Day-to-day variability in sleep parameters and depression risk: a prospective cohort study of training physicians

High levels of anxiety and depression in women farmers from Ecuador: A cross-section study in Coastal and Highlands regions

BACKGROUND

Previous studies have shown that women farmers are particularly vulnerable to mental health disorders such as depression and anxiety.

AIMS

This study aimed to investigate the prevalence of anxiety and depression in women farmers from Ecuador Coastal and Highlands regions.

METHOD

General Anxiety Disorder‑7 (GAD‑7) and Patient Health Questionnaire‑9 (PHQ‑9) were applied. In addition, self-reported number of children, days off, hours of work, pesticide use, sleep habits and years of work in agriculture, were also collected. This cross-sectional study occurred during 2023 with 443 women, for Coastal (197) and Highlands (246), respectively. Multivariable binary logistic regression models were performed to obtained adjusted odds ratios (aOR) and their 95% confidence intervals (95% CI).

RESULTS

34.5% of Coastal women had depression, while 27.2% of Highlands women had depression. 20.3% of coastal women farmers had anxiety, while in the Highlands 24.8% had anxiety. Coastal mestizo and montubio women exhibited lower probability of depression, but this was not significant in the Highlands. Coastal women farmers that did not have children showed lower odds of depression (aOR 0.05, 95% CI [0.01, 0.34]). A lower likelihood of depression was observed in coastal women that worked more than 8 hours (aOR 0.22, 95% CI [0.07, 0.72]). Women from the Highlands that had shortened sleep duration exhibited lower odds of depression and anxiety.

CONCLUSIONS

A higher proportion of depressed women farmers was observed in the Coast region and slightly higher numbers of anxiety cases in the Highlands. The number of children may cause workload and is correlated with depression in Coastal women.

Resident physician depression: systemic challenges and possible solutions

Resident physicians face intense stressors that significantly heighten their depression risk. This article discusses research findings on critical factors contributing to depression among resident physicians. Understanding these factors is essential to developing targeted interventions, fostering healthy work environments, and ultimately improving physician wellbeing and patient care.

The affective factors of depression symptoms in hypertensive patients and the protective effect of physical activity

OBJECTIVE

To investigate the potential affective factors of depressive symptoms in patients with hypertension and explore the protective effects of physical activity.

METHODS

211 hypertensive patients aged over 18 years were consecutively recruited. All patients completed a self-designed questionnaire and the Hospital Anxiety and Depression Scale (HADS) to assess the coexistence of depressive symptoms, and psychiatrists were invited to diagnose depression when necessary. Full-night polysomnography was performed to detect the sleep pattern. The association between sleep structure and depressive symptoms was tested by using logistic regression analysis, and contributing factors as well as the effect of physical activity were assessed among patients with and without depressive symptoms.

RESULTS

Of the 211 subjects, 33.6% of cases were coexistent with depressive symptoms. Female gender [OR (95%CI): 2.83 (1.44-5.57), P = 0.003) and the greater percentage of REM stage [OR (95%CI): 1.09 (1.01-1.18), P = 0.024] were the risk factors of depressive symptoms, while doing physical activity showed as the protective factor. Patients with REM stage ≥ 20% showed a higher score on HADS-D than those with REM stage < 20% [(4.9 ± 3.8) vs. (3.7 ± 3.1), P = 0.018]. Compared to individuals who never did physical activity, those who did physical activity 1-2 times per week and ≥ 3 times per week had a 52% and 62% risk reduction in depressive symptoms respectively. Patients who did physical activity had lower levels of high-sensitivity C-reactive protein (hs-CRP) compared to those who never did physical activity.

CONCLUSION

Female gender and a higher percentage of REM stage are risk factors for depressive symptoms in hypertension, while physical activity may benefit depressive symptoms by reducing serum levels of hs-CRP.

Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program

Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.

Monitoring Daily Sleep, Mood, and Affect Using Digital Technologies and Wearables: A Systematic Review

Background: Sleep and affective states are closely intertwined. Nevertheless, previous methods to evaluate sleep-affect associations have been limited by poor ecological validity, with a few studies examining temporal or dynamic interactions in naturalistic settings. Objectives: First, to update and integrate evidence from studies investigating the reciprocal relationship between daily sleep and affective phenomena (mood, affect, and emotions) through ambulatory and prospective monitoring. Second, to evaluate differential patterns based on age, affective disorder diagnosis (bipolar, depression, and anxiety), and shift work patterns on day-to-day sleep-emotion dyads. Third, to summarise the use of wearables, actigraphy, and digital tools in assessing longitudinal sleep-affect associations. Method: A comprehensive PRISMA-compliant systematic review was conducted through the EMBASE, Ovid MEDLINE(R), PsycINFO, and Scopus databases. Results: Of the 3024 records screened, 121 studies were included. Bidirectionality of sleep-affect associations was found (in general) across affective disorders (bipolar, depression, and anxiety), shift workers, and healthy participants representing a range of age groups. However, findings were influenced by the sleep indices and affective dimensions operationalised, sampling resolution, time of day effects, and diagnostic status. Conclusions: Sleep disturbances, especially poorer sleep quality and truncated sleep duration, were consistently found to influence positive and negative affective experiences. Sleep was more often a stronger predictor of subsequent daytime affect than vice versa. The strength and magnitude of sleep-affect associations were more robust for subjective (self-reported) sleep parameters compared to objective (actigraphic) sleep parameters.

