and CD potentially share underlying
pathophysiological disturbances such as systemic inflammation,
autonomic dysfunction of hypothalamic-pituitary-adrenal (HPA)
axis (Angermann & Ertl, 2018), and immune system dysregulation
(Halaris, 2017).
HR parameters may be used as diagnostic and predictive bio-
markers of depression severity. A key indicator of the autonomous
nervous system (ANS) function is the heart rate (HR) variability
(HRV), consisting of the fluctuations in either the instantaneous
HR or the length of heartbeats intervals. Increased variability
indicates an improved autonomic nervous system regulation
(Berntson et al., 1997). A reduced resting HRV has been related
to difficulties in emotion regulation (Williams et al., 2015).
Previous studies have also reported that individuals with MDD
have a reduced resting mean HRV (Kemp & Quintana, 2013;
Koenig, Kemp, Beauchaine, Thayer, & Kaess, 2016; Nabi et al.,
2011). More severe depressive symptoms have been associated
with elevated HR (Carney et al., 2008; Carney, Freedland, &
Veith, 2005; Nabi et al., 2011) and reduced HRV (Caldwell &
Steffen, 2018; Hartmann, Schmidt, Sander, & Hegerl, 2019;
Kemp et al., 2010). Some studies reported that HR differences
between individuals with MDD and without MDD might be
more evident at night (Carney et al., 2008; Taillard, Lemoine,
Boule, Drogue, & Mouret, 1993). Furthermore, people with
MDD frequently report irregularities in sleep/wakes states
(Walker, Walton, DeVries, & Nelson, 2020), which can also affect
HR. However, most of these studies have several significant lim-
itations: (i) either they were conducted in individuals of the gen-
eral population including those without a diagnosis of MDD
(Nabi et al., 2011; Silva et al., 2020); (ii) included only a small
study population (Hartmann et al., 2019; Li, Hu, Shen, Xu, &
Retcliffe, 2015; Narziev et al., 2020; Silva et al., 2020); or (iii)
were conducted under laboratory conditions such as using elec-
trocardiography (ECG) in hospital or research settings
(Hartmann et al., 2019; Kemp, Quintana, Felmingham,
Matthews, & Jelinek, 2012; Koch, Wilhelm, Salzmann, Rief, &
Euteneuer, 2019; Koenig et al., 2016; Nabi et al., 2011).
The recording of the ECG during daily life and long periods
has several limitations. For example, the electrodes of the ECG,
can cause skin irritation in long recordings due to its wet com-
pound and adhesive properties, or the gel might dry, resulting
in a reduction of the contact between the electrode and the skin
negatively affecting the quality of the recording. There are other
types of electrodes that are not adhesive but they are highly sen-
sitive to motion artefacts. Morever, Holter devices used for long
data acquisitions interfere with the daily life routine, being unfeas-
ible for continuous monitoring (Dias & Cunha, 2018).
Wrist worn devices that are available today may facilitate the
measurement of HR in naturalistic conditions. These technologies
have several advantages over previous devices, including being
non-invasive, low burden, low cost, and allowing the acquisition
and processing in near-real time of a large amount of informa-
tion. In fact, these technologies are able to provide 24 h of HR
monitoring, comfortable design and allowed to worn constantly
(Castaneda, Esparza, Ghamari, Soltanpur, & Nazeran, 2018;
Lam, Aratia, Wang, & Tung, 2020; Nelson & Allen, 2019).
All devices based on the photoplethysmographic (PPG) signal,
obtained by illuminating the skin with the light from a light-
emitting diode (LED) and then measuring the amount of light
reflected to a photodiode to detect blood volume changes in the
capillaries above the wrist (Subasi, 2019) from which HR infor-
mation can be derived. The feasibility of deriving HRV from
the PPG signal instead of the ECG signal has been widely inves-
tigated. In general, HRV derived from the PPG can be used as a
surrogate of HRV derived from the ECG, when pulses can be
accurately detected from the PPG, but this is challenging when
PPG is recorded at wrist during daily life, mainly due to the sen-
sitivity of PPG to movement artefacts. Only few studies have vali-
dated HRV derived from wrist PPG and always in resting
conditions (i.e. Hernando, Roca, Sancho, Alesanco, & Bailón,
2018). However, many studies have validated the use of HR series
provided by these devices to provide mean HR estimates over dif-
ferent periods of time (Fuller et al., 2020; Liu et al., 2022; Nazari,
Macdermid, Sinden, Richardson, & Tang, 2019; Nelson & Allen,
2019). Despite the fact that these devices, based on wrist PPG,
do not usually allow the study of HRV, they can still be useful
to study HR trends and slow dynamics during the day, and
might provide an indicator of ANS regulation of HR.
