Preliminary evidence of psychological improvements and increased maternal-fetal attachment associated with a mindfulness sleep programme: secondary analysis of uncontrolled data in 11 pregnant women with insomnia disorder.

Treating insomnia during pregnancy improves sleep and depressed mood. However, given well-established links between poor sleep and a broad spectrum of adverse maternal outcomes, the benefits of insomnia care may reach beyond sleep and depression. The present study evaluated the preliminary efficacy of 'Perinatal Understanding of Mindful Awareness for Sleep' (PUMAS)-a mindfulness sleep programme tailored to pregnancy that combines behavioural sleep strategies and meditation-for enhancing everyday mindfulness and maternal-fetal attachment, as well as for alleviating anxiety, repetitive thinking, and sleep-related daytime impairment. We conducted a secondary analysis of a single-arm proof-of-concept trial of 11 pregnant women with fifth edition of the Diagnostic and Statistical Manual of Mental Disorders diagnosed insomnia disorder who completed PUMAS (six sessions), which was delivered in an individual format via telemedicine video. Pre- and post-treatment outcomes included the Cognitive and Affective Mindfulness Scale-Revised (CAMS-R), Maternal-Fetal Attachment Scale (MFAS), Generalised Anxiety Disorder seven-item survey (GAD-7), Perseverative Thinking Questionnaire (PTQ), Daytime Insomnia Symptoms Response Scale (DISRS), and the Patient-Reported Outcomes Measurement Information System Sleep-Related Impairment Scale (PROMIS-SRI). Symptom changes were evaluated with paired-samples t tests. Results showed PUMAS patients reported large increases in CAMS-R (Cohen's dz = 1.81) and medium-large increases in MFAS scores (Cohen's dz = 0.73). Moreover, PUMAS patients reported large reductions in scores on the GAD-7 (Cohen's dz = 1.09), PTQ (Cohen's dz = 1.26), DISRS (Cohen's dz = 1.38), and PROMIS-SRI (Cohen's dz = 1.53). Preliminary evidence suggests that a mindfulness-based perinatal sleep programme may benefit several domains of maternal wellbeing beyond sleep and depression. PUMAS substantially enhanced patient ratings of everyday mindfulness and maternal-fetal attachment, while reporting alleviations in anxiety, perseverative thinking, insomnia-focused rumination, and sleep-related daytime impairment.

Reducing cognitive arousal and sleep effort alleviates insomnia and depression in pregnant women with DSM-5 insomnia disorder treated with a mindfulness sleep program.

Objectives: Combining mindfulness with behavioral sleep strategies has been found to alleviate symptoms of insomnia and depression during pregnancy, but mechanisms for this treatment approach remain unclear. The present study examined nocturnal cognitive arousal and sleep effort as potential treatment mechanisms for alleviating insomnia and depression via a mindfulness sleep program for pregnant women. Methods: Secondary analysis from a proof-of-concept trial of 12 pregnant women with DSM-5 insomnia disorder who were treated with Perinatal Understanding of Mindful Awareness for Sleep (PUMAS), which places behavioral sleep strategies within a mindfulness framework. Data were collected across eight weekly assessments: pretreatment, six sessions, and posttreatment. Measures included the insomnia severity index (ISI), Edinburgh postnatal depression scale (EPDS), pre-sleep arousal scale's cognitive factor (PSASC), and the Glasgow sleep effort scale (GSES). We used linear mixed modeling to test cognitive arousal and sleep effort as concurrent and prospective predictors of insomnia and depression. Results: Most patients reported high cognitive arousal before PUMAS (75.0%), which decreased to 8.3% after treatment. All insomnia remitters reported low cognitive arousal after treatment, whereas half of nonremitters continued reporting high cognitive arousal. Both nocturnal cognitive arousal and sleep effort were associated with same-week changes in insomnia throughout treatment, and sleep effort yielded a prospective effect on insomnia. Lower levels of nocturnal cognitive arousal and sleep effort prospectively predicted reductions in depression. Conclusions: The present study offers preliminary evidence that reducing sleep effort and nocturnal cognitive arousal may serve as key mechanisms for alleviating insomnia and depression via mindfulness-based insomnia therapy. ClinicalTrials.gov ID: NCT04443959.