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis

BACKGROUND

Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings.

OBJECTIVE

This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts.

METHODS

Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score.

RESULTS

Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (β=-93.61, P<.001), increased sleep variability (β=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: β=0.55, P=.001; sleep offset: β=1.12, P<.001; M10 onset: β=0.73, P=.003; HR acrophase: β=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (β of PHQ-8 × spring = -31.51, P=.002) and summer (β of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (β of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer.

CONCLUSIONS

Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

Does sleep link child maltreatment to depressive symptoms among incoming first-year college students?

Study Objectives

We examined whether sleep (i.e. quality, regularity, and duration) mediated associations between child maltreatment (CM) and depressive symptoms among emerging adults undergoing the major life transition of starting college.

Methods

Students (N = 1400; 44% male; 48% non-Hispanic white, 20% non-Hispanic Asian, 15% Hispanic all races, 7% non-Hispanic black, and 10% non-Hispanic other races) completed daily sleep diaries for 9 weeks, followed by the Childhood Trauma Questionnaire-Short Form, Pittsburgh Sleep Quality Index, and the Center for Epidemiologic Studies Depression Scale (CES-D). DSD data were used to compute participants' Sleep Regularity Index and average 24-hour total sleep time. We used a nonparametric structural equation modeling bootstrap approach and full information maximum likelihood to account for missing data. In model 1, we controlled for sex and race and ethnicity. In model 2, we further adjusted for baseline CES-D scores.

Results

The prevalence of self-reported moderate-to-severe CM was 22%. Small but significant indirect effects of CM on greater depressive symptoms through worse sleep quality (β = 0.06, 95% CI = 0.04, 0.09) and lower sleep regularity (β = 0.02, 95% CI = 0.005, 0.03) were observed in model 1. In model 2, only the indirect effect of sleep quality remained significant (β = 0.03, 95% CI = 0.01, 0.06).

Conclusions

Poorer sleep quality may partially account for associations between CM and depressive symptoms during the first semester of college. Including sleep as a target in student health interventions on college campuses may not only help buffer against poor mental health outcomes for students with CM, but also poor academic and socioeconomic outcomes long-term.

Just-in-Time Adaptive Intervention for Stabilizing Sleep Hours of Japanese Workers: Microrandomized Trial

BACKGROUND

Sleep disturbance is a major contributor to future health and occupational issues. Mobile health can provide interventions that address adverse health behaviors for individuals in a vulnerable health state in real-world settings (just-in-time adaptive intervention).

OBJECTIVE

This study aims to identify a subpopulation with vulnerable sleep state in daily life (study 1) and, immediately afterward, to test whether providing mobile health intervention improved habitual sleep behaviors and psychological wellness in real-world settings by conducting a microrandomized trial (study 2).

METHODS

Japanese workers (n=182) were instructed to collect data on their habitual sleep behaviors and momentary symptoms (including depressive mood, anxiety, and subjective sleep quality) using digital devices in a real-world setting. In study 1, we calculated intraindividual mean and variability of sleep hours, midpoint of sleep, and sleep efficiency to characterize their habitual sleep behaviors. In study 2, we designed and conducted a sleep just-in-time adaptive intervention, which delivered objective push-type sleep feedback messages to improve their sleep hours for a subset of participants in study 1 (n=81). The feedback messages were generated based on their sleep data measured on previous nights and were randomly sent to participants with a 50% chance for each day (microrandomization).

RESULTS

In study 1, we applied hierarchical clustering to dichotomize the population into 2 clusters (group A and group B) and found that group B was characterized by unstable habitual sleep behaviors (large intraindividual variabilities). In addition, linear mixed-effect models showed that the interindividual variability of sleep hours was significantly associated with depressive mood (β=3.83; P=.004), anxiety (β=5.70; P=.03), and subjective sleep quality (β=-3.37; P=.03). In study 2, we found that providing sleep feedback prolonged subsequent sleep hours (increasing up to 40 min; P=.01), and this effect lasted for up to 7 days. Overall, the stability of sleep hours in study 2 was significantly improved among participants in group B compared with the participants in study 1 (P=.001).

CONCLUSIONS

This is the first study to demonstrate that providing sleep feedback can benefit the modification of habitual sleep behaviors in a microrandomized trial. The findings of this study encourage the use of digitalized health intervention that uses real-time health monitoring and personalized feedback.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.

