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.     

A validation study of Fitbit Charge 2™ compared with polysomnography in adults

We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2?), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults.

In-lab PSG and Fitbit Charge 2? data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19—61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2? sleep data were compared using paired t-tests, Bland–Altman plots, and epoch-by-epoch (EBE) analysis.

In the main group, Fitbit Charge 2? showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep (“light sleep”), 0.49 accuracy in detecting N3 sleep (“deep sleep”), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2? significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2? outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland–Altman agreement limits for all sleep measures. Fitbit Charge 2? correctly identified 82% of PSG-defined non-REM–REM sleep cycles across the night. Similar outcomes were found for the PLMS group.

Fitbit Charge 2? shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2? accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).     

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)     

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.     

Effects of Filtering Visual Short Wavelengths During Nocturnal Shiftwork on Sleep and Performance

Circadian phase resetting is sensitive to visual short wavelengths (450–480?nm). Selectively filtering this range of wavelengths may reduce circadian misalignment and sleep impairment during irregular light-dark schedules associated with shiftwork. We examined the effects of filtering short wavelengths (<480?nm) during night shifts on sleep and performance in nine nurses (five females and four males; mean age?±?SD: 31.3?±?4.6 yrs). Participants were randomized to receive filtered light (intervention) or standard indoor light (baseline) on night shifts. Nighttime sleep after two night shifts and daytime sleep in between two night shifts was assessed by polysomnography (PSG). In addition, salivary melatonin levels and alertness were assessed every 2?h on the first night shift of each study period and on the middle night of a run of three night shifts in each study period. Sleep and performance under baseline and intervention conditions were compared with daytime performance on the seventh day shift, and nighttime sleep following the seventh daytime shift (comparator). On the baseline night PSG, total sleep time (TST) (p?<?0.01) and sleep efficiency (p?=?0.01) were significantly decreased and intervening wake times (wake after sleep onset [WASO]) (p?=?0.04) were significantly increased in relation to the comparator night sleep. In contrast, under intervention, TST was increased by a mean of 40?min compared with baseline, WASO was reduced and sleep efficiency was increased to levels similar to the comparator night. Daytime sleep was significantly impaired under both baseline and intervention conditions. Salivary melatonin levels were significantly higher on the first (p?<?0.05) and middle (p?<?0.01) night shifts under intervention compared with baseline. Subjective sleepiness increased throughout the night under both conditions (p?<?0.01). However, reaction time and throughput on vigilance tests were similar to daytime performance under intervention but impaired under baseline on the first night shift. By the middle night shift, the difference in performance was no longer significant between day shift and either of the two night shift conditions, suggesting some adaptation to the night shift had occurred under baseline conditions. These results suggest that both daytime and nighttime sleep are adversely affected in rotating-shift workers and that filtering short wavelengths may be an approach to reduce sleep disruption and improve performance in rotating-shift workers. (Author correspondence: casper@lunenfeld.ca)     

Comparison of Two Different Actigraphs with Polysomnography in Healthy Young Subjects

The present study aimed to compare two commercially available actigraphs, with a concurrent polysomnographic (PSG) recording. Twelve healthy volunteers (six women; age range 19–28 yrs) simultaneously wore the Basic Mini‐Motionlogger® and Actiwatch® for seven overnight polysomnographic recordings. Comparisons of the following sleep measures were focused on: sleep onset latency (SOL), total sleep time, wake after sleep onset, and sleep efficiency. Both devices underestimated SOL in comparison to PSG, but they had similar performance compared to PSG for the other sleep measures. A limit of the study is that the results can be only generalized to healthy young subjects.     

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/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.     

Comparison of two different actigraphs with polysomnography in healthy young subjects

The present study aimed to compare two commercially available actigraphs, with a concurrent polysomnographic (PSG) recording. Twelve healthy volunteers (six women; age range 19-28 yrs) simultaneously wore the Basic Mini-Motionlogger^® and Actiwatch^® for seven overnight polysomnographic recordings. Comparisons of the following sleep measures were focused on: sleep onset latency (SOL), total sleep time, wake after sleep onset, and sleep efficiency. Both devices underestimated SOL in comparison to PSG, but they had similar performance compared to PSG for the other sleep measures. A limit of the study is that the results can be only generalized to healthy young subjects.     

