Background: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings.
Objective: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8).
Methods: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature.
Results: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires.
Conclusions: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.
According to the report of the World Health Organization, the total number of people with depression was estimated to exceed 300 million in 2015, equivalent to 4.4% of the world’s population . There are several depression-related adverse outcomes, including premature mortality [ ], decline in quality of life [ ], and loss of occupational function [ ].
Sleep disturbances are prevalent among depression patients; more than 90% of patients with depression reported poor sleep quality . Sleep disturbances cover a wide range of different symptoms and disorders including insomnia, hypersomnia, excessive daytime sleepiness, and circadian rhythm disturbance [ ]. Insomnia and sleep quality have been observed to be bidirectionally related to depression in several longitudinal studies [ ]. Hypersomnia is more frequently present in depressive episodes of bipolar patients [ , ]. Changes in sleep architecture, such as reduced deep sleep, increased rapid eye movement (REM) sleep, and shortened REM latency, are also significant predictors of depression [ , ].
The gold standard for sleep evaluation is polysomnography (PSG), which involves several physiological measurements including electroencephalogram, electrocardiogram, electromyogram, and accelerometers . Using PSG to assess sleep lacks ecological validity and is time-consuming, expensive, and labor-intensive, requiring dedicated equipment and separate laboratory rooms as well as experts to analyze the physiological signals. Since depression can affect patients for an extended period, long-term monitoring of sleep quality is essential. Due to the above shortcomings, PSG is not suitable for long-term sleep monitoring [ ]. A sleep questionnaire, such as the Pittsburgh Sleep Quality Index (PSQI) [ ], is another useful method to assess sleep. This method relies on the self-reporting of subjective factors, like low recall of sleep, that may affect the accuracy of the assessment [ ].
Several recent studies have used wearable devices to estimate sleep quality and sleep-related parameters [- ] and analyzed the relationship between sleep and depression [ - ]. Miwa et al [ ] estimated sleep quality by detecting rollover movements during sleep and observed a significant difference in sleep quality between nondepressed and depressed people. Mark et al [ ] estimated the sleep duration of 40 information workers for 12 days using a Fitbit wristband and found that sleep duration was positively correlated with mood. DeMasi et al [ ] found that sleep was significantly related to changes in depressive symptoms. These studies have mostly been performed on single center and relatively small datasets (number of participants fewer than 100). Moreover, most of these studies only used basic sleep parameters, such as sleep duration; detailed information on sleep architecture, sleep patterns, and stability of sleep was not considered. The relationship between detailed sleep features, as estimated from data supplied by wearable devices, and depression is yet to be fully explored.
The first aim of this study was to design more sleep-related features, from wearable device data, that reflect the sleep architecture, sleep stability, sleep quality, and sleep disturbances (insomnia and hypersomnia) of the participant. The second aim was to explore associations between these sleep features and depressive symptom severity on a relatively large, multisite dataset. The third aim was to compare our findings with previous studies that used other measurements to assess sleep such as PSG and sleep questionnaires.
Study Participants and Settings
The data we used in this paper were collected from a major EU Innovative Medicines Initiative research project, Remote Assessment of Disease and Relapse–Central Nervous System (RADAR-CNS) . This project aims to investigate the use of remote measurement technologies to monitor people with depression, epilepsy, and multiple sclerosis in real-world settings. The study protocol for the depression component (Remote Assessment of Disease and Relapse–Major Depressive Disorder [RADAR-MDD]) is described in detail in Matcham et al [ ]. The RADAR-MDD project aims to recruit 600 participants with a recent history of depression in 3 study sites (King’s College London [KCL], UK; Vrije Universiteit Medisch Centrum [VUMC], Amsterdam, The Netherlands; and Centro de Investigación Biomédican en Red [CIBER], Barcelona, Spain). Recruitment procedures vary slightly across sites and eligible participants are identified either through existing research cohorts (in KCL and VUmc) who had given consent to be contacted for research purposes; advertisements in general practices, psychologist practices, newspapers, and Hersenonderzoek.nl [ ], which is a Dutch online registry (VUmc); or through mental health services (in KCL and CIBER) [ ]. Participants from KCL and VUmc are community-based, while the participants from CIBER come from a clinical population. As part of the study, participants are asked to install several remote monitoring technology apps and use an activity tracker for up to 2 years of follow-up. Many categories of passive and active data are being collected and uploaded to an open-source platform, RADAR-base [ ]. In this paper, we focus on the sleep and Patient Health Questionnaire 8-item (PHQ-8) data [ ].
According to the American Academy of Sleep Medicine manual for the scoring of sleep and associated events, sleep can be divided into 2 phases, REM sleep and non-REM (NREM) sleep, and NREM sleep can be subdivided into N1, N2, and N3 stages according to characteristic patterns of brain waves collected by PSG . In our project, the daily sleep records of participants were collected by the Charge 2 or Charge 3 (Fitbit Inc). An entire night’s sleep is divided into 4 stages: awake, light, deep, and REM. The light stage provides estimates for the N1 and N2 stages in PSG, while the deep stage provides estimates for the N3 stage in PSG. According to several validation studies of Fitbit, the Fitbit wristband had limited specificity in sleep stages estimation [ - ]. Therefore, in this study, we were not expecting the Fitbit devices to provide information as accurate as PSG would have provided. However, the Fitbit devices were deemed sensitive enough to detect changes in sleep-wake states [ - ]; therefore, the provided sleep stage information could be used to determine estimates for detailed sleep parameters based on known sleep pathology.
The variability of each participant’s depressive symptom severity was measured via the PHQ-8, conducted by mobile phone every 2 weeks. The questionnaire contains 8 questions, with the score of each subitem ranging from 0 to 3. The total score (range 0 to 24) of all subitems is the PHQ-8 score, which can evaluate depressive symptom severity of the participant for the past 2 weeks. A PHQ-8 score ≥10 is the most commonly recommended cutpoint for clinically significant depressive symptoms  (ie, if the PHQ-8 of a participant is ≥10, the participant is likely to have had depressive symptoms in the previous 2 weeks). In the PHQ-8, subitem 3 refers to sleep. The content of subitem 3 is “Trouble falling or staying asleep, or sleeping too much” [ ]. A higher score in subitem 3 indicates worse self-reported sleep in the past 2 weeks. For reading convenience, we denoted the score of subitem 3 as the sleep subscore in this paper.
