Wrist Actigraphy Data Research Articles

Work Habit-Related Sleep Debt; Insights From Factor Identification Analysis of Actigraphy Data.

The present study investigates the factors of “Weekday sleep debt (WSD)” by comparing activity data collected from persons with and without WSD. Since it has been reported that the amount of sleep debt as well the difference between the social clock and the biological clock is associated with WSD, specifying the factors of WSD other than chronotype may contribute to sleep debt prevention. We recruited 324 healthy male employees working at the same company and collected their 1-week wrist actigraphy data and answers to questionnaires. Because 106 participants were excluded due to measurement failure of the actigraphy data, the remaining 218 participants were included in the analysis. All participants were classified into WSD or non-WSD groups, in which persons had WDS if the difference between their weekend sleep duration and the mean weekday sleep duration was more than 120 min. We evaluated multiple measurements derived from the collected actigraphy data and trained a classifier that predicts the presence of WSD using these measurements. A support vector machine (SVM) was adopted as the classifier. In addition, to evaluate the contribution of each indicator to WSD, permutation feature importance was calculated based on the trained classifier. Our analysis results showed significant importance of the following three out of the tested 32 factors: (1) WSD was significantly related to persons with evening tendency. (2) Daily activity rhythms and sleep were less stable in the WSD group than in the non-WSD group. (3) A specific day of the week had the highest importance in our data, suggesting that work habit contributes to WSD. These findings indicate some WSD factors: evening chronotype, instability of the daily activity rhythm, and differences in work habits on the specific day of the week. Thus, it is necessary to evaluate the rhythms of diurnal activities as well as sleep conditions to identify the WSD factors. In particular, the diurnal activity rhythm influences WSD. It is suggested that proper management of activity rhythm may contribute to the prevention of sleep debt.

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3–6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2–34.3% higher than comparator IAs), and 58.7% Kappa agreement (16–23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters—sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs.

Influences Of Sleep Disturbances And Mild Traumatic Brain Injury On Gait Performance Among College Students

Sleep disturbances from stress are common among college students. In addition, student athletes in contact sports (e.g., football and soccer) are at a higher risk of receiving a mild traumatic brain injury (mTBI). Stress-related sleep disturbances and mTBI may provoke cognitive and brain changes associated with gait abnormalities. PURPOSE: to examine the association between stress, sleep disturbances, mTBI and gait performance among college students. METHODS: The control group included 28 college students (11 males, 17 females, age: 22.6±3.2 yrs) and the mTBI group consisted of 12 NCAA Division II men’s and women’s soccer players who indicated a history of concussion (3 males, 9 females, age: 19.6±1.3 yrs). Participants were asked to perform gait analysis with an in-shoe pressure measurement system during and after the midterm exam. Cadence, step time, stride time, stance time and swing time were measured. Meanwhile, 14-days consecutive wrist actigraphy data and three sets of questionnaires were collected to assess their stress, sleep and fatigue. A mixed-design ANOVA was used to compare gait and sleep parameters of mTBI and control groups. RESULTS: The mTBI group had significantly shorter step time (0.54±0.01 vs. 0.58±0.01 s, p=.011) and stance time (0.66±0.01 vs. 0.70±0.01 s, p=.013) compared to the control group. The control group had significantly longer step time and stance time during the midterm exam than after the midterm exam. However, no significant differences of gait parameters were observed for the mTBI group during and after midterm exam. During the exam week, participants in both control and mTBI groups perceived moderate stress and reported 2-3 nights of sleep disturbances. Meanwhile, stress level was positively associated with sleep disturbances, poor daytime functioning, and poor activity correlation. CONCLUSION: Midterm exam as a stressor resulted in sleep disturbances in both mTBI and control groups. This stressor may also cause gait abnormalities of the control group, but not the mTBI group. Another interesting finding is that shorter step time and stance time were observed in the mTBI group. Nevertheless, further studies need to examine whether this is indeed due to mTBI, or the difference between student athletes and non-athletes. Supported by Office of Research and Scholarship at UT Tyler.