Time-of-Day Mediates the Influences of Extended Wake and Sleep Restriction on Simulated Driving

Although a nonlinear time-of-day and prior wake interaction on performance has been well documented, two recent studies have aimed to incorporate the influences of sleep restriction into this paradigm. Through the use of sleep-restricted forced desynchrony protocols, both studies reported a time-of-day?×?sleep restriction interaction, as well as a time-of-day?×?prior wake?×?sleep dose three-way interaction. The current study aimed to investigate these interactions on simulated driving performance, a more complex task with ecological validity for the problem of fatigued driving. The driving performance of 41 male participants (mean?±?SD: 22.8 ±2.2 yrs) was assessed on a 10-min simulated driving task with the standard deviation of lateral position (SDLAT) measured. Using a between-group design, participants were subjected to either a control condition of 9.33?h of sleep/18.66?h of wake, a moderate sleep-restriction (SR) condition of 7?h of sleep/21?h of wake, or a severe SR condition of 4.66?h of sleep/23.33?h of wake. In each condition, participants were tested at 2.5-h intervals after waking across 7?×?28-h d of forced desynchrony. Driving sessions occurred at nine doses of prior wake, within six divisions of the circadian cycle based on core body temperature (CBT). Mixed-models analyses of variance (ANOVAs) revealed significant main effects of time-of-day, prior wake, sleep debt, and sleep dose on SDLAT. Additionally, significant two-way interactions of time-of-day?×?prior wake and time-of-day?×?sleep debt, as well as significant three-way interactions of time-of-day?×?prior wake?×?sleep debt and time-of-day?×?sleep debt?×?sleep dose were observed. Although limitations such as the presence of practice effects and large standard errors are noted, the study concludes with three findings. The main effects demonstrate that extending wake, reducing sleep, and driving at poor times of day all significantly impair driving performance at an individual level. In addition to this, combining either extended wake or a sleep debt with the early morning hours greatly decreases driving performance. Finally, operating under the influence of a reduced sleep dose can greatly decrease performance at all times of the day. (Author correspondence: raymond.matthews@unisa.edu.au)     

Gender Differences in Polysomnographic Sleep in Young Healthy Sleepers

Middle‐aged and elderly populations exhibit gender differences in polysomnographic (PSG) sleep; however, whether young men and women also show such differences remains unclear. Thirty‐one young healthy sleepers (16 men and 15 women, aged 18 to 30 yr, mean±SD, 20.5±2.4 yr) completed 3 consecutive overnight sessions in a sleep laboratory, after maintaining a stable sleep‐wake cycle for 1 wk before study entry. Standard PSG sleep and self‐rated sleepiness data were collected each night. Across nights, women showed better sleep quality than men: they fell asleep faster (shorter sleep onset latency) and had better sleep efficiency, with more time asleep and less time awake (all differences showed large effect sizes, d=0.98 to 1.12). By contrast, men were sleepier than women across nights. Both men and women demonstrated poorer overall sleep quality on the first night compared with the subsequent 2 nights of study. We conclude young adult healthy sleepers show robust gender differences in PSG sleep, like older populations, with better sleep quality in women than in men. These results highlight the importance of gender in sleep and circadian rhythm research studies employing young subjects and have broader implications for women's health issues relating to these topics.     

Ring the Bell for Matins: Circadian Adaptation to Split Sleep by Cloistered Monks and Nuns