Sociodemographic of participants were collected during the enrollment session. According to previous studies on the associations between depression and sociodemographic characteristics [, ], we considered baseline age, gender, education level, and annual income as potential confounding variables in our analyses. Due to the different educational systems in different countries, we simply divided the education level into 2 levels: degree (or above) and below degree. The annual income levels of Spain and the Netherlands were transformed into equivalent British levels.
Feature Window Size
For each PHQ-8 record, we extracted sleep features from a 2-week time window prior to the PHQ-8 completion time, as the PHQ-8 score is used to represent the depressive symptom severity of the participant for the past 2 weeks. The feature window is denoted as ∆t in this paper.
According to known sleep pathology and our experience, 18 sleep features extracted in this paper were divided into the following 5 categories (): sleep architecture, representing the basic and cyclical patterns of sleep; sleep stability, representing the variance of sleep in the feature window; sleep quality, measures relating to total sleep and wake times; insomnia, trouble falling or staying asleep; and hypersomnia, excessive sleepiness.
|Av_tst||Mean total sleep time||Hour|
|Av_time_bed||Mean time in bed||Hour|
|Deep_pct||Mean percentage of deep sleep||%|
|Light_pct||Mean percentage of light sleep||%|
|REM_pct||Mean percentage of REMa sleep||%|
|NREM_pct||Mean percentage of NREMb sleep||%|
|Awake_pct||Mean percentage of awake time||%|
|Av_onset||Mean sleep onset time||Hour|
|Av_offset||Mean sleep offset time||Hour|
|REM_L||Mean REM latency time||Hour|
|Std_tst||Standard deviation of total sleep time||Hour|
|Std_onset||Standard deviation of sleep onset time||Hour|
|Std_offset||Standard deviation of sleep offset time||Hour|
|Efficiency||Mean sleep efficiency||%|
|Awake_5||Mean number of awakenings (>5 minutes) per night||Times|
|WKD_diff||Total sleep time difference between weekend and weekdays||Hour|
|M_insomnia||Percentage of days with potential middle insomnia||%|
|Dur_10||Percentage of days with total sleep time >10 hours||%|
aREM: rapid eye movement.
bNon-REM: non–rapid eye movement.
The features of sleep architecture were intended to describe the basic and cyclical patterns of sleep. Therefore, we extracted some features similar to those in the PSG report (total sleep time, time in bed, sleep onset time, sleep offset time, and REM latency) , and features of the percentages of all sleep stages. Total sleep time of one night is defined as the sum of all nonawake stages (light, deep, and REM) [ ]. The mean total sleep time in ∆t was denoted as Av_tst. Time in bed of one night is defined as the sum of all sleep stages (awake, light, deep, and REM) of the entire night [ ]. The mean time in bed in ∆t was denoted as Av_time_bed. Percentage of each sleep stage is defined as the percentage of the time in the sleep stage to the time in bed of the entire night. Different sleep stages have different functions and can reflect the quality of sleep. Deep sleep is considered essential for memory consolidation [ ], and REM sleep favors the preservation of memory [ ]. A previous sleep report has shown that more deep sleep and fewer awakenings represent better sleep quality [ ]. Therefore, we extracted the mean percentages of these 4 sleep stages in ∆t, and denoted them as Deep_pct, Light_pct, REM_pct, Awake_pct, respectively. The combination of deep and light sleep is NREM sleep. The mental activity that occurs in NREM and REM sleep is a result of 2 different mind generators, which also explains the difference in mental activity [ ]. So, we extracted the mean percentage of NREM sleep in ∆t, which was denoted as NREM_pct. We calculated the mean sleep onset time (the first nonawake stage) in ∆t, denoted as Av_onset. Mean sleep offset time (the last nonawake stage) in ∆t was calculated and denoted as Av_offset. Previous literature has shown that shortened REM latency can be considered as a biological mark of depression relapse [ ]. REM latency is defined as the interval between sleep onset and occurrence of the first REM stage. The mean REM latency in ∆t was denoted as REM_L.
The features in this category were used to estimate the variance of sleep during ∆t. We extracted the standard deviation of total sleep time, sleep onset time, and sleep offset time in ∆t, which were denoted as Std_tst, Std_onset, and Std_offset, respectively.
In this paper, we used features of sleep efficiency, awakenings, and weekend catch-up sleep to describe sleep quality. The definition of sleep efficiency is the percentage of total sleep time to time in bed . Mean sleep efficiency in ∆t was denoted as Efficiency. The definition of awakenings (>5 minutes) for one night is the number of episodes in which an individual is awake for more than 5 minutes [ ]. The average number of awakenings in ∆t was denoted as Awake_5. Weekend catch-up sleep is an indicator of insufficient weekday sleep, which might be associated with depression level [ ]. A longer total sleep time during the weekend compared with weekdays may reflect the actual sleep needed [ ]. Therefore, we calculated the mean total sleep time difference between weekend and weekdays in ∆t, which was denoted as WKD_diff.
A review of several longitudinal studies suggested that insomnia is bidirectionally related to depression . According to the diagnostic features provided in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [ ], insomnia manifests as initial insomnia (difficulty initiating sleep at bedtime), middle insomnia (frequent or prolonged awakening throughout the night), and late insomnia (early-morning awakening with an inability to return to sleep).
For initial insomnia and late insomnia, mean sleep onset time (Av_onset) and sleep offset time (Av_offset) can be used to partially reflect them, respectively. We define potential middle insomnia to be whether the total sleep time is less than 6 hours and there is at least one prolonged awakening (≥30 minutes) during the night. The percentage of days with potential middle insomnia in the feature window was denoted as M_Insomnia.
