Retino-hypothalamic regulation of light-induced murine sleep

The temporal organization of sleep is regulated by an interaction between the circadian clock and homeostatic processes. Light indirectly modulates sleep through its ability to phase shift and entrain the circadian clock. Light can also exert a direct, circadian-independent effect on sleep. For example, acute exposure to light promotes sleep in nocturnal animals and wake in diurnal animals. The mechanisms whereby light directly influences sleep and arousal are not well understood. In this review, we discuss the direct effect of light on sleep at the level of the retina and hypothalamus in rodents. We review murine data from recent publications showing the roles of rod-, cone- and melanopsin-based photoreception on the initiation and maintenance of light-induced sleep. We also present hypotheses about hypothalamic mechanisms that have been advanced to explain the acute control of sleep by light. Specifically, we review recent studies assessing the roles of the ventrolateral preoptic area (VLPO) and the suprachiasmatic nucleus (SCN). We also discuss how light might differentially promote sleep and arousal in nocturnal and diurnal animals respectively. Lastly, we suggest new avenues for research on this topic which is still in its early stages

Response of the Human Circadian System to Millisecond Flashes of Light

Ocular light sensitivity is the primary mechanism by which the central circadian clock, located in the suprachiasmatic nucleus (SCN), remains synchronized with the external geophysical day. This process is dependent on both the intensity and timing of the light exposure. Little is known about the impact of the duration of light exposure on the synchronization process in humans. In vitro and behavioral data, however, indicate the circadian clock in rodents can respond to sequences of millisecond light flashes. In a cross-over design, we tested the capacity of humans (n = 7) to respond to a sequence of 60 2-msec pulses of moderately bright light (473 lux) given over an hour during the night. Compared to a control dark exposure, after which there was a 3.5±7.3 min circadian phase delay, the millisecond light flashes delayed the circadian clock by 45±13 min (p<0.01). These light flashes also concomitantly increased subjective and objective alertness while suppressing delta and sigma activity (p<0.05) in the electroencephalogram (EEG). Our data indicate that phase shifting of the human circadian clock and immediate alerting effects can be observed in response to brief flashes of light. These data are consistent with the hypothesis that the circadian system can temporally integrate extraordinarily brief light exposures

A Temporal Threshold for Distinguishing Off-Wrist from Inactivity Periods: A Retrospective Actigraphy Analysis

(1) Background. To facilitate accurate actigraphy data analysis, inactive periods have to be distinguished from periods during which the device is not being worn. The current analysis investigates the degree to which off-wrist and inactive periods can be automatically identified. (2) Methods. In total, 125 actigraphy records were manually scored for &lsquo;off-wrist&rsquo; and &lsquo;inactivity&rsquo; (99 collected with the Motionlogger AMI, 26 (sampling frequency of 60 (n = 20) and 120 (n = 6) s) with the Philips Actiwatch 2.) Data were plotted with cumulative frequency percentage and analyzed with receiver operating characteristic curves. To confirm findings, the thresholds determined in a subset of the Motionlogger dataset (n = 74) were tested in the remaining dataset (n = 25). (3) Results. Inactivity data lasted shorter than off-wrist periods, with 95% of inactive events being shorter than 11 min (Motionlogger), 20 min (Actiwatch 2; 60 s epochs) or 30 min (Actiwatch 2; 120 s epochs), correctly identifying 35, 92 or 66% of the off-wrist periods. The optimal accurate detection of both inactive and off-wrist periods for the Motionlogger was 3 min (Youden&rsquo;s Index (J) = 0.37), while it was 18 (J = 0.89) and 16 min (J = 0.81) for the Actiwatch 2 (60 and 120 s epochs, respectively). The thresholds as determined in the subset of the Motionlogger dataset showed similar results in the remaining dataset. (4) Conclusion. Off-wrist periods can be automatically identified from inactivity data based on a temporal threshold. Depending on the goal of the analysis, a threshold can be chosen to favor inactivity data&rsquo;s inclusion or accurate off-wrist detection

Temporal integration of light flashes by the human circadian system

10.1172/JCI82306JOURNAL OF CLINICAL INVESTIGATION1263938-94

Moving time zones in a flash with light therapy during sleep

Abstract 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

Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device compared to an actigraphy device

Abstract Background Actigraphs are widely used portable wrist-worn devices that record tri-axial accelerometry data. These data can be used to approximate amount and timing of sleep and wake. Their clinical utility is limited, however, by their expense. Tri-axial accelerometer-based consumer wearable devices (so-called fitness monitors) have gained popularity and could represent cost-effective research alternatives to more expensive devices. Lack of independent validation of minute-to-minute accelerometer data for consumer devices has hindered their utility and acceptance. Methods We studied a consumer-grade wearable device, Arc (Huami Inc., Mountain View CA), for which minute-to-minute accelerometer data (vector magnitude) could be obtained. Twelve healthy participants and 19 sleep clinic patients wore on their non-dominant wrist, both an Arc and a research-grade actigraph (Actiwatch Spectrum, Philips, Bend OR) continuously over a period of 48 h in free-living conditions. Time-stamped data from each participant were aligned and the Cole-Kripke algorithm was used to assign a state of “sleep” or “wake” for each minute-long epoch recorded by the Arc. The auto and low scoring settings on the Actiwatch software (Actiware) were used to determine sleep and wake from the Actiwatch data and were used as the comparators. Receiver operating characteristic curves were used to optimize the relationship between the devices. Results Minute-by-minute Arc and Actiwatch data were highly correlated (r = 0.94, Spearman correlation) over the 48-h study period. Treating the Actiwatch auto scoring as the gold standard for determination of sleep and wake, Arc has an overall accuracy of 99.0% ± 0.17% (SEM), a sensitivity of 99.4% ± 0.19%, and a specificity of 84.5% ± 1.9% for the determination of sleep. As compared to the Actiwatch low scoring, Arc has an overall accuracy of 95.2% ± 0.36%, a sensitivity of 95.7% ± 0.47%, and a specificity of 91.7% ± 0.60% for the determination of sleep. Conclusions The Arc, a consumer wearable device in which minute-by-minute activity data could be collected and compared, yielded fundamentally similar sleep scoring metrics as compared to a commonly used clinical-grade actigraph (Actiwatch). We found high degrees of agreement in minute-to-minute data scoring for sleep and wake periods between the two devices

Impact of blue-depleted white light on pupil dynamics, melatonin suppression and subjective alertness following real-world light exposure

10.1186/s41606-018-0022-2Sleep Science and Practice2

PSG Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device.

BackgroundActigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs.MethodsSleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm.ResultsBased on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc.ConclusionsAn optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography