Sleep Start Time Research Articles

Game-Changer: Unlocking Student-Athlete Success with Sleep and Later School Start Times

This editorial explores the crucial yet often overlooked role of sleep in enhancing student-athlete performance and well-being. It highlights sleep’s impact on physical recovery, cognitive function, and emotional stability, emphasizing the unique benefits for athletes. The editorial examines the detrimental effects of early school start times on adolescent sleep patterns and their subsequent impact on academic and athletic performance. By advocating for policy changes that prioritize sleep, such as delaying school start times, this editorial presents a novel approach to optimizing student-athlete success. Through a synthesis of current research and practical insights, it urges stakeholders in education and sports to recognize and act upon the foundational importance of sleep, positioning it as a key component of athletic and academic excellence.

An exploration into the sleep of workers on block-calving, pasture-based dairy farms

The benefits of sufficient and high-quality sleep for people are well documented. Insufficient sleep increases the risk of accidents, injuries, and negative health implications for people. This is especially relevant for farmers because they work with large animals and machinery. Dairy farming often requires early start times and long days, particularly over the high-workload calving period in block-calving, pasture-based systems. However, there is little published data quantifying the sleep quantity and quality of farmers over this period. In this study, the sleep patterns of workers (n = 33) on 10 New Zealand dairy farms were measured for 90 d over the spring calving period using a sleep measuring device (Oura ring, Oura Health Ltd., Oulu, Finland). Total sleep time (TST) averaged 6 h 15 min, lower than the required 7 to 9 h for optimal wellbeing and cognitive functioning. The TST decreased over the calving period and was significantly correlated with both sleep start and wake times. Factors such as work start time, farm location, and role on farm influenced sleep quantity and quality. Further research is required to better understand sleep and its effect on dairy farmers, over both the calving period and the remaining months of the year.

Metrology of two wearable sleep trackers against polysomnography in patients with sleep complaints.

Sleep trackers are used widely by patients with sleep complaints, however their metrological validation is often poor and relies on healthy subjects. We assessed the metrological validity of two commercially available sleep trackers (Withings Activité/Fitbit Alta HR) through a prospective observational monocentric study, in adult patients referred for polysomnography (PSG). We compared the total sleep time (TST), REM time, REM latency, nonREM1 + 2 time, nonREM3 time, and wake after sleep onset (WASO). We report absolute and relative errors, Bland-Altman representations, and a contingency table of times spent in sleep stages with respect to PSG. Sixty-five patients were included (final sample size 58 for Withings and 52 for Fitbit). Both devices gave a relatively accurate sleep start time with a median absolute error of 5 (IQR -43; 27) min for Withings and -2.0 (-12.5; 4.2) min for Fitbit but both overestimated TST. Withings tended to underestimate WASO with a median absolute error of -25.0 (-61.5; -8.5) min, while Fitbit tended to overestimate it (median absolute error 10 (-18; 43) min. Withings underestimated light sleep and overestimated deep sleep, while Fitbit overestimated light and REM sleep and underestimated deep sleep. The overall kappas for concordance of each epoch between PSG and devices were low: 0.12 (95%CI 0.117-0.121) for Withings and VPSG indications 0.07 (95%CI 0.067-0.071) for Fitbit, as well as kappas for each VPSG indication 0.07 (95%CI 0.067-0.071). Thus, commercially available sleep trackers are not reliable for sleep architecture in patients with sleep complaints/pathologies and should not replace actigraphy and/or PSG.