Hypersomnia can be another symptom of depression . The hypersomnia criteria used in Tam et al [ ] is sleeping more than 10 hours per day, 3 days per week. In this paper, the percentage of days with total sleep time greater than 10 hours was extracted in ∆t and denoted as Dur_10.
Data Inclusion Criteria
Sleep and PHQ-8 records were missing in our data cohort for a variety of expected reasons, including the participants not wearing the Fitbit wristband when they slept, participants forgetting to complete the PHQ-8, and the Fitbit wristband being damaged during follow-up. We, therefore, specified the following inclusion criteria: (1) PHQ-8 record should be completed (ie, participant answered all 8 questions in the questionnaire); (2) number of days with sleep records in the feature window should be at least 12 days (approximately 85% of the feature window size) ; (3) number of PHQ-8 records for each participant should be greater than or equal to 3 [ ]; (4) date of PHQ-8 records should be before February 2020, because the impact of the COVID-19 pandemic on sleep needs to be excluded [ ].
In our study, each participant had multiple PHQ-8 records and repeated sleep measures. For this reason, we used linear mixed models, which allow for accounting of both within and between-individual variability over time . For each sleep feature, a 3-level linear mixed model with a participant-specific random intercept and a site-specific random intercept was built on the entire dataset to explore the association between this sleep feature and depressive symptom severity (PHQ-8) by bivariate analysis. We then used 2-level linear mixed models with participant-specific random intercepts to test these associations on the 3 subsets (KCL, CIBER, and VUmc) separately. We similarly analyzed the associations between sleep features and sleep subscore. All models were adjusted for baseline age, gender, education level, and annual income, which were specified as fixed effects. Model assumptions were checked by the histograms of residuals and Q-Q plots. If the residuals are not normally distributed, the Box-Cox transformation was performed [ ]. The z score was used to evaluate the statistical significance of the coefficient of each model. All P values of these tests were corrected by using the Benjamini-Hochberg method [ ] for multiple comparisons, and the significance level of the corrected P value was set to .05. All linear mixed models were implemented by using the lme4 package for R software version 3.6.1 (R Foundation for Statistical Computing).
In order to identify and compare the relationship between self-reported sleep and self-reported depression among different study sites, Spearman correlations were calculated between the PHQ-8 score and sleep subscore on the 3 study sites separately.
An example of such a 3-level linear mixed model is as follows:
Sleepijk = δ000 + V00k + U0jk + β1(PHQ8ijk) + β2(agejk) + β3(genderjk) + β4(educationjk) + β5(incomejk) + εijk
where PHQ8ijk is the ith PHQ-8 score of the participant j of the site k, Sleepijk is one sleep feature extracted in ∆t before the ith PHQ-8 record of the participant j of the site k, agejk, genderjk , educationjk, and incomejk are potential confounding variables of the participant j of the site k, εijk is the residual, δ000 is the fixed effect on intercept, U0jk is the random intercept of the participant j in the site k, and V00k is the random intercept of the site k.
According to our data inclusion criteria, from June 2018 to February 2020, 2812 PHQ-8 records from 368 participants collected from 3 study sites were included for our analysis. A summary of the sociodemographic characteristics of these participants at baseline and scores of all PHQ-8 records is shown in. The Kruskal-Wallis test was used to determine whether there were any significant differences for these characteristics between the sites. These tests revealed that, except for gender, sociodemographic characteristics and distribution of PHQ-8 scores differed between the study sites. The histograms of PHQ-8 scores of the study sites and the entire dataset are shown in . We can observe that the KCL site had the most PHQ-8 records among the sites. PHQ-8 scores from the CIBER site were relatively high, probably because participants in the CIBER site came from a clinical population. presents pairwise Spearman correlation coefficients between all 18 sleep features. shows the results of Spearman correlation analysis; we can observe there was a strong positive correlation between the sleep subscore and PHQ-8 score (r=.73, z=54.48, P<.001) on the entire dataset, but this correlation was relatively weaker on the VUmc data (r=.64, z=18.75, P<.001).
|PHQ-8f records, n||1547||708||557||—|
|PHQ-8 scores, median (Q1, Q3)||8 (4, 12)||14 (8, 19)||9 (5, 13)||<.001|
|The PHQ-8 score ≥10, n (%)||599 (38.7)||492 (69.5)||248 (44.5)||<.001|
|Age at baseline, median (Q1, Q3)||46 (30.3, 59.0)||55 (49.3, 60.8)||42 (28.0, 57.0)||<.001|
|Female sex, n (%)||144 (76.2)||69 (71.9)||65 (81.9)||.62|
|Educationg, n (%)||—||—||—||<.001|
|Degree or above||116 (61.4)||21 (21.9)||40 (48.2)||—|
|Below degree||73 (38.6)||75 (78.1)||43 (51.8)||—|
|Annual incomeh (₤), n (%)||—||—||—||.009|
|<15,000||40 (21.2)||28 (29.2)||24 (28.9)||—|
|15,000-40,000||80 (42.3)||53 (55.2)||34 (41.0)||—|
|>40,000||67 (35.5)||15 (15.6)||14 (16.9)||—|
|Not mentioned||2 (1.1)||0 (0)||11 (13.3)||—|
aKCL: King’s College London.
bCIBER: Centro de Investigación Biomédican en Red.
cVUmc: Vrije Universiteit Medisch Centrum.
dP value of Kruskal-Wallis test.
fPHQ-8: Patient Health Questionnaire 8-item.
gEducation levels of Spain and the Netherlands transformed into equivalent British education levels.
hAnnual income levels of Spain and the Netherlands transformed into equivalent British levels.
|Study site||r||95% CI||z score||P value|
aSleep subscore represents the score of subitem 3 in the PHQ-8.
bKCL: King’s College London.
cCIBER: Centro de Investigación Biomédican en Red.
dVUmc: Vrije Universiteit Medisch Centrum.
