Automatic sleep/wake scoring from body motion in bed: validation of a newly developed sensor placed under a mattress

The purpose of this study was to formulate a "sleep/wake" scoring algorithm for processing activity measurements obtained using a newly developed nonwear actigraphy (NWA) device, and to test its validity. The NWA device has a highly sensitive pressure sensor and is placed under a mattress. It can continuously record the activity of a person lying on the mattress and identify an "in-bed/out-of-bed" state from the vibrations of the mattress. We formulated the sleep/wake scoring algorithm by using data obtained simultaneously by wrist actigraphy (Act) and the NWA device in 33 healthy participants. Agreement rate, sensitivity, and specificity with Act were 95.7%, 97.6%, and 75.8% (33 healthy people); the corresponding values were 85.9%, 89.1%, and 79.8% for 12 nursing home residents and 93.7%, 97.2%, and 60.8% for 60 nights for 6 healthy persons who slept 10 nights on their futons. Agreement rate, sensitivity, and specificity with polysomnography were in almost perfect agreement with Act (12 nights; 6 healthy persons who slept 2 nights). All our validation results indicate that the NWA device, placed under a mattress or a futon, can produce almost identical sleep/wake scores to Act. It is expected that the NWA device, a nonwear device for scoring sleep/wake and in-bed/out-of-bed, enables convenient long-term sleep-related evaluation in various fields, including hospital settings, home-care settings, and care facility settings such as nursing homes.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.     

A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy

ABSTRACT

Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. An unsupervised algorithm is useful in large-scale population studies and in cases where polysomnography (PSG) is unavailable, as it does not require sleep outcome labels to train the model but utilizes information solely contained in actigraphy to learn sleep and wake characteristics and separate the two states. In this study, we proposed a machine learning unsupervised algorithm based on the Hidden Markov Model (HMM) for sleep/wake identification. The proposed algorithm is also an individualized approach that takes into account individual variabilities and analyzes each individual actigraphy profile separately to infer sleep and wake states. We used Actiwatch and PSG data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis study to evaluate the method performance. Epoch-by-epoch comparisons and sleep variable comparisons were made between our algorithm, the unsupervised algorithm embedded in the Actiwatch software (AS), and the pre-trained supervised UCSD algorithm. Using PSG as the reference, the accuracy was 85.7% for HMM, 84.7% for AS, and 85.0% for UCSD. The sensitivity was 99.3%, 99.7%, and 98.9% for HMM, AS, and UCSD, respectively, and the specificity was 36.4%, 30.0%, and 31.7%, respectively. The Kappa statistic was 0.446 for HMM, 0.399 for AS, and 0.311 for UCSD, suggesting fair to moderate agreement between PSG and actigraphy. The Bland–Altman plots further show that the total sleep time, sleep latency, and sleep efficiency estimates by HMM were closer to PSG with narrower 95% limits of agreement than AS and UCSD. All three methods tend to overestimate sleep and underestimate wake compared to PSG. Our HMM approach is also able to differentiate relatively active and sedentary individuals by quantifying variabilities in activity counts: individuals with higher estimated activity variabilities tend to show more frequent sedentary behaviors. Our unsupervised data-driven HMM algorithm achieved better performance than the commonly used Actiwatch software algorithm and the pre-trained UCSD algorithm. HMM can help expand the application of actigraphy in cases where PSG is hard to acquire and supervised methods cannot be trained. In addition, the estimated HMM parameters can characterize individual activity patterns and sedentary tendencies that can be further utilized in downstream analysis.     