Perinatal Understanding of Mindful Awareness for Sleep (PUMAS): A single-arm proof-of-concept clinical trial of a mindfulness-based intervention for DSM-5 insomnia disorder during pregnancy.

OBJECTIVES: Cognitive-behavioral therapy is effective for prenatal insomnia, but unresolved cognitive arousal limits patient outcomes. Therapies aimed at reducing cognitive arousal may benefit pregnant women with insomnia. This proof-of-concept trial evaluated Perinatal Understanding of Mindful Awareness for Sleep (PUMAS, which combines mindfulness with behavioral sleep strategies) on insomnia, depression, and cognitive arousal. METHODS: A single-arm trial of 12 pregnant women with DSM-5 insomnia disorder (n = 5/12 with comorbid depression) who received six sessions of PUMAS delivered individually via telemedicine. Pretreatment and posttreatment outcomes included the insomnia severity index (ISI), Edinburgh postnatal depression scale (EPDS), pre-sleep arousal scale's cognitive factor (PSASC; nocturnal cognitive arousal), perinatal-focused rumination (appended to PSASC), and Glasgow sleep effort scale. RESULTS: Eleven of 12 patients completed all sessions. Intent-to-treat analyses revealed a 10.83-point reduction in ISI (Cohen's dz = 3.05), resulting in 83.3% insomnia remission. PUMAS produced large reductions in EPDS (Cohen's dz = 2.76 in depressed group), resulting in all five baseline depressed patients remitting from depression. PUMAS produced large reductions in nocturnal cognitive arousal, perinatal-focused rumination, and sleep effort (all Cohen's dzs>2.00). Patients were highly satisfied with PUMAS and identified the telemedicine format and meditation app as positive features of its delivery. Patients rated sleep restriction and guided meditations as the most helpful treatment components. CONCLUSION: Prenatal insomnia patients were highly engaged in PUMAS, which produced large acute reductions in insomnia, depression, and cognitive arousal. These findings support the concept and feasibility of PUMAS for pregnant women with insomnia who present with or without comorbid depression. GOV ID: NCT04443959.

Impact of Light Schedules and Model Parameters on the Circadian Outcomes of Individuals.

Key differences exist between individuals in terms of certain circadian-related parameters, such as intrinsic period and sensitivity to light. These variations can differentially impact circadian timing, leading to challenges in accurately implementing time-sensitive interventions. In this work, we parse out these effects by investigating the impact of parameters from a macroscopic model of human circadian rhythms on phase and amplitude outputs. Using in silico light data designed to mimic commonly studied schedules, we assess the impact of parameter variations on model outputs to gain insight into the different effects of these schedules. We show that parameter sensitivity is heavily modulated by the lighting routine that a person follows, with darkness and shift work schedules being the most sensitive. We develop a framework to measure overall sensitivity levels of the given light schedule and furthermore decompose the overall sensitivity into individual parameter contributions. Finally, we measure the ability of the model to extract parameters given light schedules with noise and show that key parameters like the circadian period can typically be recovered given known light history. This can inform future work on determining the key parameters to consider when personalizing a model and the lighting protocols to use when assessing interindividual variability.

Validation of the Entrainment Signal Regularity Index and associations with children's changes in BMI.

OBJECTIVE: This study examined the validity of a novel metric of circadian health, the Entrainment Signal Regularity Index (ESRI), and its relationship to changes in BMI during the school year and summer. METHODS: In a longitudinal observational data set, this study examined the relationship between ESRI score and children's (n = 119, 5- to 8-year-olds) sleep and physical activity levels during the school year and summer, differences in ESRI score during the school year and summer, and the association of ESRI score during the school year and summer with changes in BMI across those time periods. RESULTS: The ESRI score was higher during the school year (0.70 ± 0.10) compared with summer (0.63 ± 0.11); t(111) = 5.484, p < 0.001. Whereas the ESRI score at the beginning of the school year did not significantly predict BMI change during the school year (ß = 0.05 ± 0.09 SE, p = 0.57), having a higher ESRI score during summer predicted smaller increases in BMI during summer (ß = -0.22 ± 0.10 SE, p = 0.03). CONCLUSIONS: Overall, children demonstrated higher entrainment regularity during the school year compared with the summer. During summer, having a higher entrainment signal was associated with smaller changes in summertime BMI. This effect was independent of the effects of children's sleep midpoint, sleep regularity, and physical activity on children's BMI.