1196 Machine Learning Derived-Interpretative Algorithm Better Differentiates Sleep and Wake Epochs and Estimates Sleep Parameters from Wrist Actigraphy Data

Abstract Introduction Several different interpretive algorithms (IAs) are available for scoring actigraphy-obtained body movement data for sleep and wake epochs. Although most have high sensitivity in detecting sleep epochs, they identify wake epochs poorly. We derived a machine learning (ML) based IA that improves differentiation of sleep and wake epoch to better estimate sleep parameters. Methods Forty-one adults (18 females) 26.6±12.0 years old underwent at-home single-night sleep assessment. Motionlogger® Micro Watch Actigraph recorded in zero crossing mode body movement per 30s epoch, with automated sleep scoring by single-channel electroencephalography (EEG) device (Zmachine® Insight+) as reference. The popular Cole-Kripke IA was applied to score body movement time series data of the following combination of current 1, preceding 4, and following 2 minute long epochs. Data of 21 subjects were utilized to train/derive the ML IA (logistic regression), and data of the other 20 subjects were used to test performance of it and the Cole-Kripke IA. Results In reference to the EEG, the Cole-Kripke actigraphy IA showed sensitivity of 0.98±0.02, specificity of 0.48±0.19, and kappa agreement of 0.53±0.16 in detecting sleep epochs, while the ML-derived IA showed corresponding values of 0.90±0.06, 0.71±0.14, and 0.57±0.11. The Cole-Kripke IA, relative to EEG, method significantly (P<0.05) underestimated sleep onset latency (SOL) by 18.0 min and wake after sleep onset (WASO) by 35.1 min, and overestimated total sleep time (TST) by 53.1 min and sleep efficiency (SE) by 9.6%. The ML-derived IA, relative to EEG significantly underestimated SOL by 15.1 min, but comparably (P>0.05) estimated WASO, TST, and SE. Conclusion The ML-derived IA, in comparison to Cole-Kripke IA, when applied to sleep-time wrist actigraphy data significantly better differentiates wake from sleep epochs and better estimates sleep parameters. Support This work was supported by the Robert and Prudie Leibrock Professorship in Engineering at the University of Texas at Austin.

0825 Sleep During Pregnancy Through One Year Postpartum: Correspondence Between Actigraphy and Self-Report Measures

Abstract Introduction Disrupted sleep and shorter sleep duration is common in pregnancy, due to hormonal changes and physical discomfort, and in postpartum due to new infants. Objective data in this population studied over more than one year are lacking. The current analysis focuses on actigraphy-based and self-reported sleep. We examined the level of correspondence between these two complementary measurement modalities. Methods Pregnant women were enrolled in the Stress and Health in Pregnancy and Postpartum (SHIPP) study. Study visits were conducted from 2016-2019 during the 3rd trimester, 4-6 weeks postpartum, and 4 months, 8 months, and 12 months postpartum. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and provided wrist-actigraphy data (Actiwatch, Philips Respironics) for one week prior to each study visit. Actigraphy-based time in bed (TIB), total sleep time (TST), sleep efficiency (SE), WASO and sleep latency (SL) were calculated. Correlations were conducted between actigraphy-based and self-reported PSQI sleep measures. Results 79 women (28.9±4.6 years) provided complete actigraphy data (≥3 valid days; 6.4±1.0 days) for at least one time point. Objective TST from the 3rd trimester to 12-months postpartum was 7.19, 6.87, 6.86, 6.89, and 6.78 hours, respectively. Actigraphy-based TIB was positively correlated with self-reported TIB at all five visits (r’s: 0.37-0.62, p’s<0.01). Actigraphy-based TST was positively correlated with self-reported TST (r’s: 0.27-0.48, p’s<0.05) at all visits. Actigraphy-based SE was negatively correlated with the PSQI Global Score at 4, 8, and 12 months postpartum (r’s: -0.29 to -0.39, p’s<0.05). Of note, actigraphy-based WASO and SL were not consistently correlated with any self-reported PSQI sleep measures. Conclusion Ensuring collection of accurate sleep data during pregnancy and postpartum is important, as poor sleep is associated with negative health outcomes for both mother and child. Self-reported data is common in large, epidemiologic studies yet actigraphy-based measures may capture different aspects of sleep than self-report. Support This study was supported by the National Institutes of Health (R01 NR01366).