Sleep, mental health and physical health in new shift workers transitioning to shift work: Systematic review and meta-analysis

This systematic review and meta-analysis (PROSPERO registration CRD42022309827) aimed to describe how shift work impacts new workers' sleep, mental health, and physical health during the transition to shift work and to consolidate information regarding predictors of shift work tolerance (SWT) during this transition period. Inclusion criteria included: new shift workers; sleep, mental health, or physical health outcomes; prospective study design with the first timepoint assessing workers within three months of starting shift work; and written in English. Searches from six databases returned 12,172 articles as of August 2023. The final sample included 48 papers. Publication quality and risk of bias was assessed using the critical appraisal skills program. Forty-five studies investigated longitudinal changes in sleep, mental health, or physical health outcomes and 29 studies investigated predictors of SWT (i.e., better sleep, mental and physical health). Sleep and mental health outcomes worsened following the onset of shift work, while physical health did not significantly change. Pre-shift work mental health, sleep, and work characteristics predicted SWT later in workers' careers. Shift work adversely impacts new workers' sleep and mental health early in their career, and interventions before beginning shift work are needed to promote better SWT.

The Google Health Digital Well-Being Study: Protocol for a Digital Device Use and Well-Being Study

BACKGROUND

The impact of digital device use on health and well-being is a pressing question. However, the scientific literature on this topic, to date, is marred by small and unrepresentative samples, poor measurement of core constructs, and a limited ability to address the psychological and behavioral mechanisms that may underlie the relationships between device use and well-being. Recent authoritative reviews have made urgent calls for future research projects to address these limitations. The critical role of research is to identify which patterns of use are associated with benefits versus risks and who is more vulnerable to harmful versus beneficial outcomes, so that we can pursue evidence-based product design, education, and regulation aimed at maximizing benefits and minimizing the risks of smartphones and other digital devices.

OBJECTIVE

The objective of this study is to provide normative data on objective patterns of smartphone use. We aim to (1) identify how patterns of smartphone use impact well-being and identify groups of individuals who show similar patterns of covariation between smartphone use and well-being measures across time; (2) examine sociodemographic and personality or mental health predictors and which patterns of smartphone use and well-being are associated with pre-post changes in mental health and functioning; (3) discover which nondevice behavior patterns mediate the association between device use and well-being; (4) identify and explore recruitment strategies to increase and improve the representation of traditionally underrepresented populations; and (5) provide a real-world baseline of observed stress, mood, insomnia, physical activity, and sleep across a representative population.

METHODS

This is a prospective, nonrandomized study to investigate the patterns and relationships among digital device use, sensor-based measures (including both behavioral and physiological signals), and self-reported measures of mental health and well-being. The study duration is 4 weeks per participant and includes passive sensing based on smartphone sensors, and optionally a wearable (Fitbit), for the complete study period. The smartphone device will provide activity, location, phone unlocks and app usage, and battery status information.

RESULTS

At the time of submission, the study infrastructure and app have been designed and built, the institutional review board of the University of Oregon has approved the study protocol, and data collection is underway. Data from 4182 enrolled and consented participants have been collected as of March 27, 2023. We have made many efforts to sample a study population that matches the general population, and the demographic breakdown we have been able to achieve, to date, is not a perfect match.

CONCLUSIONS

The impact of digital devices on mental health and well-being raises important questions. The Digital Well-Being Study is designed to help answer questions about the association between patterns of smartphone use and well-being.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/49189.

Sleep and Activity Patterns in Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD.

Methods

We recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data.

Results

This analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives.

Conclusions

The average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.

Neurobehavioral functions during recurrent periods of sleep restriction: effects of intra-individual variability in sleep duration

STUDY OBJECTIVES

To investigate whether neurobehavioral impairments are exacerbated during successive cycles of sleep restriction and recovery in young adults, and whether a variable short sleep schedule can mitigate these impairments relative to a stable one.

METHODS

Fifty-two healthy young adults (25 males, aged: 21-28) were randomly assigned to the stable short sleep group, the variable short sleep group, or the control group in this laboratory-based study. They underwent two baseline nights of 8-hour time-in-bed (TIB), followed by two cycles of "weekday" sleep opportunity manipulation and "weekend" recovery (8-hour TIB). During each manipulation period, the stable short sleep and the control groups received 6- and 8-hour TIBs each night respectively, while the variable short sleep group received 8-hour, 4-hour, 8-hour, 4-hour, and 6-hour TIBs from the first to the fifth night. Neurobehavioral functions were assessed five times each day.

RESULTS

The stable short sleep group showed faster vigilance deterioration in the second week of sleep restriction as compared to the first. This effect was not observed in the variable short sleep group. Subjective alertness and practice-based improvement in processing speed were attenuated in both short sleep groups.

CONCLUSIONS

In young adults, more variable short sleep schedules incorporating days of prophylactic or recovery sleep might mitigate compounding vigilance deficits resulting from recurrent cycles of sleep restriction. However, processing speed and subjective sleepiness were still impaired in both short sleep schedules. Getting sufficient sleep consistently is the only way to ensure optimal neurobehavioral functioning.

CLINICAL TRIAL

Performance, Mood, and Brain and Metabolic Functions During Different Sleep Schedules (STAVAR), https://www.clinicaltrials.gov/study/NCT04731662, NCT04731662.