Hubungan Kualitas Tidur Dengan Prestasi Belajar Murid Kelas X-Xii Di SMA Muhammadiyah 7 Yogyakarta

Introduction: Learning achievement is a student's effort to achieve mastery of knowledge, abilities, habits and skills and attitudes after following the learning process which can be proven by test results. Learning achievement is very important in the world of education because it evaluates the achievement of changes in accordance with learning objectives or not. One of the factors that influence learning achievement is sleep quality.
 Purpose: This research aims to determine the relationship between sleep quality and the learning achievement of students in grades X-XII at SMA Muhammadiyah 7 Yogyakarta.
 Method: This research is quantitative research with correlation studies and cross-sectional data collection. The research subjects were 79 students in class X-XII at SMA Muhammadiyah 7 Yogyakarta with with a simple random sampling method. Data collection was measured using the PSQI questionnaire and documentation data on student learning achievement was analyzed using the Chi-Square test.
 Result: The research results showed that 58 respondents (73.4%) had good sleep quality and 21 respondents (26.6%) had poor sleep quality. It was found that 67 respondents (84.8%) had good learning achievements and 12 respondents (15.2%) had poor learning achievements. It was found that there was a significant relationship between sleep quality and the learning achievement of students in grades X-XII at SMA Muhammadiyah 7 Yogyakarta (p=0.000, r=0.703)
 Conclusions: Based on the research results, there is a significant relationship between sleep quality and the learning achievement of students in grades Therefore, students can maintain and improve sleep quality such as sleep duration and sleep start time.

Tailored lighting intervention (TLI) for improving sleep-wake cycles in older adults living with dementia.

Introduction: Sleep disturbance is a hallmark of Alzheimer's disease and related dementias, and caregiver stress caused by patients' nighttime wandering, injuries, and agitation are frequently at the root of decisions to move them to assisted living facilities, where typically dim institutional lighting can further exacerbate their sleep problems. This study explored the effects of a circadian-effective lighting intervention on actigraphic sleep measures and subjective assessments of sleep disturbance, depression, and sleep-disturbed behaviors. Methods: Fourteen older adult (≥60years) participants (11 females, mean age = 84.1 [SD 8.9]), all diagnosed with moderate to severe dementia and sleep disturbance, were recruited from 3 assisted living and memory care facilities. Following a crossover, placebo-controlled design, 3 different lighting modes were used to deliver high levels of circadian stimulus to the participants' eyes for two 8-week intervention periods in a counter balanced order with a 4-week washout between the study's 2 conditions (dim light control vs. active intervention). Actigraphy and questionnaire data were collected over 7-day assessment periods that preceded (baseline weeks 1 and 9) and concluded (post-intervention week 9 and 22) the intervention periods. Actigraphic outcomes included sleep duration, sleep time, sleep efficiency, sleep start time, and sleep end time. Subjective assessments included the Cornell Scale for Depression in Dementia (CSDD), Pittsburgh Sleep Quality Index (PSQI), and Sleep Disorders Inventory (SDI) instruments. Results: Under the active condition, sleep duration significantly (p = 0.018) increased and sleep start time significantly (p = 0.012) advanced after the intervention compared to baseline. Also under the active condition, PSQI (p = 0.012), CSDD (p = 0.007), Sleep Disorders Inventory frequency (p = 0.015), and SDI severity (p = 0.015) scores were significantly lower after the intervention compared to baseline. Discussion: This study demonstrates that a circadian-effective lighting intervention delivering bright days and dark nights improves measures of sleep and mood in dementia patients living in controlled environments.

What a difference a year makes: Objective rest/activity patterns, circadian phase markers, and sleep quality before and during the COVID-19 pandemic