Racial disparities in treatment engagement and outcomes in digital cognitive behavioral therapy for insomnia among pregnant women.

OBJECTIVES: In the United States, Black women are disproportionately afflicted with prenatal insomnia. Although cognitive-behavioral therapy for insomnia (CBTI) may represent a strategy to reduce disparities in insomnia, racial minorities attend fewer healthcare appointments and have poorer outcomes from prenatal care and mental health treatment relative to white patients. The present study examined differences in treatment engagement and patient-reported outcomes in non-Hispanic Black and white pregnant women receiving digital CBTI. METHODS: Secondary analysis of 39 pregnant women with clinical insomnia who received digital CBTI. Treatment engagement was operationalized as the number of sessions completed (≥4 considered an adequate dose). Treatment outcomes were assessed using the Insomnia Severity Index (ISI; insomnia) and Pittsburgh Sleep Quality Index (PSQI; global sleep disturbance). RESULTS: Black women were 4 times more likely than white women to discontinue CBTI before receiving an adequate dose (8.3% vs. 33.3%). Regarding treatment outcomes, white women reported a mean reduction of 5.75 points on the ISI and a reduction of 3.33 points on the PSQI (Cohen's dz = 1.10-1.19). By comparison, Black women reported reductions of 2.13 points on the ISI and 1.53 points on the PSQI, which were statistically non-significant. Differences in treatment engagement did not account for the disparities in patient-reported outcomes. CONCLUSIONS: During pregnancy, Black women completed fewer CBTI sessions and experienced poorer treatment outcomes in response to digital CBTI relative to white women. Enhancements to insomnia therapy and its digital delivery may improve adherence and outcomes in Black pregnant women.

Associations between time zone changes, travel distance and performance: A retrospective analysis of 2013-2020 National Hockey League Data.

OBJECTIVES: Many studies have investigated the role that travel plays in athletic performance. However, these studies lacked a holistic representation of travel. For instance, they do not consider travel distance and uniquely focuses on travel direction. DESIGN: An open source (www.evolving-hockey.com) provided NHL (2013-2020) game data. In total, this resulted in 17,088 regular season games. METHODS: Linear and quadratic versions of time zone change (TZΔ) and adjusted jet lag (AJL) were formulated. TZΔ captured circadian delay/advance based on travel for a game, with each TZ going eastward and westward reflected by -1 and +1, respectively. AJL advances TZΔ by allowing TZ acclimation, with each day resulting in a 1-unit change towards circadian neutral. AJL is a season-long rolling summation, which was computed using two different travel approaches: Approach A (AJL_A) assumes travel the day before each game, whereas Approach B (AJL_B) was designed to prioritize being home. A standardized flight tracker determined travel distance for each game. Team ability differences, characterized as difference in total season points, served as an analytic covariate. Outcome variables included goal differential, difference between actual and expected Fenwick save percentage (dFSv%), and goals saved above average (GSAA). RESULTS: GameDistance (ß = -0.14, p = 0.0007), AJL_B2 (ß = -0.15, p = 0.0006), and their interaction (p = 0.0004) associated with GoalDifferential. GameDistance (ß = -0.18, p = 0.02) and AJL_B2 (ß = 0.12, p = 0.03) associated with dFSv%, whereas only AJL_B2 (ß = 0.03, p = 0.05) associated with GSAA. CONCLUSIONS: Results suggest that circadian change, in both direction, and greater traveled distance can negatively impact NHL athletes.

Associations of circadian change, travel distance, and their interaction with basketball performance: a retrospective analysis of 2014-2018 National Basketball Association data.

