Perils of the nighttime: Impact of behavioral timing and preference on mental health in 73,888 community-dwelling adults.

Mental health is independently influenced by the inclination to sleep at specific times (chronotype) and the actual sleep timing (behavior). Chronotype and timing of actual sleep are, however, often misaligned. This study aims to determine how chronotype, sleep timing, and the alignment between the two impact mental health. In a community-dwelling cohort of middle- and older-aged adults (UK Biobank, n = 73,888), we examined the impact of chronotype (questionnaire-based), the timing of behavior (determined with 7-day accelerometry), and the alignment between the two on mental, behavioral, neurodevelopmental disorders (MBN), depression, and anxiety, as assessed through ICD-10 codes. As compared to morning types with early behavior (aligned), morning types with late behavior (misaligned) had an increased risk of having MBN, depression, and anxiety (p's<0.001). As compared to evening-types with late behavior (aligned), however, evening-types with early behavior (misaligned) had a decreased risk of depression (p < 0.01), with a trend for MBN (p = 0.04) and anxiety (p = 0.05). Longitudinal analyses, in which the likelihood of developing de novo mental health disorders was associated with chronotype, behavioral timing, and alignment between the two, confirmed cross-sectional findings. To age healthily, individuals should start sleeping before 1AM, despite chronobiological preferences.

A deep transfer learning approach for sleep stage classification and sleep apnea detection using wrist-worn consumer sleep technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.

Dyadic Investigation of Posttraumatic Stress Symptoms and Daily Sleep Health in Patients With Cancer and Their Caregivers.

OBJECTIVE: Cancer can be a traumatic experience affecting multidimensional aspects of sleep among patients and caregivers. This study examined the differential associations of cancer-related posttraumatic stress symptoms (PTSS) with various sleep markers in this population. METHODS: Patients newly diagnosed with colorectal cancer ( n = 138, mean age = 56.93 years, 31.88% female, 60.14% Hispanic, 6.53 months after diagnosis) and their sleep-partner caregivers ( n = 138, mean age = 55.32 years, 68.12% female, 57.97% Hispanic) completed questionnaires assessing the four PTSS clusters (intrusion, avoidance, alterations in arousal and reactivity, negative alterations in cognitions and mood). Participants also completed daily sleep diaries for 14 consecutive days, from which sleep onset latency (SOL), wake after sleep onset (WASO), and sleep duration were derived. RESULTS: Actor-partner interdependence model revealed that caregivers' greater alterations in arousal and reactivity were associated with their own longer SOL ( b = 15.59, p < .001) and their patients' longer sleep duration ( b = 0.61, p = .014), whereas patients' arousal and reactivity were associated with their caregivers' shorter SOL ( b = -8.47, p = .050). Patients' and caregivers' greater negative alterations in cognitions and mood were associated with patients' longer SOL ( b = 9.15, p = .014) and shorter sleep duration ( b = -0.41, p = .050), respectively. Caregivers' greater intrusion was related to their own shorter SOL ( b = -10.14, p = .004). CONCLUSIONS: The four PTSS clusters, particularly arousal and reactivity and negative cognitions and mood, have distinct associations with sleep markers individually and dyadically in patients and caregivers affected by cancer. Investigations of psychosocial and biobehavioral pathways underlying these relations are warranted. Tailored trauma treatments and sleep interventions may improve the well-being of this population.

Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study.

OBJECTIVE: Sleepiness and fatigue are common complaints among individuals with sleep disorders. The two concepts are often used interchangeably, causing difficulty with differential diagnosis and treatment decisions. The current study investigated sleep disorder patients to determine which factors best differentiated sleepiness from fatigue. METHODS: The study used a subset of participants from a multi-site study (n = 606), using a cross-sectional study design. We selected 60 variables associated with either sleepiness or fatigue, including demographic, mental health, and lifestyle factors, medical history, sleep questionnaires, rest-activity rhythms (actigraphy), polysomnographic (PSG) variables, and sleep diaries. Fatigue was measured with the Fatigue Severity Scale and sleepiness was measured with the Epworth Sleepiness Scale. A Random Forest machine learning approach was utilized for analysis. RESULTS: Participants' average age was 47.5 years (SD 14.0), 54.6% female, and the most common sleep disorder diagnosis was obstructive sleep apnea (67.4%). Sleepiness and fatigue were moderately correlated (r = 0.334). The model for fatigue (explained variance 49.5%) indicated depression was the strongest predictor (relative explained variance 42.7%), followed by insomnia severity (12.3%). The model for sleepiness (explained variance 17.9%), indicated insomnia symptoms was the strongest predictor (relative explained variance 17.6%). A post hoc receiver operating characteristic analysis indicated depression could be used to discriminate fatigue (AUC = 0.856) but not sleepiness (AUC = 0.643). CONCLUSIONS: The moderate correlation between fatigue and sleepiness supports previous literature that the two concepts are overlapping yet distinct. Importantly, depression played a more prominent role in characterizing fatigue than sleepiness, suggesting depression could be used to differentiate the two concepts.

