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.     

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.     

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).     

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).     

Treatment of Chronic Insomnia with Yoga: A Preliminary Study with Sleep–Wake Diaries

There is good evidence for cognitive and physiological arousal in chronic insomnia. Accordingly, clinical trial studies of insomnia treatments aimed at reducing arousal, including relaxation and meditation, have reported positive results. Yoga is a multicomponent practice that is also known to be effective in reducing arousal, although it has not been well evaluated as a treatment for insomnia. In this preliminary study, a simple daily yoga treatment was evaluated in a chronic insomnia population consisting of sleep-onset and/or sleep-maintenance insomnia and primary or secondary insomnia. Participants maintained sleep–wake diaries during a pretreatment 2-week baseline and a subsequent 8-week intervention, in which they practiced the treatment on their own following a single in-person training session with subsequent brief in-person and telephone follow-ups. Sleep efficiency (SE), total sleep time (TST), total wake time (TWT), sleep onset latency (SOL), wake time after sleep onset (WASO), number of awakenings, and sleep quality measures were derived from sleep–wake diary entries and were averaged in 2-week intervals. For 20 participants completing the protocol, statistically significant improvements were observed in SE, TST, TWT, SOL, and WASO at end-treatment as compared with pretreatment values.     

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.     

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.     

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)     

Persistence of social jetlag and sleep disruption in healthy young adults

Sleep disruption has been associated with increased risks for several major chronic diseases that develop over decades. Differences in sleep/wake timing between work and free days can result in the development of social jetlag (SJL), a chronic misalignment between a person’s preferred sleep/wake schedule and sleep/wake timing imposed by his/her work schedule. Only a few studies have examined the persistence of SJL or sleep disruption over time. This prospective investigation examined SJL and sleep characteristics over a 2-year period to evaluate whether SJL or poor sleep were chronic conditions during the study period. SJL and sleep measures (total sleep time [TST], sleep onset latency [SOL], wake after sleep onset [WASO]), and sleep efficiency [SE]), were derived from armband monitoring among 390 healthy men and women 21–35 years old. Participants wore the armband for periods of 4–10 days at 6-month intervals during the follow-up period (N = 1431 repeated observations).

The consistency of SJL or sleep disruption over time was analyzed using generalized linear mixed models (GLMMs) for repeated measures. Repeated measures latent class analysis (RMLCA) was then used to identify subgroups among the study participants with different sleep trajectories over time. Individuals in each latent group were compared using GLMMs to identify personal characteristics that differed among the latent groups.

Minor changes in mean SJL, chronotype, or TST were observed over time, whereas no statistically significant changes in SOL, WASO, or SE were observed during the study period. The RMLCA identified two groups of SJL that remained consistent throughout the study (low SJL, mean ± SE: 0.4 ± 0.04 h, 42% of the study population; and high SJL, 1.4 ± 0.03 h, 58%). Those in the SJL group with higher values tended to be employed and have an evening chronotype.

Similarly, two distinct subgroups were observed for SOL, WASO, and SE; one group with a pattern suggesting disrupted sleep over time, and another with a consistently normal sleep pattern. Analyses of TST identified three latent groups with relatively short (5.6 ± 1.0 h, 21%), intermediate (6.5 ± 1.0 h, 44%), and long (7.3 ± 1.0 h, 36%) sleep durations, all with temporally stable, linear trajectories. The results from this study suggest that sleep disturbances among young adults can persist over a 2 year period. Latent groups with poor sleep tended to be male, African American, lower income, and have an evening chronotype relative to those with more normal sleep characteristics. Characterizing the persistence of sleep disruption over time and its contributing factors could be important for understanding the role of poor sleep as a chronic disease risk factor.     

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.     

Sleep quality in adult patients with sleep related bruxism

Sleep related bruxism (SB) is the grinding of teeth during sleep and may also be associated with various sleep disorders. However, little is known about sleep structures and disturbances of SB. This study aims to further understand sleep architectures using overnight polysomnography (PSG) in patients with SB. We analyze sleep parameters and architectures in 33 healthy subjects and 25 patients with SB. PSG and sleep questionnaires measured sleep variables including proportions of rapid eye movement (REM) sleep, non-REM sleep (N1, N2 and N3), latency to sleep onset, sleep efficiency, wake after sleep onset (WASO), apnea hypopnea index (AHI), respiratory disturbance index (RDI), and periodic limb movement index (PLMI) during sleep for both groups. Sleep efficiency and the proportion of N3 in the SB group were significantly lower than in the control group (P < 0.05). In addition latency to onset of sleep and WASO were markedly increased in the SB group (P < 0.05). AHI, RDI, and PLMI showed no differences between the groups. Epworth Sleepiness Scale was significantly higher in the SB group than in the control group (P < 0.05). In contrast to previous studies, we conclude that patients with SB are not good sleepers based on PSG study. Further studies are required to assess the relationship between sleep quality and the severity of SB.