This investigation aimed to clarify the influence of circadian change and travel distance on National Basketball Association (NBA) team performance using a dataset from the 2014-2018 seasons. Data from 9,840 games were acquired from an open-access source. Game point differential and team free-throw percentage served as outcome variables. Time zone change (TZΔ) captured raw circadian delay/advance based on travel for a game and adjusted TZΔ (AdjTZΔ) evolved TZΔ by allowing acclimation to a novel TZ. We also further categorized AdjTZΔ into AdjTZΔ_A, which assumed travel the day before each game and AdjTZΔ_B, which assumed teams spent as many days in their home city as possible. Travel distance for each game was calculated. Linear mixed-effects modeling estimated associations, with games nested within team and year. Adjusted associations accounted for differences in team ability, whether the game was home or away, and whether the game occurred on the second half of a back-to-back game sequence. Greater circadian misalignment, regardless of delay or advance, and increasing travel distance negatively influenced NBA game performance. Yet, results suggest that performance outcomes may be more influenced by travel distance than circadian misalignment. Moreover, circadian misalignment and travel distance interacted to significantly influence game point differential. Furthermore, differences in results across analyses were observed between AdjTZΔ_A and AdjTZΔ_B, which suggests that subtle differences in constructed travel schedules can have notable impact on NBA performance outcomes. Lastly, playing on the second half of a back-to-back sequence emerged as a robust predictor of performance disadvantage, which corroborates the existing literature and provides further support for NBA schedule changes purposed to enhance competitive equity by reducing the number of back-to-back games across a season. These findings can help guide NBA teams on key strategies for reducing travel-related disadvantages and inform schedule makers on critical factors to prioritize across future schedules to attenuate competitive inequity from travel. Furthermore, they can help direct teams towards scenarios that are best to target for load management purposes due to the cumulative disadvantage arising from travel-related factors, opponent quality, game location, and game sequence.

Estimating circadian phase in elementary school children: leveraging advances in physiologically informed models of circadian entrainment and wearable devices.

STUDY OBJECTIVES: Examine the ability of a physiologically based mathematical model of human circadian rhythms to predict circadian phase, as measured by salivary dim light melatonin onset (DLMO), in children compared to other proxy measurements of circadian phase (bedtime, sleep midpoint, and wake time). METHODS: As part of an ongoing clinical trial, a sample of 29 elementary school children (mean age: 7.4 ± .97 years) completed 7 days of wrist actigraphy before a lab visit to assess DLMO. Hourly salivary melatonin samples were collected under dim light conditions (<5 lx). Data from actigraphy were used to generate predictions of circadian phase using both a physiologically based circadian limit cycle oscillator mathematical model (Hannay model), and published regression equations that utilize average sleep onset, midpoint, and offset to predict DLMO. Agreement of proxy predictions with measured DLMO were assessed and compared. RESULTS: DLMO predictions using the Hannay model outperformed DLMO predictions based on children's sleep/wake parameters with a Lin's Concordance Correlation Coefficient (LinCCC) of 0.79 compared to 0.41-0.59 for sleep/wake parameters. The mean absolute error was 31 min for the Hannay model compared to 35-38 min for the sleep/wake variables. CONCLUSION: Our findings suggest that sleep/wake behaviors were weak proxies of DLMO phase in children, but mathematical models using data collected from wearable data can be used to improve the accuracy of those predictions. Additional research is needed to better adapt these adult models for use in children. CLINICAL TRIAL: The i Heart Rhythm Project: Healthy Sleep and Behavioral Rhythms for Obesity Prevention https://clinicaltrials.gov/ct2/show/NCT04445740.

Distinct Circadian Assessments From Wearable Data Reveal Social Distancing Promoted Internal Desynchrony Between Circadian Markers.

Mobile measures of human circadian rhythms (CR) are needed in the age of chronotherapy. Two wearable measures of CR have recently been validated: one that uses heart rate to extract circadian rhythms that originate in the sinoatrial node of the heart, and another that uses activity to predict the laboratory gold standard and central circadian pacemaker marker, dim light melatonin onset (DLMO). We first find that the heart rate markers of normal real-world individuals align with laboratory DLMO measurements when we account for heart rate phase error. Next, we expand upon previous work that has examined sleep patterns or chronotypes during the COVID-19 lockdown by studying the effects of social distancing on circadian rhythms. In particular, using data collected from the Social Rhythms app, a mobile application where individuals upload their wearable data and receive reports on their circadian rhythms, we compared the two circadian phase estimates before and after social distancing. Interestingly, we found that the lockdown had different effects on the two ambulatory measurements. Before the lockdown, the two measures aligned, as predicted by laboratory data. After the lockdown, when circadian timekeeping signals were blunted, these measures diverged in 70% of subjects (with circadian rhythms in heart rate, or CRHR, becoming delayed). Thus, while either approach can measure circadian rhythms, both are needed to understand internal desynchrony. We also argue that interventions may be needed in future lockdowns to better align separate circadian rhythms in the body.