Three-Level Linear Mixed Models on the Entire Dataset
shows the results from 3-level linear mixed regression models that reflect the associations between sleep features and the PHQ-8 score and sleep subscore, respectively. A total of 14 sleep features were found to be significantly associated with the PHQ-8 score, among them awake percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. The percentages of light sleep (Light_pct) and NREM sleep (NREM_pct) and sleep efficiency (Efficiency) were significantly and negatively associated with the PHQ-8 score, whereas the rest of the significant features were positively associated with the PHQ-8 score.
|Features||PHQ-8c score||Sleep subscore|
|Coeff.d||95% CI||z score||P value||Coeff.||95% CI||z score||P value|
|Av_tst||0.013||0.006, 0.019||3.93||<.001||–0.004||–0.034, 0.025||–0.28||.78|
|Av_time_bed||0.016||0.009, 0.023||4.45||<.001||0.005||–0.028, 0.038||0.29||.77|
|Deep_pct||–0.007||–0.026, 0.011||–0.75||.45||–0.104||–0.191, –0.017||–2.34||.02|
|Light_pct||–0.032||–0.064, –0.001||–2.02||.04||0.090||–0.057, 0.237||1.20||.23|
|REM_pct||0.003||–0.021, 0.027||0.25||.80||–0.125||–0.238, –0.012||–2.17||.03|
|NREM_pct||–0.038||–0.062, –0.014||–3.12||.002||–0.014||–0.127, 0.098||–0.25||.80|
|Awake_pct||0.035||0.022, 0.048||5.45||<.001||0.139||0.079, 0.199||4.58||<.001|
|Av_onset||0.007||–0.001, 0.015||1.71||.09||0.078||0.040, 0.115||4.03||<.001|
|Av_offset||0.025||0.017, 0.033||6.19||<.001||0.097||0.060, 0.135||5.10||<.001|
|REM_L||0.034||–0.021, 0.088||1.21||.23||0.085||–0.178, 0.347||0.63||.53|
|Std_tst||0.008||0.004, 0.012||4.07||<.001||0.047||0.028, 0.067||4.77||<.001|
|Std_onset||0.012||0.004, 0.019||3.11||.002||0.060||0.022, 0.097||3.13||.002|
|Std_offset||0.012||0.005, 0.018||3.58||<.001||0.069||0.037, 0.100||4.26||<.001|
|Efficiency||–0.025||–0.037, –0.012||–3.91||<.001||–0.108||–0.167, –0.050||–3.65||<.001|
|Awake_5||0.016||0.010, 0.022||5.53||<.001||0.038||0.011, 0.065||2.77||.006|
|WKD_diff||0.134||0.039, 0.230||2.76||.006||0.747||0.255, 1.240||2.98||.003|
|M_insomnia||0.370||0.211, 0.530||4.55||<.001||2.373||1.595, 3.151||5.98||<.001|
|Dur_10||0.309||0.195, 0.423||5.30||<.001||0.909||0.357, 1.462||3.23||.001|
aDefinitions of sleep features in this table are shown in.
bSleep subscore represents the score of subitem 3 in the PHQ-8.
cPHQ-8: Patient Health Questionnaire 8-item.
dSlope coefficient estimates for all sleep features.
For sleep subscore, we can notice that deep sleep percentage (Deep_pct), REM sleep percentage (REM_pct), and sleep efficiency (Efficiency) were significantly and negatively associated with the sleep subscore, whereas features of the percentage of awake time (Awake_pct), unstable sleep (Std_tst, Std_onset, Std_offset), awakening times (Awake_5), weekend catch-up sleep (WKD_diff), sleep onset time (Av_onset), sleep offset time (Av_offset), insomnia (M_insomnia), and hypersomnia (Dur_10) were significantly and positively associated with the sleep subscore.
Two-Level Linear Mixed Models on Different Research Sites
provides the results from 2-level linear mixed models which show the associations between sleep features and the PHQ-8 score on different research sites separately. On the KCL data, most associations between sleep features and depression were consistent with the results on the entire dataset. On the CIBER data, some features were no longer significantly associated with the PHQ-8 score. However, on the VUmc data, most features lost their significance except features of total sleep time (Av_tst), time in bed (Av_time_bed), REM latency (REM_L), and awakenings (Awake_5).
shows associations between sleep features and the sleep subscore on different research sites. The significance of associations between sleep features and the sleep subscore were different among the 3 study sites. Notably, the insomnia feature (M_insomnia) and at least one feature of sleep stability were significantly positively associated with sleep subscore on the data of all 3 sites.
|Coeff.e||95% CI||P value||Coeff.||95% CI||P value||Coeff.||95% CI||P value|
|Av_tst||0.013||0.005, 0.020||.001||0.016||–0.001, 0.033||.06||0.011||0, 0.022||.049|
|Av_time_bed||0.016||0.008, 0.024||<.001||0.021||0.002, 0.040||.03||0.013||0.001, 0.025||.04|
|Deep_pct||–0.005||–0.028, 0.018||.69||0.024||–0.022, 0.071||.31||–0.037||–0.074, 0.001||.06|
|Light_pct||–0.046||–0.087, –0.006||.03||–0.081||–0.155, –0.007||.03||0.019||–0.043, 0.082||.55|
|REM_pct||0.013||–0.018, 0.043||.43||0.015||–0.042, 0.071||.62||–0.007||–0.055, 0.041||.77|
|NREM_pct||–0.049||–0.080, –0.018||.002||–0.060||–0.116, –0.005||.04||–0.016||–0.062, 0.030||.50|
|Awake_pct||0.037||0.020, 0.054||<.001||0.043||0.015, 0.071||.003||0.022||–0.003, 0.047||.09|
|Av_onset||0.010||0.000, 0.020||.047||0.004||–0.018, 0.025||.74||–0.005||–0.021, 0.010||.52|
|Av_offset||0.029||0.018, 0.039||<.001||0.024||0.004, 0.043||.02||0.012||–0.004, 0.029||.14|
|REM_L||0.019||–0.049, 0.088||.58||0.106||–0.026, 0.237||.12||–0.126||–0.231, –0.020||.02|
|Std_tst||0.008||0.003, 0.013||.001||0.009||0, 0.019||.06||0.002||–0.006, 0.010||.62|
|Std_onset||0.007||–0.002, 0.016||.14||0.019||–0.001, 0.039||.06||0.001||–0.011, 0.013||.93|
|Std_offset||0.009||0.001, 0.017||.03||0.019||0.002, 0.036||.03||0.003||–0.008, 0.015||.56|
|Efficiency||–0.025||–0.041, –0.008||.004||–0.043||–0.071, –0.016||.002||–0.012||–0.037, 0.013||.34|
|Awake_5||0.014||0.006, 0.022||<.001||0.022||0.009, 0.035||.001||0.016||0.005, 0.027||.01|
|WKD_diff||0.211||0.084, 0.339||.001||0.071||–0.126, 0.268||.48||0.077||–0.144, 0.299||.49|
|M_insomnia||0.472||0.259, 0.685||<.001||0.381||0.028, 0.734||.04||–0.048||–0.385, 0.289||.78|
|Dur_10||0.331||0.191, 0.472||<.001||0.340||0.052, 0.627||.02||0.181||–0.051, 0.413||.13|
aDefinitions of sleep features in this table are shown in.