A Non-Drug Study Detecting And Quantifying Nocturnal Scratch Behaviors From Wrist Actigraphy Data In Adult Healthy Volunteers And Participants With Atopic Dermatitis (AD)

A Non-Drug Study Detecting And Quantifying Nocturnal Scratch Behaviors From Wrist Actigraphy Data In Adult Healthy Volunteers And Participants With Atopic Dermatitis (AD)

Detection and analysis of pulse waves during sleep via wrist-worn actigraphy.

The high temporal and intensity resolution of modern accelerometers gives the opportunity of detecting even tiny body movements via motion-based sensors. In this paper, we demonstrate and evaluate an approach to identify pulse waves and heartbeats from acceleration data of the human wrist during sleep. Specifically, we have recorded simultaneously full-night polysomnography and 3d wrist actigraphy data of 363 subjects during one night in a clinical sleep laboratory. The acceleration data was segmented and cleaned, excluding body movements and separating episodes with different sleep positions. Then, we applied a bandpass filter and a Hilbert transform to uncover the pulse wave signal, which worked well for an average duration of 1.7 h per subject. We found that 81 percent of the detected pulse wave intervals could be correctly associated with the R peak intervals from independently recorded ECGs and obtained a median Pearson cross-correlation of 0.94. While the low-frequency components of both signals were practically identical, the high-frequency component of the pulse wave interval time series was increased, indicating a respiratory modulation of pulse transit times, probably as an additional contribution to respiratory sinus arrhythmia. Our approach could be used to obtain long-term nocturnal heartbeat interval time series and pulse wave signals from wrist-worn accelerometers without the need of recording ECG or photoplethysmography. This is particularly useful for an ambulatory monitoring of high-risk cardiac patients as well as for assessing cardiac dynamics in large cohort studies solely with accelerometer devices that are already used for activity tracking and sleep pattern analysis.

Sleep Patterns and Obesity: Hispanic Community Health Study/Study of Latinos Sueño Ancillar Study

The relationship of poor sleep patterns to the increased risk of obesity has been reported, but the results are variable. This study evaluated the association between objectively measured sleep patterns and obesity in a representative adult population of Hispanic/Latino subjects living in the United States. This cross-sectional study was an analysis of a multicenter, community-based cohort of 2,156 participants aged 18 to 64 years from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Recruitment was conducted in San Diego, California; Chicago, Illinois; Bronx, New York; and Miami, Florida. Models were controlled for age, sex, ethnic background, site, income, education, and apnea-hypopnea index. Seven days of wrist actigraphy data were collected. Obesity was defined as BMI≥ 30kg/m2, and abdominal obesity was defined as waist circumference≥ 88cm in women and≥ 102cm in men. Napping was defined as more than one 15-min nap per week. An inverse linear relationship was found between sleep duration and prevalence of obesity (P linear trend≤ 0.01). A reduction of 1h sleep increased obesity prevalence by 4.1%(95%CI, 1.6-6.6; P= .002) and abdominal obesity prevalence by 3.6%(95%CI, 1.1-6.1; P= .007). Daytime napping increased obesity prevalence by 10.4%(95%CI, 3.5-17.3; P= .004) and abdominal obesity prevalence by 7.1%(95%CI, 1.0-13.2; P= .02). In a population of young to older adult Hispanic/Latino subjects, we found an inverse linear association between sleep duration and the prevalence of obesity. Daytime napping was strongly associated with greater adiposity. Interventional and longitudinal studies are needed to better understand how abnormal sleep patterns contribute to the obesity epidemic.