Emotion regulation and heart rate variability may identify the optimal posttraumatic stress disorder treatment: analyses from a randomized controlled trial.

Introduction: High variability in response and retention rates for posttraumatic stress disorder (PTSD) treatment highlights the need to identify "personalized" or "precision" medicine factors that can inform optimal intervention selection before an individual commences treatment. In secondary analyses from a non-inferiority randomized controlled trial, behavioral and physiological emotion regulation were examined as non-specific predictors (that identify which individuals are more likely to respond to treatment, regardless of treatment type) and treatment moderators (that identify which treatment works best for whom) of PTSD outcome. Methods: There were 85 US Veterans with clinically significant PTSD symptoms randomized to 6 weeks of either cognitive processing therapy (CPT; n = 44) or a breathing-based yoga practice (Sudarshan kriya yoga; SKY; n = 41). Baseline self-reported emotion regulation (Difficulties in Emotion Regulation Scale) and heart rate variability (HRV) were assessed prior to treatment, and self-reported PTSD symptoms were assessed at baseline, end-of-treatment, 1-month follow-up, and 1-year follow-up. Results: Greater baseline deficit in self-reported emotional awareness (similar to alexithymia) predicted better overall PTSD improvement in both the short- and long-term, following either CPT or SKY. High self-reported levels of emotional response non-acceptance were associated with better PTSD treatment response with CPT than with SKY. However, all significant HRV indices were stronger moderators than all self-reported emotion regulation scales, both in the short- and long-term. Veterans with lower baseline HRV had better PTSD treatment response with SKY, whereas Veterans with higher or average-to-high baseline HRV had better PTSD treatment response with CPT. Conclusions: To our knowledge, this is the first study to examine both self-reported emotion regulation and HRV, within the same study, as both non-specific predictors and moderators of PTSD treatment outcome. Veterans with poorer autonomic regulation prior to treatment had better PTSD outcome with a yoga-based intervention, whereas those with better autonomic regulation did better with a trauma-focused psychological therapy. Findings show potential for the use of HRV in clinical practice to personalize PTSD treatment. Clinical trial registration: ClinicalTrials.gov identifier, NCT02366403.

Impaired 24-h activity patterns are associated with an increased risk of Alzheimer's disease, Parkinson's disease, and cognitive decline.

BACKGROUND: Sleep-wake regulating circuits are affected during prodromal stages in the pathological progression of both Alzheimer's disease (AD) and Parkinson's disease (PD), and this disturbance can be measured passively using wearable devices. Our objective was to determine whether accelerometer-based measures of 24-h activity are associated with subsequent development of AD, PD, and cognitive decline. METHODS: This study obtained UK Biobank data from 82,829 individuals with wrist-worn accelerometer data aged 40 to 79 years with a mean (± SD) follow-up of 6.8 (± 0.9) years. Outcomes were accelerometer-derived measures of 24-h activity (derived by cosinor, nonparametric, and functional principal component methods), incident AD and PD diagnosis (obtained through hospitalization or primary care records), and prospective longitudinal cognitive testing. RESULTS: One hundred eighty-seven individuals progressed to AD and 265 to PD. Interdaily stability (a measure of regularity, hazard ratio [HR] per SD increase 1.25, 95% confidence interval [CI] 1.05-1.48), diurnal amplitude (HR 0.79, CI 0.65-0.96), mesor (mean activity; HR 0.77, CI 0.59-0.998), and activity during most active 10 h (HR 0.75, CI 0.61-0.94), were associated with risk of AD. Diurnal amplitude (HR 0.28, CI 0.23-0.34), mesor (HR 0.13, CI 0.10-0.16), activity during least active 5 h (HR 0.24, CI 0.08-0.69), and activity during most active 10 h (HR 0.20, CI 0.16-0.25) were associated with risk of PD. Several measures were additionally predictive of longitudinal cognitive test performance. CONCLUSIONS: In this community-based longitudinal study, accelerometer-derived metrics were associated with elevated risk of AD, PD, and accelerated cognitive decline. These findings suggest 24-h rhythm integrity, as measured by affordable, non-invasive wearable devices, may serve as a scalable early marker of neurodegenerative disease.