A method for characterizing daily physiology from widely used wearables.

Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the "Social Rhythms" iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method.

M1-Type, but Not M4-Type, Melanopsin Ganglion Cells Are Physiologically Tuned to the Central Circadian Clock.

Proper circadian photoentrainment is crucial for the survival of many organisms. In mammals, intrinsically photosensitive retinal ganglion cells (ipRGCs) can use the photopigment melanopsin to sense light independently from rod and cone photoreceptors and send this information to many brain nuclei such as the suprachiasmatic nucleus (SCN), the site of the central circadian pacemaker. Here, we measure ionic currents and develop mathematical models of the electrical activity of two types of ipRGCs: M1, which projects to the SCN, and M4, which does not. We illustrate how their ionic properties differ, mainly how ionic currents generate lower spike rates and depolarization block in M1 ipRGCs. Both M1 and M4 cells have large geometries and project to higher visual centers of the brain via the optic nerve. Using a partial differential equation model, we show how axons of M1 and M4 cells faithfully convey information from the soma to the synapse even when the signal at the soma is attenuated due to depolarization block. Finally, we consider an ionic model of circadian photoentrainment from ipRGCs synapsing on SCN neurons and show how the properties of M1 ipRGCs are tuned to create accurate transmission of visual signals from the retina to the central pacemaker, whereas M4 ipRGCs would not evoke nearly as efficient a postsynaptic response. This work shows how ipRGCs and SCN neurons' electrical activities are tuned to allow for accurate circadian photoentrainment.

Predicting circadian phase across populations: a comparison of mathematical models and wearable devices.

From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase noninvasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 h, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.

Predicting circadian misalignment with wearable technology: validation of wrist-worn actigraphy and photometry in night shift workers.

STUDY OBJECTIVES: A critical barrier to successful treatment of circadian misalignment in shift workers is determining circadian phase in a clinical or field setting. Light and movement data collected passively from wrist actigraphy can generate predictions of circadian phase via mathematical models; however, these models have largely been tested in non-shift working adults. This study tested the feasibility and accuracy of actigraphy in predicting dim light melatonin onset (DLMO) in fixed night shift workers. METHODS: A sample of 45 night shift workers wore wrist actigraphs before completing DLMO in the laboratory (17.0 days ± 10.3 SD). DLMO was assessed via 24 hourly saliva samples in dim light (<10 lux). Data from actigraphy were provided as input to a mathematical model to generate predictions of circadian phase. Agreement was assessed and compared to average sleep timing on non-workdays as a proxy of DLMO. Model code and an open-source prototype assessment tool are available (www.predictDLMO.com). RESULTS: Model predictions of DLMO showed good concordance with in-lab DLMO, with Lin's concordance coefficient of 0.70, which was twice as high as agreement using average sleep timing as a proxy of DLMO. The absolute mean error of the predictions was 2.88 h, with 76% and 91% of the predictions falling with 2 and 4 h, respectively. CONCLUSION: This study is the first to demonstrate the use of wrist actigraphy-based estimates of circadian phase as a clinically useful and valid alternative to in-lab measurement of DLMO in fixed night shift workers. Future research should explore how additional predictors may impact accuracy.

Optimal adjustment of the human circadian clock in the real world.

Which suggestions for behavioral modifications, based on mathematical models, are most likely to be followed in the real world? We address this question in the context of human circadian rhythms. Jet lag is a consequence of the misalignment of the body's internal circadian (~24-hour) clock during an adjustment to a new schedule. Light is the clock's primary synchronizer. Previous research has used mathematical models to compute light schedules that shift the circadian clock to a new time zone as quickly as possible. How users adjust their behavior when provided with these optimal schedules remains an open question. Here, we report data collected by wearables from more than 100 travelers as they cross time zones using a smartphone app, Entrain. We find that people rarely follow the optimal schedules generated through mathematical modeling entirely, but travelers who better followed the optimal schedules reported more positive moods after their trips. Using the data collected, we improve the optimal schedule predictions to accommodate real-world constraints. We also develop a scheduling algorithm that allows for the computation of approximately optimal schedules "on-the-fly" in response to disruptions. User burnout may not be critically important as long as the first parts of a schedule are followed. These results represent a crucial improvement in making the theoretical results of past work viable for practical use and show how theoretical predictions based on known human physiology can be efficiently used in real-world settings.

Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device.

Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and "clock proxy") to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns.

Daily rhythms in human physiology and behavior are driven by the interplay of circadian rhythms, environmental cycles, and social schedules. Much research has focused on the mechanism and function of circadian rhythms in constant conditions or in idealized light-dark environments. There have been comparatively few studies into how social pressures, such as work and school schedules, affect human activity rhythms day to day and season to season. To address this issue, we analyzed activity on Twitter in >1,500 US counties throughout the 2012-2013 calendar years in 15-min intervals using geographically tagged tweets representing ≈0.1% of the total population each day. We find that sustained periods of low Twitter activity are correlated with sufficient sleep as measured by conventional surveys. We show that this nighttime lull in Twitter activity is shifted to later times on weekends relative to weekdays, a phenomenon we term "Twitter social jet lag." The magnitude of this social jet lag varies seasonally and geographically-with the West Coast experiencing less Twitter social jet lag compared to the Central and Eastern US-and is correlated with average commuting schedules and disease risk factors such as obesity. Most counties experience the largest amount of Twitter social jet lag in February and the lowest in June or July. We present evidence that these shifts in weekday activity coincide with relaxed social pressures due to local K-12 school holidays and that the direct seasonal effect of altered day length is comparatively weaker.

A global quantification of "normal" sleep schedules using smartphone data.

The influence of the circadian clock on sleep scheduling has been studied extensively in the laboratory; however, the effects of society on sleep remain largely unquantified. We show how a smartphone app that we have developed, ENTRAIN, accurately collects data on sleep habits around the world. Through mathematical modeling and statistics, we find that social pressures weaken and/or conceal biological drives in the evening, leading individuals to delay their bedtime and shorten their sleep. A country's average bedtime, but not average wake time, predicts sleep duration. We further show that mathematical models based on controlled laboratory experiments predict qualitative trends in sunrise, sunset, and light level; however, these effects are attenuated in the real world around bedtime. Additionally, we find that women schedule more sleep than men and that users reporting that they are typically exposed to outdoor light go to sleep earlier and sleep more than those reporting indoor light. Finally, we find that age is the primary determinant of sleep timing, and that age plays an important role in the variability of population-level sleep habits. This work better defines and personalizes "normal" sleep, produces hypotheses for future testing in the laboratory, and suggests important ways to counteract the global sleep crisis.

Characterizing and modeling the intrinsic light response of rat ganglion-cell photoreceptors.

Intrinsically photosensitive retinal ganglion cells (ipRGCs) mediate both image-forming vision and non-image-forming visual responses such as pupillary constriction and circadian photoentrainment. Five types of ipRGCs, named M1-M5, have been discovered in rodents. To further investigate their photoresponse properties, we made multielectrode array spike recordings from rat ipRGCs, classified them into M1, M2/M4, and M3/M5 clusters, and measured their intrinsic, melanopsin-based responses to single and flickering light pulses. Results showed that ipRGC spiking can track flickers up to ∼0.2 Hz in frequency and that flicker intervals between 5 and 14 s evoke the most spikes. We also learned that melanopsin's integration time is intensity and cluster dependent. Using these data, we constructed a mathematical model for each cluster's intrinsic photoresponse. We found that the data for the M1 cluster are best fit by a model that assumes a large photoresponse, causing the cell to enter depolarization block. Our models also led us to hypothesize that the M2/M4 and M3/M5 clusters experience comparable photoexcitation but that the M3/M5 cascade decays significantly faster than the M2/M4 cascade, resulting in different response waveforms between these clusters. These mathematical models will help predict how each ipRGC cluster might respond to stimuli of any waveform and could inform the invention of lighting technologies that promote health through melanopsin stimulation.