ObjectivesThe COVID-19 pandemic led to numerous changes in sleep duration, quality, and timing. The goal of this study was to examine objective and self-reported changes in sleep and circadian timing before and during the pandemic. MethodsData were utilized from an ongoing longitudinal study of sleep and circadian timing with assessments at baseline and 1-year follow-up. Participants had baseline assessment between 2019 and March 2020 (before pandemic) and 12-month follow-up between September 2020 and March 2021 (during pandemic). Participants completed 7 days of wrist actigraphy, self-report questionnaires, and laboratory-collected circadian phase assessment (dim light melatonin onset). ResultsActigraphy and questionnaire data were available for 18 participants (11 women and 7 men, Mean = 38.8 years, SD = 11.8). Dim light melatonin onset was available for 11 participants. Participants demonstrated statistically significant decreases in sleep efficiency (Mean = −4.11%, SD = 3.22, P = .001), worse scores on Patient-Reported Outcome Measurement Information System sleep disturbance scale (Mean increase = 4.48, SD = 6.87, P = .017), and sleep end time delay (Mean = 22.4 mins, SD = 44.4 mins, P = .046). Chronotype was significantly correlated with change in dim light melatonin onset (r = 0.649, P = .031). This suggests that a later chronotype is associated with a greater delay in dim light melatonin onset. There were also non-significant increases in total sleep time (Mean = 12.4 mins, SD = 44.4 mins, P = .255), later dim light melatonin onset (Mean = 25.2 mins, SD = 1.15 hrs, P = .295), and earlier sleep start time (Mean = 11.4 mins, SD = 48 mins, P = .322). ConclusionOur data demonstrate objective and self-reported changes to sleep during the COVID-19 pandemic. Future studies should look at whether some individuals will require intervention to phase advance sleep when returning to previous routines such as returning to office and school settings.

걷기 Apps을 이용한 Power Walking이 교대 여학생의 체력, 체조성 및 생활습관에 미치는 영향

In this study, 40 students out of 6 classes took a track and field class in the 2nd semester of B University in Busan, walked a 10cm extension per a step for 12 weeks, more than 3times a week, and at least 1 hour per once. Physical fitness, body composition, and exercise environment were analyzed, and the results of analysis using SPSS Ver 23.0 statistical program are as follows.BR There were significant differences between the groups on the physical fitness of teacher’s college women students in 50m running, grip strength, and 20m shuttle run. The results of body composition of teacher’s college women students between groups showed significantly differences in body fat mass, %fat, and body development index. The results of the change in the exercise environment of the walking exercise group in the group of teacher’s college women students showed significant differences in the frequency of going out, outing time, exercise frequency, number of walking steps, exercise time, and the number of other exercie participants. As a result of the change of living environment in the walking exercise group in the group of teacher’s college women students, there were significant differences in breakfast, snack frequency, sleep start time, and health interest.BR Summarizing the results, walking with a 10cm extension for 12 weeks was a valuable opportunity to directly experience the development of the body and changes in lifestyle during the semester, and bringing positive effects to school life.

Posttraumatic stress disorder, sleep and medical cannabis treatment: A daily diary study

Despite increasing use of Medical Cannabis (MC) among posttraumatic stress disorder (PTSD) patients, research is lacking on how MC treatment relates to PTSD symptomatology, in particular sleep disturbances. This study examines the time gap between MC use and sleep onset and its association with (1) number of awakenings throughout the night, (2) early awakenings, (3) nightmares. Each morning over a two week period, 77 licensed MC patients suffering from PTSD reported on the timing of previous night MC use and sleep disturbances. Within-person analyses found that shorter time gaps between previous night MC use and sleep start time was associated with lower likelihood of experiencing nightmares throughout the night, but it was not associated with nightly awakenings or waking up too early. Between-person analyses showed that individuals who used MC products with higher CBD concentrations reported fewer early awakenings. These preliminary results indicate that future research should test causal relations between MC use and sleep problems in PTSD patients. Future research is warranted in order to explore causal relationships between MC use, nightmares and insomnia in PTSD patients.

