Impact of Recent Job Loss on Sleep, Energy Consumption and Diet

To examine how sleep quality and sleep duration affect caloric intake among those experiencing involuntary job loss. Methods; Adequate sleep and self reported dietary recall data from the Assessing Daily Activity Patterns through Occupational Transitions (ADAPT) study was analyzed. Primary sleep indices used were total sleep time, time spent in bed after final awakening, and sleep quality as measured by the Daily Sleep Diary (DSD). Mean Energy consumption (MEC) was the primary nutritional index. Secondary indices included diet quality using the Health Eating Index 2015 (HEI), and self-reported intake of protein, carbohydrates and fats. Results The study participants were comprised mainly of women (61%) and non-Hispanic white. The participants had at least 2 years of college education and mean body mass index of 30.2 ± 8.08 (kg/m 2 ). The average time in bed was 541.8 ± 77.55 minutes and total sleep time was 461.1 ±56.49 minutes. Mean sleep efficiency was 91 ± 6%, self-reported sleep quality was 2.40 ±0.57 (0-4 scale, 4 = very good), and minutes earlier than planned morning awakening were 14.36 ±24.15. Mean HEI score was 47.41 ± 10.92. Although the MEC was below national average for both men and women, male sex was associated with higher MEC. In a fully adjusted model sleep quality was positively associated with MEC. Conclusion Daily overall assessments of sleep quality among recently unemployed persons were positively associated with mean energy consumption. Additionally, the diet quality of unemployed persons was found to unhealthier than the average American and consistent with the relationship between poor socioeconomic status and lower diet quality.

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Personality Traits and Insomnia Symptoms in Shift Workers

Frontiers in Psychology ◽

10.3389/fpsyg.2021.689741 ◽

2021 ◽

Vol 12 ◽

Author(s):

Brigitte Holzinger ◽

Lucille Mayer ◽

Gerhard Klösch

Keyword(s):

Sleep Quality ◽

Personality Traits ◽

Sleep Duration ◽

Total Sleep Time ◽

Sleep Time ◽

Work Schedule ◽

Shift Workers ◽

Time In Bed ◽

Shift Schedules ◽

Insomnia Symptoms

The discrepancy between natural sleep-wake rhythm and actual sleep times in shift workers can cause sleep loss and negative daytime consequences. Irregular shift schedules do not follow a fixed structure and change frequently, which makes them particularly harmful and makes affected individuals more susceptible to insomnia. The present study compares insomnia symptoms of non-shift workers, regular shift workers, and irregular shift workers and takes into account the moderating role of the Big Five personality traits and levels of perfectionism. Employees of an Austrian railway company completed an online survey assessing shift schedules, sleep quality and duration, daytime sleepiness, and personality traits. A total of 305 participants, of whom 111 were non-shift workers, 60 regular shift workers, and 134 irregular shift workers, made up the final sample. Irregular shift workers achieved significantly worse scores than one or both of the other groups in time in bed, total sleep time, sleep efficiency, sleep duration, sleep quality, sleep latency, and the number of awakenings. However, the values of the irregular shifts workers are still in the average range and do not indicate clinical insomnia. Participants working regular shifts reported the best sleep quality and longest sleep duration and showed the least nocturnal awakenings, possibly due to higher conscientiousness- and lower neuroticism scores in this group. Agreeableness increased the effect of work schedule on total sleep time while decreasing its effect on the amount of sleep medication taken. Perfectionism increased the effect of work schedule on time in bed and total sleep time. Generalization of results is limited due to the high percentage of males in the sample and using self-report measures only.