Unraveling the interplay of circadian rhythm and sleep deprivation on mood: A Real-World Study on first-year physicians

The interplay between circadian rhythms, time awake, and mood remains poorly understood in the real-world. Individuals in high-stress occupations with irregular schedules or nighttime shifts are particularly vulnerable to depression and other mood disorders. Advances in wearable technology have provided the opportunity to study these interactions outside of a controlled laboratory environment. Here, we examine the effects of circadian rhythms and time awake on mood in first-year physicians using wearables. Continuous heart rate, step count, sleep data, and daily mood scores were collected from 2,602 medical interns across 168,311 days of Fitbit data. Circadian time and time awake were extracted from minute-by-minute wearable heart rate and motion measurements. Linear mixed modeling determined the relationship between mood, circadian rhythm, and time awake. In this cohort, mood was modulated by circadian timekeeping (p<0.001). Furthermore, we show that increasing time awake both deteriorates mood (p<0.001) and amplifies mood's circadian rhythm nonlinearly. These findings demonstrate the contributions of both circadian rhythms and sleep deprivation to underlying mood and show how these factors can be studied in real-world settings using Fitbits. They underscore the promising opportunity to harness wearables in deploying chronotherapies for psychiatric illness.

Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data

Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.

Another benefit of regular sleep

A large observational study has found that irregular sleep-wake patterns are associated with a higher risk of overall mortality, and also mortality from cancers and cardiovascular disease.

Circadian aspects in the aetiology and pathophysiology of insomnia

Because the endogenous circadian pacemaker is a very strong determinant of alertness/sleep propensity across the 24 h period, its mistiming may contribute to symptoms of insomnia (e.g., difficulties initiating sleep and maintaining sleep) and to the development of insomnia disorder. Despite the separation of insomnia and circadian rhythm disorders in diagnostic nosology implying independent pathophysiology, there is considerable evidence of co-morbidity and interaction between them. Sleep onset insomnia is associated with later timed circadian rhythms and can be treated with morning bright light to shift rhythms to an earlier timing. It is also possible that the causal link may go in both directions and that having a delayed circadian rhythm can result in enough experiences of delayed sleep onset to lead to some conditioned insomnia or insomnia disorder further exacerbating a delayed circadian rhythm. Early morning awakening insomnia is associated with an advanced circadian phase (early timing) and can be treated with evening bright light resulting in a delay of rhythms and an improved ability to sleep later in the morning and to obtain more sleep. There is some evidence suggesting that sleep maintenance insomnia is associated with a blunted amplitude of circadian rhythm that may be treated with increased regularity of sleep and light exposure timing. However, this is an insomnia phenotype that requires considerably more circadian research as well as further insomnia clinical research with the other insomnia phenotypes incorporating circadian timing measures and treatments.

Sleep and mental health in recruit paramedics: a 6-month longitudinal study

STUDY OBJECTIVES

To explore potential relationships and longitudinal changes in sleep and mental health in recruit paramedics over the first 6 months of work, and whether sleep disturbances pre-emergency work predict future mental health outcomes.

METHODS

Participants (N = 101, 52% female, Mage = 26 years) completed questionnaires prior to (baseline), and after 6 months of emergency work to assess for symptoms of insomnia, obstructive sleep apnea, post-traumatic stress disorder (PTSD), depression, anxiety, and trauma exposure. At each timepoint, participants also completed a sleep diary and wore an actigraph for 14 days to assess sleep patterns. Correlations between baseline sleep and mental health were conducted and changes in these variables across timepoints were examined using linear mixed models. Hierarchical regressions assessed whether sleep at baseline predicted mental health at follow-up.

RESULTS

Insomnia and depression symptoms, and total sleep time increased while sleep onset latency decreased across the first 6 months of emergency work. Participants experienced an average of 1 potentially traumatic event during the 6-month period. Baseline insomnia predicted increased depression symptoms at the 6-month follow-up, while baseline wake after sleep onset predicted follow-up PTSD symptoms.

CONCLUSION

Results highlight an increase in insomnia and depression across the initial months of emergency work, while sleep disturbances before emergency work were identified as potential risk factors for the development of depression and PTSD among paramedics in their early career. Screening and early interventions targeting poor sleep at the beginning of emergency employment may assist in reducing the risk of future mental health outcomes in this high-risk occupation.

Circadian variability of objective sleep measures predicts the relapse of a mood episode in bipolar disorder: findings from the APPLE cohort

AIM

Sleep disturbance, a core feature of bipolar disorder, is closely associated with mood symptoms. We examined the association between actigraphy sleep parameters and mood episode relapses in patients with bipolar disorder.

METHODS

This prospective cohort study analyzed 193 outpatients with bipolar disorder who participated in the Association between the Pathology of Bipolar Disorder and Light Exposure in Daily Life (APPLE) cohort study. The participants' sleep was objectively evaluated via actigraphy over seven consecutive days for the baseline assessment and then at the 2-year follow-up appointment for mood episode relapses. The actigraphy sleep parameters were presented using the mean and variability (standard deviation) of each sleep parameter for 7 days.