Cloistered monks and nuns adhere to a 10-century-old strict schedule with a common zeitgeber of a night split by a 2- to 3-h-long Office (Matins). The authors evaluated how the circadian core body temperature rhythm and sleep adapt in cloistered monks and nuns in two monasteries. Five monks and five nuns following the split-sleep night schedule for 5 to 46 yrs without interruption and 10 controls underwent interviews, sleep scales, and physical examination and produced a week-long sleep diary and actigraphy, plus 48-h recordings of core body temperature. The circadian rhythm of temperature was described by partial Fourier time-series analysis (with 12- and 24-h harmonics). The temperature peak and trough values and clock times did not differ between groups. However, the temperature rhythm was biphasic in monks and nuns, with an early decrease at 19:39?±?4:30?h (median?±?95% interval), plateau or rise of temperature at 22:35?±?00:23?h (while asleep) lasting 296?±?39?min, followed by a second decrease after the Matins Office, and a classical morning rise. Although they required alarm clocks to wake-up for Matins at midnight, the body temperature rise anticipated the nocturnal awakening by 85?±?15?min. Compared to the controls, the monks and nuns had an earlier sleep onset (20:05?±?00:59?h vs. 00:00?±?00:54?h, median?±?95% confidence interval, p?=?.0001) and offset (06:27?±?0:22?h, vs. 07:37?±?0:33?h, p?=?.0001), as well as a shorter sleep time (6.5?±?0.6 vs. 7.6?±?0.7?h, p?=?.05). They reported difficulties with sleep latency, sleep duration, and daytime function, and more frequent hypnagogic hallucinations. In contrast to their daytime silence, they experienced conversations (and occasionally prayers) in dreams. The biphasic temperature profile in monks and nuns suggests the human clock adapts to and even anticipates nocturnal awakenings. It resembles the biphasic sleep and rhythm of healthy volunteers transferred to a short (10-h) photoperiod and provides a living glance into the sleep pattern of medieval time. (Author correspondence: isabelle.arnulf@psl.aphp.fr)     

Newly developed waist actigraphy and its sleep/wake scoring algorithm

The purpose of this study was to formulate an algorithm for assessing sleep/waking from activity intensities measured with a waist-worn actigraphy, the Lifecorder PLUS (LC; Suzuken Co. Ltd., Nagoya, Japan), and to test the validity of the algorithm. The study consisted of 31 healthy subjects (M/F = 20/11, mean age 31.7 years) who underwent one night of simultaneous measurement of activity intensity by LC and polysomnography (PSG). A sleep(S)/wake(W) scoring algorithm based on a linear model was determined through discriminant analysis of activity intensities measured by LC over a total of 235 h and 56 min and the corresponding PSG-based S/W data. The formulated S/W scoring algorithm was then used to score S/W during the monitoring epochs (2 min each, 7078 epochs in total) for each subject. The mean agreement rate with the corresponding PSG-based S/W data was 86.9%, with a mean sensitivity (sleep detection) of 89.4% and mean specificity (wakefulness detection) of 58.2%. The agreement rates for the individual stages of sleep were 60.6% for Stage 1, 89.3% for Stage 2, 99.2% for Stage 3 + 4, and 90.1% for Stage REM. These results demonstrate that sleep/wake activity in young to middle-aged healthy subjects can be assessed with a reliability comparable to that of conventional actigraphy through LC waist actigraphy and the optimal S/W scoring algorithm.

     

Assessing Sleep Disturbance in Low Back Pain: The Validity of Portable Instruments

Although portable instruments have been used in the assessment of sleep disturbance for patients with low back pain (LBP), the accuracy of the instruments in detecting sleep/wake episodes for this population is unknown. This study investigated the criterion validity of two portable instruments (Armband and Actiwatch) for assessing sleep disturbance in patients with LBP. 50 patients with LBP performed simultaneous overnight sleep recordings in a university sleep laboratory. All 50 participants were assessed by Polysomnography (PSG) and the Armband and a subgroup of 33 participants wore an Actiwatch. Criterion validity was determined by calculating epoch-by-epoch agreement, sensitivity, specificity and prevalence and bias- adjusted kappa (PABAK) for sleep versus wake between each instrument and PSG. The relationship between PSG and the two instruments was assessed using intraclass correlation coefficients (ICC 2, 1). The study participants showed symptoms of sub-threshold insomnia (mean ISI = 13.2, 95% CI = 6.36) and poor sleep quality (mean PSQI = 9.20, 95% CI = 4.27). Observed agreement with PSG was 85% and 88% for the Armband and Actiwatch. Sensitivity was 0.90 for both instruments and specificity was 0.54 and 0.67 and PABAK of 0.69 and 0.77 for the Armband and Actiwatch respectively. The ICC (95%CI) was 0.76 (0.61 to 0.86) and 0.80 (0.46 to 0.92) for total sleep time, 0.52 (0.29 to 0.70) and 0.55 (0.14 to 0.77) for sleep efficiency, 0.64 (0.45 to 0.78) and 0.52 (0.23 to 0.73) for wake after sleep onset and 0.13 (−0.15 to 0.39) and 0.33 (−0.05 to 0.63) for sleep onset latency, for the Armband and Actiwatch, respectively. The findings showed that both instruments have varied criterion validity across the sleep parameters from excellent validity for measures of total sleep time, good validity for measures of sleep efficiency and wake after onset to poor validity for sleep onset latency.     

