Circadian skin temperature rhythms, circadian activity rhythms and sleep in individuals with self-reported depressive symptoms

Lorenz, Noah; Spada, Janek; Sander, Christian; Riedel-Heller, S; Hegerl, Ulrich

doi:10.1016/j.jpsychires.2019.06.022

Cited by 21 publications

(31 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, regarding circadian rhythm metrics, we found that a lower steps-based weekday IS—a nonparametric measure of rhythm regularity—was robustly associated with a greater severity of depressive symptoms independent of confounders, supporting and extending existing evidence [ 31 , 60 ], including one large-sample study [ 34 ]. However, there are several studies that did not find IS to be related to depressive symptoms or other mental disorders [ 27 , 35 , 36 ]. In support of this association, we also found steps-based autocorrelation during weekdays—a complementary and alternative nonparametric measure of rhythm regularity—to be robustly correlated to symptom severity independent of all confounders.…”

Section: Discussionmentioning

confidence: 99%

“…First, we used step counts and heart rate data from consumer wearables instead of “activity counts”—a measure of total linear acceleration—from research-grade devices commonly used in other studies. There are few studies that used alternative source data for circadian rhythm analysis; for example, heart rate [ 32 ] or skin temperature [ 36 ]. Second, we analyzed weekday circadian rhythms separately that were found to be stronger predictors rather than rhythms based on all days.…”

Section: Discussionmentioning

confidence: 99%

“…Third, the cross-sectional design of the study does not allow causal inferences. Fourth, the poor predictability of depressive symptomatology in the whole sample highlights a possible limitation, in principle, of using this set of digital biomarkers alone for depression screening universally due to prominent interindividual differences and heterogeneity that appear in naturalistic settings [ 36 ]. Finally, predictive models were retrained using balanced and contrasted subsamples equally comprised of depressed participants and healthy participants with no risk of depression; therefore, they probably suffer from overfitting and would perform worse on new samples.…”

Section: Discussionmentioning

confidence: 99%

“…We extracted a range of digital biomarkers characterizing physical activity, sleep, circadian rhythms, and physiological parameters from the raw data. This set of digital biomarkers relied on previous findings and was substantially extended with novel metrics hypothetically indicative of depressive symptoms [ 15 - 18 , 20 , 21 , 24 - 26 , 30 , 32 , 34 , 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…Depressive symptoms were also associated with heart rate–based mesor [ 32 ] and amplitude [ 32 ] as well as increased heart rate in the night and morning hours [ 33 ]. Few studies using nonparametric rhythm measures demonstrated that participants with more severe depressive symptoms had lower interdaily stability (IS) and higher intradaily variability (IV) of circadian activity rhythm [ 31 , 34 ], reduced relative amplitude (RA) between the highest and the lowest activity levels [ 28 , 35 ], and reduced RA of skin temperature [ 36 ]. Although findings on separate circadian rhythm indicators are mixed, the evidence suggests that depression is often associated with disturbed, irregular, and delayed circadian behavioral and physiological rhythms.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Rykov¹,

Thach²,

Bojić³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. Objective The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. Methods This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. Results A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. Conclusions Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

show abstract