bluuebunny/crossref_metadata_2025 huggingface dataset:
261 Accelerometer-Derived Sleep and Circadian Domains and Sociodemographic Correlates in the UK Biobank
Authors: Gehrman Philip , Malone Susan , Patterson Freda , Mitchell Jonathan , Mazzotti Diego
Published: May 2021

Introduction

Sleep health encompasses sleep regularity, duration, timing, efficiency and satisfaction. Accelerometry is an established method to estimate sleep in ecologically valid contexts, capturing key characteristic of rest-activity patterns, and facilitating population sleep health research. While hundreds of traits can be generated from open-source algorithms applied to raw acceleration data, the lack of clarity around their meaningful use beyond conventional measures limit the ability of these data to systematically inform evidence-based practices promoting sleep health. Here, we propose a method to identify key sleep and circadian domains, using data reduction methods for hundreds of accelerometer-derived traits to inform population-based sleep heath research. We also aimed to validate our findings by assessing whether the identified domains captured known sociodemographic associations.

Methods

We analyzed sociodemographic and raw triaxial accelerometer data recorded for 7 days from 79,876 adults (mean age 56.3±2.1 years, 56.3% women) participating in the UK Biobank. Standardized data processing using the open-source package GGIR (v1.7-1) resulted in the generation of 107 sleep and circadian traits. Variable clustering was used to identify key sleep and circadian domains, pertinent to sleep health, representing interpretable biological constructs minimizing correlation with other domains. Associations between identified domains and sociodemographic factors were evaluated using general linear models, and clinically significant differences were determined by standardized mean differences (SMD) ≥0.3.

Results

We identified 25 sleep and circadian domains explaining ≥80% of the variability of all 107 included traits. Domains capturing measures of variability tended to cluster together. The most clinically significant associations with sociodemographic characteristics were: women (vs. men) had higher sleep efficiency and lower accumulation of diurnal sleep periods; older (vs. younger) individuals had earlier most active starting time, lower acceleration amplitude and lower number of nocturnal sleep periods; and shift (vs. non-shift) workers had higher variability in sleep timing on weekends.

Conclusion

We demonstrate that variable clustering on accelerometer-derived data can identify meaningful sleep and circadian domains. In addition, identified domains captured known sociodemographic associations commonly observed in the sleep and circadian literature, suggesting that they could be relevant to inform public health practices that promote sleep health.

Support (if any)

NHLBI 5R01HL143790-02(PG); NIMHHD R01MD012734(FP); NIDA R01DA051321(FP); NIH/NHLBI K01HL123612(JM)
263 Use of an Air Purifier in the Bedroom Improves Objective and Perceived Sleep
Authors: Rus Holly , Danoff-Burg Sharon , Weaver Morgan , Rodriguez Rodolfo , Raymann Roy
Published: May 2021

Introduction

This study examined if an air purifier in the bedroom would improve sleep on a broad sample of healthy people (non-smokers without allergies) with sub-optimal sleep living in urban and suburban areas. This way of testing has the advantage of providing insight into the effectiveness of a product under real-life conditions, yielding more ecologically valid results.

Methods

36 adults whose objective data indicated suboptimal sleep participated in an 8-week field study. A within subjects, pre-post intervention design was implemented, comparing 4 weeks of nightly product use to 4 weeks without using the product. Intervention consisted of the use of an Alen BreatheSmart 45i air purifier in the bedroom for 24 hours a day during the duration of the intervention period. Sleep was measured objectively with SleepScore Max every night and by self-report. Multilevel regression and paired t-tests were used to test for statistical significance.

Results

Across all participants there were 1591 nights of tracked sleep. Participants (55% male, age 25-74, avg. 44) showed improvement in both objective and perceived sleep during the intervention. While using the air purifier, there was a small but significant improvement in objective sleep quality (p<.05). In the subgroup of participants who had the poorest objective sleep at baseline (n=16), there were significant improvements while using the air purifier, reflecting improved sleep continuity after falling asleep: fewer awakenings during the night, decreased time awake during the night, increased sleep efficiency, and increased sleep maintenance (all ps<.05). Self-report data showed that, when using the air purifier, participants felt they fell asleep faster, woke up fewer times, spent less time awake at night, and were better able to sleep through the night. In the morning, they were more likely to feel well-rested. They also felt satisfied with their sleep more often and had better sleep quality (all ps<.05).

Conclusion

Using the air purifier in the bedroom can improve sleep. Objectively measured sleep improvement supported self-reported sleep improvements, especially in the subset of participants who showed compromised sleep at baseline.

Support (if any)

Alen Corporation
267 Perioperative sleep study in geriatric cardiac surgical patients using wireless wearable devices
Authors: Kafashan MohammadMehdi , Labonte Alyssa , Smith Kendall , Guay Christian , Hyche Orlandrea , Nguyen Thomas , Wilson Elizabeth , Guan Michael , Lucey Brendan , Ju Yo-El , Palanca Ben
Published: May 2021

Introduction

Sleep is a fundamental necessity for health and is commonly disrupted in the perioperative period. Technological improvements leveraging dry electroencephalographic (EEG) sensors have opened the door for large-scale quantitative assessments of sleep in relation to perioperative outcomes.