Circadian protein expression patterns in healthy young adults.

OBJECTIVES: To explore how the blood plasma proteome fluctuates across the 24-hour day and identify a subset of proteins that show endogenous circadian rhythmicity. METHODS: Plasma samples from 17 healthy adults were collected hourly under controlled conditions designed to unmask endogenous circadian rhythmicity; in a subset of 8 participants, we also collected samples across a day on a typical sleep-wake schedule. A total of 6916 proteins were analyzed from each sample using the SomaScan aptamer-based multiplexed platform. We used differential rhythmicity analysis based on a cosinor model with mixed effects to identify a subset of proteins that showed circadian rhythmicity in their abundance. RESULTS: One thousand and sixty-three (15%) proteins exhibited significant daily rhythmicity. Of those, 431 (6.2%) proteins displayed consistent endogenous circadian rhythms on both a sleep-wake schedule and under controlled conditions: it included both known and novel proteins. When models were fitted with two harmonics, an additional 259 (3.7%) proteins exhibited significant endogenous circadian rhythmicity, indicating that some rhythmic proteins cannot be solely captured by a simple sinusoidal model. Overall, we found that the largest number of proteins had their peak levels in the late afternoon/evening, with another smaller group peaking in the early morning. CONCLUSIONS: This study reveals that hundreds of plasma proteins exhibit endogenous circadian rhythmicity in humans. Future analyses will likely reveal novel physiological pathways regulated by circadian clocks and pave the way for improved diagnosis and treatment for patients with circadian disorders and other pathologies. It will also advance efforts to include knowledge about time-of-day, thereby incorporating circadian medicine into personalized medicine.

The Negative Effects of Travel on Student Athletes Through Sleep and Circadian Disruption.

Collegiate athletes must satisfy the academic obligations common to all undergraduates, but they have the additional structural and social stressors of extensive practice time, competition schedules, and frequent travel away from their home campus. Clearly such stressors can have negative impacts on both their academic and athletic performances as well as on their health. These concerns are made more acute by recent proposals and decisions to reorganize major collegiate athletic conferences. These rearrangements will require more multi-day travel that interferes with the academic work and personal schedules of athletes. Of particular concern is additional east-west travel that results in circadian rhythm disruptions commonly called jet lag that contribute to the loss of amount as well as quality of sleep. Circadian misalignment and sleep deprivation and/or sleep disturbances have profound effects on physical and mental health and performance. We, as concerned scientists and physicians with relevant expertise, developed this white paper to raise awareness of these challenges to the wellbeing of our student-athletes and their co-travelers. We also offer practical steps to mitigate the negative consequences of collegiate travel schedules. We discuss the importance of bedtime protocols, the availability of early afternoon naps, and adherence to scheduled lighting exposure protocols before, during, and after travel, with support from wearables and apps. We call upon departments of athletics to engage with sleep and circadian experts to advise and help design tailored implementation of these mitigating practices that could contribute to the current and long-term health and wellbeing of their students and their staff members.

Fully Automated Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Using Actigraphy.

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is caused by motor disinhibition during REM sleep and is a strong early predictor of Parkinson's disease. However, screening questionnaires for iRBD lack specificity due to other sleep disorders that mimic the symptoms. Nocturnal wrist actigraphy has shown promise in detecting iRBD by measuring sleep-related motor activity, but it relies on sleep diary-defined sleep periods, which are not always available. Our aim was to precisely detect iRBD using actigraphy alone by combining two actigraphy-based markers of iRBD - abnormal nighttime activity and 24-hour rhythm disruption. In a sample of 42 iRBD patients and 42 controls (21 clinical controls with other sleep disorders and 21 community controls) from the Stanford Sleep Clinic, the nighttime actigraphy model was optimized using automated detection of sleep periods. Using a subset of 38 iRBD patients with daytime data and 110 age-, sex-, and body-mass-index-matched controls from the UK Biobank, the 24-hour rhythm actigraphy model was optimized. Both nighttime and 24-hour rhythm features were found to distinguish iRBD from controls. To improve the accuracy of iRBD detection, we fused the nighttime and 24-hour rhythm disruption classifiers using logistic regression, which achieved a sensitivity of 78.9%, a specificity of 96.4%, and an AUC of 0.954. This study preliminarily validates a fully automated method for detecting iRBD using actigraphy in a general population.Clinical relevance- Actigraphy-based iRBD detection has potential for large-scale screening of iRBD in the general population.