     

Association of rest–activity and light exposure rhythms with sleep quality in insomnia patients

ABSTRACT

The relevance of altered rest-activity rhythm (RAR) and light exposure rhythm (LER) in insomnia patients under natural conditions remains unclear. The aim of this study was to compare the parametric and nonparametric circadian variables of RAR and those of LER under natural conditions between insomnia patients and normal controls (NC) in a community-dwelling setting. The relationship of the nonparametric variables with sleep quality was also explored in both groups. Participants above 18 years old were recruited from three Public Health Centers in a rural area of Korea. Actigraphy (Actiwatch 2; Philips Respironics, Murrysville PA, USA) recording was conducted for 7 days. Subjects were eligible for our study if they had an insomnia disorder (ID) for at least 1 month. Actigraphy data of 78 normal control (NC) subjects (Age, 55.95 ± 13.22 years) and 104 patients with insomnia disorder (ID) (Age, 62.14 ± 12.34 years) were included for the analysis. Acrophases and amplitudes of RAR and LER were estimated using cosinor analysis. Interdaily stability (IS), intradaily variability (IV), and relative amplitude (RA) of these rhythms were determined using nonparametric methods. Parametric cosinor and nonparametric variables of RAR and LER were compared between the NC and ID groups. Generalized linear models (GLMs) were applied to evaluate the main effects of group and each nonparametric variable as well as a group by each variable interaction on the sleep onset latency (SOL), sleep efficiency (SE), and wake after sleep onset (WASO) reflecting sleep quality. Among sleep parameters, the ID group showed significantly lower SE and greater WASO than the NC group. There were no significant differences in the acrophase and amplitude of RAR and LER between the two groups. There were no significant differences in IV, IS, and RA of RAR and LER between the two groups either. GLMs for RAR revealed a significant interaction between the group and IS on the SOL (β = ?46.39, p < 0.01), indicating a negative relationship of the IS with SOL in ID unlike its positive relationship in NC. There were no significant main effects of IV on the SOL, SE, and WASO, but significant main effects of RA on the SE and WASO (β = 63.65 and β = ?221.43, respectively, p < 0.01). GLMs for LER revealed no significant main effects of IS, IV or RA on the SOL, SE, and WASO, but significant interactions between group and RA on the SE and WASO (β = 56.17 and β = ?171.93, respectively, p < 0.05), indicating a stronger positive relationship of the RA with SE in ID compared to NC, and a negative relationship of the RA with WASO in ID, unlike its positive relationship in NC. Although our study did not reveal group differences in circadian variables of RAR and LER, it suggested that the regularity of RAR could be positively associated with sleep initiation, while the robustness of LER could be positively associated with sleep maintenance in insomnia patients.     

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.     

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.     

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.     

Duration of Sleep Inertia after Napping during Simulated Night Work and in Extended Operations

Due to the mixed findings of previous studies, it is still difficult to provide guidance on how to best manage sleep inertia after waking from naps in operational settings. One of the few factors that can be manipulated is the duration of the nap opportunity. The aim of the present study was to investigate the magnitude and time course of sleep inertia after waking from short (20-, 40- or 60-min) naps during simulated night work and extended operations. In addition, the effect of sleep stage on awakening and duration of slow wave sleep (SWS) on sleep inertia was assessed. Two within-subject protocols were conducted in a controlled laboratory setting. Twenty-four healthy young men (Protocol 1: n?=?12, mean age?=?25.1 yrs; Protocol 2: n?=?12, mean age?=?23.2 yrs) were provided with nap opportunities of 20-, 40-, and 60-min (and a control condition of no nap) ending at 02:00?h after ～20?h of wakefulness (Protocol 1 [P1]: simulated night work) or ending at 12:00?h after ～30?h of wakefulness (Protocol 2 [P2]: simulated extended operations). A 6-min test battery, including the Karolinska Sleepiness Scale (KSS) and the 4-min 2-Back Working Memory Task (WMT), was repeated every 15?min the first hour after waking. Nap sleep was recorded polysomnographically, and in all nap opportunities sleep onset latency was short and sleep efficiency high. Mixed-model analyses of variance (ANOVA) for repeated measures were calculated and included the factors time (time post-nap), nap opportunity (duration of nap provided), order (order in which the four protocols were completed), and the interaction of these terms. Results showed no test x nap opportunity effect (i.e., no effect of sleep inertia) on KSS. However, WMT performance was impaired (slower reaction time, fewer correct responses, and increased omissions) on the first test post-nap, primarily after a 40- or 60-min nap. In P2 only, performance improvement was evident 45?min post-awakening for naps of 40?min or more. In ANOVAs where sleep stage on awakening was included, the test x nap opportunity interaction was significant, but differences were between wake and non-REM Stage 1/Stage 2 or wake and SWS. A further series of ANOVAs showed no effect of the duration of SWS on sleep inertia. The results of this study demonstrate that no more than 15?min is required for performance decrements due to sleep inertia to dissipate after nap opportunities of 60?min or less, but subjective sleepiness is not a reliable indicator of this effect. Under conditions where sleep is short, these findings also suggest that SWS, per se, does not contribute to more severe sleep inertia. When wakefulness is extended and napping occurs at midday (i.e., P2), nap opportunities of 40- and 60-min have the advantage over shorter duration sleep periods, as they result in performance benefits ～45?min after waking.     