RESULTS

Of the 193 participants, 110 (57%) experienced mood episodes during follow-up. The participants with higher variability in total sleep time had a significantly shorter mean estimated time to mood episode relapses than those with lower variability (12.5 vs. 16.8 months; P < 0.001). The Cox proportional hazards model, when adjusted for potential confounders, demonstrated that variability in total sleep time was significantly associated with an increase in the mood episode relapses (per hour; hazard ratio [HR], 1.407; 95% confidence interval (CI), 1.057-1.873), mainly in the depressive episodes (per hour; HR, 1.477; 95% CI, 1.088-2.006).

CONCLUSIONS

Our findings suggest that consistency in sleep time might be useful, as an adjunct therapy, in preventing the recurrence or relapse of mood episodes in bipolar disorder.

Association of longitudinal patterns of nighttime sleep duration and daytime napping duration with risk of multimorbidity

OBJECTIVES

To determine whether longitudinal trajectories of nighttime sleep duration and daytime napping duration are related to subsequent multimorbidity risk. To explore whether daytime napping can compensate for negative effects of short nighttime sleep.

METHODS

The current study included 5262 participants from China Health and Retirement Longitudinal Study. Self-reported nighttime sleep duration and daytime napping duration were collected from 2011 to 2015. The 4-year sleep duration trajectories were conducted by group-based trajectory modeling. The 14 medical conditions were defined by self-reported physician diagnoses. Multimorbidity was diagnosed as participants with 2 or more of the 14 chronic diseases after 2015. Associations between sleep trajectories and multimorbidity were assessed by Cox regression models.

RESULTS

During 6.69 years of follow-up, we observed multimorbidity in 785 participants. Three nighttime sleep duration trajectories and three daytime napping duration trajectories were identified. Participants with persistent short nighttime sleep duration trajectory had the higher risk of multimorbidity (hazard ratio = 1.37, 95% confidence interval: 1.06-1.77), compared with those with persistent recommended nighttime sleep duration trajectory. Participants with persistent short nighttime sleep duration and persistent seldom daytime napping duration had the highest risk of multimorbidity (hazard ratio = 1.69, 95% confidence interval: 1.16-2.46).

CONCLUSIONS

In this study, persistent short nighttime sleep duration trajectory was associated with subsequent multimorbidity risk. Daytime napping could compensate for the risk of insufficient night sleep.

Sleep Duration on Workdays Is Correlated with Subjective Workload and Subjective Impact of High Workload on Sleep in Young Healthy Adults

Psychosocial stress is widespread worldwide and particularly affects young adults. There is a close and bidirectional relationship between sleep quality and mental health. Sleep duration, which is an important feature of sleep quality, shows both intra-individual variations and inter-individual differences. Internal clocks control individual sleep timing, which, in turn, defines the chronotype. On workdays, however, the end and duration of sleep are largely limited by external factors, such as alarm clocks, especially in later chronotypes. The aim of this study is to investigate whether there is a relationship between sleep timing and duration on workdays and measures for psychosocial stress, such as anxiety and depression; subjective workload; and the subjective impact of a high workload on sleep. We used a combination of Fitbit wearable actigraphy data and a questionnaire survey of young, healthy medical students and calculated correlations between the respective variables. We found that a shorter sleep duration on workdays is associated with a higher subjective workload and a higher subjective impact of a high workload on sleep, which, in turn, are associated with higher measures of anxiety and depression. Our study contributes to understanding the importance of sleep timing/duration and their regularity on weekdays for subjectively perceived psychosocial stress.

Investigating sleep, stress, and mood dynamics via temporal network analysis

OBJECTIVE/BACKGROUND

Prior research has emphasized the bidirectional relationships between sleep, stress, and affective states, such as depression. Given the inherent variability and fluctuations associated with sleep, assessing how sleep and affective variables function within a dynamic system may help further uncover possible causes and consequences of sleep disturbances, as well as find candidate targets for intervention. To this end, we examined dynamic relationships between self-reported stress, depressed mood, and clinically-relevant sleep parameters via temporal network analysis.

METHODS

Participants were 401 nurses (92% female, 78% White, Mage = 39.47 years) who completed 14 days of sleep diaries incorporating self-reported stress and depression, as well as total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset.

RESULTS AND CONCLUSIONS

Overall, total sleep time emerged as a highly influential variable in the context of "outstrength centrality," meaning total sleep time had numerous outward connections with other variables (e.g., stress and sleep efficiency). The high outstrength centrality of total sleep time suggests this variable is a source of activation within this dynamic system. Conversely, stress showed high "instrength centrality," suggesting this variable was highly impacted by other variables in the system, such as depressed mood and sleep efficiency. These findings emphasize the importance of assessing unfolding sleep processes within a naturalistic setting, and implicate the role of total sleep time in fueling depressed mood and stress. Discussion emphasizes implications of these results for understanding the connections between sleep, stress, and depression as well as clinical relevance of these findings.