Moving time zones in a flash with light therapy during sleep.

In humans, exposure to continuous light is typically used to change the timing of the circadian clock. This study examines the efficiency of a sequence of light flashes ("flash therapy") applied during sleep to shift the clock. Healthy participants (n = 10) took part in two 36-h laboratory stays, receiving a placebo (goggles, no light) during one visit and the intervention (goggles, 2-ms flashes broad-spectrum light for 60 min, delivered every 15 s, starting 30 min after habitual sleep onset) during the other. Circadian phase shift was assessed with changes in salivary dim light melatonin onset (DLMO). Sleep, measured with polysomnography, was analyzed to assess changes in sleep architecture and spectral power. After 1 h of flashes, DLMO showed a substantial delay (1.13 ± 1.27 h) compared to placebo (12 ± 20 min). Two individuals exhibited very large shifts of 6.4 and 3.1 h. There were no substantive differences in sleep architecture, but some evidence for greater instability in sleep. 1 h of flash therapy during sleep evokes large changes in circadian timing, up to 6 h, and does so with only minimal, if any, impact on sleep. Flash therapy may offer a practical option to delay the circadian clock in shift workers and jet travelers.

Early time-restricted eating advances sleep in late sleepers: a pilot randomized controlled trial.

STUDY OBJECTIVES: This study evaluated the effects of early time-restricted eating (eTRE) on shifting the timing of sleep among late sleepers. Primary outcomes included actigraphy- and sleep diary-derived sleep onset, midsleep phase, and wake time with total sleep time as a secondary outcome. METHODS: Fifteen healthy adults with habitual late sleep timing were randomized to receive either eTRE or sleep and nutrition hygiene (control) via a single 30-minute synchronous video session. Participants completed an initial 1-week baseline phase followed by a 2-week intervention phase. Measures included continuous sleep monitoring and sleep and nutrition diaries. RESULTS: Linear mixed-effects modeling demonstrated that eTRE significantly advanced sleep timing compared with controls. Self-reported sleep onset (56.1 [95% confidence interval: 20.5, 91.7] minutes), midpoint (19.5 [7.2, 31.9] minutes), and offset (42.2 [2.9, 81.5] minutes) each moved earlier in eTRE as compared with controls. Similarly, objectively determined sleep onset (66.5 [29.6, 103.4] minutes), midpoint (21.9 [9.1, 34.7] minutes), and offset (39.3 [1.3, 77.3] minutes) each moved earlier in eTRE as compared with controls. Total sleep time showed a nonsignificant increase in the eTRE group as compared with controls. CONCLUSIONS: Late sleepers who were instructed in a single session about eTRE significantly advanced their sleep timing, especially sleep onset. eTRE shows potential as a clinical strategy for advancing sleep timing in late sleepers. CLINICAL TRIAL REGISTRATION: Registry: Chinese Clinical Trial Registry; Name: FAST Asleep: It's All About Timing; URL: https://www.chictr.org.cn/showproj.html?proj=122504; Identifier: ChiCTR2100043691. CITATION: Blum DJ, Hernandez B, Zeitzer JM. Early time-restricted eating advances sleep in late sleepers: a pilot randomized controlled trial. J Clin Sleep Med. 2023;19(12):2097-2106.

International e-Delphi Consensus Recommendations for the Assessment and Diagnosis of Circadian rest-Activity Rhythm Disorders (CARDs) in Patients with Cancer.