0699 CIRCADIAN ACTIVITY RHYTHMS AND SLEEP AMONG CHINESE COLLEGE STUDENTS

Sleep problems are prevalent for young adults. For college students, sleep disturbances could be associated with their stress derived from school works or daily life. Circadian activity rhythms (CAR) have been found to be altered among dementia, cancer patients, and distressed postpartum women. To date, studies exploring CAR in healthy adults are rare. The study aimed to: 1) describe the characteristics of CAR, and 2) explore the associations between CAR and stress and stress-related symptoms in college students. Impaired Sleep Model was used to guide for this descriptive correlational study. A total of 26 (12 males, 14 females) college students completed this study. A battery of questionnaires was used during the week after midterm examination to assess stress and stress-related symptoms (sleep disturbances, fatigue, physical symptoms). One-week consecutive wrist actigraphy data, including total sleep time (TST) and wake after sleep onset (WASO) were collected. Cosinor analysis was used for computing the CAR, including mesor, acrophase (time of the peak of the fitted activity curve), and amplitude. The mean age for the students was 21.04 (SD= .92) and in average they need about 8 hours of sleep to feel refreshed; however, the actual nocturnal TST (M= 396.7 minutes, SEM= 12.57) was significantly less then what they needed (paired t[25]= -3.7, p= .001). They perceived moderate stress, reported a clinically significant fatigue severity, and half of them experienced at least two physical symptoms. The CAR was desynchronized (M= .63, SEM= .03) and WASO was 7.2% (SEM=.7). The average peak activity level was at 17:18 indicates delayed sleep phase syndrome. A trend was seen between poor CAR and poor daytime functioning and more physical symptoms. After control for gender, CAR along with perceived stress explained 64% of variance in nocturnal total sleep time. Results showed that college student’s sleep and CAR were disturbed, and they also experienced distress and fatigue. It appears that CAR plays a role in stress-related symptoms. Educational materials in the areas of sleep hygiene and mind-body exercise to regulate CAR should be mandatorily included in the college education.

Sleep and circadian variability in people with delayed sleep–wake phase disorder versus healthy controls

Objective/BackgroundTo compare sleep and circadian variability in adults with delayed sleep–wake phase disorder (DSWPD) to healthy controls. Patients/MethodsForty participants (22 DSWPD, 18 healthy controls) completed a ten-day protocol, consisting of DLMO assessments on two consecutive nights, a five-day study break, followed by two more DLMO assessments. All participants were instructed to sleep within one hour of their self-reported average sleep schedule for the last four days of the study break. We analyzed the participants' wrist actigraphy data during these four days to examine intraindividual variability in sleep timing, duration and efficiency. We also examined shifts in the DLMO from before and after the study break. Results and conclusionsUnder the same conditions, people with DSWPD had significantly more variable wake times and total sleep time than healthy controls (p ≤ 0.015). Intraindividual variability in sleep onset time and sleep efficiency was similar between the two groups (p ≥ 0.30). The DLMO was relatively stable across the study break, with only 11% of controls but 27% of DSWPDs showed more than a one hour shift in the DLMO. Only in the DSWPD sample was greater sleep variability associated with a larger shift in the DLMO (r = 0.46, p = 0.03). These results suggest that intraindividual variability in sleep can be higher in DSWPD versus healthy controls, and this may impact variability in the DLMO. DSWPD patients with higher intraindividual variability in sleep are more likely to have a shifting DLMO, which could impact sleep symptoms and the optimal timing of light and/or melatonin treatment for DSWPD. Clinical trialCircadian Phase Assessments at Home, http://clinicaltrials.gov/show/NCT01487252, NCT01487252.