Preliminary findings for the validity of the Morningness–Eveningness-Stability Scale improved (MESSi): Correlations with activity levels and personality

Aim of the present study is an additional validation of the Morningness–Eveningness-Stability Scale improved (MESSi). We screened a total of 97 German students using the reduced Morningness–Eveningness Questionnaire (rMEQ) to identify a subsample (N = 42) of definite morning and evening types (31% males, mean age: 24.8 ± 5.8?years). The participants provided information about their sleep–wake rhythm (diary), personality traits (questionnaire) and experienced actigraphic monitoring. Correlations of the MESSi components “Morning affect subscale” (MA) (r = 0.91, p < 0.01) and “Eveningness subscale” (r = ?0.87, p < 0.01) with the rMEQ showed good convergent validity. MA was also significantly negatively correlated with the acrophase and the midpoint of sleep as measured by actigraphy.     

Sleep,Sleepiness, and Neurobehavioral Performance While on Watch in a Simulated 4 Hours on/8 Hours off Maritime Watch System

Seafarer sleepiness jeopardizes safety at sea and has been documented as a direct or contributing factor in many maritime accidents. This study investigates sleep, sleepiness, and neurobehavioral performance in a simulated 4?h on/8?h off watch system as well as the effects of a single free watch disturbance, simulating a condition of overtime work, resulting in 16?h of work in a row and a missed sleep opportunity. Thirty bridge officers (age 30?±?6 yrs; 29 men) participated in bridge simulator trials on an identical 1-wk voyage in the North Sea and English Channel. The three watch teams started respectively with the 00–04, the 04–08, and the 08–12 watches. Participants rated their sleepiness every hour (Karolinska Sleepiness Scale [KSS]) and carried out a 5-min psychomotor vigilance test (PVT) test at the start and end of every watch. Polysomnography (PSG) was recorded during 6 watches in the first and the second half of the week. KSS was higher during the first (mean?±?SD: 4.0?±?0.2) compared with the second (3.3?±?0.2) watch of the day (p?<?0.001). In addition, it increased with hours on watch (p?<?0.001), peaking at the end of watch (4.1?±?0.2). The free watch disturbance increased KSS profoundly (p?<?0.001): from 4.2?±?0.2 to 6.5?±?0.3. PVT reaction times were slower during the first (290?±?6?ms) compared with the second (280?±?6?ms) watch of the day (p?<?0.001) as well as at the end of the watch (289?±?6?ms) compared with the start (281?±?6?ms; p?=?0.001). The free watch disturbance increased reaction times (p?<?0.001) from 283?±?5 to 306?±?7?ms. Similar effects were observed for PVT lapses. One third of all participants slept during at least one of the PSG watches. Sleep on watch was most abundant in the team working 00–04 and it increased following the free watch disturbance. This study reveals that—within a 4?h on/8?h off shift system—subjective and objective sleepiness peak during the night and early morning watches, coinciding with a time frame in which relatively many maritime accidents occur. In addition, we showed that overtime work strongly increases sleepiness. Finally, a striking amount of participants fell asleep while on duty.     