PURPOSE: Circadian rest-Activity Rhythm Disorders (CARDs) are common in patients with cancer, particularly in advanced disease. CARDs are associated with increased symptom burden, poorer quality of life, and shorter survival. Research and reporting practices lack standardization, and formal diagnostic criteria do not exist. This electronic Delphi (e-Delphi) study aimed to formulate international recommendations for the assessment and diagnosis of CARDs in patients with cancer. METHODS: An international e-Delphi was performed using an online platform (Welphi). Round 1 developed statements regarding circadian rest-activity rhythms, diagnostic criteria, and assessment techniques. Rounds 2 and 3 involved participants rating their level of agreement with the statements and providing comments until consensus (defined internally as 67%) and stability between rounds were achieved. Recommendations were then created and distributed to participants for comments before being finalized. RESULTS: Sixteen participants from nine different clinical specialties and seven different countries, with 5-35 years of relevant research experience, were recruited, and thirteen participants completed all three rounds. Of the 164 generated statements, 66% achieved consensus, and responses were stable between the final two rounds. CONCLUSIONS: The e-Delphi resulted in international recommendations for assessing and diagnosing CARDs in patients with cancer. These recommendations should ensure standardized research and reporting practices in future studies.

24 h Rest/Activity Rhythms in Older Adults with Memory Impairment: Associations with Cognitive Performance and Depressive Symptomatology.

Little is known about links of circadian rhythm alterations with neuropsychiatric symptoms and cognition in memory impaired older adults. Associations of actigraphic rest/activity rhythms (RAR) with depressive symptoms and cognition are examined using function-on-scalar regression (FOSR). Forty-four older adults with memory impairment (mean: 76.84 ± 8.15 years; 40.9% female) completed 6.37 ± 0.93 days of actigraphy, the Beck depression inventory-II (BDI-II), mini-mental state examination (MMSE) and consortium to establish a registry for Alzheimer's disease (CERAD) delayed word recall. FOSR models with BDI-II, MMSE, or CERAD as individual predictors adjusted for demographics (Models A1-A3) and all three predictors and demographics (Model B). In Model B, higher BDI-II scores are associated with greater activity from 12:00-11:50 a.m., 2:10-5:50 p.m., 8:40-9:40 p.m., 11:20-12:00 a.m., higher CERAD scores with greater activity from 9:20-10:00 p.m., and higher MMSE scores with greater activity from 5:50-10:50 a.m. and 12:40-5:00 p.m. Greater depressive symptomatology is associated with greater activity in midafternoon, evening, and overnight into midday; better delayed recall with greater late evening activity; and higher global cognitive performance with greater morning and afternoon activity (Model B). Time-of-day specific RAR alterations may affect mood and cognitive performance in this population.

Brief structured respiration practices enhance mood and reduce physiological arousal.

Controlled breathwork practices have emerged as potential tools for stress management and well-being. Here, we report a remote, randomized, controlled study (NCT05304000) of three different daily 5-min breathwork exercises compared with an equivalent period of mindfulness meditation over 1 month. The breathing conditions are (1) cyclic sighing, which emphasizes prolonged exhalations; (2) box breathing, which is equal duration of inhalations, breath retentions, and exhalations; and (3) cyclic hyperventilation with retention, with longer inhalations and shorter exhalations. The primary endpoints are improvement in mood and anxiety as well as reduced physiological arousal (respiratory rate, heart rate, and heart rate variability). Using a mixed-effects model, we show that breathwork, especially the exhale-focused cyclic sighing, produces greater improvement in mood (p < 0.05) and reduction in respiratory rate (p < 0.05) compared with mindfulness meditation. Daily 5-min cyclic sighing has promise as an effective stress management exercise.

A Flexible Deep Learning Architecture for Temporal Sleep Stage Classification Using Accelerometry and Photoplethysmography.

Wrist-worn consumer sleep technologies (CST) that contain accelerometers (ACC) and photoplethysmography (PPG) are increasingly common and hold great potential to function as out-of-clinic (OOC) sleep monitoring systems. However, very few validation studies exist because raw data from CSTs are rarely made accessible for external use. We present a deep neural network (DNN) with a strong temporal core, inspired by U-Net, that can process multivariate time series inputs with different dimensionality to predict sleep stages (wake, light-, deep-, and REM sleep) using ACC and PPG signals from nocturnal recordings. The DNN was trained and tested on 3 internal datasets, comprising raw data both from clinical and wrist-worn devices from 301 recordings (PSG-PPG: 266, Wrist-worn PPG: 35). External validation was performed on a hold-out test dataset containing 35 recordings comprising only raw data from a wrist-worn CST. An accuracy = 0.71 ± 0.09, 0.76 ± 0.07, 0.73 ± 0.06, and κ = 0.58 ± 0.13, 0.64 ± 0.09, 0.59 ± 0.09 was achieved on the internal test sets. Our experiments show that spectral preprocessing yields superior performance when compared to surrogate-, feature-, raw data-based preparation. Combining both modalities produce the overall best performance, although PPG proved to be the most impactful and was the only modality capable of detecting REM sleep well. Including ACC improved model precision to wake and sleep metric estimation. Increasing input segment size improved performance consistently; the best performance was achieved using 1024 epochs (∼8.5 hrs.). An accuracy = 0.69 ± 0.13 and κ = 0.58 ± 0.18 was achieved on the hold-out test dataset, proving the generalizability and robustness of our approach to raw data collected with a wrist-worn CST.