bKCL: King’s College London.
cCIBER: Centro de Investigación Biomédican en Red.
dVUmc: Vrije Universiteit Medisch Centrum.
eSlope coefficient estimates for all sleep features.
|Coeff.f||95% CI||P value||Coeff.||95% CI||P value||Coeff.||95% CI||P value|
|Av_tst||0.015||–0.021, 0.050||.41||–0.035||–0.116, 0.047||.41||–0.017||–0.070, 0.035||.52|
|Av_time_bed||0.026||–0.013, 0.066||.19||–0.025||–0.116, 0.065||.58||–0.015||–0.074, 0.043||.61|
|Deep_pct||–0.027||–0.134, 0.081||.63||–0.196||–0.412, 0.020||.07||–0.191||–0.369, –0.014||.04|
|Light_pct||–0.024||–0.213, 0.166||.81||0.098||–0.250, 0.445||.58||0.312||0.016, 0.608||.04|
|REM_pct||–0.116||–0.260, 0.028||.12||–0.037||–0.304, 0.230||.79||–0.169||–0.398, 0.060||.15|
|NREM_pct||–0.048||–0.194, 0.098||.52||–0.123||–0.389, 0.143||.37||0.125||–0.096, 0.346||.27|
|Awake_pct||0.165||0.085, 0.245||<.001||0.150||0.020, 0.280||.02||0.049||–0.073, 0.170||.43|
|Av_onset||0.055||0.008, 0.101||.02||0.075||–0.023, 0.172||.13||0.128||0.054, 0.202||.001|
|Av_offset||0.102||0.053, 0.150||<.001||0.048||–0.040, 0.135||.29||0.133||0.056, 0.210||.001|
|REM_L||0.073||–0.255, 0.401||.66||0.146||–0.494, 0.787||.65||–0.171||–0.683, 0.340||.51|
|Std_tst||0.046||0.022, 0.071||<.001||0.046||–0.002, 0.094||.06||0.043||0.004, 0.082||.03|
|Std_onset||0.028||–0.015, 0.070||.21||0.089||–0.018, 0.195||.10||0.079||0.020, 0.139||.01|
|Std_offset||0.046||0.008, 0.084||.02||0.109||0.022, 0.195||.01||0.072||0.016, 0.127||.01|
|Efficiency||–0.118||–0.196, –0.041||.003||–0.152||–0.280, –0.024||.02||–0.044||–0.162, 0.074||.46|
|Awake_5||0.047||0.011, 0.083||.01||0.037||–0.022, 0.097||.22||0.013||–0.042, 0.067||.65|
|WKD_diff||1.169||0.534, 1.804||<.001||0.210||–0.864, 1.284||.70||0.283||–0.830, 1.395||.62|
|M_insomnia||2.302||1.274, 3.329||<.001||2.777||1.070, 4.485||.001||1.823||0.180, 3.465||.03|
|Dur_10||1.057||0.387, 1.728||.002||0.576||–0.844, 1.995||.43||0.706||–0.411, 1.823||.22|
aThe definitions of sleep features in this table are shown in.
bThe sleep subscore represents the score of subitem 3 in the PHQ-8.
cKCL: King’s College London.
dCIBER: Centro de Investigación Biomédican en Red.
eVUmc: Vrije Universiteit Medisch Centrum.
fSlope coefficient estimates for all sleep features.
In this study, we extracted 18 sleep features through Fitbit data to quantitatively describe participant sleep characteristics in 5 categories (sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia) associated with the severity of depression. Along with the depressive status worsening, the following changes may be seen in the past 2 weeks: (1) percentage of light/NREM sleep decreased and the percentage of wakefulness during sleep increased (sleep architecture); (2) sleep duration/onset/offset were unstable (sleep stability); (3) reduced sleep efficiency, more awakenings during sleep, and longer weekend catch-up sleep were observed (sleep quality); (4) more days with insomnia were observed (insomnia); (5) more days with hypersomnia were observed (hypersomnia).illustrated that our sleep features of these 5 categories could reflect both the participant sleep condition (sleep subscore) and depressive symptom severity (PHQ-8 score) of the past 2 weeks.
Potential Factors Affecting Associations
We evaluated our models on the research sites separately. Fromand , we can notice that the associations between sleep features and PHQ-8 score/sleep subscore varied across different sites. Several factors may affect the associations. First, the populations of the 3 sites were significantly different ( ). For example, participants in the CIBER site came from a clinical population and their average age was oldest, so one speculation is that there was less difference between their weekday sleep and weekend sleep for inpatients or people in retirement. Therefore, this may be the reason why the feature of weekend catch-up sleep (WKD_diff) lost significance on the CIBER data. In addition, the reduced significance of features related to sleep onset and offset time on the CIBER site might be related to the regular sleep pattern in CIBER site favors going to bed later, as seen in our previous study [ ].