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Testing the contiguity of the sleep and fatigue relationship: a daily diary study

SLEEP ◽

10.1093/sleep/zsaa252 ◽

2020 ◽

Author(s):

Andrea L Harris ◽

Nicole E Carmona ◽

Taryn G Moss ◽

Colleen E Carney

Keyword(s):

Sleep Quality ◽

Hierarchical Linear Modeling ◽

Total Sleep Time ◽

Sleep Time ◽

Cognitive Factors ◽

Sleep Diary ◽

Poor Sleep ◽

Daily Diary Study ◽

Insomnia Disorder ◽

The Relationship

Abstract Study Objectives There is mixed evidence for the relationship between poor sleep and daytime fatigue, and some have suggested that fatigue is simply caused by lack of sleep. Although retrospective measures of insomnia and fatigue tend to correlate, other studies fail to demonstrate a link between objectively disturbed sleep and fatigue. The current study prospectively explored the relationship between sleep and fatigue among those with and without insomnia disorder. Methods Participants meeting Research Diagnostic Criteria for insomnia disorder (n = 33) or normal sleepers (n = 32) completed the Consensus Sleep Diary (CSD) and daily fatigue ratings for 2 weeks. Baseline questionnaires evaluated cognitive factors including unhelpful beliefs about sleep and rumination about fatigue. Hierarchical linear modeling tested the within- and between-participant relationships between sleep quality, total sleep time, and daily fatigue ratings. Mediation analyses tested if cognitive factors mediated the relationship between insomnia and fatigue. Results Self-reported nightly sleep quality significantly predicted subsequent daily fatigue ratings. Total sleep time was a significant predictor of fatigue within, but not between, participants. Unhelpful sleep beliefs and rumination about fatigue mediated the relationship between insomnia and fatigue reporting. Conclusions The results suggest that perception of sleep plays an important role in predicting reports of daytime fatigue. These findings could be used in treatment to help shift the focus away from total sleep times, and instead, focus on challenging maladaptive sleep-related cognitions to change fatigue perception.

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Sleep and Physical Activity Patterns in Urban American Indian Children

American Journal of Health Behavior ◽

10.5993/ajhb.44.1.7 ◽

2020 ◽

Vol 44 (1) ◽

pp. 67-75

Author(s):

Vernon M. Grant ◽

Emily J. Tomayko ◽

Raymond D. Kingfisher

Keyword(s):

Physical Activity ◽

American Indian ◽

Screen Time ◽

Total Sleep Time ◽

Activity Patterns ◽

Sleep Time ◽

Indian Children ◽

Time In Bed ◽

Prevention Interventions ◽

Urban American Indian

Objectives: In this study, we examined patterns of obesity, physical activity (PA), sleep, and screen time in urban American Indian (AI) youth in the 6th-8th grade. Methods: A youth sample (N = 36) from 3 middle schools was recruited to participate in this observational sample of convenience. Youth completed a demographic and screen time survey, measurements of height and weight, and wore a wrist accelerometer continuously for 7 days to assess PA and sleep. Results: Approximately 42% of participants were overweight or obese. Average weekday screen time was 254.7±98.1 minutes. Compared to weekdays, weekend sedentary activity increased (weekday, 159.2±81.1 minutes vs weekend, 204.3±91.7 minutes; p = .03) and vigorous PA (weekday, 20.9±19.1 minutes vs weekend, 5.7±8.1 minutes; p = .0001) and moderate-to-vigorous PA (weekday, 192.65±62.3 minutes vs weekend, 141±71.7 minutes; p = .002) decreased. Compared to weekdays, weekend total sleep time (weekday, 512.8±48.6 minutes vs weekend, 555.3±84.3 minutes; p = .007) and time in bed (weekday, 487.3±49.6 minutes vs weekend, 528.6±71.2 minutes; p = .01) increased. Conclusions: Weekday to weekend shifts in PA and sleep must be considered when designing targeted obesity prevention interventions.

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286 Pilot Sleep During Brazil-to-China COVID-19 Humanitarian Mission Flights Compared to Biomathematical Predictions of Sleep

SLEEP ◽

10.1093/sleep/zsab072.285 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A114-A115

Author(s):

Jaime Devine ◽

Caio Garcia ◽

Audrey Simoes ◽

Jake Choynowski ◽

Marina Guelere ◽

...