Sleep/wake estimation using only anterior tibialis electromyography data

ABSTRACT: BACKGROUND: In sleep efficiency monitoring system, actigraphy is the simplest and most commonly used device. However, low specificity to wakefulness of actigraphy was revealed in previous studies. In this study, we assumed that sleep/wake estimation using actigraphy and electromyography (EMG) signals would show different patterns. Furthermore, each EMG pattern in two states (sleep, wake during sleep) was analysed. Finally, we proposed two types of method for the estimation of sleep/wake patterns using only EMG signals from anterior tibialis muscles and the results were compared with PSG data. METHODS: Seven healthy subjects and five patients (2 obstructive sleep apnea, 3 periodic limb movement disorder) participated in this study. Night time polysomnography (PSG) recordings were conducted, and electrooculogram, EMG, electroencephalogram, electrocardiogram, and respiration data were collected. Time domain analysis and frequency domain analysis were applied to estimate the sleep/wake patterns. Each method was based on changes in amplitude or spectrum (total power) of anterior tibialis electromyography signals during the transition from the sleep state to the wake state. To obtain the results, leave-one-out-cross-validation technique was adopted. RESULTS: Total sleep time of the each group was about 8 hours. For healthy subjects, the mean epoch-by-epoch results between time domain analysis and PSG data were 99%, 71%, 80% and 0.64 (sensitivity, specificity, accuracy and kappa value), respectively. For frequency domain analysis, the corresponding values were 99%, 73%, 81% and 0.67, respectively. Absolute and relative differences between sleep efficiency index from PSG and our methods were 0.8 and 0.8% (for frequency domain analysis). In patients with sleep-related disorder, our proposed methods revealed the substantial agreement (kappa > 0.61) for OSA patients and moderate or fair agreement for PLMD patients. CONCLUSIONS: The results of our proposed methods were comparable to those of PSG. The time and frequency domain analyses showed the similar sleep/wake estimation performance.     

Validity of an algorithm for determining sleep/wake states using a new actigraph

Background

This study aimed to develop an algorithm for determining sleep/wake states by using chronological data on the amount of physical activity (activity intensity) measured with the FS-750 actigraph, a device that can be worn at the waist, allows for its data to be downloaded at home, and is suitable for use in both sleep research and remote sleep medicine.

Methods

Participants were 34 healthy young adults randomly assigned to two groups, A (n =17) and B (n =17), who underwent an 8-hour polysomnography (PSG) in the laboratory environment. Simultaneous activity data were obtained using the FS-750 attached at the front waist. Sleep/wake state and activity intensity were calculated every 2 minutes (1 epoch). To determine the central epoch of the sleep/wake states (x), a five-variable linear model was developed using the activity intensity of Group A for five epochs (x_-2, x_-1, x, x₊₁, x₊₂; 10 minutes). The optimal coefficients were calculated using discriminant analysis. The agreement rate of the developed algorithm was then retested with Group B, and its validity was examined.

Results

The overall agreement rates for group A and group B calculated using the sleep/wake score algorithm developed were 84.7% and 85.4%, respectively. Mean sensitivity (agreement rate for sleep state) was 88.3% and 90.0% and mean specificity (agreement rate for wakeful state) was 66.0% and 64.9%, respectively. These results confirmed comparable agreement rates between the two groups. Furthermore, when applying an estimation rule developed for the sleep parameters measured by the FS-750, no differences were found in the average values between the calculated scores and PSG results, and we also observed a correlation between the two sets of results. Thus, the validity of these evaluation indices based on measurements from the FS-750 is confirmed.

Conclusions

The developed algorithm could determine sleep/wake states from activity intensity data obtained with the FS-750 with sensitivity and specificity equivalent to that determined with conventional actigraphs. The FS-750, which is smaller, less expensive, and able to take measurements over longer periods than conventional devices, is a promising tool for advancing sleep studies at home and in remote sleep medicine.     