The associations between sleep features and the sleep subscore on the VUmc data () were similar to that in the entire dataset ( ), which demonstrated sleep features have the same ability to capture the sleep condition of participants on the VUmc data. However, the significance of associations between these sleep features and the PHQ-8 score was reduced in the VUmc data ( ). One possible reason is that, as seen on , the correlation between the sleep subscore and PHQ-8 score in the VUmc data (r=.64) was weaker than other 2 study sites (KCL: r=.74 and CIBER: r=.78), which may be caused by confounding variables that we did not consider or record in the VUmc population such as medication and occupational status.
Sample size and heterogeneity of the dataset were other possible factors that may affect results.shows that the KCL site had the most PHQ-8 records, whereas VUmc had the least data. As depression manifests itself in distinctive symptoms on different people, it may be difficult to fully explore the associations between sleep and depression on a relatively smaller dataset (VUmc). For example, hypersomnia is specifically related to bipolar patients [ , ]; therefore, if the dataset did not contain enough bipolar patients or bipolar patients were not in depressive episodes when they completed their PHQ-8 records, it would be hard to find the association between hypersomnia and depression.
Comparison With Prior Work
Our study has a relatively larger sample size and a longer follow-up duration than previous studies on monitoring depression by using wearable devices and mobile phones [- ]. Each participant has multiple PHQ-8 records and repeated measurements of sleep, so we can not only explore the relationships between sleep and depression between individuals but also find the associations within individuals by using the linear mixed model. is an example of a possible depression relapse of one participant, showing an obvious increasing trend in PHQ-8 scores at the 13th PHQ-8 record of this participant. We can observe the sleep features in are significantly associated with the PHQ-8 score. This indicates that the sleep features extracted in this paper have the potential to be the biomarkers of depression.
We also compared our findings with previous studies that used other measurements to assess sleep, such as PSG and sleep questionnaires. Although the sample size, population, measurements, duration of these studies are different, the comparison may help to find more general associations between sleep and depression.provides a summary of the comparison. Several longitudinal studies based on sleep questionnaires have shown that insomnia and hypersomnia are both symptoms of depression [ , ], which we found in our research. Kang et al [ ] found the weekend catch-up sleep was significantly positively correlated with the severity of depression by analyzing the self-sleep questionnaires of 4553 Korean adolescents, and this is consistent with the finding in our paper. A sleep report has shown that higher sleep efficiency, more deep sleep, and fewer awakenings after sleep onset represent better sleep quality [ ], which is also consistent with the relationships we found between deep sleep percentage, awake percentage, and awakenings (>5 minutes) with sleep subscore. A review showed that according to PSG research, the shortened REM latency and increased percentage of REM sleep are biological markers of depression relapse [ ]; however, relationships between depressive symptom severity with REM sleep percentage and REM latency were not significant in our results.
|Type of feature||Findings in previous studies||Consistenta||Measurement|
|Insomnia||Insomnia is significantly related to depression .||Yes||Questionnaire|
|Hypersomnia||Prevalence of hypersomnia is high in depressed patients .||Yes||Questionnaire|
|Weekend catch-up sleep||Weekend catch-up sleep is significantly positively correlated with the severity of depression .||Yes||Questionnaire|
|Deep sleep percentage||More deep sleep represents higher sleep quality .||Yes||Questionnaire|
|Awake percentage, Awakenings (>5 mins)||Fewer awakenings after sleep onset represents better sleep quality .||Yes||Questionnaire|
|Sleep efficiency||Higher sleep efficiency represents better sleep quality .||Yes||Questionnaire|
|REM sleep percentage||Increased REM sleep percentage can be biomarkers of depression .||No||Polysomnography|
|REMb latency||Shortened REM latency can be biomarkers of depression .||No||Polysomnography|
aWhether it is consistent with our findings.
bREM: rapid eye movement.
Missing data is the major hindrance in our study. For various reasons, there were many missing records of sleep. We set the completion rate of sleep records greater than 85% (12 days) as one of the data inclusion criteria. However, the optimum threshold is unclear, which needs to be further studied in future research. Missingness could also be associated with depressive status and could be a useful marker of relapse of depression; for example, participants may not feel like complying if they are feeling depressed. In future research, we will consider missingness as a potential feature.
Although we adjusted our models for age, gender, education level, and annual income, it is hard to consider all potential confounding variables. For example, some participants with sleep disorders may take sleep medications. Sleep medications have a significant influence on the features of sleep. Unfortunately, there was no daily record of whether the participant took medication. This confounding variable may affect the result.
The data of sleep stages used in this paper were provided by the Fitbit wristband. According to their validation studies, the Fitbit wristband showed promise in detecting sleep-wake states but limitations in other sleep stages estimation [- ]. This may be the reason the features of REM percentage and REM latency in our paper did not show significant relationships with depressive symptoms. For detecting insomnia, the sleep onset latency (SOL) in the PSG report is a reliable indicator of insomnia, but the Charge 2 and 3 are not able to measure SOL directly. The features related to insomnia in our paper can partially reflect insomnia, but they may be affected by factors (such as work schedules or activities) other than insomnia. Therefore, in future research, we will combine multiple features (such as a late sleep onset time accompanied by a short total sleep time) to determine whether a participant has insomnia and try to use activity information (eg, steps) provided by Fitbit to approximate SOL. Although there are some limitations of Fitbit data, it provides a means to investigate sleep characteristic in home settings.
In feature extraction, we did not consider the impact of individual circumstances on sleep features. For example, some participants may need to shift work at night, which our features are unable to capture. We will consider the impact of sleep habits and lifestyles on sleep features in the future. Further, we did not explore the impact of individual patterns of depression —for example, the distinction between people with typical and atypical depression who report reduced and increased sleep, respectively, during depressive episodes. In future work, we will explore whether including this dimension improves specificity of our findings.
In this paper, we focused on analyzing the manifestations of depression in sleep characteristics. We will investigate whether these relationships are bidirectional in future research. We only performed bivariate analysis (ie, separately analyzing the association between each feature and the PHQ-8 score). The combination of features and nonlinear relationships was not considered. We will try to apply machine/deep learning models to predict the severity of depression by using sleep features in future research.
Although consumer wearable devices may not be a substitute for PSG to assess sleep quality accurately, we demonstrated that some derived sleep features extracted from these wearable devices show potential for remote measurement of sleep and consequently can act as a biomarker of depression in real-world settings. These findings may provide the basis for the development of clinical tools that could be used to passively monitor disease state and trajectory with minimal burden on the participant.
The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant 115902. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA). This communication reflects the views of the RADAR-CNS consortium and neither the Innovative Medicines Initiative nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Participants in the CIBER site came from the following 4 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 grant 73305095003 from ZonMw-Memorabel, a project in the context of the Dutch Deltaplan Dementie, Gieskes-Strijbis Foundation, the Alzheimer’s Society in the Netherlands, and Brain Foundation Netherlands. This paper represents independent research partially funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, NIHR, or the Department of Health and Social Care. RB is funded in part by grant MR/R016372/1 from the King’s College London Medical Research Council Skills Development Fellowship program funded by the UK Medical Research Council and by grant IS-BRC-1215-20018 from the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London.
Conflicts of Interest
VAN is an employee of Janssen Research and Development LLC and may own equity in the company.
- Depression and other common mental disorders: global health estimates. Geneva: World Health Organization; 2017. URL: https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf [accessed 2021-03-26]
- Cuijpers P, Schoevers RA. Increased mortality in depressive disorders: a review. Curr Psychiatry Rep 2004 Dec;6(6):430-437. [CrossRef] [Medline]
- Lenox-Smith A, Macdonald MTB, Reed C, Tylee A, Peveler R, Quail D, et al. Quality of life in depressed patients in UK primary care: the FINDER study. Neurol Ther 2013 Dec;2(1-2):25-42 [FREE Full text] [CrossRef] [Medline]
- Lerner D, Adler DA, Chang H, Berndt ER, Irish JT, Lapitsky L, et al. The clinical and occupational correlates of work productivity loss among employed patients with depression. J Occup Environ Med 2004 Jun;46(6 Suppl):S46-S55 [FREE Full text] [CrossRef] [Medline]
- Mendelson WB, editor. Human Sleep and Its Disorders. Berlin: Springer Science & Business Media; 2012.
- Alvaro PK, Roberts RM, Harris JK. A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep 2013 Jul 01;36(7):1059-1068 [FREE Full text] [CrossRef] [Medline]
- Detre T, Himmelhoch J, Swartzburg M, Anderson CM, Byck R, Kupfer DJ. Hypersomnia and manic-depressive disease. Am J Psychiatry 1972 Apr;128(10):1303-1305. [CrossRef] [Medline]
- Thase ME, Himmelhoch JM, Mallinger AG, Jarrett DB, Kupfer DJ. Sleep EEG and DST findings in anergic bipolar depression. Am J Psychiatry 1989 Mar;146(3):329-333. [CrossRef] [Medline]
- Palagini L, Baglioni C, Ciapparelli A, Gemignani A, Riemann D. REM sleep dysregulation in depression: state of the art. Sleep Med Rev 2013 Oct;17(5):377-390. [CrossRef] [Medline]
- Riemann D, Berger M, Voderholzer U. Sleep and depression—results from psychobiological studies: an overview. Biol Psychol 2001;57(1-3):67-103. [CrossRef] [Medline]
- Iber C, Ancoli-Israel S, Chesson A, Quan S. AASM Manual for the Scoring of Sleep and Associated Events. Darien: American Academy of Sleep Medicine; 2007.
- Sánchez-Ortuño MM, Edinger JD, Means MK, Almirall D. Home is where sleep is: an ecological approach to test the validity of actigraphy for the assessment of insomnia. J Clin Sleep Med 2010 Feb 15;6(1):21-29 [FREE Full text] [Medline]
- Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989 May;28(2):193-213. [CrossRef] [Medline]
- Moore CM, Schmiege SJ, Matthews EE. Actigraphy and sleep diary measurements in breast cancer survivors: discrepancy in selected sleep parameters. Behav Sleep Med 2015;13(6):472-490 [FREE Full text] [CrossRef] [Medline]
- Beattie Z, Oyang Y, Statan A, Ghoreyshi A, Pantelopoulos A, Russell A, et al. Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol Meas 2017 Oct 31;38(11):1968-1979. [CrossRef] [Medline]
- Van de Water ATM, Holmes A, Hurley DA. Objective measurements of sleep for non-laboratory settings as alternatives to polysomnography—a systematic review. J Sleep Res 2011 Mar;20(1 Pt 2):183-200 [FREE Full text] [CrossRef] [Medline]
- Zhang Y, Yang Z, Lan K. Sleep stage classification using bidirectional lstm in wearable multi-sensor systems. 2019 Presented at: IEEE INFOCOM–IEEE Conference on Computer Communications Workshops; 2019; Paris p. 443-448. [CrossRef]
- Zhang Y, Yang Z, Zhang Z. Breathing disorder detection using wearable electrocardiogram and oxygen saturation. Proc 16th ACM Conf Emb Netw Sensor Syst 2018:313-314. [CrossRef]
- Miwa H, Sasahara S, Matsui T. Roll-over detection and sleep quality measurement using a wearable sensor. Annu Int Conf IEEE Eng Med Biol Soc 2007:1507-1510. [CrossRef] [Medline]
- Mark G, Czerwinski M, Iqbal S. Workplace indicators of mood: behavioral and cognitive correlates of mood among information workers. Proc 6th Int Conf on Dig Health 2016:29-36. [CrossRef]