Cytokine polymorphisms are associated with daytime napping in adults living with HIV

Objective/backgroundDaytime napping longer than one hour has been associated with an increased risk for all-cause mortality. Associations between cytokine polymorphisms and daytime napping in chronic illnesses such as HIV, however, have not been well described. The purpose of this study was to examine cytokine polymorphisms associated with long daytime napping in adults living with HIV. MethodsA cross-sectional analysis was conducted using a convenience sample of 257 adults living with HIV. Daytime napping was assessed with wrist actigraphy data collected over three days. Participants categorized as long nappers (≥60 min) were compared to short nappers and non-nappers (<60 min). Single nucleotide polymorphisms (SNPs) for 15 candidate genes involved in cytokine signaling were analyzed. Genes included: interferon-gamma (IFNG), IFNG receptor 1 (IFNGR1), interleukins (IL1B, IL1R, IL1R2, IL2, IL4, IL6, IL8, IL10, IL13, IL17A), nuclear factors of kappa light polypeptide gene enhancer in B cells (NFKB1 and NFKB2), and tumor necrosis factor alpha (TNFA). ResultsAfter adjusting for relevant demographic and clinical characteristics, long daytime napping was associated with 12 SNPs from seven genes: 1) IFNG rs2069728; 2) IL1B rs1143642, rs1143627, and rs16944; 3) IL2 rs2069763; 4) IL6 rs4719714, rs1554606, and rs2069845; 5) IL17A rs3819024 and rs8193036; 6) NFKB1 rs4648110; and 7) NFKB2 rs1056890. ConclusionsCytokine genetic variations may have a role in physiological regulation of daytime napping as well as nocturnal sleep. Cytokine polymorphisms associated with long daytime napping could help identify adults with HIV who may benefit from targeted therapeutic interventions.

Sleep patterns among shift-working flight controllers of the International Space Station: an observational study on the JAXA Flight Control Team.

BackgroundFlight controllers of the International Space Station (ISS) are engaged in shift work to provide 24-h coverage to support ISS systems. The purpose of this study was to investigate the prevalence and associated factors of shift work sleep disorder (SWSD) among Japanese ISS flight controllers.MethodsA questionnaire study was conducted using the Standard Shiftwork Index to evaluate sleep-related problems and possible associated variables. Among 52 respondents out of 73 flight controllers, 30 subjects were identified as night shift workers who worked 3 or more night shifts per month. Those night shift workers who answered “almost always” to questions about experiencing insomnia or excessive sleepiness in any case of work shifts and days off were classified as having SWSD. Additionally, 7 night shift workers participated in supplemental wrist actigraphy data collection for 7 to 8 days including 3 to 4 days of consecutive night shifts.ResultsFourteen of 30 night shift workers were classified as having SWSD. Significant group differences were observed where the SWSD group felt that night shift work was harder and reported more frequent insomniac symptoms after a night shift. However, no other variables demonstrated remarkable differences between groups. Actigraphy results characterized 5 subjects reporting better perceived adaptation as having regular daytime sleep, for 6 to 9 h in total, between consecutive night shifts. On the other hand, 2 subjects reporting perceived maladaptation revealed different sleep patterns, with longer daytime sleep and large day-to-day variation in daytime sleep between consecutive night shifts, respectively.ConclusionsAs the tasks for flight control require high levels of alertness and cognitive function, several characteristics, namely shift-working schedule (2 to 4 consecutive night shifts), very short break time (5 to 10 min/h) during work shifts, and cooperative work with onboard astronauts during the evening/night shift, accounted for increasing workloads especially in the case of night shifts, resulting in higher or equal prevalence of SWSD to that among other shift-working populations. Further studies are required to collect more actigraphy data and examine the possibility of interventions to improve SWSD.

Rest-Activity Patterns in Older Adults with Heart Failure and Healthy Older Adults.