Direct comparison of two actigraphy devices with polysomnographically recorded naps in healthy young adults

The last 20 yrs have seen a marked increase in studies utilizing actigraphy in free-living environments. The aim of the present study is to directly compare two commercially available actigraph devices with concurrent polysomnography (PSG) during a daytime nap in healthy young adults. Thirty healthy young adults, ages 18–31 (mean 20.77 yrs, SD 3.14 yrs) simultaneously wore AW-64 and GT3X+ devices during a polysomnographically recorded nap. Mann-Whitney U (M-U) test, intraclass correlation coefficients, and Bland-Altman statistic were used to compare total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) between the two actigraphs and PSG. Epoch-by-epoch (EBE) agreement was calculated to determine accuracy, sensitivity, specificity, predictive values for sleep (PVS) and wake (PVW), and kappa and prevalence- and bias-adjusted kappa (PABAK) coefficients. All frequency settings provided by the devices were examined. For both actigraphs, EBE analysis found accuracy, sensitivity, specificity, PVS, and PVW comparable to previous reports of other similar devices. Kappa and PABAK coefficients showed moderate to high agreement with PSG depending on device settings. The GT3X+ overestimated TST and SE, and underestimated SOL and WASO, whereas no significant difference was found between AW-64 and PSG. However, GT3X+ showed overall better EBE agreements to PSG than AW-64. We conclude that both actigraphs are valid and reliable devices for detecting sleep/wake diurnal patterns. The choice between devices should be based on several parameters as reliability, cost of the device, scoring algorithm, target population, experimental condition, and aims of the study (e.g., sleep and/or physical activity). (Author correspondence: smednick@ucr.edu)     

Evaluation of a consumer fitness-tracking device to assess sleep in adults

Wearable fitness-tracker devices are becoming increasingly available. We evaluated the agreement between Jawbone UP and polysomnography (PSG) in assessing sleep in a sample of 28 midlife women. As shown previously, for standard actigraphy, Jawbone UP had high sensitivity in detecting sleep (0.97) and low specificity in detecting wake (0.37). However, it showed good overall agreement with PSG with a maximum of two women falling outside Bland–Altman plot agreement limits. Jawbone UP overestimated PSG total sleep time (26.6?±?35.3?min) and sleep onset latency (5.2?±?9.6?min), and underestimated wake after sleep onset (31.2?±?32.3?min) (p’s?<?0.05), with greater discrepancies in nights with more disrupted sleep. The low-cost and wide-availability of these fitness-tracker devices may make them an attractive alternative to standard actigraphy in monitoring daily sleep–wake rhythms over several days.     

Cortico-pontine theta carrier frequency phase shift across sleep/wake states following monoaminergic lesion in rat

This study was aimed to explore the sleep/wake states related cortico-pontine theta carrier frequency phase shift following a systemically induced chemical axotomy of the monoaminergic afferents within a brain of the freely moving rats. Our experiments were performed in 14 adult, male Sprague Dawley rats, chronically implanted for sleep recording. We recorded sleep during baseline condition, following sham injection (saline i.p. 1 ml/kg), and every week for 5 weeks following injection of the systemic neurotoxins (DSP-4 or PCA; 1 ml/kg, i.p.) for chemical axotomy of the locus coeruleus (LC) and dorsal raphe (DR) axon terminals. After sleep/wake states identification, FFT analysis was performed on 5 s epochs. Theta carrier frequency phase shift (?Φ) was calculated for each epoch by averaging theta Fourier component phase shifts, and the ?Φ values were plotted for each rat in control condition and 28 days following the monoaminergic lesions, as a time for permanently established DR or LC chemical axotomy. Calculated group averages have shown that ?Φ increased between pons and cortex significantly in all sleep/wake states (Wake, NREM and REM) following the monoaminergic lesions, with respect to controls. Monoaminergic lesions established the pontine leading role in the brain theta oscillations during all sleep/wake states.     

Short-term complexity of cardiac autonomic control during sleep: REM as a potential risk factor for cardiovascular system in aging

Introduction

Sleep is a complex phenomenon characterized by important modifications throughout life and by changes of autonomic cardiovascular control. Aging is associated with a reduction of the overall heart rate variability (HRV) and a decrease of complexity of autonomic cardiac regulation. The aim of our study was to evaluate the HRV complexity using two entropy-derived measures, Shannon Entropy (SE) and Corrected Conditional Entropy (CCE), during sleep in young and older subjects.