Methods

Patients utilized the Dreem (Rhythm, New York USA), a wireless EEG headband, to acquire their own preoperative nocturnal sleep records at home. Following cardiac surgery, postoperative recordings were obtained with staff assistance until postoperative night 7. Sleep records were scored as rapid eye movement (REM) and non-rapid eye movement (NREM) stages N1-N3, using modified American Academy of Sleep Medicine guidelines.

Results

Of 100 patients enrolled for perioperative sleep recordings, 74 patients provided 132 preoperative records; 80% were scorable with a median total sleep time (TST) of 209.8 minutes. TST was distributed as 8.3% N1, 70.6% N2, 2.1% N3 and 19% REM, consistent with expected sleep structure in geriatric populations. EEG markers for staging sleep were evaluated in the scorable records: 92% with sleep spindles, 98% with K-complexes, 69% with slow waves, 92% with sawtooth waves, and 80% with rapid eye movements. Among 26 patients with multiple preoperative sleep recordings, no significant within-subject differences in sleep structure were observed (all p > 0.05, paired Wilcoxon sign-rank test). 270 postoperative nocturnal sleep recordings were obtained from 83 patients, 70% of which were scorable. TST in scorable postoperative records was distributed as 14.9% N1, 78.6% N2, 0.9% N3 and 5.6% REM. Durations of REM and N3 sleep were significantly reduced in postoperative (POD 1-4) overnight recordings compared to preoperative measurements (Skillings–Mack test, p < 0.001 and p = 0.02 for REM and N3, respectively).

Conclusion

Wireless EEG devices enhance the feasibility of assaying perioperative sleep. A single night of unattended, ambulatory sleep monitoring is sufficient to establish a preoperative baseline. Multiple preoperative and postoperative sleep studies were tolerated by patients, which showed reductions of N3 and REM sleep in the early postoperative period. This study demonstrates the feasibility of using the Dreem for monitoring sleep macro- and microstructural EEG elements in the perioperative setting.

Support (if any):
268 Smart polymer implants as an emerging technology for treating airway collapse in OSA: a proof of concept study
Authors: Sideris Anders , Wallace Gordon , Lam Matthew , Kitipornchai Leon , Lewis Richard , Jones Andrew , Jeiranikhameneh Ali , Hingley Lachlan , Beirne Stephen , Mackay Stuart G
Published: May 2021

Introduction

Implantable 3D printed ‘smart’ polymers are an emerging technology with potential applications in treating collapse in adult obstructive sleep apnea through mechanical airway manipulation. There is a paucity of devices that are commercially available or in research and development stage. Limited studies have investigated the use of implantable smart polymers in reversing the collapsibility of the pharyngeal airway by creating counter forces during sleep. This paper describes an application of implantable magnetic polymer technology in an in-vivo porcine model. Study Objectives: To assess the use of a novel magnetic polymer implant in reversing airway collapse and identifying potential anatomical targets for airway implant surgery in an in-vivo porcine model.

Methods

Target sites of airway collapse were genioglossus muscle, hyoid bone and middle constrictor. Magnetic polymer implants were sutured to these sites and external magnetic forces, through magnets with pull forces rated at 102kg and 294kg, were applied at the skin. The resultant airway movement was assessed via nasendoscopy. Pharyngeal plexus branches to the middle constrictor muscle were stimulated at 0.5mA, 1.0mA and 2.0mA and airway movement assessed via nasendoscopy.

Results

At the genioglossus muscles large magnetic forces were required to produce airway movement. At the hyoid bone, anterior movement of the airway was noted when using a 294kg rated magnet. At the middle constrictor muscle, an anterolateral (or rotatory) pattern of airway movement was noted when using the same magnet. Stimulation of pharyngeal plexus branches to the middle constrictor revealed contraction and increasing rigidity of the lateral walls of the airway as stimulation amplitude increased. The resultant effect was prevention of collapse, a previously unidentified pattern of airway movement.

Conclusion

Surgically implanted smart polymers are an emerging technology showing promise in the treatment of airway collapse in obstructive sleep apnea. Future research should investigate their biomechanical role as an adjunct to treatment of airway collapse through nerve stimulation.

Support (if any)

Garnett-Passe and Rodney Williams Memorial Foundation, Conjoint Grant, 2016-18.
270 Sleep Enhancement Technology: A Survey of Devices
Authors: Klosterman Grace , Stekl Emily , Simonelli Guido , Collen Jacob , Doty Tracy Jill
Published: May 2021

Introduction

Innovations in consumer sleep technologies have risen exponentially and providers struggle to keep up with patient expectations in this arena. Although there is high quality data on the validity on commercial sleep monitoring devices, there has been a rise in devices for the explicit purpose of sleep enhancement. We sought to identify existing consumer sleep devices that claim to enhance sleep, provide a comprehensive review of the main characteristics of these devices, and look into the types of evidence the developers offered to support their claims.