Keyword(s):

Sleep Quality ◽

Intraclass Correlation ◽

Correlation Coefficients ◽

Sleep Time ◽

Prediction Algorithm ◽

Sleep Diary ◽

Flight Period ◽

Paired Samples ◽

Time In Bed ◽

Scheduling Tool

Abstract Introduction n response to the COVID-19 pandemic, Azul Airlines organized and conducted five separate humanitarian missions to China between May and July, 2020. Each mission consisted of 4 flight legs between 11-15 hours long crewed by a team of 8 pilots. Each pilot was given a 9-hour sleep opportunity during the flight period. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model Fatigue Avoidance Scheduling Tool (FAST) was used to predict in-flight time in bed (TIB) and total sleep time (TST). During missions, pilots wore a wrist actigraph and completed a sleep diary. These analyses compare the accuracy of SAFTE-FAST AutoSleep predictions against pilots’ sleep diary and actigraphy from Azul’s COVID-19 humanitarian missions. Methods Pilots wore a sleep-tracking actigraphy device (Zulu Watch, Institutes for Behavior Resources), and reported the TIB and sleep quality of their in-flight rest periods using a sleep diary. Diary TST was estimated from TIB and sleep quality. AutoSleep, diary, and actigraphy measures were compared using paired samples t-tests. Agreement was compared using intraclass correlation coefficients (ICC). Results Twenty (n=20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. AutoSleep predictions of TIB (235±20 minutes) and TST (193±16 minutes) were significantly lower than diary (TIB: 330±123, t=6.80, p≤0.001; TST: 262±108, t=5.60, p≤0.001) and comparable to actigraphy (TIB: 246±127, t=0.78, p=0.43; TST: 212±113, t=1.59, p=0.12). ICC values were >0.90, indicating excellent agreement, for TIB (0.94) and TST (0.91). Conclusion Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances like the COVID-19 pandemic. Pilots may overestimate the amount of sleep that they receive during extreme flights-duty periods, which could constitute a fatigue risk. Support (if any) NA

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An investigation of the relationship between sleep and fatigue in those with and without insomnia

10.32920/ryerson.14645433 ◽

2021 ◽

Author(s):

Andrea L. Harris

Keyword(s):

Sleep Quality ◽

Linear Models ◽

Total Sleep Time ◽

Sleep Time ◽

Sleep Diary ◽

Poor Sleep ◽

Sleep Quantity ◽

Cognitive Behavioural ◽

Multi Method Approach ◽

The Relationship

There is currently mixed evidence for the relationship between poor sleep and daytime fatigue. It is well documented that retrospective measures of insomnia and fatigue are highly correlated with one another. However, other studies fail to demonstrate a link between objectively less sleep and fatigue; that is, individuals with shorter sleep times do not necessarily report increased fatigue. As such, the relationship between these two constructs remains unclear. The current investigation will help to elucidate the complex relationship between sleep and fatigue among those with and without insomnia by advancing the existing literature in two important ways. First, this study proposed to examine the temporal relationship between sleep and fatigue across two weeks, thereby investigating whether sleep and fatigue occur in accordance with one anotherover time. Second, this study utilized a multi-method approach by collecting subjective (i.e.,sleep diary) and objective (i.e., actigraphy) measures of sleep, as well as retrospective (i.e.,visual analogue scales: VAS) and prospective (i.e., momentary ratings) measures of fatigue. Two separate hierarchical linear models were used to test whether sleep (measured by sleep quality and total sleep time) predicted daytime fatigue on the VAS and actigraph, respectively. The secondary objective asked whether cognitive-behavioural variables (i.e., maladaptive sleep beliefs, fear and avoidance of fatigue, and fatigue-based rumination) may help account for the relationship between sleep and fatigue using mediation. The results of the primary analyses suggested that sleep quality significantly predicted VAS fatigue ratings, whereas total sleep time was a significant predictor of fatigue within- but not between-persons. No significant relationships were found between objective measures of sleep and momentary fatigue ratings. Finally, each of the cognitive-behavioural variables, with the exception of avoidance of fatigue, were significant mediators of the relationship between sleep and fatigue. The results demonstrated that compared to sleep quantity, our perception of sleep may play a more important role in predicting reports of daytime fatigue. These findings could help decrease the burden that individuals with insomnia place on their total sleep times, and instead, treatment could focus on challenging maladaptive sleep-related cognitions, which ultimately could lessen the overall sleep-related anxiety.

