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.     

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).     

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).     

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)     

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.     

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.     

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.

     

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.     

Association Between Thyroid Function and Objective and Subjective Sleep Quality in Older Men: The Osteoporotic Fractures in Men (MrOS) Study

ObjectiveTo determine the association between thyroid hormone levels and sleep quality in community-dwelling men.MethodsAmong 5,994 men aged ≥ 65 years in the Osteoporotic Fractures in Men (MrOS) study, 682 had baseline thyroid function data, normal free thyroxine (FT₄) (0.70 ≤ FT₄ ≤ 1.85 ng/dL), actigraphy measurements, and were not using thyroid-related medications. Three categories of thyroid function were defined: subclinical hyperthyroid (thyroid-stimulating hormone [TSH] < 0.55 mIU/L), euthyroid (TSH, 0.55 to 4.78 mIU/L), and subclinical hypothyroid (TSH > 4.78 mIU/L). Objective (total hours of nighttime sleep [TST], sleep efficiency [SE], wake after sleep onset [WASO], sleep latency [SL], number of long wake episodes [LWEP]) and subjective (TST, Pittsburgh Sleep Quality Index score, Epworth Sleepiness Scale score) sleep quality parameters were measured. The association between TSH and sleep quality was examined using linear regression (continuous sleep outcomes) and log-binomial regression (categorical sleep outcomes).ResultsAmong the 682 men examined, 15 had subclinical hyperthyroidism and 38 had subclinical hypothyroidism. There was no difference in sleep quality between subclinical hypothyroid and euthyroid men. Compared to euthyroid men, subclinical hyperthyroid men had lower mean actigraphy TST (adjusted mean difference [95% confidence interval (CI)], − 27.4 [− 63.7 to 8.9] minutes), lower mean SE (− 4.5% [− 10.3% to 1.3%]), and higher mean WASO (13.5 [− 8.0 to 35.0] minutes]), whereas 41% had increased risk of actigraphy-measured TST < 6 hours (relative risk [RR], 1.41; 95% CI, 0.83 to 2.39), and 83% had increased risk of SL ≥ 60 minutes (RR, 1.83; 95% CI, 0.65 to 5.14) (all P > .05).ConclusionNeither subclinical hypothyroidism nor hyperthyroidism is significantly associated with decreased sleep quality. (Endocr Pract. 2014;20:576-586)     

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)     

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.     

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.     

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.     

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.     

Effects of essential oil inhalation on objective and subjective sleep quality in healthy university students

Aromatherapy with essential oils is one of the most popular complementary medical tools for improving sleep quality. However, only a few reports have objectively measured the effects of essential oils on sleep. Here, we used objective and subjective measures to analyze the effects of the essential oils of lavender (Lavandula angustifolia) and sweet orange (Citrus sinensis) on the sleep quality of healthy university students. The participants were monitored for 15 consecutive nights as they inhaled lavender oil and sweet orange oil, in a crossover design. Their sleep was monitored objectively by actigraphy, and total sleep time (TST), sleep efficiency, sleep latency, and wake after sleep onset (WASO) were analyzed. Their sleep was analyzed subjectively using Oguri–Shirakawa–Azumi (OSA) sleep inventory scores. Inhalation of an essential oil improved sleep measures only in participant whose sleep quality was poor in the control condition. Lavender seemed more effective than sweet orange in objective measures, especially in improving sleep latency. In the subjective sleep analysis, the essential oils improved sleep maintenance, dreaming, and sleep length in subjects who had poor sleep quality. Sweet orange seemed more effective than lavender in the subjective sleep measures. The difference between the two oils suggests that expectancy bias had little effect on the hypnotic effect of lavender on objective sleep. Although no obvious effect was observed in good sleepers, the inhalation of lavender oil could be effective for helping poor sleepers improve objective sleep quality.