Effects of neuromodulation on cognitive and emotional responses to psychosocial stressors in healthy humans

Physiological and psychological stressors can exert wide-ranging effects on the human brain and behavior. Research has improved understanding of how the sympatho-adreno-medullary (SAM) and hypothalamic-pituitary-adrenocortical (HPA) axes respond to stressors and the differential responses that occur depending on stressor type. Although the physiological function of SAM and HPA responses is to promote survival and safety, exaggerated psychobiological reactivity can occur in psychiatric disorders. Exaggerated reactivity may occur more for certain types of stressors, specifically, psychosocial stressors. Understanding stressor effects and how the body regulates these responses can provide insight into ways that psychobiological reactivity can be modulated. Non-invasive neuromodulation is one way that responding to stressors may be altered; research into these interventions may provide further insights into the brain circuits that modulate stress reactivity. This review focuses on the effects of acute psychosocial stressors and how neuromodulation might be effective in altering stress reactivity. Although considerable research into stress interventions focuses on treating pathology, it is imperative to first understand these mechanisms in non-clinical populations; therefore, this review will emphasize populations with no known pathology and consider how these results may translate to those with psychiatric pathologies.

The Updating and Individualizing of Sleep Hygiene Rules for Non-clinical Adult Populations

Sleep hygiene is essential for the prevention of somatic and mental disorders, including the prevention of sleep disorders. However, it does not typically address individual differences. The aim of this review is threefold: first, to outline the empirical evidence for particular components of sleep hygiene rules; second, to indicate the importance of individualized sleep hygiene application with regard to the varying degree of validity of sleep hygiene rules in the population; third, to highlight a new field of sleep hygiene, namely light hygiene. PubMed and Google Scholar were used to identify studies that were published between 2007 and 2022. A search was conducted for studies related to sleeping rules topics: sleep regularity, regular exercise, alcohol, caffeine, napping, relaxation and meditation, food intake and light exposure. In applying these sleep hygiene principles, it is essential to pay attention to individual variables such as age, genetic predisposition, health status, and substance (caffeine, alcohol) possible dependence.

A caveat to using wearable sensor data for COVID-19 detection: The role of behavioral change after receipt of test results

BACKGROUND

Recent studies indicate that wearable sensors can capture subtle within-person changes caused by SARS-CoV-2 infection and play a role in detecting COVID-19 infections. However, in addition to direct effects of infection, wearable sensor data may capture changes in behavior after the receipt of COVID test results. At present, it remains unclear to what extent the observed discriminative performance of the wearable sensor data is affected by behavioral changes upon receipt of the test results.

METHODS

We conducted a retrospective study of wearable sensor data in a sample of medical interns who had symptoms and received COVID-19 test results from March to December 2020, and calculated wearable sensor metrics incorporating changes in step, sleep, and resting heart rate for interns who tested positive (cases, n = 22) and negative (controls, n = 83) after symptom onset. All these interns had wearable sensor data available for > 50% of the days in pre- and post-symptom onset periods. We assessed discriminative accuracy of the metrics via area under the curve (AUC) and tested the impact of behavior changes after receiving test results by comparing AUCs of three models: all data, pre-test-result-only data, and post-test-result-only data.

RESULTS

Wearable sensor metrics differentiated between symptomatic COVID-19 positive and negative individuals with good accuracy (AUC = 0.75). However, the discriminative capacity of the model with pre-test-result-only data substantially decreased (AUC from 0.75 to 0.63; change = -0.12, p = 0.013). The model with post-test-result-only data did not produce similar reductions in discriminative capacity.

CONCLUSIONS

Changes in wearable sensor data, especially physical activity and sleep, are robust indicators of COVID-19 infection, though they may be reflective of a person's behavior change after receiving a positive test result as opposed to a physiological signature of the virus. Thus, wearable sensor data could facilitate the monitoring of COVID-19 prevalence, but not yet replace SARS-CoV-2 testing.

Reducing chronic disease may just be a walk in the park

Wearable technology allows the collection of real-world granular data at scales that would be impossible using traditional collection methods. Master et al. demonstrate the power of this technology to estimate the risk of disease based on daily step counts.¹.

Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns

BACKGROUND

Intensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings.

METHODS

Medical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3.

RESULTS

Passively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy.

LIMITATIONS

Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity.

CONCLUSIONS

Mobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.

Family and Academic Stress and Their Impact on Students' Depression Level and Academic Performance

Current research examines the impact of academic and familial stress on students' depression levels and the subsequent impact on their academic performance based on Lazarus' cognitive appraisal theory of stress. The non-probability convenience sampling technique has been used to collect data from undergraduate and postgraduate students using a modified questionnaire with a five-point Likert scale. This study used the SEM method to examine the link between stress, depression, and academic performance. It was confirmed that academic and family stress leads to depression among students, negatively affecting their academic performance and learning outcomes. This research provides valuable information to parents, educators, and other stakeholders concerned about their childrens' education and performance.