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262 Comparing Two Methods for Scoring Wrist Actigraphy to Polysomnography for Estimating Total Sleep Time in Healthy Men

SLEEP ◽

10.1093/sleep/zsab072.261 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A105-A105

Author(s):

Emma Tussey ◽

Corey Rynders ◽

Christine Swanson

Keyword(s):

Decision Tree ◽

Total Sleep Time ◽

Reference Method ◽

Sleep Time ◽

Sleep Diary ◽

Scoring Methods ◽

Scoring Method ◽

Time In Bed ◽

Wrist Actigraphy ◽

Rest Intervals

Abstract Introduction This analysis assessed whether manually setting rest (i.e., time in bed) intervals prior to using a proprietary software package (Actiware, version 6.09) to analyze wrist actigraphy data improved estimates of total sleep time (TST) compared to polysomnography (PSG). Methods The Phillips Actiwatch 2 and PSG (reference method) were used to calculate TST on two separate nights in twelve men (age=28.3 ± 5.7). Participants had an 8-hour sleep opportunity on night one and a 5-hour sleep opportunity and on night two. Estimates of TST from actigraphy data were calculated using two scoring methods. For scoring method 1, we allowed the software to automatically choose rest intervals and then applied a proprietary algorithm to calculate TST. For scoring method 2, we manually entered rest intervals using a published decision tree that incorporates activity, light, event marker, and sleep diary data. After the rest intervals were set in method 2, the proprietary algorithm was applied to calculate TST. Mean bias and limits of agreement (LOA) from Bland-Altman plots compared TST derived from both actigraphy scoring methods to PSG estimates. Results On night 1 (n=8) TST measured by PSG was 398.4 ± 40.6 minutes, compared to 395.5 ± 70.9 minutes using actigraphy scoring method 1 and 396 ± 44.5 minutes using scoring method 2. Mean bias was similar when comparing both scoring methods to PSG, but the LOA were wider in method 1 compared to method 2 (method 1 vs. PSG: -2.9 [-110.4, 104.7]; method 2 vs. PSG: -2.4 [-66.5, 61.7]; minutes). On night 2 (n=12) TST determined by PSG was 283.3 ± 11.2 minutes, compared to 302.1 ± 84.4 minutes using actigraphy scoring method 1 and 273.1 ± 14.5 minutes using scoring method 2. Again, LOA for TST estimated by actigraphy scoring method 1 were wider compared to scoring method number 2 (method 1 vs. PSG: 18.8 [-136.9, 174.6]; method 2 vs. PSG: -10.2 [-35.1, 14.8]). Conclusion These data demonstrate that applying a decision tree to manually set time in bed intervals prior to running analyses in the software results in better agreement when estimating TST from wrist actigraphy compared to PSG. Support (if any) UL1RR025780, K23AR070275.

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Sleep Quality in Family Caregivers and Matched Non-Caregiving Controls: The REGARDS Study

Innovation in Aging ◽

10.1093/geroni/igab046.2982 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 818-818

Author(s):

Marcela Blinka ◽

Adam Spira ◽

Orla Sheehan ◽

Tansu Cidav ◽

J David Rhodes ◽

...

Keyword(s):