Methods

A polysomnographic study was performed in 12 healthy young (21.1±0.8 years) and 12 healthy older subjects (64.9±1.9 years). After the sleep scoring, heart period time series were divided into wake (W), Stage 1–2 (S1-2), Stage 3–4 (S3-4) and REM. Two complexity indexes were assessed: SE(3) measuring the complexity of a distribution of 3-beat patterns (SE(3) is higher when all the patterns are identically distributed and it is lower when some patterns are more likely) and CCE_min measuring the minimum amount of information that cannot be derived from the knowledge of previous values.

Results

Across the different sleep stages, young subjects had similar RR interval, total variance, SE(3) and CCE_min. In the older group, SE(3) and CCE_min were reduced during REM sleep compared to S1-2, S3-4 and W. Compared to young subjects, during W and sleep the older subjects showed a lower RR interval and reduced total variance as well as a significant reduction of SE(3) and CCE_min. This decrease of entropy measures was more evident during REM sleep.

Conclusion

Our study indicates that aging is characterized by a reduction of entropy indices of cardiovascular variability during wake/sleep cycle, more evident during REM sleep. We conclude that during aging REM sleep is associated with a simplification of cardiac control mechanisms that could lead to an impaired ability of the cardiovascular system to react to cardiovascular adverse events.     

Validation of an innovative method,based on tilt sensing,for the assessment of activity and body position

Since there is less movement during sleep than during wake, the recording of body movements by actigraphy has been used to indirectly evaluate the sleep–wake cycle. In general, most actigraphic devices are placed on the wrist and their measures are based on acceleration detection. Here, we propose an alternative way of measuring actigraphy at the level of the arm for joint evaluation of activity and body position. This method analyzes the tilt of three axes, scoring activity as the cumulative change of degrees per minute with respect to the previous sampling, and measuring arm tilt for the body position inference. In this study, subjects (N?=?13) went about their daily routine for 7 days, kept daily sleep logs, wore three ambulatory monitoring devices and collected sequential saliva samples during evenings for the measurement of dim light melatonin onset (DLMO). These devices measured motor activity (arm activity, AA) and body position (P) using the tilt sensing of the arm, with acceleration (wrist acceleration, WA) and skin temperature at wrist level (WT). Cosinor, Fourier and non-parametric rhythmic analyses were performed for the different variables, and the results were compared by the ANOVA test. Linear correlations were also performed between actimetry methods (AA and WA) and WT. The AA and WA suitability for circadian phase prediction and for evaluating the sleep–wake cycle was assessed by comparison with the DLMO and sleep logs, respectively. All correlations between rhythmic parameters obtained from AA and WA were highly significant. Only parameters related to activity levels, such as mesor, RA (relative amplitude), VL5 and VM10 (value for the 5 and 10 consecutive hours of minimum and maximum activity, respectively) showed significant differences between AA and WA records. However, when a correlation analysis was performed on the phase markers acrophase, mid-time for the 10 consecutive hours of highest (M10) and mid-time for the five consecutive hours of lowest activity (L5) with DLMO, all of them showed a significant correlation for AA (R?=?0.607, p?=?0.028; R?=?0.582, p?=?0.037; R?=?0.620, p?=?0.031, respectively), while for WA, only acrophase did (R?=?0.621, p?=?0.031). Regarding sleep detection, WA showed higher specificity than AA (0.95?±?0.01 versus 0.86?±?0.02), while the agreement rate and sensitivity were higher for AA (0.76?±?0.02 versus 0.66?±?0.02 and 0.71?±?0.03 versus 0.53?±?0.03, respectively). Cohen’s kappa coefficient also presented the highest values for AA (0.49?±?0.04) and AP (0.64?±?0.04), followed by WT (0.45?±?0.06) and WA (0.37?±?0.04). The findings demonstrate that this alternative actigraphy method (AA), based on tilt sensing of the arm, can be used to reliably evaluate the activity and sleep–wake rhythm, since it presents a higher agreement rate and sensitivity for detecting sleep, at the same time allows the detection of body position and improves circadian phase assessment compared to the classical actigraphic method based on wrist acceleration.     