Methods

Using a scoping review framework we identified and mapped out the main characteristics of sleep enhancement devices in the consumer market. We systematically used a common search engine and the FDA database using various combinations of sleep-related search terms, such as “sleep enhancement device”. Through an iterative process, we identified and categorized devices based on the intervention target. Devices that were exclusively for clinical use and required a prescription, such as for the treatment of obstructive sleep apnea or diagnosed insomnia, were excluded.

Results

We identified 34 sleep enhancement devices, all 34 were found via web search and one was also found in the FDA Database. We defined the following overlapping categories: reduce sleep latency (94.1%), increase restorative sleep (17.6%), and/or “other” (32.4%). About half of the devices use sound (44.1%), 26.5% use visual stimuli, and 11.8% use vibration. Additionally, roughly a third of all devices claim to entrain brain signals associated with sleep. Half of devices found operate near the bed without being in contact with the consumer, 44.1% are worn on the body, and the remaining 5.9% operate in bed, near the consumer.

Conclusion

For the most part, commercial sleep enhancement devices target sleep latency or claim to increase the restorative power of sleep. These devices generally use auditory, visual, and vibratory stimuli, and half are worn on the body. Lack of evidence supporting whether these devices actually improve sleep questions the utility of such devices and demonstrates the need for validation standards for consumer sleep enhancement devices.

Support (if any)

Department of Defense Military Operational Medicine Research Program (MOMRP)
271 Sleep staging performance of a signal-agnostic cloud-based real-time sleep analytics platform
Authors: Siting Zhao , Kishan Kishan , Patanaik Amiya
Published: May 2021

Introduction

The coronavirus pandemic has brought unprecedented changes to the health care system, including sleep medicine. Remote monitoring and telemedicine played a significant role in this shift. We anticipate these changes to continue in the future with internet-connected wearables (ICWs) playing an important role in measuring and managing sleep remotely. As these ICWs measures a small subset of signals traditionally measured during polysomnography (PSG), manual sleep staging becomes non-trivial and sometimes impossible. The ability to do accurate and reliable automatic sleep staging using different modalities of physiological signals remotely is becoming ever more important.

Methods

The current work seeks to quantify the sleep staging performance of Z3Score-Neo (https://z3score.com, Neurobit Technologies, Singapore), a signal agnostic, cloud-based real-time sleep analytics platform. We tested its staging performance on the CINC open dataset with N=994 subjects using various combinations of signals including Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), and Instantaneous Heart Rate (IHR) derived from Electrocardiogram (ECG). The staging was compared against manual scoring based on PSG. For IHR based staging, N1 and N2 were combined.

Results

We achieved substantial agreement (all Cohen’s Kappa > 0.7) between automatic and manual staging using various combinations of EEG, EOG and EMG channels with accuracies varying between 81.76% (two central EEGs, one EOG, one EMG), 79.31% (EEG+EOG), 78.73% (EEG only) and 78.09% (one EOG). We achieved moderate agreement (accuracy: 72.8% κ=0.54) with IHR derived from ECG.

Conclusion

Our results demonstrated the accuracy of a cloud-based sleep analytics platform on an open dataset, using various combinations of ecologically valid physiological signals. EOG and EMG channels can be easily self-administered using sticker-based electrodes and can be added to existing home sleep apnea test (HSAT) kits significantly improving their utility. ICWs are already capable of accurately measuring EEG/EOG (Muse, InteraXon Inc., Toronto, Canada; Dreem band, Dreem, USA) and IHR derived from ECG (Movesense, Suunto, Finland) or photoplethysmogram (Oura Ring, Oura Health Oy, Finland) or through non-contact ballistocardiogram/radio-based measurements (Dozee, Turtle Shell Technologies, India; Sleepiz, Sleepiz AG, Switzerland). Therefore, a well-validated cloud-based staging platform solves a major technological hurdle towards the proliferation of remote monitoring and telehealth in sleep medicine.

Support (if any):
272 Long-term monitoring of trait-like characteristics of the sleep electroencephalogram using ear-EEG
Authors: Hemmsen Martin , Mikkelsen Kaare , Rank Mike , Kidmose Preben
Published: May 2021

Introduction

Wearable electroencephalogram (EEG) monitoring has a remarkable potential, it is safe, scalable and can track neural signatures for long periods. One such signature is the power spectra of non-rapid-eye-movement (NREM) sleep which has been shown to demonstrate a trait-like characteristic. Changes in personalized signatures has been associated with biomarkers of Alzheimer’s disease and is of great interest for early detection and clinical management. This work investigates monitoring of signatures using a wearable device that records EEG from the ear (ear-EEG) and compares the intra- and inter-individual similarity of the neural signatures with that from central scalp-EEG.

Methods

We initiated a two phased in-home study, monitoring 20 subjects for 4 nights (A), followed by a delayed but continued monitoring of 10 subjects for 12 nights (B). In A, subjects wore a dry-electrode ear-EEG system and a partial PSG, in B the subjects wore only the ear-EEG system. Subjects were instructed to follow their usual time schedule and lifestyle. Sleep stages were scored manually according to AASM in A and automatically in B. The grand average power spectra of NREM2 sleep were computed and log-transformed prior to calculating the cosine similarity for determination of the intra- and inter-individual similarity.