0144 Identification of sleep factors related to blood pressure in emergency medicine healthcare workers

Abstract Introduction Emergency Department (ED) healthcare workers (HCWs) may be at elevated risk for the development of cardiovascular disease (CVD), due in part to sleep and/or circadian disturbances. This study aimed to evaluate the relationship of sleep factors with blood pressure, a primary marker of CVD risk, in ED HCWs. Methods Participants were HCWs (physicians, nurses, advanced practice providers, technicians, etc.) from 4 EDs in New York City who completed study procedures between November 2020-October 2021. Participants completed a 2-week data burst, which included sleep/activity (Fitbit Inspire) and home blood pressure monitoring (Omron 5 Series BP7250; preceding and following their main daily sleep episode). Linear regression models, adjusted for age, gender, and race/ethnicity, were conducted predicting blood pressure from sleep factors. Results The sample included n=74 ED HCWs (mean [SD] age=38.4 [8.7] years). Most were female (62.2%) and non-Hispanic/Latino White (55.6%). Mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 116.1 (12.5) mmHg and 75.1 (7.6) mmHg, respectively. Actigraphy-derived sleep factor means for the data burst period were: a) Total sleep time (TST): 6.8 (1.0) hours; b) Sleep efficiency (SE): 94.5 (2.2)%; c) Percentage of main sleep episodes throughout the burst with TST <6 hours: 25.9 (20.8)%; d) Sleep start time: 24:06 (01:24); and e) Within-subject inter-daily bedtime variability (i.e., SD of sleep start times): 2.4 (1.8) hours. Higher TST was associated with lower SBP (B [SE] =-0.50 [0.30] mmHg/10 min, p=.04) and DBP (B [SE] =-0.50 [0.20] mmHg/10 min, p=.01). Greater SE was associated with lower SBP (B [SE] =-1.23 [0.55], mmHg/%, p=.03) and DBP (B [SE] =-1.05 [0.39], mmHg/%, p=.01). A higher proportion of nights with TST <6 hours was associated with higher DBP (B [SE] =1.4 [0.40], mmHg/10%, p<0.01) but not SBP. Sleep start time and bedtime variability were not associated with BP. Conclusion These findings provide support for the relationship between sleep and blood pressure. Of note, data were collected during the COVID-19 pandemic, which may impact observed relationships. Because this is a cross-sectional analysis, the causal direction of the association may be (at least partially) reversed. Further research should examine psychological and work-related factors in ED HCWs that may modify these relationships, e.g., stress/anxiety, burnout, and job strain, and include assessments of the circadian system. Support (If Any) R01HL146911

Sleep behavior and chronotype before and throughout pregnancy

Study objectiveTo compare sleep behavior before and during pregnancy. MethodsIn this prospective cohort study, healthy women were followed from pre-pregnancy until delivery. At pre-pregnancy and each trimester, participants completed validated questionnaires of chronotype and sleep quality and timing, including the Munich ChronoType Questionnaire, Epworth Sleepiness Scale, and Pittsburgh Sleep Quality Index. The primary outcomes were sleep period start and end times, sleep duration, sleep midpoint, and social jetlag, compared between pre-pregnancy and each trimester. Wrist actigraphy was used to measure the same outcomes in a subset of participants. ResultsEighty-six women were included in analysis of questionnaires. Of these, 37 provided complete actigraphy data. Questionnaire and actigraphy data indicate that participants had less social jetlag during pregnancy than before pregnancy. Sleep period start times were earlier on both work and free days in the first and second trimesters than pre-pregnancy, and returned to pre-pregnancy times by the third trimester. Actigraphy data revealed that, compared to pre-pregnancy, participants had longer sleep periods in all trimesters on work days and in the first trimester on free days. Sleep surveys revealed that participants had poorer sleep quality in the first and third trimesters and more sleepiness in the first trimester than pre-pregnancy. ConclusionThe first trimester of pregnancy is characterized by earlier sleep period start time, longer sleep duration, and poorer sleep quality than pre-pregnancy. Sleep quality temporarily improves in the second trimester, and sleep period start time returns to pre-pregnancy time by the third trimester. Study rationaleMultiple parameters of sleep have been studied in the context of pregnancy and pregnancy outcomes, but rarely in comparison to pre-pregnancy or longitudinally through pregnancy. Study impactActigraphy and questionnaire data reveal sleep timing and quality change throughout pregnancy. These data on sleep changes in healthy pregnancy can be used as a baseline to identify sleep-related risk factors throughout pregnancy.