Prevalence and risk factors for depression among training physicians in China and the United States

During their first year of medical residency (internship), 35% of training physicians in the United States suffer at least one depression episode. We assessed whether there is a similar increase of depression among first year residents in China, and identified predictors of depression in the two systems. 1006 residents across three cohorts (2016-2017, 2017-2018 and 2018-2019) at Shanghai Jiao Tong University and Peking Union Medical College were assessed in parallel with three cohorts of 7028 residents at 100 + US institutions. The Patient Health Questionnaire-9 (PHQ-9) depressive symptoms were measured at baseline and quarterly. Demographic, personal and residency factors were assessed as potential predictors of PHQ-9 depression scores. Similar to training interns in the US, the proportion of participants in China who met depression criteria at least once during the first year of residency increased substantially, from 9.1 to 35.1%. History of depression and symptoms at baseline were common factors significantly associated with depression during residency. By contrast, neuroticism, early family environment, female gender and not being coupled were associated with depression risk only in the US, while young age was a predictor of depression only in China. Fear of workplace violence also was a predictor in China. Long duty hours and reduced sleep duration emerged as training predictors of depression in both countries. The magnitude of depression increase and work-related drivers of depression were similar between China and the US, suggesting a need for effective system reforms in both systems.

The moderating role of personality traits in the associations between seasonal fluctuations in chronotype and depressive symptoms

Recent research provided evidence that the well-established association between morningness-eveningness and depressive symptoms may be moderated by personality features - conscientiousness and neuroticism. In the present study, we attempted to broaden these findings using a longitudinal design. We hypothesized that these personality traits may influence the degree to which morningness-eveningness and depressiveness covary in time. Participants (n = 380) filled measures of morningness-eveningness, the Big Five personality, and depressive symptoms twice, in December and in June. Consistent with previous results, we observed a significant seasonal shift towards morningness and lower depressive symptoms from December to June. Seasonal shifts in chronotype and depressive symptoms were interrelated: a seasonal shift towards morningness was associated with a decrease in depressive symptoms. The strength of this association was exaggerated by neuroticism but attenuated by conscientiousness, suggesting that among neurotic individuals seasonal changes in depressive symptomatology are more dependent on seasonal shifts in morningness-eveningness but less dependent among conscientious ones. This result suggests that conscientiousness and emotional stability play a protective role against maladaptive consequences of eveningness.

Prior sleep-wake behaviors are associated with mental health outcomes during the COVID-19 pandemic among adult users of a wearable device in the United States

Objectives

To characterize objective sleep patterns among U.S. adults before and during the COVID-19 pandemic, and to assess for associations between adverse mental health symptoms and (1) sleep duration and (2) the consistency of sleep timing before and during the pandemic.

Design

Longitudinal objective sleep-wake data during January-June 2020 were linked with mental health and substance use assessments conducted during June 2020 for The COVID-19 Outbreak Public Evaluation (COPE) Initiative.

Setting

Adult users of WHOOP—a commercial, digital sleep wearable.

Participants

Adults residing in the U.S. and actively using WHOOP wearable devices, recruited by WHOOP, Inc.

Intervention

The COVID-19 pandemic and its mitigation.

Measurements

Anxiety or depression symptoms, burnout symptoms, and new or increased substance use to cope with stress or emotions.

Results

Of 4912 participants in the primary analytic sample (response rate, 14.9%), we observed acutely increased sleep duration (0.25 h or 15 m) and sleep consistency (3.51 points out of 100) and delayed sleep timing (onset, 18.7 m; offset, 36.6 m) during mid-March through mid-April 2020. Adjusting for demographic and lifestyle variables, participants with persistently insufficient sleep duration and inconsistent sleep timing had higher odds of adverse mental health symptoms and substance use in June 2020.

Conclusions

U.S. adult wearable users displayed increased sleep duration, more consistent sleep timing, and delayed sleep onset and offset times after the COVID-19 pandemic onset, with subsample heterogeneity. Associations between adverse mental health symptoms and pre- and mid-pandemic short sleep duration and inconsistent sleep timing suggest that these characteristics warrant further investigation as potential modifiable mental health and substance use risk factors.

Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach

The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.

Association between sleep disturbance and mental health of healthcare workers: A systematic review and meta-analysis

OBJECTIVES

Sleep disturbance and mental health are challenges for healthcare workers (HCWs). Especially during the COVID-19 pandemic, they experienced more severe sleep and mental health problems. However, the association between sleep disturbance and the mental health of HCWs is still controversial. This study aimed to systematically review the relationship by conducting a systematic review and meta-analysis.

METHOD

Two researchers retrieved the literature from Web of Science, PubMed, EMBASE, CINAHL, Psyclnfo, and Cochrane Library from the establishment of the databases until November 20, 2021. We used the New Castle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) to evaluate the risk of bias in prospective research and cross-sectional research, respectively. The major exposure was HCWs' sleep disturbance, and the major outcome was mental health. The correlation coefficients (r), regression coefficients (β) and odds ratios (OR) of the included studies were integrated.

