bluuebunny/crossref_metadata_2025_split huggingface dataset:
285 Pilot field study of Ambulatory Sleep-Staging in Shift-Working Air Traffic Controllers
Authors: Kosmadopoulos Anastasi , Boudreau Philippe , Boivin Diane 
Published: May 2021

Abstract

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&lt;.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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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&gt;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

Abstract

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&lt;.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

Abstract

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).
312 Associations Between Sleep Regularity and Body Mass Index: Findings from a Prospective Study of First-Year College Students
Authors: Wong Patricia , Barker David , Gredvig-Ardito Caroline , Carskadon Mary 
Published: May 2021

Abstract

Introduction
College students often experience irregular sleep timing, short sleep duration, and weight gain. Using data from a large, prospective study on sleep in first-year college students, we examined whether students’ sleep regularity index (SRI; Phillips et al., 2017) was associated with body mass index (BMI) and BMI change (∆BMI) during the first nine weeks of their college semester.

Methods
Analyses included data from 583 students (mean age = 18.7± 0.5 years; 59% Female; 48% non-White) who had their height and weight assessed at the start of classes (T1) and end (T2) of nine weeks. ∆BMI was calculated as the difference between T2 and T1, with a positive value indicating an increase in BMI. Throughout the semester, participants completed on-line daily sleep diaries that included bedtime, wake-time, sleep onset latency, and wake after sleep onset for the previous major sleep episode and daytime naps. Based on this data, total sleep time (TST) was calculated as time spent asleep between bedtime and wake-time, and SRI was calculated by comparing participants’ sleep/wake states across adjacent 24-hour periods. Average SRI reflects participants’ sleep regularity (0 (random) to 100 (perfect regularity)) across the study. Data were analyzed with hierarchical linear regressions that controlled for sex and average TST.

Results
Average SRI was 74.1±8.7 (range 25.7–91.6). Average BMI at T1 was 22.0±3.5; 6% of participants were underweight (BMI less than 18.5), 6% overweight (≥25 and &lt;30) and 3% obese (≥30). Greater BMI at T1 was correlated with less ∆BMI by T2 (r=-.16, p&lt;.001). On average, participants gained 1.8±2.4kg (range: -7.2–11.4); 6% of participants lost ≥2kg, 39% gained 2-5kg, 8% gained more than 5kg. Average TST was not significantly correlated with BMI or ∆BMI. Lower SRI was associated with greater BMI at T1 (B= -.06 [95% CI: -.09– -.02], p=.001) but less ∆BMI (B= .01 [.002–.018], p=.018).

Conclusion
We found that lower sleep-wake regularity associated with greater baseline BMI but less BMI increase during the initial transition to college. Given that the majority of our participants were normal weight young adults, our findings may indicate that sleep regularity associates with healthy growth in this population.

Support (if any)
R01MH079179, T32MH019927(P.W.)