A 4-year longitudinal study investigating the relationship between flexible school starts and grades

The mismatch between teenagers’ late sleep phase and early school start times results in acute and chronic sleep reductions. This is not only harmful for learning but may reduce career prospects and widen social inequalities. Delaying school start times has been shown to improve sleep at least short-term but whether this translates to better achievement is unresolved. Here, we studied whether 0.5–1.5 years of exposure to a flexible school start system, with the daily choice of an 8 AM or 8:50 AM-start, allowed secondary school students (n = 63–157, 14–21 years) to improve their quarterly school grades in a 4-year longitudinal pre-post design. We investigated whether sleep, changes in sleep or frequency of later starts predicted grade improvements. Mixed model regressions with 5111–16,724 official grades as outcomes did not indicate grade improvements in the flexible system per se or with observed sleep variables nor their changes—the covariates academic quarter, discipline and grade level had a greater effect in our sample. Importantly, our finding that intermittent sleep benefits did not translate into detectable grade changes does not preclude improvements in learning and cognition in our sample. However, it highlights that grades are likely suboptimal to evaluate timetabling interventions despite their importance for future success.

Social Jetlag and Excessive Daytime Sleepiness from a Sample of Russian Children and Adolescents.

PurposeInsufficient nocturnal sleep is a primary source of excessive daytime sleepiness. Most previous research has focused on the disparity between sleep demands and study start times in adolescents. Fewer studies have focused on elementary schoolchildren. We hypothesize that late sleep timing is connected to excessive daytime sleepiness in a sample of Russian children and adolescents. The major goals of our study were to evaluate excessive daytime sleepiness in Russian schoolchildren and adolescents using the Russian version of the Pediatric Daytime Sleepiness Scale (PDSS) and to estimate its relationship with sleep-wake parameters using the Munich Chronotype Questionnaire (MCTQ).Materials and MethodsStudent subjects were from public educational facilities in the Republic of Karelia. They completed both the PDSS and the Munich Chronotype Questionnaire to estimate sleep parameters and chronotype (MSFsc). Five hundred and eleven students provided data for the PDSS and sleep-wake variables, and 479 for the full MCTQ data.ResultsThe overall prevalence of Excessive Daytime Sleepiness (EDS) in our sample was 18%. The total PDSS score was inversely correlated with sleep length on school nights and was independent of respondents’ sex. Higher PDSS scores were associated with later bedtimes on school days and free days, and shorter sleep duration on school days. Late chronotype and more pronounced social jetlag were both positively correlated with high PDSS scores. A negative correlation was found between chronotype and the duration of the sleep period on weekdays (p < 0.001) and a positive correlation was found on weekends (p < 0.001). Longer average sleep duration was positively related to less daytime sleepiness.ConclusionThis study suggests that excessive daytime sleepiness is chronotype-dependent. School start times could be shifted to a later hour to prolong sleep and reduce EDS.

019 A Simple, Objective Estimate of Dietary Timing as a Circadian Biomarker

Abstract Introduction The importance of diet for health has been recognized for centuries, yet, the focus has mostly been on what someone eats. Recently, however, focus has expanded to include when someone eats. Experimental studies that manipulated timing of food intake demonstrated effects on metabolic function. Most population-based studies have had to rely on self-reported methods to assess dietary behavior, including food logs or dietary recall, but self-report is subjective, error-prone, and burdensome. The goal of this analysis was to identify an objective estimate of dietary timing in clock time format. Further, we planned to examine whether sleep “chronotype” and this dietary “chronotype” are correlated. Methods We used continuous glucose monitoring (CGM), which involves inserting a small sensor into the back of a participant’s upper arm. It measures interstitial glucose levels every 5 minutes continuously for up to 2 weeks. We had 13 participants wear a CGM device for 2–10 days. We characterized dietary timing by simply identifying the clock time at which glucose levels were the highest. We identified this time point for each day and then calculated the mean clock time for each participant. Participants also wore wrist actigraphy for approximately one week and we calculated mean sleep start, sleep end, and midpoint times. We calculated intervals between highest glucose and sleep timing measures. Results The mean time of highest glucose was 16:57 (SD 3:05) and ranged from 11:16 to 22:27. The mean (SD) intervals were 10.4 (2.9) hours for sleep end to highest glucose, 13.5 (2.8) hours for midpoint to highest glucose, and 5.8 (2.8) hours for highest glucose to sleep start. The correlations between highest glucose and sleep timing were .13 (p=.70) for sleep start, .05 (p=.88) for sleep end, and -.19 (p=.55) for midpoint. Conclusion There was a wide range in the timing of highest glucose values in this sample, Further, the timing of highest glucose does not correlate with sleep timing, which indicates that these two timing measures are not redundant with one another. The timing of highest glucose from CGM may provide an objective, simple circadian biomarker of dietary behavior. Support (if any) P01A1011412-18A1