Results

The ear-EEG and scalp-EEG analysis showed that mean intra-individual similarity was higher than mean inter-individual similarity. Permutation tests indicate that the observed mean difference is statistically significant p<0.01 for both montages. Comparing the distributions of intra-individual similarities for ear-EEG and scalp-EEG, the observed mean difference is statistically significant p<0.05, in favor of a more stable ear-EEG signature. Comparing ear-EEG signatures between A and B, considering nights from A as reference, all subjects from B were most similar with its own reference signature. Considering signatures from individual nights the accuracy paring subjects from A and B were 88% correct.

Conclusion

Nocturnal ear-EEG measures trait-like characteristics as reliable as scalp-EEG. The neural signature is stable over time within healthy subjects and demonstrated its ability as a personalized signature.

Support (if any):
274 Multimodal longitudinal sleep tracking combining wearable, smartphone tap analysis and electronic questionnaires
Authors: Massar Stijn A , Chua Xin Yu , Soon Chun Siong , Ng Alyssa , Ong Ju Lynn , Chee Nick , Chee Michael
Published: May 2021

Introduction

The proliferation of wearable and smartphone technologies has enabled continuous monitoring of sleep using data from different channels (physiological [wearables], behavioural [phone usage] and ecological momentary assessment [EMA self-report]). As these modalities use different methods to assess sleep, information gaps suggested by discrepancies between estimates may be filled in through cross-referencing among the modalities to produce a more accurate sleep measurement. Moreover, the pattern of discrepancies could inform about specific sleep and peri-sleep behaviors (e.g. phone use before bedtime).

Methods

198 staff and students from the National University of Singapore (61 male, mean age 26.15±5.83 years) were recruited for an 8-week study. Sleep timings were assessed daily from three modalities: a wearable sleep and activity tracker (Oura ring), estimations from smartphone touchscreen interactions (tappigraphy) and smartphone derived EMA self-reports. Sleep estimates from the different modalities were compared for agreement (bivariate correlation) and discrepancies (t-test). Additionally, clustering analysis of high-discrepancy nights (>1h discrepancy between modalities) was performed to identify pattens of sleep behaviors that could lead to specific discrepancies.

Results

Adherence throughout the 8-week monitoring period (total 11,088 nights) was = high for the Oura ring; 9826 nights [80%]), Tappigraphy; 9740 nights [88%)), and EMA; 9166 nights [83%]). Sleep estimates across the three modalities showed high agreement (r=0.79-.91), with some discrepancies: Relative to self-report data, Oura wake time tended to be a later (Mean diff=9mins, t=18.58, p<.001), while tappigraphy estimates of bedtime tended to be early (Mean diff=15mins, t=26.48, p<.001). On 23% of nights (1755 nights), however, large discrepancies were detected (>1h). K-means clustering identified three distinct patterns of discrepancy, which were dominantly expressed in different individuals. Group comparison revealed that these individuals differed in demographic variables (age, student/work status), sleep variables (sleep timing, duration, subjective sleepiness), and phone usage characteristics (overall and pre-bedtime phone usage).

Conclusion

These data show that the combined use of three streams of data concerning sleep is complementary. Moreover, discrepancy patterns provide specific insights into sleep and peri-sleep behaviors facilitating digital phenotyping.

Support (if any)

This research was supported by the National Medical Research Council Singapore (NMRC/STaR/015/2013 and NMRC/STaR19may-0001).
281 Feasibility, Acceptability, and Preliminary Impact of a School-Based Sleep Promotion Program for Adolescents
Authors: Levenson Jessica , Goodkind Sara , Albert Sara , Macuga Bryan , Golden Dawn , Karadus Clay , Wehrer Edward , Miller Elizabeth
Published: May 2021

Introduction

Poor sleep is common among adolescents and has been linked with school absenteeism. Though improved sleep has been associated with improved school outcomes, intervention programs for insufficient sleep are not standard in schools, few have used an individualized approach, and none have focused on youth with absenteeism. We conducted an open trial of the feasibility, acceptability, and initial impact of our school-based sleep promotion program among adolescents.

Methods

Participants included 8th and 9th grade students with mild absenteeism, insufficient sleep, and sleep timing shift on the School Sleep Habits Survey (SSHS). The program included a smartphone-based sleep diary and 1-2 tailored sessions with a school staff member. Participants completed baseline and follow-up research assessments. The sleep program focused on education about sleep; cognitive strategies targeting sleep beliefs; stimulus control; and regularization of sleep and wake. We descriptively report program feasibility, acceptability, and change in sleep duration.

Results

Of 33 participants approached by school staff, 12 agreed to participate and 10 completed the study. Seven participants had two program sessions and 5 participants had one program session. Sleep duration increased by 19 minutes across the entire week based on daily sleep diary, and by 31 minutes on weekdays on the SSHS. Most participants (89%) reported that the program was not a burden on their time, that the program length was just right (88%), and that they were likely to participate in research again (75%). While program feasibility was high, most youth (56%) rated program relevance as low or unknown. The most salient implementation barrier was the challenge of embedding the program into each school’s existing procedures.