In order to analyse the use of wrist-worn technologies in asses-
sing individuals with a history of recurrent MDD, the Remote
Assessment of Disease and Relapse –Central Nervous System
(RADAR-CNS) (www.radar-cns.org) project, involving the
patients, took the decision to use a commercially available device
which is minimally invasive, easy to use and has the sensitivity
and precision to generate the desired multimodal information
(i.e. HR, activity, sleep) (Owens, 2020; Polhemus et al., 2020;
Simblett et al., 2019). In this project, a wrist worn fitness wearable
device was used to track the HR dynamics during the whole day,
outside the medical environment. This device makes use of the
PPG signal from which HR is estimated using a proprietary algo-
rithm and output at different time intervals. HR can vary signifi-
cantly over 24 h and under different conditions (Shaffer &
Ginsberg, 2017), so it is essential to take this information into
account when analysing and interpreting HR dynamics as a
marker of ANS.
This study assessed the relationship of HR parameters during
different periods of the day and night and different activity levels
with depression severity in a cohort of individuals with a recent
history of recurrent MDD. Our first objective was to explore
and test the association of HR parameters with the severity of
depression. Based on previous literature, we expected that an
increase in mean HR and reduced HR variation during the day
would be related to an increased depression severity across the
follow-up. Furthermore, we expected a similar pattern during
the resting periods of the night: an increased mean HR and
reduced HR variation associated with higher level of depression.
The second objective was to examine whether this relationship
can be affected by an individual’s characteristics (age, gender
body mass index (BMI), smoking and alcohol habit, comorbidity
with medical health conditions and antidepressant medication).
We also expected that these factors might impact on the associ-
ation between HR changes and depression severity.
Method
Study design and sample
This study uses data collected from the RADAR-MDD study, as a
part of the research RADAR-CNS project. The study was
co-developed with service users in our Patient Advisory Board.
They were involved in the choice of measures, the timing and
issues of engagement and have also been involved in developing
the analysis plan and representative (s) are authors of this paper
and critically reviewed it.
2 S. Siddi et al.
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
The RADAR-MDD study explored the use of active and pas-
sive remote monitoring technology (RMT), including a wrist-
worn Fitbit device to track disease course in people with a recent
history of recurrent MDD (with the latest episode within the past
2 years) and follow them up for a 2 years. The active and passive
data are collected via the active and passive RMT and then send
into the RADAR-base platform (Ranjan et al., 2019). For the
Fitbit data, they are uploaded to the vendor data warehouse and
provided to developers via a Web API. Getting these data into
the RADAR system is achieved by implementing a server-side
Kafka Source Connector, which continuously queries data from
the vendor’s Web API and dumps it into Kafka inside the
RADAR-base platform; this approach can be used to integrate
other Web API/OAuth2 data sources [GitHub. (2019-03-04).
RADAR-base/RADAR-REST-fitbit https://github.com/RADAR-
base/RADAR-REST-fitbit website].
The full protocol has already been published (Matcham et al.,
2019; Matcham et al., 2022). The RADAR-MDD is a multi-centre
cohort study involving 623 individuals recruited from three sites:
Centro de Investigación Biomédica en Red (CIBER); Barcelona,
Vrije Universiteit Medisch Centrum (VUmc), Amsterdam) and
the King’s College London (KCL). Participants were recruited
through primary and secondary mental health care networks
(Barcelona and London) and through existing research cohorts
(participants from Amsterdam were partially recruited through
Hersenonderzoek.nl (https://hersenonderzoek.nl/) and other
ways such as advertisements in the https://www.radar-cns.org/
participate and mental health charity websites. The study was
approved by the ethical committees of participating centres and
all participants provided written consent.
Instruments
Depression severity
Depression severity was assessed with the Patient Health
Questionnaire 8 items (PHQ-8) (Kroenke et al., 2009) instrument
delivered through an app installed in an Android smartphone
(Ranjan et al., 2019). Participants were asked by push-notification
to complete the PHQ-8 every two weeks. The PHQ-8 score ranges
from 0 to 24 (increasing severity). A cut-off score of ⩾10 is the
most recommended cut-off point for ‘clinically significant’
depressive symptoms (severe or moderate depression = 1; v. no
depression or mild depression = 0), which means that the partici-
pant is likely to meet diagnostic criteria for a depressive episode
(or moderate and severe depression) in the previous two weeks
(Kroenke et al., 2009). Ratings below 10 are usually defined as
an asymptomatic state or sub-threshold (no or mild depression).
Internal consistency was calculated with Cronbach’s alpha, and it
was 0.91.
Heart rate features
HR parameters, such as the mean or the standard deviation, were
computed daily from the HR signal provided by Fitbit charge 2
and 3 (Fitbit Inc, San Francisco, CA, USA), which is obtained
from the PPG sensor of the device with a a narrowest resolution
up to 5 s between samples. This device was previously demon-
strate proven to accurately measuring HR (Fuller et al., 2020;
Liu et al., 2022; Nazari et al., 2019; Nelson & Allen, 2019). The
HR values were not estimated when the Fitbit was not worn.
HR was computed during the whole day (24h) and just at
night (from 00:00 to 05:59), as well as just during resting periods
and during active periods separately. The HR during nighttime
calculated by the Fitbit was strongly associated with resting HR
at night (00:00–05:59) (ρ= 0.94, p< 0.0001). However, a previous
study (i.e.(Stucky et al., 2021) proved that the Fitbit underesti-
mated the sleep transition dynamics. For this reason, we selected
period from 00:00 to 05:59 as a conservative night time that might
work for a large part of the population.