     

Accuracy of PurePulse photoplethysmography technology of Fitbit Charge 2 for assessment of heart rate during sleep

Elevated asleep heart rate (HR) is a risk factor for cardiovascular disease and other-cause morbidity and mortality. We assessed the accuracy of Fitbit Inc. PurePulse® photoplethysmography with reference to three-lead electrocardiography (ECG) in determining HR during sleep. HR of 35 (17 female) healthy adults 25.1 ± 10.6 years of age (mean ± SD) was continuously recorded throughout a single night of sleep. There was no significant difference in asleep HR mean (0.09 beats per minute [bpm], P = 0.426) between Fitbit photoplethysmography and ECG; plus, there was excellent intraclass correlation (0.998) and narrow Bland–Altman agreement range (2.67 bpm). The regression analysis of Bland–Altman plot of mean asleep HR indicates Fitbit tends to slightly overestimate reference values in the lower range of HR (HR < 50 bpm) by 0.51 bpm and slightly underestimate reference values in the higher range of HR (HR > 80 bpm) by 0.63 bpm. Mixed model analysis of epoch-by-epoch (5-min epochs) asleep HR showed significant “U” shape trend (P < 0.001) in amount of Fitbit error (absolute amount of difference between ECG and Fitbit values regardless of overestimation or underestimation) in regard to HR, i.e. smaller error in the medium range of HR (60–80 bpm) and slightly larger error for lower (<60 bpm) and higher (>80 bpm) ranges of HR. However, effect of age, body mass index, gender, and subjective sleep quality measured by Pittsburgh sleep quality index (good/poor sleepers) on error in estimating HR by the Fitbit method was not significant. It is concluded that Fitbit photoplethysmography suitably tracks HR during sleep in healthy young adults.     

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.     

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.     

Polyphasic Wake/Sleep Episodes in the Fire Ant, Solenopsis Invicta

Sleep is a well-studied biological process in vertebrates, particularly birds and mammals. Less is know about sleep in solitary and social invertebrates, particularly the ants. This paper reports a study of light/dark periods on worker activity as well as sleep location, posture and the wake/sleep cycles of fire ant workers and queens located in an artificial nest chamber. Workers slept in one of three locations: on the ceiling, against the chamber wall or in the center of the chamber floor. Workers on the ceiling or against the chamber wall slept for longer periods than those at the center of the chamber floor where most grooming and feeding activity occurred. When sleeping, queens huddled together. Their close contact generated synchronized wake/sleep cycles with each other. Sleep posture was distinctly different than wake posture. During deep sleep, queens and workers folded their antennae and were non-responsive to contact by other ants. Another indicator of deep sleep was rapid antennal movement (RAM sleep). Sleep episodes were polyphasic. Queens averaged ~92 sleep episodes per day, each episode lasting ~6 min, for a total of ~9.4 h of sleep per day. Workers averaged ~253 sleep episodes lasting 1.1 min each for a total of ~4.8 h of sleep per day. Activity episodes were unaffected by light/dark periods. Workers were hypervigilant with an average of 80% of the labor force completing grooming, feeding or excavation tasks at any given time. These findings reinforce the parental exploitation hypothesis—sterile workers are a caste of disposable, short-lived helpers whose vigilance and hyperactivty increases the queen’s fitness by buffering her and her fertile offspring from environmental stresses.     

Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep-provoking conditions

Age-associated changes in different bandwidths of the human electroencephalographic (EEG) spectrum are well documented, but their functional significance is poorly understood. This spectrum seems to represent summation of simultaneous influences of several sleep–wake regulatory processes. Scoring of its orthogonal (uncorrelated) principal components can help in separation of the brain signatures of these processes. In particular, the opposite age-associated changes were documented for scores on the two largest (1st and 2nd) principal components of the sleep EEG spectrum. A decrease of the first score and an increase of the second score can reflect, respectively, the weakening of the sleep drive and disinhibition of the opposing wake drive with age. In order to support the suggestion of age-associated disinhibition of the wake drive from the antagonistic influence of the sleep drive, we analyzed principal component scores of the resting EEG spectra obtained in sleep deprivation experiments with 81 healthy young adults aged between 19 and 26 and 40 healthy older adults aged between 45 and 66 years. At the second day of the sleep deprivation experiments, frontal scores on the 1st principal component of the EEG spectrum demonstrated an age-associated reduction of response to eyes closed relaxation. Scores on the 2nd principal component were either initially increased during wakefulness or less responsive to such sleep-provoking conditions (frontal and occipital scores, respectively). These results are in line with the suggestion of disinhibition of the wake drive with age. They provide an explanation of why older adults are less vulnerable to sleep deprivation than young adults.