Conclusion

A school-based sleep promotion program for adolescents with insufficient sleep and mild absenteeism is feasible, acceptable, and associated with improved sleep. An individualized approach, with sessions delivered by school staff, shows great promise for being feasible and clinically significant. Future work should focus on selecting a sample of youth with greater investment in the program to improve school attendance and implementing the program flexibly and with fidelity across schools.

Support (if any)

University of Pittsburgh Center for Interventions for Improve Community Health
282 Effect of circadian misalignment on the sleep of police officers across a series of night shifts
Authors: Kosmadopoulos Anastasi , Kervezee Laura , Boudreau Philippe , Boivin Diane
Published: May 2021

Introduction

Misalignment of behavior and circadian rhythms due to night work can impair sleep and waking function. While both simulated and field-based studies suggest that circadian adaptation to a nocturnal schedule is slow, the rates of adaptation in real-world shift-work conditions are still largely unknown. The aim of this study was to evaluate the extent of adaptation of 24-h rhythms in 6-sulfatoxymelatonin (aMT6s) and cortisol in police officers across night shifts and to compare their effect on sleep.

Methods

A total of 76 police officers (20 women; aged 32.3±5.5 years, mean±SD) from the province of Québec, Canada, participated in a field study comprising their 28- or 35-day work-cycle. Urine samples were collected for ~24-h before and after a series of 3–7 night shifts. Rhythms of urinary aMT6s were considered adapted if midpoints following night shifts occurred during participants’ average daytime sleep period. Cortisol was considered adapted if midpoints occurred within 2h of their average daytime sleep offset. Sleep was measured with actigraphy and sleep logs on a cell phone. Data were analyzed with circular and linear mixed-effects models.

Results

Analyses were based on a subset of 37 participants with rhythms of both hormones suitable for circadian phase assessment before and after their series of night shifts. After night shifts, the group acrophase of adapted rhythms (aMT6: n=11, cortisol: n=9) occurred significantly later than for non-adapted rhythms (aMT6s: 10.9 h vs. 3.4 h, p<.001; cortisol: 14.9 vs. 9.9 h, p<.001). Participants with adapted aMT6s rhythms obtained cumulatively more sleep per day throughout the series of shifts than those with non-adapted rhythms (average 6.4 h per day vs 5.8 h per day; p=.026).

Conclusion

Consistent with prior research, our results from both urinary aMT6s and cortisol midpoints indicate that a large proportion of police officers remained in a state of circadian misalignment following their night shifts. The minority of officers who adapted to night work were able to obtain more sleep across consecutive night shifts.

Support (if any)

Project funded by the Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST). A.K. received a postdoctoral fellowship from the Fonds de Recherche en Santé du Québec (FRQS).
285 Pilot field study of Ambulatory Sleep-Staging in Shift-Working Air Traffic Controllers
Authors: Kosmadopoulos Anastasi , Boudreau Philippe , Boivin Diane
Published: May 2021

Introduction

The simplicity of wrist actigraphy for sleep-wake monitoring in the field contributes to its ubiquity in shift-work research. However, devices based solely on recording activity levels are generally not suitable to quantify sleep architecture. This is a limitation as quantifying changes in sleep stages caused by circadian misalignment is important to better assess the consequences of sleep-wake disruption in shift-working populations. This pilot study was conducted to evaluate whether sleep stages vary with respect to different shift types.

Methods

Six male air traffic controllers aged 48.5±8.4 years (mean±SD) completed the protocol which entailed two ~9-day periods, each with up to 6 workdays. Schedules comprised 1 or 2 early night shifts (19:30–03:30h), followed by an evening shift (15:00–23:00h), day shift (09:00–17:00h), morning shift (06:30–14:30h), and 1 or 2 full night shifts (23:00–7:00h). A portable sleep-staging device (Somno-Art, Paris, France) that monitored activity levels and heart rate was worn on the non-dominant forearm during bedtime and produced estimates of REM and NREM sleep stages with a proprietary algorithm. Total sleep time (TST) and sleep stages were assessed per shift type with mixed-effects models.

Results

Final analyses were based on 70 sleep periods preceding workdays, standardized to 24 h to account for the different intervals between consecutive shifts. Analyses revealed significant effects of shift type for TST (p=.016), stages N1 (p=.010) and N2 (p=.043), but none for N3 (p=.055) or REM (p=.117) sleep. TST and stage N1 sleep prior to night shifts was shorter than for day, evening, or early night shifts (all p<.05). Participants obtained less stage N2 sleep prior to night shifts than days shifts (p=.049).

Conclusion

This pilot study suggests variations in TST across shifts were predominantly due to differences in light sleep stages, whereas no significant differences in N3 and REM sleep were observed. Thus, while TST was reduced for night shifts, participants obtained similar durations of the most recuperative stages. These findings highlight the importance of refined monitoring of sleep in field research involving shift-work.