Resting periods were defined when the number of steps and
activity level, derived from the accelerometer data were equal to 0.
A total of seven HR features were derived for each day: total
mean and standard deviation of HR (mHR/day and stdHR/
day), mean and standard deviation of HR during the resting per-
iod (resting mHR/day and resting stdHR/day, respectively), mean
and standard deviation of HR during resting period at night (rest-
ing mHR/night and resting stdHR/night, respectively) and mean
HR for the activity periods (activity mHR) (Table 1). We com-
puted the average of the each of the daily HR parameters in the
week before the PHQ-8 assessment across the follow-up. An
example of the HR parameters for an individual with different
depression score during the study was represented in Fig. 1.
(Fig. 1). One week mean was considered appropriate to smooth
day-to-day variability, especially during weekdays and weekend
Sociodemographic, smoking habits and medical health
conditions
Information about sociodemographic (age, gender, education
years, marital status) and medical history including heaviness of
smoking index (yes/no), and current alcohol habit (yes/no) mea-
sured trough the questionnaire Alcohol use disorders identifica-
tion test (Daeppen, Yersin, Landry, Pecoud, & Decrey, 2000)
during all the follow-up, self-reported BMI and comorbidity with
Table 1. Features legend: HR parameters derived from the Fitbit
Variable No Observations Features
mHR/day 6666 Mean HR data across all day (24 h)
Std HR/day 6666 Standard deviation of HR data across all day (24 h)
Resting mHR/day 6613 Mean HR during resting periods, identified by activity level= resting and number of steps = 0 during the day (24 h)
Resting stdHR /day 6613 Standard deviation of HR during resting periods during the day (24 h)
Resting mHR/night 6261 Mean HR during resting periods at night time.
Resting stdHR/
night
6261 Standard deviation of HR during resting periods, identified by number of steps = 0 only during night time (0:00–05:59)
Activity mHR/day 6466 Mean of HR during activity periods (the physical activity was classified as lightly, moderate and vigorous).
Psychological Medicine 3
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
pre-existing medical health conditions(yes/no), and current anti-
depressant medications (yes/no) during the study were collected
through a the Research Electronic Data Capture (REDCap) package
during the enrolment session (Harris et al., 2019,2009).
Data analysis
First, we described the sociodemographic characteristics reporting
frequencies and percentages for categorical variables. Categorical
comparisons were made with the χ
2
test. Median with standard
deviation (Std) and interquartile ranges (IQR) were reported for
continuous variables. Differences by country in continuous vari-
ables were explored with the Kruskal–Wallis test. Spearman cor-
relation (ρ) was calculated between the HR parameters and
PHQ-8 to assess the association without considering the clustered
structure of repeated observations per individual. We also looked
at the association between the PHQ-8 and the number of obser-
vations to explore if depression severity was negatively related to
the assessment rate (i.e. patients with severe depression might be
significantly less likely to complete the assessment because of
their symptoms of abulia and apathy). Second, we compared
the HR parameters between the observations in individuals with
severe or moderate depression = 1; v. no depression or mild
depression = 0) to discover HR features explaining the variance
of PHQ-8. Cohen’s d effect size was calculated for the compari-
sons: the effect size is considered as small, medium, and large
using the 0.2, 0.5 and 0.8 cut-offs. Finally, linear mixed models
with PHQ-8 as the outcome were computed, in two steps. In
both, random intercepts for the participant and country levels
were included as random effects, and data normalisation (z
score) of the HR parameters was performed within-participants,
so estimates in the mixed models indicate the effect of changes
in the HR parameters from the participant-specific mean. In
the first step a mixed model was computed separately for each
HR feature (mean and standard deviation of HR during the
day, resting periods, resting at night and activity periods), having
that feature and the baseline of PHQ-8 (first measure of PHQ-8
in the dataset for each participant) as independent variables. In
the second step various HR features were included as predictors
simultaneously together with the baseline of PHQ-8 to estimate
their joint effect. Further, in the second step, sociodemographic
and clinical factors were also included as covariates to test their
effect in the model, specifically age and BMI as continuous vari-
ables and gender, smoking, alcohol and antidepressant consump-
tion, and medical comorbidity as dichotomous variables. All
analyses were performed using the R software package
‘lme4’(Bates, Mächler, Bolker, & Walker, 2015) software R (R
Core Team, R Development Core Team, & R Core Team, 2016).
Results
Descriptive analyses
A total of 510 participants (with a total number of HR observations
of 6666) in relationship to HR were included in the analyses. The
majority of participants were female (n= 386, 76%) and the median
age was 50 years old (mean 46.6, std 15.1). About half of the parti-
cipants were single, separated or widowed (268, 53%). The majority
were receiving antidepressant medication during the follow-up
(84.3%). The median education years were 15 (Mean: 15.4, std
6.6). The mean BMI was 26 (IQR: 7.6). The sociodemographic
information across sites is shown in the online Supplementary
materials (Table S1). Only 21.1% (N= 107) reported smoking habits
at enrolment. More than half (N= 284, 56%) reported comorbidity
with medical conditions (please see details in online Supplementary
materials Table S2). The Table 2 displays the descriptives of the
variables included in the statistical models.