The purpose of this investigation is to examine differences in rest-activity patterns and sleep characteristics in older adults with heart failure (HF) and healthy older adults. The sample included older adults with HF (n = 20) and a reference group of healthy older adults (n = 20). Traditional cosinor analysis was used to assess three parameters of rest-activity from wrist actigraphy data: amplitude (range of activity), mesor (mean activity), and acrophase (time of peak activity). Traditional sleep characteristics were also determined from actigraphy data: total sleep time (TST), sleep latency (SL), sleep efficiency (SE), and wake after sleep onset (WASO). The HF group demonstrated significantly lower mesor and amplitude than the reference group (p < .01). The HF group had significantly greater TST (p < .01), but the groups had similar SE, SL, and WASO. Despite similar sleep characteristics to healthy older adults, overall rest-activity patterns were significantly dampened in those with HF.

The effect of oral care on intracranial pressure in critically ill adults.

A major goal in the care of patients with neurological problems is to prevent or minimize episodes of increased intracranial pressure (ICP). Elevations in ICP in response to nursing interventions have been acknowledged since the 1960s when ICP monitoring was first introduced in the clinical setting. Until recently, few studies have specifically examined the effect of oral care on ICP, and oral care and other hygiene measures were combined or not specified, prohibiting a direct interpretation of the influence of oral care alone on ICP. The purpose of this study was to describe the relationship between routine oral care interventions and the changes in ICP specifically focusing on the effect of intensity and duration of this intervention. Twenty-three patients with a clinical condition requiring ICP monitoring were enrolled over a 12-month period. Oral care provided by neuroscience intensive care nurses was observed and videotaped. Characteristics of the intervention were documented including products used, patient positioning, and duration of the intervention. A 1-5 subjective scale was used to score intensity of oral care. Wrist actigraphy data were collected from the nurses to provide an objective measure of intensity. Patient physiologic data were collected at 12-second epochs 5 minutes before, during, and 5 minutes after oral care. The mixed-effect repeated measures analysis of variance model indicated that there was a statistically significant increase in ICP in response to oral care (p = .0031). There was, however, no clinically significant effect on ICP. This study provides evidence that oral care is safe to perform in patients in the absence of preexisting elevated ICP.

Examination of wrist and hip actigraphy using a novel sleep estimation procedure

ObjectiveImproving and validating sleep scoring algorithms for actigraphs enhances their usefulness in clinical and research applications. The MTI® device (ActiGraph, Pensacola, FL) had not been previously validated for sleep. The aims were to (1) compare the accuracy of sleep metrics obtained via wrist- and hip-mounted MTI® actigraphs with polysomnographic (PSG) recordings in a sample that included both normal sleepers and individuals with presumed sleep disorders; and (2) develop a novel sleep scoring algorithm using spline regression to improve the correspondence between the actigraphs and PSG. MethodsOriginal actigraphy data were amplified and their pattern was estimated using a penalized spline. The magnitude of amplification and the spline were estimated by minimizing the difference in sleep efficiency between wrist- (hip-) actigraphs and PSG recordings. Sleep measures using both the original and spline-modified actigraphy data were compared to PSG using the following: mean sleep summary measures; Spearman rank-order correlations of summary measures; percent of minute-by-minute agreement; sensitivity and specificity; and Bland–Altman plots. ResultsThe original wrist actigraphy data showed modest correspondence with PSG, and much less correspondence was found between hip actigraphy and PSG. The spline-modified wrist actigraphy produced better approximations of interclass correlations, sensitivity, and mean sleep summary measures relative to PSG than the original wrist actigraphy data. The spline-modified hip actigraphy provided improved correspondence, but sleep measures were still not representative of PSG. DiscussionThe results indicate that with some refinement, the spline regression method has the potential to improve sleep estimates obtained using wrist actigraphy.

Diurnal fatigue patterns, sleep timing, and mental health outcomes among healthy postpartum women.