Support (if any)

Project funded by NAV Canada. Devices lent by the Somno-Art company. A.K. received a postdoctoral fellowship from the Fonds de Recherche en Santé du Québec (FRQS).
288 Chronotype-dependent impact of napping on sleep behavior in rotating shift workers
Authors: Kervezee Laura , Gonzales-Aste Fernando , Boudreau Philippe , Boivin Diane
Published: May 2021

Introduction

Rotating shift work is known to adversely impact sleep. Napping is one of the strategies that workers can use to mitigate the effect of shift work on their sleep. In this study, we investigated the effect of chronotype on napping behavior in police officers involved in rotating shift work.

Methods

Actigraphy-based sleep measures and chronotype information was available from 74 police officers (20 women and 54 men; age [mean ± SD]: 32 ± 5.4 years) that participated in a 35-day field study during which they worked morning, evening, and night shifts. A generalized linear mixed model was used to assess the effect of shift type, chronotype, and their interaction on the likelihood to take a nap, adjusted for relevant covariates. In addition, linear mixed models were used to determine the effect of shift type, chronotype, and their interaction on sleep duration with and without taking into account napping duration.

Results

The likelihood to take a nap was influenced by an interaction between shift type and chronotype (χ2(2) = 11.2, p = 0.004). Earlier chronotype was linked to a lower likelihood to take naps during days with morning shifts and a higher likelihood during days with night shifts. Napping modulated the effect of shift type and chronotype on daily sleep duration, most notably during night shifts: while chronotype was associated with the duration of the main sleep period during night shifts, with the main sleep period being 1.7 h [95% C.I.: 0.6 – 2.8] shorter in the earliest chronotypes compared to the latest chronotypes, this effect was attenuated and no longer significant when napping duration was taken into account (difference in total sleep duration in latest chronotypes vs earliest chronotypes during nights shifts: 0.9 [−0.1 to 1.9] h).

Conclusion

Napping attenuates the chronotype-dependent effect of atypical work schedules on sleep duration in this population of shift-working police officers. These findings highlight the need to take into account chronotype when assessing the effect of shift work on sleep behavior.

Support (if any)

The Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRRST) and Fonds de Recherche du Québec–Santé (FRQS).
290 Depression, anxiety, and resting heart rate variability during sleep in a sample of active duty service members
Authors: Shattuck Nita , Matsangas Panagiotis , Boyle Joshua
Published: May 2021

Introduction

Depression and anxiety are among the most prevalent mental health outcomes in the military population with rates ranging between 11% and 15% in Army active duty service members (ADSMs). Oftentimes both maladies are comorbid with insomnia and other sleep-related disorders. We explored the association between self-reported depression and anxiety levels and resting heart rate variability (HRV) metrics during sleep using a wearable device, the Oura ring.

Methods

We conducted a longitudinal, naturalistic assessment of fit-for-duty ADSMs (N=44; 21-40 years of age, 38 males) attending the Naval Postgraduate School. Depression was assessed by the Beck Depression Inventory; anxiety was assessed by the State-Trait Anxiety Inventory. HRV (average nightly HRV and average nightly HRVmaximal during sleep) was assessed with the Oura devices during a period of MD=8 days (range 8–18).

Results

The median BDI score was 5.50 (IQR=9.50; range 0–23). Most participants had minimal depression (36, 81.8%) with seven (16.9%) having mild depression and one (2.27%) moderate depression. The median state anxiety score was 29.5 (IQR=16.8; range 20 – 56), whereas the median trait anxiety score was 31.0 (IQR=15.8; range 21–56). Correlation analysis (Spearman’s rho) showed that lower depression and anxiety scores were associated with higher nightly HRV during sleep. Specifically, average nightly HRV was correlated with BDI scores (rho=-0.384, p=0.010), state anxiety scores (rho=-0.343, p=0.023), and trait anxiety (rho=-0.356, p=0.018). Average nightly HRVmaximal was negatively correlated with BDI scores (rho=-0.435, p=0.003), state anxiety scores (rho=-0.339, p=0.024), and trait anxiety (rho=-0.339, p=0.025).

Conclusion

Our findings suggest that HRV during sleep is associated with self-reported depression and anxiety levels in this sample of ADSMs. Further research is needed to assess the utility and limitations of the Oura devices to collect data in field settings.

Support (if any):
296 Sex differences in sleep and wakefulness of police officers working shifts: evidence from a field study
Authors: Caetano Gabriela , Kervezee Laura , Gonzales-Aste Fernando , Boudreau Philippe , Boivin Diane
Published: May 2021

Introduction

National reports of work-related injuries found the excess risk of work injury attributed to shift work to be significantly higher among women. The Working Time Society (WTS) concluded that male sex is one of the few factors that is “consistently associated with perceived or actual shift work tolerance”. However, it is unclear if physiological parameters are involved. Laboratory-controlled studies report sex differences in circadian rhythms (body temperature, melatonin). In sleep deprivation protocols, alertness and cognitive performances were affected by sex, menstrual cycle phase and hormonal contraceptives [HC] use. Nevertheless, field studies that compare male and female shift workers are scarce.