Effects of Transitions into and out of Daylight Saving Time on the Quality of the Sleep/Wake Cycle: an Actigraphic Study in Healthy University Students

The main goal of the present study was to examine the effects of transition into and out of daylight saving time (DST) on the quality of the sleep/wake cycle, assessed through actigraphy. To this end, 14 healthy university students (mean age: 26.86?±?3.25?yrs) wore an actigraph for 7?d before and 7?d after the transition out of and into DST on fall 2009 and spring 2010, respectively. The following parameters have been compared before and after the transition, separately for autumn and spring changes: bedtime (BT), get-up time (GUT), time in bed (TIB), sleep onset latency (SOL), fragmentation index (FI), sleep efficiency (SE), total sleep time (TST), wake after sleep onset (WASO), mean activity score (MAS), and number of wake bouts (WB). After the autumn transition, a significant advance of the GUT and a decrease of TIB and TST were observed. On the contrary, spring transition led to a delay of the GUT, an increase of TIB, TST, WASO, MAS, and WB, and a decrease of SE. The present results highlight a more strong deterioration of sleep/wake cycle quality after spring compared with autumn transition, confirming that human circadian system more easily adjusts to a phase delay (autumn change) than a phase advance (spring transition).     

Performance comparison of different interpretative algorithms utilized to derive sleep parameters from wrist actigraphy data

ABSTRACT

We compared performance of four popular interpretative algorithms (IAs), i.e., Cole–Kripke, Rescored Cole–Kripke, Sadeh, and UCSD, utilized to derive sleep parameters from wrist actigraphy data. We conducted in-home sleep study of 40 healthy adults (17 female/23 male; age 26.7 ± 12.1 years), assessing sleep variables both by Motionlogger® Micro Watch Actigraphy (MMWA) and Zmachine® Insight+ electroencephalography (EEG). Data of MMWA were separately scored per 30 sec epochs by each of the four popular IAs, and data of the Zmachine were also scored per 30 sec epochs by its proprietary IA. In reference to the EEG Zmachine method, all four of the MMWA algorithms showed high (~94 to 98%) sensitivity and moderate (~42 to 54%) specificity in detecting Sleep epochs. All of them significantly underestimated Sleep Onset Latency (SOL: ~9 to 20 min), and all of them, except the Sadeh IA, significantly underestimated Wake After Sleep Onset (WASO: ~22 to 25 min) and overestimated Total Sleep Time (TST: ~32 to 45 min) and Sleep Efficiency (SE: ~7 to 9%). The Sadeh IA showed significantly smaller bias than the other three IAs in deriving WASO, TST, and SE. Overall, application of ‘Rescoring Rules’ improved performance of the Cole–Kripke IA. The Sadeh and Rescored Cole–Kripke IAs exhibited highest agreement with the EEG Zmachine method (Cohen’s Kappa: ~51%), while the UCSD IA exhibited lowest agreement (Cohen’s kappa: ~47%). However, minimum detectable change across all sleep parameters was smallest with use of the UCSD IA and, except for SOL, largest with use of the Sadeh algorithm. Findings of this study indicate the Sadeh IA is most appropriate for deriving sleep parameters of healthy adults, while the UCSD IA is most appropriate for evaluating change in sleep parameters over time or in response to medical intervention.     

酒石酸唑吡坦对非器质性失眠症患者Actigraphy检测指标的影响

目的:应用Actigraphy仪检测酒石酸唑吡坦对非器质性失眠患者睡眠质量的影响。方法:选择非器质性失眠症患者36例,实验第二晚给予10 mg酒石酸唑吡坦,实验第一晚和第四晚采用Actigraph仪监测,观察用药后Actigraph指标的变化。同时设置正常对照组24名,进行基础Actigraph监测。结果:失眠组患者服用酒石酸唑吡坦后,夜间Actigraphy检测显示实际觉醒时间(AWT)、睡眠潜入期(SL)、平均每次觉醒时间(MWB T)与服药前相比,显著缩短(P0.01);睡眠效率(SE)、平均静息状态时长(MLI)与服药前相比,显著提高(P0.01),同时反映身体活动的参数平均活动分数(MAS)和睡眠总体破碎程度的割裂指数(FI)与对照组相比,显著降低(P0.05)。结论:酒石酸唑吡坦能明显改善非器质性失眠患者睡眠,在非器质性失眠症的诊断治疗中Actigraphy仪是一种有效、便捷的方法。     

The effect of consecutive transmeridian flights on alertness,sleep–wake cycles and sleepiness: A case study

ABSTRACT

Travel across time zones disrupts circadian rhythms causing increased daytime sleepiness, impaired alertness and sleep disturbance. However, the effect of repeated consecutive transmeridian travel on sleep–wake cycles and circadian dynamics is unknown. The aim of this study was to investigate changes in alertness, sleep–wake schedule and sleepiness and predict circadian and sleep dynamics of an individual undergoing demanding transmeridian travel. A 47-year-old healthy male flew 16 international flights over 12 consecutive days. He maintained a sleep–wake schedule based on Sydney, Australia time (GMT + 10?h). The participant completed a sleep diary and wore an Actiwatch before, during and after the flights. Subjective alertness, fatigue and sleepiness were rated 4 hourly (08:00–00:00), if awake during the flights. A validated physiologically based mathematical model of arousal dynamics was used to further explore the dynamics and compare sleep time predictions with observational data and to estimate circadian phase changes. The participant completed 191?h and 159 736?km of flying and traversed a total of 144 time-zones. Total sleep time during the flights decreased (357.5?min actigraphy; 292.4?min diary) compared to baseline (430.8?min actigraphy; 472.1?min diary), predominately due to restricted sleep opportunities. The daily range of alertness, sleepiness and fatigue increased compared to baseline, with heightened fatigue towards the end of the flight schedule. The arousal dynamics model predicted sleep/wake states during and post travel with 88% and 95% agreement with sleep diary data. The circadian phase predicted a delay of only 34?min over the 16 transmeridian flights. Despite repeated changes in transmeridian travel direction and flight duration, the participant was able to maintain a stable sleep schedule aligned with the Sydney night. Modelling revealed only minor circadian misalignment during the flying period. This was likely due to the transitory time spent in the overseas airports that did not allow for resynchronisation to the new time zone. The robustness of the arousal model in the real-world was demonstrated for the first time using unique transmeridian travel.     

Sleep logs of young adults with self-selected sleep times predict the dim light melatonin onset

The purpose of this study was to determine whether a sleep log parameter could be used to estimate the circadian phase of normal, healthy, young adults who sleep at their normal times, and thus naturally have day-to-day variability in their times of sleep. Thus, we did not impose any restrictions on the sleep schedules of our subjects (n = 26). For 14 d, they completed daily sleep logs that were verified with wrist activity monitors. On day 14, salivary melatonin was sampled every 30 min in dim light from 19:00 to 07:30 h to determine the dim light melatonin onset (DLMO). Daily sleep parameters (onset, midpoint, and wake) were taken from sleep logs and averaged over the last 5, 7, and 14 d before determination of the DLMO. The mean DLMO was 22:48 +/- 01:30 h. Sleep onset and wake time averaged over the last 5 d were 01:44 +/- 01:41 and 08:44 +/- 01:26 h, respectively. The DLMO was significantly correlated with sleep onset, midpoint, and wake time, but was most strongly correlated with the mean midpoint of sleep from the last 5 d (r = 0.89). The DLMO predicted using the mean midpoint of sleep from the last 5 d was within 1 h of the DLMO determined from salivary melatonin for 92% of the subjects; in no case did the difference exceed 1.5 h. The correlation between the DLMO and the score on the morningness-eveningness questionnaire was significant but comparatively weak (r = -0.48). We conclude that the circadian phase of normal, healthy day-active young adults can be accurately predicted using sleep times recorded on sleep logs (and verified by actigraphy), even when the sleep schedules are irregular.     