Circadian Phase,Sleepiness, and Light Exposure Assessment in Night Workers With and Without Shift Work Disorder

Most night workers are unable to adjust their circadian rhythms to the atypical hours of sleep and wake. Between 10% and 30% of shiftworkers report symptoms of excessive sleepiness and/or insomnia consistent with a diagnosis of shift work disorder (SWD). Difficulties in attaining appropriate shifts in circadian phase, in response to night work, may explain why some individuals develop SWD. In the present study, it was hypothesized that disturbances of sleep and wakefulness in shiftworkers are related to the degree of mismatch between their endogenous circadian rhythms and the night-work schedule of sleep during the day and wake activities at night. Five asymptomatic night workers (ANWs) (3 females; [mean?±?SD] age: 39.2?±?12.5 yrs; mean yrs on shift?=?9.3) and five night workers meeting diagnostic criteria (International Classification of Sleep Disorders [ICSD]-2) for SWD (3 females; age: 35.6?±?8.6 yrs; mean years on shift?=?8.4) participated. All participants were admitted to the sleep center at 16:00?h, where they stayed in a dim light (<10 lux) private room for the study period of 25 consecutive hours. Saliva samples for melatonin assessment were collected at 30-min intervals. Circadian phase was determined from circadian rhythms of salivary melatonin onset (dim light melatonin onset, DLMO) calculated for each individual melatonin profile. Objective sleepiness was assessed using the multiple sleep latency test (MSLT; 13 trials, 2-h intervals starting at 17:00?h). A Mann-Whitney U test was used for evaluation of differences between groups. The DLMO in ANW group was 04:42?±?3.25?h, whereas in the SWD group it was 20:42?±?2.21?h (z = 2.4; p?<?.05). Sleep did not differ between groups, except the SWD group showed an earlier bedtime on off days from work relative to that in ANW group. The MSLT corresponding to night work time (01:00–09:00?h) was significantly shorter (3.6?±?.90?min: [M?±?SEM]) in the SWD group compared with that in ANW group (6.8?±?.93?min). DLMO was significantly correlated with insomnia severity (r = ?.68; p < .03), indicating that the workers with more severe insomnia symptoms had an earlier timing of DLMO. Finally, SWD subjects were exposed to more morning light (between 05:00 and 11:00?h) as than ANW ones (798 vs. 180 lux [M?±?SD], respectively z?=??1.7; p?<?.05). These data provide evidence of an internal physiological delay of the circadian pacemaker in asymptomatic night-shift workers. In contrast, individuals with SWD maintain a circadian phase position similar to day workers, leading to a mismatch/conflict between their endogenous rhythms and their sleep-wake schedule. (Author correspondence: vgumeny1@hfhs.org)     

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).     

Dim Light Melatonin Onset in Alcohol-Dependent Men and Women Compared with Healthy Controls

Sleep disturbances in alcohol-dependent (AD) individuals may persist despite abstinence from alcohol and can influence the course of the disorder. Although the mechanisms of sleep disturbances of AD are not well understood and some evidence suggests dysregulation of circadian rhythms, dim light melatonin onset (DLMO) has not previously been assessed in AD versus healthy control (HC) individuals in a sample that varied by sex and race. The authors assessed 52 AD participants (mean?±?SD age: 36.0?±?11.0 yrs of age, 10 women) who were 3–12 wks since their last drink (abstinence: 57.9?±?19.3 d) and 19 age- and sex-matched HCs (34.4?±?10.6 yrs, 5 women). Following a 23:00–06:00?h at-home sleep schedule for at least 5 d and screening/baseline nights in the sleep laboratory, participants underwent a 3-h extension of wakefulness (02:00?h bedtime) during which salivary melatonin samples were collected every 30?min beginning at 19:30?h. The time of DLMO was the primary measure of circadian physiology and was assessed with two commonly used methodologies. There was a slower rate of rise and lower maximal amplitude of the melatonin rhythm in the AD group. DLMO varied by the method used to derive it. Using 3 pg/mL as threshold, no significant differences were found between the AD and HC groups. Using 2 standard deviations above the mean of the first three samples, the DLMO in AD occurred significantly later, 21:02?±?00:41?h, than in HC, 20:44?±?00:21?h (t?=??2.4, p?=?.02). Although melatonin in the AD group appears to have a slower rate of rise, using well-established criteria to assess the salivary DLMO did not reveal differences between AD and HC participants. Only when capturing melatonin when it is already rising was DLMO found to be significantly delayed by a mean 18?min in AD participants. Future circadian analyses on alcoholics should account for these methodological caveats. (Author correspondence: daconroy@med.umich.edu)