Methods

An observational study including 76 police officers working on patrol: 56 males and 20 females (11 using [HC], 6 not using [non-HC] and 3 with unknown use of hormonal contraception) aged 32.0 ± 5.3 years. Participants were followed throughout a month-long work cycle (1,457 morning, evening, night, or other shifts, plus rest days). They filled out time-stamped questionnaires (Samn-Perelli, KSS, Visual Analogue Scales, ~5/day; sleep and work-related information, ~1–2/day), completed 5-min Psychomotor Vigilance Tasks (PVT, ~2/day), and wore an actigraph to collect activity data. Linear mixed-effects models were used to analyze the effects of group, time awake and time-of-day on fatigue, sleepiness, alertness, mood and PVT measures.

Results

Self-reported measures and psychomotor performance significantly varied with time awake and time-of-day. Fatigue and sleepiness levels were significantly higher among female compared to male police officers, both with time awake and across the 24-h day. These variations were similar between non-HC females and the other groups. Compared to males, HC females were more fatigued and less alert, both with time awake and across the 24-h day, and sleepier with time awake. Having children at home did not explain these differences.

Conclusion

The results of this study expand our knowledge on the sex differences in the sleep and circadian physiology and demonstrate a critical effect of HC on women fatigue, sleepiness and alertness when working shifts. Sex and hormonal parameters must be considered in occupational medicine as well as in future laboratory and field studies on shift workers and circadian rhythms.

Support (if any)

IRSST, FRQS.
298 Sleep Stability and Academic Performance in College Students
Authors: Reynolds Alexandria , Shell Madelynn
Published: May 2021

Introduction

There is considerable research demonstrating poor sleep patterns in college students; however, few studies actually examine sleep stability over a typical undergraduate career. Considering that the transition to college involves significant shifts in independence and potentially creating a foundation of lifelong behavioral patterns, it is important to identify whether these poor sleep patterns change throughout college. Additionally, studies show that shorter sleep duration predicts poorer academic performance. In the current study, it was expected that students would report poor sleep on average, and that poorer sleep would predict worse academic performance.

Methods

Participants included 27 full-time first-year undergraduate students who completed an online survey every spring for four years to examine sleep habits as part of a larger longitudinal study on the transition to college at a small liberal arts school. The Pittsburgh Sleep Quality Index was used to assess total sleep time (TST), sleep efficiency, and quality; the Epworth Sleepiness Scale (ESS) was used to determine sleepiness. Semester GPA was obtained via college registrar records.

Results

Repeated measures ANOVAs revealed no differences in participants’ sleep variables (TST, sleep quality, sleep efficiency, and sleepiness) across all four time points. Average TST was 6.85 hours per night, and overall sleep quality (PSQI) was poor (M = 6.12). Mean sleep efficiency was 86.70%; mean ESS score was 5.35. Preliminary analyses revealed no significant differences between GPA values over the course of the four years; sleep factors did not predict GPA.

Conclusion

Overall, students reported short sleep, poor sleep quality, decent sleep efficiency, and borderline higher than normal daytime sleepiness. However, sleep factors and GPA were stable over all time points. These results suggest that poor sleep habits start early and continue throughout students’ college career, as opposed to developing throughout college, or starting out poor and improving. Surprisingly, preliminary results indicated that sleep factors did not predict academic performance. Limitations include subjective sleep assessments, limited testing, and small sample size; however, this longitudinal study sheds interesting light on the general sleep patterns of college students over the course of their entire academic career.

Support (if any)

None.
302 Sleep, Sleepiness, and Performance Across Three In-Flight Bunk Rest Opportunities
Authors: Hilditch Cassie , Arsintescu Lucia , Gregory Kevin , Evans Erin
Published: May 2021

Introduction

Airline pilots are required to take a rest break in a bunk during long-haul flights in an effort to reduce sleepiness during critical phases of flight. It is unclear, however, whether each rest break affords equal opportunity for sleep. We aimed to characterize sleep, sleepiness, and performance outcomes across three in-flight rest breaks during long-haul flights.

Methods

Thirty-seven pilots wore actiwatches and completed sleep diaries for approximately two weeks while flying a variety of long-haul routes (n=126 flights). Self-reported in-flight bunk rest (BR) periods were used to set rest intervals and sleep was estimated within these intervals using actigraphy software (wake threshold set to medium). Pilots provided Karolinska Sleepiness Scale ratings (KSS) and performed a 5-minute psychomotor vigilance task (PVT) before landing. A linear mixed-effects model with participant included as a random effect and allowed to vary by intercept was used to assess differences between BR opportunities.

Results

The majority (97%, n=122) of BR periods contained sleep (as estimated by actigraphy). The mean (+/- standard deviation) sleep duration for the first, middle, and third BR opportunity was 152.8 (69.7), 149.2 (44.1), 125.2 (44.9) minutes, respectively. There was a significant effect of BR opportunity for sleep duration (F(2,54) = 3.747, p=.03) and KSS (F(2,44) = 7.869, p=.001). Bonferroni adjusted planned pairwise contrasts revealed that pilots using the third BR obtained significantly less sleep than in the first BR (p=.029). KSS ratings prior to landing were higher for the third BR compared to both the first (p=.001) and middle BR (p=.017). There were no significant differences for PVT speed or lapses (all p>05).

Conclusion

These results suggest that the last rest break is associated with shorter sleep, lower alertness, and no differences in performance relative to the other rest breaks. Further analysis is required to determine whether the higher KSS ratings following the third rest break are associated with sleep inertia, or whether other factors may be involved.