Circadian regulation gene polymorphisms are associated with sleep disruption and duration,and circadian phase and rhythm in adults with HIV

Genes involved in circadian regulation, such as circadian locomotor output cycles kaput [CLOCK], cryptochrome [CRY1] and period [PER], have been associated with sleep outcomes in prior animal and human research. However, it is unclear whether polymorphisms in these genes are associated with the sleep disturbances commonly experienced by adults living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS). Thus, the purpose of this study was to describe polymorphisms in selected circadian genes that are associated with sleep duration or disruption as well as the sleep–wake rhythm strength and phase timing among adults living with HIV/AIDS. A convenience sample of 289 adults with HIV/AIDS was recruited from HIV clinics and community sites in the San Francisco Bay Area. A wrist actigraph was worn for 72?h on weekdays to estimate sleep duration or total sleep time (TST), sleep disruption or percentage of wake after sleep onset (WASO) and several circadian rhythm parameters: mesor, amplitude, the ratio of mesor to amplitude (circadian quotient), and 24-h autocorrelation. Circadian phase measures included clock time for peak activity (acrophase) from actigraphy movement data, and bed time and final wake time from actigraphy and self-report. Genotyping was conducted for polymorphisms in five candidate genes involved in circadian regulation: CLOCK, CRY1, PER1, PER2 and PER3. Demographic and clinical variables were evaluated as potential covariates. Interactions between genotype and HIV variables (i.e. viral load, years since HIV diagnosis) were also evaluated. Controlling for potentially confounding variables (e.g. race, gender, CD4+ T-cell count, waist circumference, medication use, smoking and depressive symptoms), CLOCK was associated with WASO, 24-h autocorrelation and objectively-measured bed time; CRY1 was associated with circadian quotient; PER1 was associated with mesor and self-reported habitual wake time; PER2 was associated with TST, mesor, circadian quotient, 24-h autocorrelation and bed and wake times; PER3 was associated with amplitude, 24-h autocorrelation, acrophase and bed and wake times. Most of the observed associations involved a significant interaction between genotype and HIV. In this chronic illness population, polymorphisms in several circadian genes were associated with measures of sleep disruption and timing. These findings extend the evidence for an association between genetic variability in circadian regulation and sleep outcomes to include the sleep–wake patterns experienced by adults living with HIV/AIDS. These results provide direction for future intervention research related to circadian sleep–wake behavior patterns.     

CONTRIBUTION OF CORE BODY TEMPERATURE,PRIOR WAKE TIME,AND SLEEP STAGES TO COGNITIVE THROUGHPUT PERFORMANCE DURING FORCED DESYNCHRONY