096 The Association Between Sleep Regularity Index and Self-Reported Behavioral and Emotional Symptoms in Adolescents

Abstract Introduction Among adolescents, sleep health has been associated with emotional and mood regulation, cognitive functioning, and behavior. Few studies, however, have examined the Sleep Regularity Index (SRI, Phillips et. al, 2017) and its associations with mental health and well-being in this age group. For this study, we examined whether SRI in 15-16-year-old adolescents would predict internalizing and externalizing symptoms as measured by Youth Self-Report (YSR) scores two years later. We hypothesized that a higher baseline sleep regularity would predict lower internalizing and externalizing YSR scores at the 2-year follow-up. Methods The sample included 32 adolescents (14 male) ages 15-16yr (mean = 15.6) at baseline and 2 years later (mean age = 17.7). Actigraphy data and YSR scores were collected at baseline, and YSR was examined at follow-up. Participant’s SRIs were calculated using 24-hour actigraphy data scored for sleep and wake. YSR T-scores of 60 or above indicate borderline clinical internalizing (n = 2) and externalizing (n = 4) symptoms at follow-up. We used linear regression modeling to determine whether baseline SRI predicted YSR scores 2 years later. Covariates included sleep start time, sleep duration, sex, and baseline YSR scores. Results At baseline, average SRI and YSR scores were not significantly correlated (internalizing: r = 0.10; externalizing: r = 0.24, p’s &amp;gt; 0.1). SRI score at baseline (mean = 80.5 ± 7.4) significantly predicted YSR internalizing scores (mean = 42 ± 9) at the 2-year follow up (t(26) = 2.57, p = 0.016) but not externalizing scores (mean = 44.8 ± 10.3, t(26) = .78, p = 0.44). Conclusion We observed that sleep regularity was associated with internalizing symptoms two years later; however, the association was not in the expected direction: higher SRI was correlated with increased YSR internalizing scores at the 2-year follow-up. As most participants were in a healthy range for YSR scores at both assessments, a possible explanation for this finding is that those with higher SRIs have greater self-awareness in assessing their internal feelings. Future work will examine SRI values and YSR in this sample across 6 assessments acquired at 6-month intervals. Support (if any) AA13252 (NIH)

Researcher Choices for Infant Sleep Assessment: Parent Report, Actigraphy, and a Novel Video System

Incorporating infant sleep, either as a predictor or as an outcome variable, into interdisciplinary work has become increasingly popular. Sleep researchers face many methodological choices that have implications for the reliability and validity of the data. Here, the authors directly investigated the impact of design and measurement choices in a small, longitudinal sample of infants. Three sleep measurement techniques—parent-reported sleep diaries, actigraphy (Micromini Sleep Watch), and a commercial videosomnography (Nanit)—were included, using actigraphy as the baseline. Nine infants’ sleep (4 girls) was measured longitudinally using all three measurement techniques. Nanit provided summary statistics, using a proprietary algorithm, for nightly sleep parameters. The actigraphy data were analyzed with both the Sadeh Infant and Sadeh algorithms. The extent to which measurements converged on sleep start and end time, number of wake episodes, sleep efficiency, and sleep duration was assessed. Measures were positively correlated. Difference scores revealed similar patterns of greater sleep estimation in parent reports and Nanit compared with actigraphy. Bland-Altman plots revealed that much of the data were within the limits of agreement, tentatively suggesting that Nanit and actigraphy may be used interchangeably. Graphs display significant variability within and between individual infants as well as across measurement techniques. Potential confounding variables that may explain the discrepancies between parent report, Sadeh Infant, Sadeh, and Nanit are discussed. The findings are also used to speak to the advantages and disadvantages of design and measurement choices. Future directions focus on the unique contributions of each measurement technique and how to capitalize on them.