Spearman correlation between depression severity and HR
features, and comparison between two groups with different
current depression severity
Depression severity, as measured with the PHQ-8 was positively
related to total mHR/day (ρ= 0.13) and negatively related to the
total stdHR/day (ρ=−0.21), ( p < 0.001)(Fig. 2). The same
pattern of correlations was observed during the resting
Figure 1. An example of 7-day mean HR prior to the PHQ-8 assessment from the same participant at the mild depression level (left) and moderately severe level
(right) during the follow-up.
4 S. Siddi et al.
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
Table 2. Baseline, clinical and HR features
Variable Median or N(%) IQR Min Max
Age 50 27 18 76
Gender (Female) (n, %) 386 (75.7)
BMI 26 7.6 14 71.7
PHQ-8 9.00 10.00 0 24
Smoking status (yes) (N, %) 107 (21.1)
Alcohol habit (yes) (n, %) 7982 (87.4)
Antidepressant (yes) (n, %) 7768 (84.3)
Comorbidity (yes) (n, %) 284 (56)
HR parameters Period Median IQR Min Max
mHR Day 75.55 11.82 46.62 132.65
stdHR Day 12.70 3.93 0.00 40.18
Resting mHR Day 73.64 11.80 46.63 132.65
Resting stdHR Day 11.46 4.18 0.00 39.26
Activity mHR Day 81.87 14.19 44.63 135.06
Resting mHR Night 66.70 12.42 43.16 124.65
Resting stdHR Night 5.05 2.30 0.00 23.72
Note: m, mean; IQR, interquartile ranges; std, standard deviation.
Figure 2. Depression and HR during the day. Scatter plot on the left side showed a correlation between PHQ-8 and mHR (above) and between PHQ-8 and stdHR
(below). Boxplots on the right showed a comparison on total mHR (above) and total stdHR (below) between the groups depression v. no depression.
Psychological Medicine 5
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
period: depression severity was positively related to resting mHR/
day (ρ= 0.17) and negatively related to resting stdHR/day (ρ=
−0.12) ( p < 0.001). During the activity period, depression severity
was positively related to activity mHR during all day but with a
small correlation (ρ= 0.03, p= 0.001). During the night, depres-
sion severity was positively related with both measures: resting
mHR/night (ρ= 0.21) and resting stdHR/night (ρ= 0.13)(p<
0.001). In fact, participants with no depression or mild depression
(PHQ-8 < 10) and moderate or severe depression (PHQ-8 ⩾10)
severity had different total mHR/day (t=−7.99, Cohen’sd=
−0.20) and total stdHR/day (t = 16.61, Cohen’sd=0.41)(Fig. 2)
(p < 0.001); resting mHR/night (t=−15.30, Cohen’sd=−0.39)
and resting stdHR/night (t=−7.31, Cohen’sd=−0.18)(p<
0.001)(Fig. 3). However, we did not find any significant difference
in activity mHR/day (t=−1.67, p = 0.09).
We did not find any significant association between depression
severity as assessed with the PHQ-8 and the number of observa-
tions (ρ=−0.08, p=0.07).
Linear multilevel regression analyses for each of the HR
parameters
The models included the PHQ-8 rating as dependent variable and
each of the HR features and the baseline of PHQ-8 as independ-
ent variables. The within-participant coefficient z-scores of the
mHR/day (β=−0.18) and stdHR/day (β=−0.34) were negatively
associated with depression severity (Table 3). We also observed a
similar pattern for resting mHR/day (β=−0.14) and resting
stdHR/day (β=−0.32), both were negatively associated with
depression severity. While, resting mHR/night was positively
associated with depression severity (β= 0.09). On the other
hand, no association was found for resting stdHR/night and
depression severity.
Linear multilevel regression analyses for each of the HR para-
meters adjusting for all parameters and covariates.
The Table 4 shows the results of the multilevel analysis includ-
ing mean and standard deviation of HR during the day, and
resting HR during the night as independent variables
(Model 1). Table 4 does shows the same model when adding
the sociodemographic and clinical characteristics (adjusted
model). The findings are consistent in the two models.
Increases in mHR/day (β=−0.23) and stdHR/day (β=−0.30)
were associated with a decrease on PHQ-8, while the increasing
resting mHR/night (β=0.19) was related with an increase on
PHQ-8. We then replicated the analyses, replacing the HR during
all day with HR at resting state during the day (Table 5).
