Industrialization has greatly changed human lifestyle; work and leisure activities have been moved indoors, and artificial light has been used to illuminate the night. As cyclic environmental cues such as light and feeding become weak and/or irregular, endogenous circadian systems are increasingly being disrupted. These disruptions are associated with metabolic dysfunction, possibly contributing to increased rates of overweight and obesity worldwide. Here, we aimed to investigate how activity-rest rhythms, patterns of light exposure, and levels of urbanization may be associated with body mass index (BMI) in a sample of rural and urban Quilombola communities in southern Brazil. These are characterized as remaining social groups who resisted the slavery regime that prevailed in Brazil. Quilombola communities were classified into five groups according to their stage of urbanization: from rural areas with no access to electricity to highly urbanized communities. We collected anthropometric data to calculate BMI, which was categorized as follows: from ≥ 18.5 kg/m2 to < 25 kg/m2 = normal weight; from ≥ 25 kg/m2 to < 30 kg/m2 = overweight; and ≥ 30 kg/m2 = obese. Subjects were asked about their sleep routines and light exposure on workdays and work-free days using the Munich Chronotype Questionnaire (N = 244 included). In addition, we analyzed actimetry data from 121 participants with seven consecutive days of recordings. Living in more urbanized areas and higher intradaily variability (IV) of activity-rest rhythms were associated with an increased risk of belonging to the overweight or obese group, when controlling for age and sex. These findings are consistent with preclinical data and point to potential strategies in obesity prevention and promotion of healthy metabolic profiles.

Study Objectives Major Depressive Disorder (MDD) in adolescence is associated with irregularities in circadian rhythms and sleep. The characterization of such impairment may be critical to design effective interventions to prevent development of depression among adolescents. This study aimed to examine self-reported and actimetry-based circadian rhythms and sleep-wake behavior associated with current MDD and high-risk for MDD among adolescents. Methods Ninety-six adolescents who took part in the IDEA-RiSCo study were recruited using an empirically-developed depression-risk stratification method: 26 classified as low-risk (LR), 31 as high-risk (HR), and 39 as a current depressive episode (MDD). We collected self-report data on insomnia, chronotype, sleep schedule, sleep hygiene as well as objective data on sleep, rest-activity and light exposure rhythms using actimetry for 10 days. Results Adolescents with MDD exhibited more severe insomnia, shorter sleep duration, higher social jetlag (SJL), lower relative amplitude (RA) of activity and higher exposure to artificial light at night (ALAN) compared to the other groups. They also presented poorer sleep hygiene compared to the LR group. The HR group also showed higher insomnia, lower RA, higher exposure to ALAN and higher SJL compared to the LR group. Conclusions High-risk adolescents shared sleep and rhythm alterations with the MDD group, which may constitute early signs of depression, suggesting that preventive strategies targeting sleep should be examined in future studies. Furthermore, we highlight that actimetry-based parameters of motor activity (particularly RA) and light exposure are promising constructs to be explored as tools for assessment of depression in adolescence.

Objective: To assess the adherence to a set of evidence-based recommendations to support mental health during the coronavirus disease 2019 (COVID-19) pandemic and its association with depressive and anxiety symptoms. Methods: A team of health workers and researchers prepared the recommendations, formatted into three volumes (1: COVID-19 prevention; 2: Healthy habits; 3: Biological clock and sleep). Participants were randomized to receive only Volume 1 (control), Volumes 1 and 2, Volumes 1 and 3, or all volumes. We used a convenience sample of Portuguese-speaking participants over age 18 years. An online survey consisting of sociodemographic and behavioral questionnaires and mental health instruments (Patient Health Questionnaire-9 [PHQ-9] and Generalized Anxiety Disorder-7 [GAD-7]) was administered. At 14 and 28 days later, participants were invited to complete follow-up surveys, which also included questions regarding adherence to the recommendations. A total of 409 participants completed the study -mostly young adult women holding university degrees. Results: The set of recommendations contained in Volumes 2 and 3 was effective in protecting mental health, as suggested by significant associations of adherence with PHQ-9 and GAD-7 scores (reflecting anxiety and depression symptoms, respectively). Conclusion:The recommendations developed in this study could be useful to prevent negative mental health effects in the context of the pandemic and beyond.