Sleep in New Zealand children aged 7-9: associations with ethnicity, socioeconomic status, and achievement in reading and mathematics.

The aims were (1) to investigate differences by ethnicity and socioeconomic status (SES) in objective measures of sleep in children aged 7-9 years and (2) determine whether measures of sleep predict child achievement in reading or mathematics after controlling for ethnicity and SES. Four groups of parent-child dyads were recruited: Māori, low-SES schools (n = 18); Māori, high-SES schools (n = 17); New Zealand European, low-SES schools (n = 18); New Zealand European, high-SES schools (n = 17). Child sleep was measured by actigraphy. Parents and teachers reported child daytime sleepiness and behavior, and children completed a self-report of anxiety symptoms. Teachers also reported on child achievement in reading and mathematics. Children from low-SES schools went to bed later on school nights (F[1,68] = 12.150, P = .001) and woke later (F[1,68] = 15.978, P < .001) than children from high-SES schools but had similar sleep duration. There were no differences related to ethnicity. Children from low-SES schools were almost 3 times more likely to be below national standards for mathematics. Children not meeting academic standards in mathematics had a later sleep start time, lower sleep period efficiency, and a decreased total sleep time. However, when SES and sleep period efficiency were modeled together neither were found to significantly influence achievement in mathematics. In this study, SES influenced sleep timing but not the quality and quantity of sleep in 7- to 9-year-old children, and a significant independent effect of sleep efficiency on learning could not be demonstrated.

Is time elapsed between cannabis use and sleep start time associated with sleep continuity? An experience sampling method

BackgroundA substantial proportion of people using cannabis report using it to improve sleep. Yet, little research exists on the associations between the timing of cannabis use and sleep. This study examines the time elapsed between cannabis use and sleep start time and its association with two of the main indicators of sleep continuity: (1) sleep onset latency (SOL) and (2) number of awakenings (NOA) throughout the night. MethodsEach morning, for 7 consecutive days, daily cannabis users (n = 54) reported on the timing of previous night’s cannabis use and sleep indicators on their smartphones. Mixed effects models examined the relations of within- and between-subjects’ time elapsed between previous night cannabis use and sleep start time, with (1) SOL and (2) NOA. ResultsWithin subjects, shorter time elapsed between cannabis use and sleep start time was associated with shorter SOL (β = 0.519, p = 0.010), but not NOA (β = -0.030, p = 0.535). Furthermore, between individuals, the time gap between the previous night cannabis use and sleep start time was not associated with SOL or NOA (p > 0.05). ConclusionsIt is possible that cannabis use proximal to bedtime is associated with shorted sleep onset latency but not nighttime awakenings. Cannabis users should be informed about both the potential sleep aid effects of cannabis and its limitations. Pending further evidence of the effects of cannabis on sleep, cannabis users experiencing sleep problems should be provided with evidence-based alternatives to improve sleep, e.g., pharmacological and behavioral treatments.