We observed similar patterns for resting HR during the day:
depression severity was negatively related to resting mHR/day
(β=−0.16) and resting stdHR (β=−0.30), while a positive associ-
ation was observed with resting mHR/night (β=0.14,p=0.04)
Figure 3. Depression and resting HR at night. Scatter plot on the left side showed a correlation between PHQ-8 and mHR (above) and between PHQ-8 and stdHR
(below). Boxplots on the right side showed a comparison on mHR (above) and stdHR (below) between the groups depression v. no depression.
6 S. Siddi et al.
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(Table 5). No significant findings were found for resting stdHR
at night. Depression severity was negatively associated with age
(β=−0.03, p = 0.01) and positively to medical health condition
(β= 1.12, p < 0.0001).
We then replicated the previous analyses including mHR dur-
ing the activity period and the resting mHR/day and resting
mHR/night to confirm that the activity mHR does not have an
association with depression severity (Table 6). Resting mHR/day
and resting mHR/night maintained their association with depres-
sion severity, as also the other two covariates did, but activity
mHR/day was not associated to depression severity (adjusted
model).
Discussion
To the best of our knowledge, this is the first study exploring the
association between depression severity and HR changes using a
wearable device in a sample of people with MDD during a long-
term period of monitoring. The two main findings of this study
are: first, lower resting HR variation measured with the standard
deviation of HR during the day is associated with higher depres-
sion severity; and second, resting mean HR at night increases with
depression severity. These relationships were maintained when we
adjusted for gender, age, smoking and alcohol habits, also pre-
existing comorbid medical health conditions, and antidepressant
treatment.
These findings are consistent with the study hypotheses. A
previous study demonstrated that individuals with more severe
depression were less active and did not perform moderate and
vigorous activities that increase the HR during the day
(Kandola, Lewis, Osborn, Stubbs, & Hayes, 2020). Accordingly,
one possible explanation could, therefore, be that individuals
with MDD have low physical activity. The relationship was main-
tained when we also adjusted for mean HR during activity
periods.
At odds with the study hypothesis, we also observed decrease
in daily mean HR and daily resting HR associated with more
severe depression. However, this finding was only present in the
regression analysis. Accordingly, this association between mean
HR/day and depression severity could have been affected by
Simpson’s paradox, given that the direction of the correlation
between the two parameters changed from positive in the bivariate
analyses to negative in the regression analyses when the HR data
were within-participants normalised. The negative association was
Table 3. Multilevel analyses for exploring the associations between the HR
features and the depressive symptoms severity (PHQ-8)
Features βS.E. (95% CI) pvalue
mHR/day −0.18 0.04 (−0.26 to −0.10) <0.0001
stdHR/day −0.34 0.04 (−0.42 to −0.26) <0.0001
Resting mHR/day −0.14 0.04 (−0.22 to −0.06) 0.0004
Resting stdHR/day −0.32 0.04 (−0.40 to −0.24) <0.0001
Resting mHR/night 0.09 0.04 (0.01–0. 17) 0.037
Resting stdHR/night −0.01 0.04 (−0.09 to 0.07) 0.824
Activity mHR/day −0.05 0.04 (−0.13 to 0.03) 0.215
Note: each model includes a HR parameter together with the baseline PHQ-8 as
independent variables to predict.
PHQ-8 changes (continuous variable).
Table 4. Mixed model with HR features during the day (24 h) and night related to depression severity and sociodemographic covariates
Model 1 Adjusted Model
Features βS.E. 95% CI pvalue βS.E. 95% CI pvalue
Baseline PHQ-8 0.72 0.03 0.67–0.77 <0.0001 0.69 0.03 0.63–0.75 <0.001
mHR/day −0.23 0.07 −0.38 to −0.09 0.001 −0.24 0.07 −0.39 to −0.09 0.001
stdHR /day −0.30 0.06 −0.41 to −0.19 <0.0001 −0.29 0.06 −0.40 to −0.18 <0.0001
Resting mHR/night 0.19 0.07 0.05–0.33 0.006 0.18 0.07 0.04–0.32 0.01
Resting stdHR/night 0.003 0.05 −0.09 to 0.09 0.93 0.02 0.05 −0.07 to 0.11 0.68
Age −0.03 0.01 −0.05 to −0.01 0.01
Gender (women)
a
−0.14 0.37 −0.88 to 0.59 0.71
BMI 0.01 0.02 −0.04 to 0.05 0.84
Smoking habits 0.44 0.42 −0.39 to 1.27 0.30
Alcohol −0.22 0.35 −0.9 to 0.48 0.54
Comorbidity 1.12 0.34 0.46–1.78 <0.001
Antidepressant −0.21 0.22 −0.64 to 0.21 0.32
R
2
marginal 0.487 R
2
marginal 0.749
AIC 33 347.92 AIC 32 446.4
BIC 33 401.85 BIC 32 547.1
Adjusted ICC 0.512 Adjusted ICC 0.497
Note: Model 1. Daily measure of HR and HR during nighttime parameters were included together with the baseline PHQ-8 as independent variables in this model. Adjusted model includes all
the previous independent variables and covariates.
a
The reference group is men.