Associations of 24-Hour Light Exposure and Activity Patterns and Risk of Cognitive Impairment and Decline in Older Men: The MrOS Sleep Study.

BACKGROUND: Older men with the worse alignment of activity and light may have lower levels of cognition and increased rates of cognitive decline. METHODS: This cohort consisted of 1 036 older men (81.1 ± 4.6 years) from the MrOS Sleep Study (2009-2012). Light and activity levels were gathered by wrist actigraphy. Phasor analysis was used to quantify the alignment of light-dark and rest-activity patterns (magnitude) and their temporal relationship (angle). Global cognitive function (Modified Mini-Mental State examination [3MS]) and executive function (Trails B test) were measured, then repeated 4.2 ± 0.8 years later. Linear regression models examined the associations of phasor magnitude and angle with cognition and cognitive decline. Models were adjusted for age, clinic, race, education, and season. RESULTS: Smaller phasor magnitude (worse aligned light and activity patterns) was associated with lower initial level and increased decline in executive function. Compared to those with higher phasor magnitude, those with lower magnitude took an average of 11.1 seconds longer to complete the Trails B test (quartile 1 vs quartile 4, p = .02). After follow-up, Trails B completion time increased an average of 5.5 seconds per standard deviation decrease in phasor magnitude (95% confidence interval [CI] 0.7-10.4, p = .03). There were no associations with phasor angle, and none with magnitude and global cognition (3MS). CONCLUSION: Among older men, worse alignment of light and activity patterns was associated with worse initial performance and increased decline in executive function, but not related to global cognition. Interventions that improve the alignment of light and activity may slow cognitive decline in older adults.

Optimizing Light Flash Sequence Duration to Shift Human Circadian Phase.

Unlike light input for forming images, non-image-forming retinal pathways are optimized to convey information about the total light environment, integrating this information over time and space. In a variety of species, discontinuous light sequences (flashes) can be effective stimuli, notably impacting circadian entrainment. In this study, we examined the extent to which this temporal integration can occur. A group of healthy, young (n = 20) individuals took part in a series of 16-day protocols in which we examined the impact of different lengths of light flash sequences on circadian timing. We find a significant phase change of -0.70 h in response to flashes that did not differ by duration; a 15-min sequence could engender as much change in circadian timing as 3.5-h sequences. Acute suppression of melatonin was also observed during short (15-min) exposures, but not in exposures over one hour in length. Our data are consistent with the theory that responses to light flashes are mediated by the extrinsic, rod/cone pathway, and saturate the response of this pathway within 15 min. Further excitation leads to no greater change in circadian timing and an inability to acutely suppress melatonin, indicating that this pathway may be in a refractory state following this brief light stimulation.

The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns.

The purpose of this study is to characterize the impact of the timing and duration of missing actigraphy data on interdaily stability (IS) and intradaily variability (IV) calculation. The performance of three missing data imputation methods (linear interpolation, mean time of day (ToD), and median ToD imputation) for estimating IV and IS was also tested. Week-long actigraphy records with no non-wear or missing timeseries data were masked with zeros or 'Not a Number' (NaN) across a range of timings and durations for single and multiple missing data bouts. IV and IS were calculated for true, masked, and imputed (i.e., linear interpolation, mean ToD and, median ToD imputation) timeseries data and used to generate Bland-Alman plots for each condition. Heatmaps were used to analyze the impact of timings and durations of and between bouts. Simulated missing data produced deviations in IV and IS for longer durations, midday crossings, and during similar timing on consecutive days. Median ToD imputation produced the least deviation among the imputation methods. Median ToD imputation is recommended to recapitulate IV and IS under missing data conditions for less than 24 h.