Duration of sleep inertia after napping during simulated night work and in extended operations

Due to the mixed findings of previous studies, it is still difficult to provide guidance on how to best manage sleep inertia after waking from naps in operational settings. One of the few factors that can be manipulated is the duration of the nap opportunity. The aim of the present study was to investigate the magnitude and time course of sleep inertia after waking from short (20-, 40- or 60-min) naps during simulated night work and extended operations. In addition, the effect of sleep stage on awakening and duration of slow wave sleep (SWS) on sleep inertia was assessed. Two within-subject protocols were conducted in a controlled laboratory setting. Twenty-four healthy young men (Protocol 1: n = 12, mean age = 25.1 yrs; Protocol 2: n = 12, mean age = 23.2 yrs) were provided with nap opportunities of 20-, 40-, and 60-min (and a control condition of no nap) ending at 02:00 h after ～20 h of wakefulness (Protocol 1 [P1]: simulated night work) or ending at 12:00 h after ～30 h of wakefulness (Protocol 2 [P2]: simulated extended operations). A 6-min test battery, including the Karolinska Sleepiness Scale (KSS) and the 4-min 2-Back Working Memory Task (WMT), was repeated every 15 min the first hour after waking. Nap sleep was recorded polysomnographically, and in all nap opportunities sleep onset latency was short and sleep efficiency high. Mixed-model analyses of variance (ANOVA) for repeated measures were calculated and included the factors time (time post-nap), nap opportunity (duration of nap provided), order (order in which the four protocols were completed), and the interaction of these terms. Results showed no test x nap opportunity effect (i.e., no effect of sleep inertia) on KSS. However, WMT performance was impaired (slower reaction time, fewer correct responses, and increased omissions) on the first test post-nap, primarily after a 40- or 60-min nap. In P2 only, performance improvement was evident 45 min post-awakening for naps of 40 min or more. In ANOVAs where sleep stage on awakening was included, the test x nap opportunity interaction was significant, but differences were between wake and non-REM Stage 1/Stage 2 or wake and SWS. A further series of ANOVAs showed no effect of the duration of SWS on sleep inertia. The results of this study demonstrate that no more than 15 min is required for performance decrements due to sleep inertia to dissipate after nap opportunities of 60 min or less, but subjective sleepiness is not a reliable indicator of this effect. Under conditions where sleep is short, these findings also suggest that SWS, per se, does not contribute to more severe sleep inertia. When wakefulness is extended and napping occurs at midday (i.e., P2), nap opportunities of 40- and 60-min have the advantage over shorter duration sleep periods, as they result in performance benefits ～45 min after waking.     

Effects of Low Doses of Melatonin on Late Afternoon Napping and Mood

The effects of low doses of melatonin (0.1, 0.5 and 1 mg) given at 16:00 h on induction and quality of sleep in the late afternoon (17:00-21:00 h), as well as on subjective fatigue and mood ratings before and after sleep were studied. Ten healthy male volunteers (age 26-30 years) were given on a double-blind crossover basis, tablets containing melatonin, or placebo, with one day washout between treatments. Mood and fatigue were assessed before and after bedtime. Sleep quality was objectively monitored using wrist-worn actigraphs and subjectively by using sleep logs. Data were analysed by means of analysis of variance for repeated measures with a factor of group (placebo and the three melatonin doses). The analysis revealed dose-dependent increase by melatonin in subjective evaluation of fatigue and sleepiness, and decrease in alertness, efficiency, vigor and concentration before the nap. Melatonin did not significantly affect actigraph-measured nap sleep latency and efficiency but reduced wake time after sleep onset and delayed sleep offset time compared to placebo, Melatonin did not significantly affect sleep latency and sleep efficiency in the night following the treatment. These data indicate acute effects of low doses of melatonin given at 16:00h on sleepiness and fatigue but not on sleep efficiency or latency in healthy young individuals.     

Effects of Low Doses of Melatonin on Late Afternoon Napping and Mood

The effects of low doses of melatonin (0.1, 0.5 and 1 mg) given at 16:00 h on induction and quality of sleep in the late afternoon (17:00-21:00 h), as well as on subjective fatigue and mood ratings before and after sleep were studied. Ten healthy male volunteers (age 26-30 years) were given on a double-blind crossover basis, tablets containing melatonin, or placebo, with one day washout between treatments. Mood and fatigue were assessed before and after bedtime. Sleep quality was objectively monitored using wrist-worn actigraphs and subjectively by using sleep logs. Data were analysed by means of analysis of variance for repeated measures with a factor of group (placebo and the three melatonin doses). The analysis revealed dose-dependent increase by melatonin in subjective evaluation of fatigue and sleepiness, and decrease in alertness, efficiency, vigor and concentration before the nap. Melatonin did not significantly affect actigraph-measured nap sleep latency and efficiency but reduced wake time after sleep onset and delayed sleep offset time compared to placebo, Melatonin did not significantly affect sleep latency and sleep efficiency in the night following the treatment. These data indicate acute effects of low doses of melatonin given at 16:00h on sleepiness and fatigue but not on sleep efficiency or latency in healthy young individuals.     

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.