Shiftworkers are often required to sleep at inappropriate phases of their circadian timekeeping system, with implications for the dynamics of ultradian sleep stages. The independent effects of these changes on cognitive throughput performance are not well understood. This is because the effects of sleep on performance are usually confounded with circadian factors that cannot be controlled under normal day/night conditions. The aim of this study was to assess the contribution of prior wake, core body temperature, and sleep stages to cognitive throughput performance under conditions of forced desynchrony (FD). A total of 11 healthy young adult males resided in a sleep laboratory in which day/night zeitgebers were eliminated and ambient room temperature, lighting levels, and behavior were controlled. The protocol included 2 training days, a baseline day, and 7?×?28-h FD periods. Each FD period consisted of an 18.7-h wake period followed by a 9.3-h rest period. Sleep was assessed using standard polysomnography. Core body temperature and physical activity were assessed continuously in 1-min epochs. Cognitive throughput was measured by a 5-min serial addition and subtraction (SAS) task and a 90-s digit symbol substitution (DSS) task. These were administered in test sessions scheduled every 2.5?h across the wake periods of each FD period. On average, sleep periods had a mean (± standard deviation) duration of 8.5 (±1.2) h in which participants obtained 7.6 (±1.4) h of total sleep time. This included 4.2 (±1.2) h of stage 1 and stage 2 sleep (S1–S2 sleep), 1.6 (±0.6) h of slow-wave sleep (SWS), and 1.8 (±0.6) h of rapid eye movement (REM) sleep. A mixed-model analysis with five covariates indicated significant fixed effects on cognitive throughput for circadian phase, prior wake time, and amount of REM sleep. Significant effects for S1–S2 sleep and SWS were not found. The results demonstrate that variations in core body temperature, time awake, and amount of REM sleep are associated with changes in cognitive throughput performance. The absence of significant effect for SWS may be attributable to the truncated range of sleep period durations sampled in this study. However, because the mean and variance for SWS were similar to REM sleep, these results suggest that cognitive throughput may be more sensitive to variations in REM sleep than SWS. (Author correspondence: david.darwent@unisa.edu.au)     

SLEEP LOGS OF YOUNG ADULTS WITH SELF-SELECTED SLEEP TIMES PREDICT THE DIM LIGHT MELATONIN ONSET

The purpose of this study was to determine whether a sleep log parameter could be used to estimate the circadian phase of normal, healthy, young adults who sleep at their normal times, and thus naturally have day-to-day variability in their times of sleep. Thus, we did not impose any restrictions on the sleep schedules of our subjects (n=26). For 14 d, they completed daily sleep logs that were verified with wrist activity monitors. On day 14, salivary melatonin was sampled every 30 min in dim light from 19:00 to 07:30h to determine the dim light melatonin onset (DLMO). Daily sleep parameters (onset, midpoint, and wake) were taken from sleep logs and averaged over the last 5, 7, and 14 d before determination of the DLMO. The mean DLMO was 22:48±01:30 h. Sleep onset and wake time averaged over the last 5 d were 01:44±01:41 and 08:44±01:26 h, respectively. The DLMO was significantly correlated with sleep onset, midpoint, and wake time, but was most strongly correlated with the mean midpoint of sleep from the last 5 d (r=0.89). The DLMO predicted using the mean midpoint of sleep from the last 5 d was within 1 h of the DLMO determined from salivary melatonin for 92% of the subjects; in no case did the difference exceed 1.5 h. The correlation between the DLMO and the score on the morningness-eveningness questionnaire was significant but comparatively weak (r=-0.48). We conclude that the circadian phase of normal, healthy day-active young adults can be accurately predicted using sleep times recorded on sleep logs (and verified by actigraphy), even when the sleep schedules are irregular.     

Actigraphy: A Means of Assessing Circadian Patterns in Human Activity

-Twenty-three diurnally active (0705-2333), healthy persons between 22 and 54 yrs of age and without history of sleep abnormality were monitored continuously for 120 consecutive hr (five days) by wrist actigraphy. Circadian rhythms of high amplitude were detected by cosinor analysis for each participant and for the groups of 10 males and 13 females with the average span of heightened activity timed between ∼1330 and 1605. The circadian peak-trough difference in wrist movement was marked, equalling aproximately 75% of the 24-hr mean level. In 19 of 23 participants, the 24-hr mean of wrist activity varied between 140-180 movements/min, with four persons exhibiting lesser means of 110-140 movements/min. With respect to the daytime span of activity, the mean wrist movement of individual participants ranged from 155-265 movements/min, with the majority (20/23) varying between 185-245 movements/min. During nocturnal sleep the mean wrist activity level was quite low, varying between individuals from 5 to 25 movements/min for 21 of 23 persons. Wrist actigraphy proved to be well-accepted and was a most reliable means of monitoring aspects of body movement during activity and sleep in ambulatory persons adhering to usual life habits and pursuits.