Psychological Medicine 7
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
Table 5. Linear mixed model with resting HR features during the day (24 h) and night related to depression severity and sociodemographic covariates
Model 1 Adjusted Model
Features βS.E. (95% CI) pvalue βS.E. (95% CI) pvalue
Baseline PHQ-8 0.72 0.03 0.67–0.77 <0.0001 0.69 0.03 0.64–0.75 <0.001
Resting mHR/day −0.16 0.07 −0.31 to −0.01 0.03 −0.17 0.08 −0.33 to −0.02 0.02
Resting stdHR/day −0.30 0.06 −0.43 to −0.19 <0.0001 −0.32 0.06 −0.44 to −0.20 <0.001
Resting mHR/night 0.14 0.07 0.004–0.28 0.04 0.13 0.07 −0.01 to 0.27 0.07
Resting stdHR/night 0.05 0.05 −0.04 to 0.14 0.28 0.07 0.05 −0.02 to 0.17 0.13
Age −0.03 0.01 −0.05 to −0.01 0.01
Gender(women)
a
−0.15 0.38 −0.89 to 0.59 0.69
BMI 0.005 0.02 −0.04 to 0.05 0.84
Smoking habits 0.43 0.42 −0.40 to 1.26 0.30
Alcohol −0.23 0.35 −0.92 to 0.46 0.52
Comorbidity 1.12 0.34 0.45–1.78 <0.001
Antidepressant −0.24 0.22 −0.67 to 0.18 0.27
R
2
marginal 0.487 R
2
marginal 0.501
AIC 32 505.1 AIC 32 448.09
BIC 32 505.1 BIC 32 548.79
Adjusted ICC 0.509 Adjusted ICC 0.498
Note. Model 1. Resting HR measures during the day and night were included together with the baseline PHQ-8 as independent variables, Adjusted model: previous parameters were then
included in this model with covariates.
a
The reference group is men.
Table 6. Linear mixed model with mean HR features during the day (24 h) and night related to depression severity and sociodemographic covariates
Model 1 Adjusted Model
Features βS.E. 95% CI pvalue βS.E. 95% CI pvalue
Baseline PHQ-8 0.72 0.03 0.67–0.77 <0.0001 0.69 0.03 0.64–0.75 <0.001
Resting mHR/day −0.40 0.06 −0.52 to −0.28 <0.0001 −0.42 0.06 −0.54 to −0.30 <0.001
Resting mHR/night 0.37 0.06 0.25–0.48 <0.0001 0.36 0.06 0.25–0.48 <0.001
Activity mHR/day −0.06 0.04 −0.15 to 0.02 0.18 −0.04 0.05 −0.13 to 0.05 0.42
Age −0.03 0.01 −0.05 to −0.01 0.02
Gender (women)
a
−0.15 0.37 −0.88 to 0.59 0.69
BMI 0.01 0.02 −0.04 to 0.05 0.84
Smoking habits 0.43 0.42 −0.40 to 1.26 0.32
Alcohol habit −0.20 0.35 −0.89 to 0.50 0.58
Comorbidity 1.10 0.34 0.44–1.76 0.001
Antidepressant −0.22 0.22 −0.64 to 0.21 0.32
R
2
marginal 0.488 R
2
marginal 0.502
AIC 31 888.51 AIC 31 885.92
BIC 31 942.08 BIC 31 986.35
Adjusted ICC 0.507 Adjusted ICC 0.496
Note. Model 1: Resting and activity HR measures were included together with the baseline PHQ-8 as independent variables, Adjusted model: Previous parameters were then included in this
model with covariates.
a
The reference group is men.
8 S. Siddi et al.
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
also maintained when the predictors were analysed separately,
when they were introduced together and then adjusted with the
covariates. Further research may be needed to advance in the rela-
tion between HR and depression severity.
One possible explanation of the relationship between HR and
depression severity may be that passive behaviours, such as watch-
ing TV, listening to music or any other activity that do not include
movements, may be more frequent in individuals when more
depressed (Hallgren et al., 2020). Another explanation might be
that a low resting mean HR might be provoked by the effect of
the medication; however we did not observe any effect of anti-
depressant medication when adjusted for it.. Studies conducted
both in humans and animals reported that potentially adverse
effects of antidepressant interactions could lead to an abnormal
decrease in the HR (Ababneh, Ritchie, & Webster, 2012; Azizi,
Elyasi, & Roodposhti, 2019; Woroń, Siwek, & Gorostowicz,
2019). However, a recent meta-analysis found that HR alterations
were not fully explained by antidepressant use alone (Brown et al.,
2018).
During the night, the high resting mean HR was associated
with higher depression severity. In the resting state during the
night non-activity periods were included, but they can correspond
to awake stages where HR could be higher than expected for the
night time. It is well-known that people with MDD suffer of sleep-
ing difficulties, especially insomnia, that would be one explan-
ation of the high mean HR. In healthy individuals, resting
mean HR decreases significantly during the night due to parasym-
pathetic predominance during sleep (Mancia et al., 1983). This
decrease may be less pronounced in individuals with MDD and
related to depression severity. Previous studies have also demon-
strated that increasing resting mean HR is a significant predictor
of mortality in people with MDD and heart failure (Carney et al.,
2016; Lau et al., 2021; Nabi et al., 2011). We did not find any asso-
ciation between the resting HR variation during the night and
depression severity. Further studies are warranted to explore if
the HR variation in relation to depression severity fluctuates
according to the different sleep stages.