No abstract

Background: To date, no biomarker has been able to predict antidepressant response at an early blockade of norepinephrine or serotonin uptake. The transient nocturnal increase in plasma melatonin levels is upregulated by blocking these uptakes. The aim of this study was to test whether fluoxetine increase in urinary 6-sulfatoxymelatonin (aMT6s) is an indicator of serotonin uptake blockade. Methods: A total of 20 women (35–45 years of age) recruited from the community had a diagnosis of major depressive disorder confirmed by the Structured Clinical Interview for DSM-IV. Depressive symptoms were evaluated by the Beck Depression Inventory (BDI). Participants were instructed to take 20 mg of fluoxetine every morning. Every 4 weeks, the dose could be increased by 20 mg until symptom remission. The concentration of aMT6s was evaluated in overnight urine samples collected 1 day before and 1 day after the first fluoxetine dose. Results: An increase in aMT6s correlated to a decrease in BDI score evaluated on day 45 (ρ = −0.67, p = 0.024) was observed. Conclusions: Nocturnal increase in urinary aMT6s after the first day of medication use links the early mechanism of action of fluoxetine to its clinical output 45 days later. Thus, the relationship between urinary aMT6s excretion 1 day before/1 day after is a biomarker for predicting clinical output earlier, reducing illness burden and health care costs.

No abstract

Irregular light–dark cycles and circadian/sleep disturbances have been suggested as risk or co-occurring factors in depression. Among a set of metrics developed to quantify strain on the circadian system, social jetlag (SJL) has been put forward as a measure of the discrepancy between biological and social clocks. Here, we approached the question on whether light exposure and SJL would also be associated with depressive symptoms in Quilombola communities in Southern Brazil. These rural communities are void of potential confounders of modern lifestyles and show low levels of SJL. 210 Quilombolas (age range 16–92; 56% women) were asked about their sleep times and light exposure using the Munich ChronoType Questionnaire (MCTQ). The Beck Depression Inventory (BDI) was used to assess depressive symptoms. Additionally, we analyzed 7-day actimetry recordings in 124 subjects. BDI scores higher than 10 (having clinically significant depressive symptoms; controlled for age and sex in the multivariate analysis) were positively associated with SJL >1 h and negatively associated with median light exposure during the day, especially in the morning from 8:00 to 10:00. Our results suggest that low light exposure during the day, and higher levels of SJL are associated with depressive symptoms; longitudinal and experimental studies are needed to understand the underlying mechanisms. Nevertheless, we highlight the potential of treatment strategies aimed at decreasing circadian strain and insufficient light exposure, which are suggested as areas of further research in Psychiatry.

Study objectives In field studies using wrist-actimetry, not identifying/handling off-wrist intervals may result in their misclassification as immobility/sleep and biased estimations of rhythmic patterns. By comparing different solutions for detecting off-wrist, our goal was to ascertain how accurately they detect nonwear in different contexts, and identify variables that are useful in the process. Methods We developed algorithms using heuristic (HA), and machine learning (ML) approaches. Both were tested using data from a protocol followed by 10 subjects, which was devised to mimic contexts of actimeter wear/nonwear in real-life. Self-reported data on usage according to the protocol was considered the gold standard. Additionally, the performance of our algorithms was compared to that of visual inspection (by 2 experienced investigators) and Choi algorithm. Data previously collected in field studies were used for proof-of-concept analyses. Results All methods showed similarly good performances. Accuracy was marginally higher for one of the raters (visual inspection) than for heuristically developed algorithms (HA, Choi). Short intervals (especially <2h) were either not or only poorly identified. Consecutive stretches of zeros in activity were considered important indicators of off-wrist (for both HA and ML). It took hours for raters to complete the task as opposed to the seconds or few minutes taken by the automated methods. Conclusions Automated strategies of off-wrist detection are similarly effective to visual inspection, but have the important advantage of being faster, less costly, and independent of raters’ attention/experience. In our study, detecting short intervals was a limitation across methods.