Postpartum women have frequently interrupted sleep, report high levels of fatigue, and may experience circadian rhythm disruptions. They are also susceptible to mood impairments, anxiety, and stress. The current study explored associations between maternal postpartum daily fatigue patterns, which should vary according to circadian influences and mental health. Seventy-one primiparous, healthy mothers completed multiple daily self-reports of fatigue during postpartum Weeks 2 and 12 and were categorized at each week as having either a rhythmic or random fatigue pattern during the daytime. Wrist actigraphy data were used to calculate sleep midpoints. Surveys assessed chronotype, mood, anxiety, and stress. At postpartum Week 2, there were no differences in mental health measures between fatigue groups. At postpartum Week 12, higher overall fatigue levels were associated with increased anxiety, stress, and mood disruption. However, overall fatigue levels did not differ between fatigue groups. Women with a rhythmic fatigue pattern reported significantly less stress and more vigor than women with a random fatigue pattern. An earlier sleep midpoint was associated with a rhythmic fatigue pattern during postpartum Week 12. These data suggest that, despite similar average daily fatigue levels, having a rhythmic daily pattern of fatigue may be advantageous for mental health outcomes among postpartum women.

Non-24-Hour Disorder in Blind Individuals Revisited: Variability and the Influence of Environmental Time Cues

To assess the interindividual and intraindividual variability in the circadian rhythms of blind individuals with non-24-h disorder and to quantify the influence of environmental time cues in blind subjects lacking entrainment (non-24-h individuals or N-24s). An observational study of 21 N-24s (11 females and 10 males, age 9-78 years) who kept a sleep/wake schedule of their choosing. Circadian phase was determined using the melatonin onset (MO) from plasma or saliva samples that were collected every 2 weeks. Melatonin concentrations were measured by radioimmunoassay. A total of 469 MO assessments were conducted over 5,536 days of study. The rate of drift of circadian phase was calculated using a series of MOs (total number of hours the MO drifted divided by the total number of days studied). Stability of the rest/activity rhythm was calculated using chi-squared periodogram analysis of wrist actigraphy data in 19 subjects. Academic medical center. Paid volunteers. N/A. Subjects lacked entrainment such that circadian phase drifted an average (± standard deviation) of 0.39 ± 0.29 h later per day; however, there was notable intersubject and intrasubject variability in the rate of drift including relative coordination and periods of transient entrainment during which there was little to no drift in the circadian phase. A regular, reproducible, and significant oscillation in the rate of drift was detected in 14 of the 21 subjects. A significant non-24-h rest/activity rhythm was detected in 18 of 19 subjects. There was a strong correlation (r = 0.793, P = 0.0001) between the non-24-h rest/activity rhythm and the rate of drift of the circadian phase. Most N-24s are influenced by unidentified environmental time cues and the non-entrained biological clock in such N-24s is reflected in their rest/activity rhythms. These findings may have diagnostic and treatment implications: this disorder might be diagnosed with actigraphy alone, relative coordination and transient entrainment may result in misdiagnosis and responsiveness to environmental time cues may influence treatment success with oral melatonin. Emens JS; Laurie AL; Songer JB; Lewy AJ. Non-24-hour disorder in blind individuals revisited: variability and the influence of environmental time cues. SLEEP 2013;36(7):1091-1100.

Circadian Clock Gene Polymorphisms and Sleep–Wake Disturbance in Alzheimer Disease