As we expected, we observed that low HR variation during all
day resting periods was related to increased depression severity
over time. A low HR variation indicates that the body is under
stress from psychological events, or other internal or external
stressors. The reduced resting HR variation may depend on a fail-
ure of the parasympathetic control via the vagal nerve (Olshansky,
Sabbah, Hauptman, & Colucci, 2008) that restores the body from
overworking and prior accumulated stress (Kemp & Quintana,
2013; Kim, Cheon, Bai, Lee, & Koo, 2018).
Decreased HR variation, especially during the resting period is
associated with increased cardiovascular risk and mortality across
different age groups (Brown et al., 2018; Koch et al., 2019; Koenig
et al., 2016) and it is reported in different medical health condi-
tions (Galinier et al., 2000; Tessier et al., 2017). In our group,
we observed that depression severity was positively associated
with various comorbid medical conditions including cardiovascu-
lar disease, metabolic and digestive disease rheumatic disease, pul-
monary disease and neurological disorders and other (i.e.
psychiatric, as anxiety, eating disorders and other medical condi-
tions. MDD is frequently comorbid with cardiovascular disease
(Carney et al., 2005; Penninx, 2017), long diseases (Yohannes,
Willgoss, Baldwin, & Connolly, 2010), metabolic syndrome (Pan
& Hu, 2013; Vancampfort et al., 2014), and neurological disorder
(Raskind, 2008). This comorbidity might impact the prognosis
and management of depression and increase risk of mortality.
Moreover, we also observed that younger age was associated
with depression severity. Young people may endorse more affect-
ive symptoms whereas elderly people may report more cognitive
changes and loss of interest (Fiske, Wetherell, & Gatz, 2009).
Another study found that younger age could have a greater risk
of multiple depression episodes (Fergusson & Woodward,
2002). This finding highlights the importance of a prompt and
early intervention. Finally, we did not find any association of
depression severity with gender. It might be due to the fact that
the majority of our sample was composed of women (76%) and
both women and men were diagnosed with MDD with recurrent
episodes. Neither did we find an association between depression
and smoking status, alcohol status and BMI in our group. One
explanation could be that only a small subgroup reported smok-
ing habits, and a few reported BMI over the normal range. Previous
studies reported an association between depressive symptoms, BMI
and smoking habits (Hooker, MacGregor, Funderburk, & Maisto,
2014;Strineetal.,2008;Widomeetal.,2009).
Strengths and limitations
When considering these results, we should acknowledge the limi-
tation of the device used. As stated previously, the HR data
derived from the PPG are highly correlated with the HR data
derived from the ECG, both HR and HRV (Gil et al., 2010;Lu
et al., 2008). However, the wrist-worn device used in this study
does not provide access to the PPG signal but rather to the HR
series derived by proprietary algorithms at different time intervals.
This does not allow for analysing HRV properly, as already stated.
Wrist-based device HR determination has been shown to have a <
5% error in a range of devices and activities relative to gold-
standard and closer to 1% when at rest (Shcherbina et al.,
2017). The HR variation can be used as a proxy measure of HR
fluctuations (Moser et al., 1994). The HR variation has already
been used to explore the variation of HR in other studies
(Quer, Gouda, Galarnyk, Topol, & Steinhubl, 2020; Wang,
Lizardo, & Hachen, 2022). On the other hand, the Fitbit device
presents different advantages: comfortable design, it can be used
for an extended period and is accessible to a broad population
for its low cost. Another strength of this study is the collection
of daily mood data which may be superior to retrospective reports
to measure depression severity in association with daily HR para-
meters. Moreover, we used long term longitudinal data from par-
ticipants. Additional aspects to be considered when analysing the
results are that the effect of some medications (i.e. beta-blockers
and inhaler treatments) was not analysed; that potential adverse
effect on HR might be provoked by other medications; and that
various medical conditions have been included in the analysis
but were not considered separately. However, the objective was
only to explore how co-morbid medical condition might impact
the association between HR and depression severity. Finally,
depression severity may be exacerbated by anxiety symptoms.
Further research should explore the impact of anxiety and sleep,
among others, in the association between HR and depression
severity.
Conclusion
In this paper, we have demonstrated that HR parameters may be
indicators of depression severity and that it is possible to collect
them on large numbers of participants and long follow-ups.