- Demasi O, Aguilera A, Recht B. Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity. IEEE 2016:1. [CrossRef]
- Khoulji S, Garzón-Rey J, Aguilo J. Remote Assessment of Disease and Relapse–Central Nervous System (RADAR-CNS). Transact Mach Learn Artif Intell 2017 Aug 31;5(4):1. [CrossRef]
- Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, RADAR-CNS consortium. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry 2019 Feb 18;19(1):72 [FREE Full text] [CrossRef] [Medline]
- Hersenonderzoek.nl. URL: https://hersenonderzoek.nl [accessed 2020-12-07]
- Ranjan Y, Rashid Z, Stewart C, Conde P, Begale M, Verbeeck D, RADAR-CNS Consortium. RADAR-Base: open source mobile health platform for collecting, monitoring, and analyzing data using sensors, wearables, and mobile devices. JMIR Mhealth Uhealth 2019 Aug 01;7(8):e11734 [FREE Full text] [CrossRef] [Medline]
- Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord 2009 Apr;114(1-3):163-173. [CrossRef] [Medline]
- de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2 compared with polysomnography in adults. Chronobiol Int 2018 Apr;35(4):465-476. [CrossRef] [Medline]
- Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Accuracy of wristband fitbit models in assessing sleep: systematic review and meta-analysis. J Med Internet Res 2019 Nov 28;21(11):e16273 [FREE Full text] [CrossRef] [Medline]
- Liang Z, Chapa-Martell MA. Accuracy of fitbit wristbands in measuring sleep stage transitions and the effect of user-specific factors. JMIR Mhealth Uhealth 2019 Jun 06;7(6):e13384 [FREE Full text] [CrossRef] [Medline]
- Aluoja A, Leinsalu M, Shlik J, Vasar V, Luuk K. Symptoms of depression in the Estonian population: prevalence, sociodemographic correlates and social adjustment. J Affect Disord 2004 Jan;78(1):27-35. [CrossRef] [Medline]
- Akhtar-Danesh N, Landeen J. Relation between depression and sociodemographic factors. Int J Ment Health Syst 2007 Sep 04;1(1):4 [FREE Full text] [CrossRef] [Medline]
- Ohayon M, Wickwire EM, Hirshkowitz M, Albert SM, Avidan A, Daly FJ, et al. National Sleep Foundation's sleep quality recommendations: first report. Sleep Health 2017 Feb;3(1):6-19. [CrossRef] [Medline]
- Walker MP. Sleep-dependent memory processing. Harv Rev Psychiatry 2008;16(5):287-298. [CrossRef] [Medline]
- Rasch B, Born J. About sleep's role in memory. Physiol Rev 2013 Apr;93(2):681-766 [FREE Full text] [CrossRef] [Medline]
- Manni R. Rapid eye movement sleep, non-rapid eye movement sleep, dreams, and hallucinations. Curr Psychiatry Rep 2005 Jun;7(3):196-200. [CrossRef] [Medline]
- Kang S, Lee YJ, Kim SJ, Lim W, Lee H, Park Y, et al. Weekend catch-up sleep is independently associated with suicide attempts and self-injury in Korean adolescents. Compr Psychiatry 2014 Feb;55(2):319-325. [CrossRef] [Medline]
- Liu X, Zhao Z, Jia C, Buysse DJ. Sleep patterns and problems among chinese adolescents. Pediatrics 2008 Jun;121(6):1165-1173. [CrossRef] [Medline]
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Washington: American Psychiatric Publishers; 2013.
- Tam EM, Lam RW, Robertson HA, Stewart JN, Yatham LN, Zis AP. Atypical depressive symptoms in seasonal and non-seasonal mood disorders. J Affect Disord 1997 Jun;44(1):39-44. [CrossRef] [Medline]
- Farhan AA, Yue C, Morillo R. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. 2016 Presented at: 2016 IEEE Wireless Health (WH); 2016; Bethesda p. 1-8. [CrossRef]
- Singer JD, Willett JB, Willett JB. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford: Oxford University Press; 2003.
- Sun S, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, RADAR-CNS Consortium. Using smartphones and wearable devices to monitor behavioral changes during COVID-19. J Med Internet Res 2020 Sep 25;22(9):e19992 [FREE Full text] [CrossRef] [Medline]
- Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982 Dec;38(4):963-974. [Medline]
- Box GEP, Cox DR. An analysis of transformations. J Royal Stat Soc B 2018 Dec 05;26(2):211-243. [CrossRef]
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B 2018 Dec 05;57(1):289-300. [CrossRef]
- Kaplan KA, Harvey AG. Hypersomnia across mood disorders: a review and synthesis. Sleep Med Rev 2009 Aug;13(4):275-285. [CrossRef] [Medline]
- Brailean A, Curtis J, Davis K, Dregan A, Hotopf M. Characteristics, comorbidities, and correlates of atypical depression: evidence from the UK Biobank Mental Health Survey. Psychol Med 2019 May 02;50(7):1129-1138. [CrossRef]
|CIBER: Centro de Investigación Biomédican en Red|
|EFPIA: European Federation of Pharmaceutical Industries and Associations|
|KCL: King’s College London|
|NHS: National Health Service|
|NIHR: National Institute for Health Research|
|PHQ-8: Patient Health Questionnaire 8-item|
|PSQI: Pittsburgh Sleep Quality Index|
|RADAR-CNS: Remote Assessment of Disease and Relapse–Central Nervous System|
|RADAR-MDD: Remote Assessment of Disease and Relapse–Major Depressive Disorder|
|REM: rapid eye movement|
|SOL: sleep onset latency|
|VUmc: Vrije Universiteit Medisch Centrum|
|WHO: World Health Organization|
Edited by L Buis; submitted 26.09.20; peer-reviewed by N Jacobson, Z Liang; comments to author 25.11.20; revised version received 07.12.20; accepted 03.02.21; published 12.04.21Copyright
©Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins, Rebecca Bendayan, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Faith Matcham, Katie M White, Femke Lamers, Sara Siddi, Sara Simblett, Inez Myin-Germeys, Aki Rintala, Til Wykes, Josep Maria Haro, Brenda WJH Penninx, Vaibhav A Narayan, Matthew Hotopf, Richard JB Dobson, RADAR-CNS Consortium. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 12.04.2021.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.