The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

METHODS

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

RESULTS

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

CONCLUSIONS

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Nocturnal continuous glucose and sleep stage data in adults with type 1 diabetes in real-world conditions

BACKGROUND

Sleep plays an important role in health, and poor sleep is associated with negative impacts on diabetes management, but few studies have objectively evaluated sleep in adults with type 1 diabetes mellitus (T1DM). Nocturnal glycemia and sleep characteristics in T1DM were evaluated using body-worn sensors in real-world conditions.

METHODS

Analyses were performed on data collected by the Diabetes Management Integrated Technology Research Initiative pilot study of 17 T1DM subjects: 10 male, 7 female; age 19-61 years; T1DM duration 14.9 ± 11.0 years; hemoglobin A1c (HbA1c) 7.3% ± 1.3% (mean ± standard deviation). Each subject was equipped with a continuous glucose monitor and a wireless sleep monitor (WSM) for four nights. Sleep stages [rapid eye movement (REM), light, and deep sleep] were continuously recorded by the WSM. Nocturnal glycemia (mg/dl) was evaluated as hypoglycemia (<50 mg/dl), low (50-69 mg/dl), euglycemia (70-120 mg/dl), high (121-250 mg/dl), and hyperglycemia (>250 mg/dl) and by several indices of glycemic variability. Glycemia was analyzed within each sleep stage.

RESULTS

Subjects slept 358 ± 48 min per night, with 85 ± 27 min in REM sleep, 207 ± 42 min in light sleep, and 66 ± 30 min in deep sleep (mean ± standard deviation). Increased time in deep sleep was associated with lower HbA1c (R2 = 0.42; F = 9.37; p < .01). Nocturnal glycemia varied widely between and within subjects. Glycemia during REM sleep was hypoglycemia 5.5% ± 18.1%, low 6.6% ± 18.5%, euglycemia 44.6% ± 39.5%, high 37.9% ± 39.7%, and hyperglycemia 5.5% ± 21.2%; glycemia during light sleep was hypoglycemia 4.8% ± 12.4%, low 7.3% ± 12.9%, euglycemia 42.1% ± 33.7%, high 39.2% ± 34.6%, and hyperglycemia 6.5% ± 20.5%; and glycemia during deep sleep was hypoglycemia 0.5% ± 2.2%, low 5.8% ± 14.3%, euglycemia 48.0% ± 37.5%, high 39.5% ± 37.6%, and hyperglycemia 6.2% ± 19.5%. Significantly less time was spent in the hypoglycemic range during deep sleep compared with light sleep (p = .02).

CONCLUSIONS

Increased time in deep sleep was associated with lower HbA1c, and less hypoglycemia occurred in deep sleep in T1DM, though this must be further evaluated in larger subsequent studies. Furthermore, the consumer-grade WSM device was useful for objectively studying sleep in a real-world setting.