One of the hypothesized causes of the breakdown in sleep-wake consolidation often occurring in individuals with Alzheimer disease (AD) is the dysfunction of the circadian clock. The goal of this study is to report indices of sleep-wake function collected from individuals with AD in relation to relevant polymorphisms in circadian clock-related genes. One week of ad libitum ambulatory sleep data collection. At-home collection of sleep data and in-laboratory questionnaire. Two cohorts of AD participants. Cohort 1 (N = 124): individuals with probable AD recruited from the Stanford/Veterans Affairs, National Institute on Aging Alzheimer's Disease Core Center (N = 81), and the Memory Disorders Clinic at the University of Nice School of Medicine (N = 43). Cohort 2 (N = 176): individuals with probable AD derived from the Alzheimer's Disease Neuroimaging Initiative data set. Determination of sleep-wake state was obtained by wrist actigraphy data for 7 days in Cohort 1 and by the Neuropsychiatric Inventory questionnaire for Cohort 2. Both cohorts were genotyped by using an Illumina Beadstation (Illumina, San Diego, CA), and 122 circadian-related single-nucleotide polymorphisms (SNPs) were examined. In Cohort 1, an additional polymorphism (variable-number tandem repeat in per3) was also determined. Adjusting for multiple tests, none of the candidate gene SNPs were significantly associated with the amount of wake time after sleep onset (WASO), a marker of sleep consolidation. Although the study was powered sufficiently to identify moderate-sized correlations, we found no relationships likely to be of clinical relevance. It is unlikely that a relationship with a clinically meaningful correlation exists between the circadian rhythm-associated SNPs and WASO in individuals with AD.

Relationship between Apathy and Sleep Disturbance in Mild and Moderate Alzheimer's Disease: An Actigraphic Study

Apathy is the most frequently reported neuropsychiatric symptom across all stages of Alzheimer's disease (AD). Both apathy and sleep disorders are known to have independent negative effects on the quality of life in individuals with AD. The aim of this study was to assess the relationship between apathy and sleep/wake patterns in individuals with AD using ambulatory actigraphy. One hundred and three non-institutionalized individuals with AD wore a wrist actigraph continuously over seven consecutive 24-h periods. Apathy was assessed using the Neuropsychiatric Inventory. Daytime mean motor activity (dMMA) was calculated from daytime wrist actigraphy data. Actigraphic parameters of sleep included total sleep time (TST), wake after sleep onset (WASO), time in bed (TIB), WASO normalized by TIB, sleep latency, and nighttime mean motor activity (nMMA). Among the 103 individuals with AD (aged 76.9 ± 7.2 years; MMSE = 21.4 ± 4.3), those with apathy had significantly lower dMMA, higher WASO (both raw and normalized), and spent more time in bed during the night than those without apathy. Sleep latency, nMMA and TST did not differ significantly between the two subgroups. To our knowledge, this study is the first to identify a relationship between apathy and sleep disturbance in those with mild or moderate AD: apathy was associated with increased TIB during the night and more WASO. These results suggest that AD patients with apathy have less consolidated nocturnal sleep than those without apathy.

Nocturnal Sleep–Wake Parameters of Adolescents at Home Following Cancer Chemotherapy

The purpose of this descriptive, longitudinal study was to describe objective nocturnal sleep-wake parameters of adolescents at home after receiving chemotherapy in the hospital or outpatient clinic and explore differences in sleep variables by age, gender, and corticosteroid use. We collected 7 days of wrist actigraphy and sleep diary data from 48 adolescents (10-19 years) who were receiving cancer chemotherapy for a primary or secondary cancer or a relapse. The actigraphic sleep variables included rest interval (i.e., time in bed), sleep onset, sleep offset, sleep duration, total sleep time (TST), wake after sleep onset (WASO), and %WASO. Of the 48 adolescents, 38 had at least five nights of scored actigraphy and were included in analyses. Older (13-18 years) adolescents went to bed later and had fewer minutes of TST than younger adolescents (10-12 years). Exploratory analyses revealed no differences between adolescents who were taking oral corticosteroids (i.e., prednisone, dexamethasone) and those who were not or between males and females. These adolescents had sleep durations that met or exceeded the recommended sleep duration for their age groups but experienced significant WASO. Further research is needed to estimate sleep needs of adolescents during chemotherapy and determine factors that contribute to nocturnal wake-time so that targeted interventions can be designed to improve sleep quality.