Support (if any)

NASA Airspace Operations and Safety Program, System-Wide Safety Project
303 Sleep Midpoint and Efficiency Moderate the Link Between Stress and Academic Cognitions in Latinx College Students
Authors: Sasser Jeri , Lecarie Emma , Gusman Michaela , Park HyeJung , Doane Leah
Published: May 2021

Introduction

Latinx students are the largest ethnic/racial minority group in higher education, but are also the group least likely to graduate from a four-year institution. Research suggests that heightened stress perceptions may impede college students’ ability to perform well academically. Poorer sleep may compound the impact of stress on academic functioning. The present study examined the multiplicative effect of college-stress and actigraphy-measured sleep on academic cognitions within-and-across semesters.

Methods

209 Latinx college students (Mage=18.95; 64.4% female, 85.1% Mexican descent) were assessed in the spring of the first year of college (T1) and fall of the second year (T2). At T1, participants wore an actigraph watch for 7 nights to measure total sleep time, sleep efficiency, and sleep midpoint. College-stress was assessed at T1 using the College Stress Scale. At T1 and T2, participants completed the Behavioral-Emotional-Cognitive School Engagement Scale, the Academic Self-Efficacy Scale, and a scale assessing academic motivation. Confirmatory factor analysis (CFA) was conducted to assess the model fit of a two-factor model representing academic cognitions (engagement, self-efficacy, motivation) at T1 and T2. Latent variable path analysis models testing for moderation were conducted using Mplus.

Results

The CFA indicated excellent fit (χ2(5)= 2.91, p=.71, RMSEA=.00, CFI=1.00, TLI=1.01, SRMR=.02). College-stress was concurrently (β=-.19, p=.02), but not longitudinally, associated with academic cognitions. Sleep midpoint predicted academic cognitions at T1 (β=-.24, p<.01) and T2 (β=-.18, p=.03). Sleep efficiency (β=.18, p=.01) and sleep midpoint (β=-.17, p=.02) moderated associations between college-stress and T2 academic cognitions. Higher college-stress was longitudinally linked with lower academic cognitions for students with lower sleep efficiency (b=-.12, p=.01) and later sleep midpoints (b=-.14, p=.01).

Conclusion

Greater college-stress is concurrently linked with lower academic cognitions, whereas later sleep timing has both immediate and enduring consequences on students’ academic mindsets. The impact of college-stress on academic cognitions may depend on the quality and timing of sleep at the time of these stress perceptions. Programs that address stress reduction and sleep health may be promising interventions for improving academic well-being among first-year Latinx college students.

Support (if any)

This work was supported by a William T. Grant Foundation Scholars Award (184370) to L.D. Doane.
306 Circadian activity rhythms and alertness among rapid-shift work female nurses
Authors: Zhang Xin , Lee Shih-Yu
Published: May 2021

Introduction

Circadian rhythms play an important role in regulating sleep. Sleep disturbances are prevalent in shift-work nurses, particularly for those work in rapid-shift rotation, including night shifts and day shifts. This study aimed to: 1) describe the characters of sleep-wake index (total sleep time [TST], wake after sleep onset [WASO], circadian activity rhythms [CAR]), psychomotor vigilance test (PVT), salivary cortisol, fatigue, and activity level during 8- and 12-hour rapid-shift work nurses; and 2) compare the parameters between two different shifts.

Methods

This exploratory study used registered nurses (RNs) from nine intensive care units in Beijing area. 7-days consecutive wrist actigraphy data, including TST and WASO were collected. Cosiner analysis was used for computing the CAR, including amplitude and mesor. PVT and saliva cortisol were used to assess alertness level, which measured before and after shift. Self-reported fatigue severity was measured by Lee Fatigue Scale-Short Form and assessed before and after shift.

Results

A total of 152 RNs (12-hour, n=82; 8-hour, n=70) participated this study, with a mean age of 31.81 (SD= 6.09). Compared with the 8-hour shift nurses, the 12-hour shift nurses were significantly higher in TST (456 vs. 364 minutes), median saliva cortisol level (before day shift, 0.54 vs. 0.31), and median PVT reaction time (before night shift). However, CAR were 0.53 (SD=0.13) and 0.50 (SD=0.18) for 12-hour and 8-hour shift RNs, respectively, and indicates desynchronized CAR in both groups. Regardless shift rotation, almost three-quarters of the RNs had a 500 ms PVT reaction time. For the 12-hour and 8-hour nurses, the level of activity during day shift was similar. However, during night shift work it was significantly lower in 12-hour nurses compared to the 8-hour nurses. All RNs experienced clinical significant fatigue severity (ranged 3.78 to 8.14) regardless before or after shift work; however, the 12-hour group reported lower fatigue severity than 8-hour group.

Conclusion

In this study, findings revealed shift-work RNs experienced fatigue and desynchronized CAR. The TST was low and reaction time was prolonged before and after shift work. Sleep intervention should be mandatorily included in clinical continue education.

Support (if any)

This project was supported by Chinese National Natural Science Foundation (71603279).