Effect of Hemodiafiltration on Self-Reported Sleep Duration: Results from a Randomized Controlled Trial

Introduction: Dialysis patients suffer from poor sleep duration and quality. We examined the self-reported sleep duration in patients randomized to either high-volume hemodiafiltration (HDF) or high flux hemodialysis (HD). Methods: Patients from 13 Brazilian dialysis clinics were enrolled in the HDFIT randomized controlled trial (RCT) investigating the impact of HDF on physical activity and self-reported outcomes. Self-reported sleep duration was taken from patient diaries recording sleep start and end time over a week during baseline, months 3 and 6, respectively. Sleep duration was analyzed by shift and nights relative to dialysis. Results: The HDFIT study enrolled 197 patients; sleep data were available in 173 patients (87 HD; 86 HDF). Patients’ age was 53 ± 15 years, 57% were white, 72% were male, 34% had diabetes, Kt/V was 1.54 ± 0.40, and albumin 3.97 ± 0.36 g/dL. Most patients reported sleeping 510–530 min/night. At 3 months, HDF patients slept 513 ± 71 min/night, HD patients 518 ± 76 min/night. At 6 months, HDF patients slept 532 ± 74 min/night, HD patients 519 ± 80 min/night. At baseline, 1st shift patients slept 406 ± 86 min the night before HD, 534 ± 64 min the night after HD, and 496 ± 99 min the night between 2 non-HD days. Compared to patients in the 2nd and 3rd shifts, patients dialyzed in the 1st shift slept less in the night before dialysis. Similar patterns were seen after 3 and 6 months. Conclusion: In our RCT, the dialysis modality (HDF vs. HD) had no effect on self-reported sleep duration. In both groups, dialysis in the 1st shift adversely affected self reported sleep duration.

Smartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm.

BackgroundSmartphones are becoming increasingly ubiquitous every day; they are becoming more assimilated into our everyday life, being the last thing used before going to sleep and the first one after waking up. This strong correlation between our lifestyle choices and smartphone interaction patterns enables us to use them as sensors for sleep duration assessment to understand individuals’ lifestyle and sleep patterns.ObjectivesThe objective of this study was to estimate sleep duration based on the analysis of the users’ ON-OFF interaction with their smartphone alone using the iSenseSleep algorithm.MethodsWe used smartwatch sleep assessment data as the ground truth. Results were acquired with 14 different subjects collecting smartwatch and smartphone interaction data for up to 6 months each.ResultsResults showed that based on the smartphone ON-OFF patterns, individual’s sleep duration can be estimated with an average error of 7% (24/343) [SD 4% (17/343)] min of the total duration), enabling an estimate of sleep start and wake-up times as well as sleep deprivation patterns.ConclusionsIt is possible to estimate sleep duration patterns using only data related to smartphone screen interaction.

Delayed Lactogenesis II is Associated With Lower Sleep Efficiency and Greater Variation in Nightly Sleep Duration in the Third Trimester.

Metabolic and hormonal disturbances are associated with sleep disturbances and delayed onset of lactogenesis II. The aim of this study was to measure sleep using wrist actigraphy during gestation weeks 22 and 32 to determine if sleep characteristics were associated with blood glucose, body mass index, gestational related disease, delayed onset of lactogenesis II, or work schedule. Demographic data were collected at study intake from primiparous women who wore a wrist actigraph during gestation weeks 22 (n = 50) and 32 (n = 44). Start and end sleep time, total nighttime sleep, sleep efficiency, wake after sleep onset, and sleep fragmentation were measured. Night to night variability was assessed with the root mean square of successive difference. Blood glucose levels, body mass index, and gestational disease data were abstracted from medical charts. Timing of lactogenesis II was determined by survey. Between gestation week 22 and 32, sleep efficiency decreased and fragmentation increased (p < .05). During gestation week 32, blood glucose was negatively correlated with sleep duration, and positively related to fragmentation (p < .05). Women who experienced delayed lactogenesis II had lower sleep efficiency and greater fragmentation (p < .05), and greater night-to-night variability in sleep start and end time, efficiency, and duration during gestation week 32 (p < .05). Women with better sleep efficiency and more stable nightly sleep time are less likely to experience delayed onset of lactogenesis II. Interventions to improve sleep may improve maternal health and breastfeeding adequacy.