RESULT

Fifty-nine studies were included for qualitative analysis, of which 30 studies could be combined and entered into quantitative analysis. There were 23 studies during the COVID-19 pandemic among the 59 included studies. The results of the meta-analysis showed that the correlation coefficient between sleep disturbance and mental health was 0.43 (95% CI: 0.39-0.47). HCWs with sleep disturbance had a 3.74 (95% CI: 2.76-5.07) times higher risk of mental health problems than those without sleep disturbance. The correlation coefficient during the COVID-19 epidemic was 0.45 (95% CI: 0.37-0.53), while it was 0.40 (95% CI: 0.36-0.44) during the non-epidemic period. Subgroup analysis compared the OR results in epidemic and non-epidemic periods of COVID-19, which were 4.48 (95% CI: 2.75-5.07) and 3.74 (95% CI: 2.74-7.32), respectively.

CONCLUSION

Sleep disturbance and mental health problems were positively correlated among HCWs. Particularly in the COVID-19 pandemic, more attention should be given to this issue.

Greater variability in daily sleep efficiency predicts depression and anxiety in young adults: Estimation of depression severity using the two-week sleep quality records of wearable devices

OBJECTIVES

Sleep disturbances are associated with both the onset and progression of depressive disorders. It is important to capture day-to-day variability in sleep patterns; irregular sleep is associated with depressive symptoms. We used sleep efficiency, measured with wearable devices, as an objective indicator of daily sleep variability.

MATERIALS AND METHODS

The total sample consists of 100 undergraduate and graduate students, 60% of whom were female. All were divided into three groups (with major depressive disorder, mild depressive symptoms, and controls). Self-report questionnaires were completed at the beginning of the experiment, and sleep efficiency data were collected daily for 2 weeks using wearable devices. We explored whether the mean value of sleep efficiency, and its variability, predicted the severity of depression using dynamic structural equation modeling.

RESULTS

More marked daily variability in sleep efficiency significantly predicted levels of depression and anxiety, as did the average person-level covariates (longer time in bed, poorer quality of life, lower extraversion, and higher neuroticism).

CONCLUSION

Large swings in day-to-day sleep efficiency and certain clinical characteristics might be associated with depression severity in young adults.

Individual Differences in the Affective Response to Pandemic-related Stressors in COVID-19 Healthcare Workers

Background

We investigated the evolving prevalence of mood and anxiety symptoms among healthcare workers from May, 2020 to January, 2021; risk factors for adverse outcomes; and characteristic modes of affective responses to pandemic-related stressors.

Methods

2,307 healthcare workers (78.9% female, modal age 25-34) participated in an online survey assessing depression (Patient Health Questionnaire [PHQ-9]) and anxiety symptoms (Generalized Anxiety Disorder scale [GAD-7]), demographic variables, and self-reported impact of pandemic-related stressors. 334 subjects were reassessed ∼6 months later.

Results

The prevalence of clinically significant depression and anxiety was 45.3% and 43.3%, respectively, and a majority (59.9%-62.9%) of those individuals had persistent significant symptoms at 6-month follow-up. Younger age, female gender, and specific occupations (support staff > nurses > physicians) were associated with increased depressive and anxiety symptoms. The most important risk factors were social isolation and fear of contracting COVID-19. The prevalence of clinically significant mood and anxiety symptoms increased by 39.8% from May, 2020 to January, 2021. PHQ-9 and GAD-7 scores were highly correlated and associated with nearly identical risk factors, suggesting that they are not capturing independent constructs in this sample. Principal components analysis identified seven orthogonal symptom domains with unique risk factors.

Conclusions

Clinically significant mood and anxiety symptoms are highly prevalent and persistent among healthcare workers, and are associated with numerous risk factors, the strongest of which are related to pandemic stressors and potentially modifiable. Interventions aimed at reducing social isolation and mitigating the impact of fear of infection warrant further study.

Access to Daylight at Home Improves Circadian Alignment, Sleep, and Mental Health in Healthy Adults: A Crossover Study

As the primary environmental cue for the body’s master biological clock, light–dark patterns are key for circadian alignment and are ultimately fundamental to multiple dimensions of health including sleep and mental health. Although daylight provides the proper qualities of light for promoting circadian alignment, our modern indoor lifestyles offer fewer opportunities for adequate daylight exposure. This field study explores how increasing circadian-effective light in residences affects circadian phase, sleep, vitality, and mental health. In this crossover study, 20 residents spent one week in their apartments with electrochromic glass windows and another week with functionally standard windows with blinds. Calibrated light sensors revealed higher daytime circadian-effective light levels with the electrochromic glass windows, and participants exhibited consistent melatonin onset, a 22-min earlier sleep onset, and higher sleep regularity. In the blinds condition, participants exhibited a 15-min delay in dim light melatonin onset, a delay in subjective vitality throughout the day, and an overall lower positive affect. This study demonstrates the impact of daytime lighting on the physiological, behavioral, and subjective measures of circadian health in a real-world environment and stresses the importance of designing buildings that optimize daylight for human health and wellbeing.