From a clinical perspective, the current data suggest that reduced
Psychological Medicine 9
https://doi.org/10.1017/S0033291723001034 Published online by Cambridge University Press
daily resting HR variability could represent a correlate of vulner-
ability to depression severity. Hence, the findings of specific HR
biomarkers associated to depression severity fluctuations, provid-
ing a valuable tool for the early recognition or post- depressive
monitoring of vulnerable individuals. An early warning of poten-
tial relapse allows clinicians to take responsive treatment
promptly. This represents an opportunity to offer individual tra-
jectories of HR parameters. Moreover, a longitudinal view of HR
variations provides great personal health information in real-time
and in real-world setting. Further research is needed to translate
these results into meaningful clinical recommendations. These
findings will be integrated in future analyses with multi-modal
data collected within the RADAR-MDD study in order to develop
algorithms to predict changes in clinical state.
Supplementary material. The supplementary material for this article can
be found at https://doi.org/10.1017/S0033291723001034
Acknowledgements. The RADAR-CNS project has received funding from
the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement
No 115902. This Joint Undertaking receives support from the European
Union’s Horizon 2020 research and innovation programme and EFPIA,
www.imi.europa.eu. This communication reflects the views of the
RADAR-CNS consortium and neither IMI nor the European Union and
EFPIA are liable for any use that may be made of the information contained
herein. The funding body has not been involved in the design of the study, the
collection or analysis of data, or the interpretation of data. Participants in the
CIBER site came from following four clinical communities in Spain: Parc
Sanitari Sant Joan de Déu Network services, Institut Català de la Salut,
Institut Pere Mata, and Hospital Clínico San Carlos. Participant recruitment
in Amsterdam was partially accomplished through Hersenonderzoek.nl, a
Dutch online registry that facilitates participant recruitment for neuroscience
studies Hersenonderzoek.nl is funded by ZonMw-Memorabel project no
73305095003, a project in the context of the Dutch Deltaplan Dementie,
Gieskes-Strijbis Foundation, the Alzheimer’s Society in the Netherlands and
Brain Foundation Netherlands. We thank all GLAD Study volunteers for
their participation, and gratefully acknowledge the NIHR BioResource,
NIHR BioResource centres, NHS Trusts and staff for their contribution. We
also acknowledge NIHR BRC, King’s College London, South London and
Maudsley NHS Trust and King’s Health Partners. We thank the National
Institute for Health Research, NHS Blood and Transplant, and Health Data
Research UK as part of the Digital Innovation Hub Programme. The views
expressed are those of the author(s) and not necessarily those of the NHS,
the NIHR or the Department of Health and Social Care. This paper represents
independent research part funded by the National Institute for Health
Research NIHR Maudsley Biomedical Research Centre at South London and
Maudsley NHS Foundation Trust and King’s College London. The views
expressed are those of the authors and not necessarily those of the NHS, the
NIHR or the Department of Health and Social Care. We thank all the mem-
bers of the RADAR-CNS patient advisory board for their contribution to the
device selection procedures, and their invaluable advice throughout the study
protocol design. This research was reviewed by a team with experience of men-
tal health problems and their careers who have been specially trained to advise
on research proposals and documentation through the Feasibility and
Acceptability Support Team for Researchers FAST-R: a free, confidential ser-
vice in England provided by the National Institute for Health Research
Maudsley Biomedical Research Centre via King’s College London and South
London and Maudsley NHS Foundation Trust. RADAR-MDD will be con-
ducted per the Declaration of Helsinki and Good Clinical Practice, adhering
to principles outlined in the NHS Research Governance Framework for
Health and Social Care 2nd edition. Ethical approval has been obtained in
London from the Camberwell St Giles Research Ethics Committee REC refer-
ence: 17/LO/1154, in London from the CEIC Fundacio Sant Joan de Deu CI:
PIC-128-17 and in the Netherlands from the Medische Ethische
Toetsingscommissie VUms METc VUmc registratienummer: 2018.012 –
NL63557.029.17. All authors acknowledged the contribution of the Patient
Advisory Board. This work has been dedicated to the memory of Rita Siddi.
Author contributors. SS (Sara Siddi), IG, RB, SK, JMH contributed to the
data analysis, figure drawing and manuscript writing. NC, SV contributed
with the critical revision of the analysis. AF, YR, RD contributed to the
platform design and implementation. EL and EG contributed to the extraction
of the features. FM, FL, SS (Sara Siddi), SD, BP, MH, TW, GD, p-M MT
contributed to data collection. AF MH and VAN are lead for RADAR-CNS
consortium, principal investigator of RADAR-MDD, study funding, design,
and oversight of data collection. AF, YR, AR and RJBD contributed to the
administrative, technical, and clinical support of the study. All authors have
been involved in reviewing the manuscript and have given approval for it to
be published. All authors have agreed to be accountable for all aspects of
the work, ensuring that questions relating to the accuracy or the integrity of
any part of the work are appropriately investigated and resolved.
Competing interests. VAN and VS are employees of Janssen Research and
Development LLC. PA is employed by the pharmaceutical company
H. Lundbeck A/S. JMH has received economic compensation for participating
in advisory boards or giving educational lectures from Eli Lilly & Co,
Sanofi, Lundbeck, and Otsuka. No other authors have competing interests
to declare
Availability of data and materials. The datasets used and/or analysed dur-
ing the current study are available from the corresponding author on reason-
able request.
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