Sleep Quality ◽

Family Caregivers ◽

Total Sleep Time ◽

Sleep Onset ◽

Sleep Time ◽

Care Recipient ◽

Sleep Onset Latency ◽

Onset Latency ◽

Time In Bed ◽

Regards Study

Abstract The high levels of stress experienced by family caregivers may affect their physical and psychological health, including their sleep quality. However, there are few population-based studies comparing sleep between family caregivers and carefully-matched controls. We evaluated differences in sleep and identified predictors of poorer sleep among the caregivers, in a comparison of 251 incident caregivers and carefully matched non-caregiving controls, recruited from the national REasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Incident caregivers and controls were matched on up to seven demographic and health factors (age, sex, race, education level, marital status, self-rated health, and self-reported serious cardiovascular disease history). Sleep characteristics were self-reported and included total sleep time, sleep onset latency, wake after sleep onset, time in bed, and sleep efficiency. Family caregivers reported significantly longer sleep onset latency, before and after adjusting for potential confounders, compared to non-caregiving controls (ps < 0.05). Depressive symptoms in caregivers predicted longer sleep onset latency, greater wake after sleep onset, and lower sleep efficiency. Longer total sleep time in caregivers was predicted by employment status, living with the care recipient, and number of caregiver hours. Employed caregivers and caregivers who did not live with the care recipient had shorter total sleep time and spent less time in bed than non-employed caregivers. Additional research is needed to evaluate whether sleep disturbances contributes to health problems among caregivers.

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An investigation of the relationship between sleep and fatigue in those with and without insomnia

10.32920/ryerson.14645433.v1 ◽

2021 ◽

Author(s):

Andrea L. Harris

Keyword(s):

Sleep Quality ◽

Linear Models ◽

Total Sleep Time ◽

Sleep Time ◽

Sleep Diary ◽

Poor Sleep ◽

Sleep Quantity ◽

Cognitive Behavioural ◽

Multi Method Approach ◽

The Relationship

There is currently mixed evidence for the relationship between poor sleep and daytime fatigue. It is well documented that retrospective measures of insomnia and fatigue are highly correlated with one another. However, other studies fail to demonstrate a link between objectively less sleep and fatigue; that is, individuals with shorter sleep times do not necessarily report increased fatigue. As such, the relationship between these two constructs remains unclear. The current investigation will help to elucidate the complex relationship between sleep and fatigue among those with and without insomnia by advancing the existing literature in two important ways. First, this study proposed to examine the temporal relationship between sleep and fatigue across two weeks, thereby investigating whether sleep and fatigue occur in accordance with one anotherover time. Second, this study utilized a multi-method approach by collecting subjective (i.e.,sleep diary) and objective (i.e., actigraphy) measures of sleep, as well as retrospective (i.e.,visual analogue scales: VAS) and prospective (i.e., momentary ratings) measures of fatigue. Two separate hierarchical linear models were used to test whether sleep (measured by sleep quality and total sleep time) predicted daytime fatigue on the VAS and actigraph, respectively. The secondary objective asked whether cognitive-behavioural variables (i.e., maladaptive sleep beliefs, fear and avoidance of fatigue, and fatigue-based rumination) may help account for the relationship between sleep and fatigue using mediation. The results of the primary analyses suggested that sleep quality significantly predicted VAS fatigue ratings, whereas total sleep time was a significant predictor of fatigue within- but not between-persons. No significant relationships were found between objective measures of sleep and momentary fatigue ratings. Finally, each of the cognitive-behavioural variables, with the exception of avoidance of fatigue, were significant mediators of the relationship between sleep and fatigue. The results demonstrated that compared to sleep quantity, our perception of sleep may play a more important role in predicting reports of daytime fatigue. These findings could help decrease the burden that individuals with insomnia place on their total sleep times, and instead, treatment could focus on challenging maladaptive sleep-related cognitions, which ultimately could lessen the overall sleep-related anxiety.

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Differential Effects of DHA- and EPA-Rich Oils on Sleep in Healthy Young Adults: A Randomized Controlled Trial

Nutrients ◽

10.3390/nu13010248 ◽

2021 ◽

Vol 13 (1) ◽

pp. 248

Author(s):

Michael J. Patan ◽

David O. Kennedy ◽

Cathrine Husberg ◽

Svein Olaf Hustvedt ◽

Philip C. Calder ◽

...

Keyword(s):

Sleep Quality ◽

Controlled Trial ◽

Sleep Time ◽

Sleep Efficiency ◽

Omega 3 ◽

Double Blind ◽

Differential Effects ◽

Positive Effects ◽

Time In Bed ◽

Oily Fish

Emerging evidence suggests that adequate intake of omega-3 polyunsaturated fatty acids (n-3 PUFAs), which include docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), might be associated with better sleep quality. N-3 PUFAs, which must be acquired from dietary sources, are typically consumed at suboptimal levels in Western diets. Therefore, the current placebo-controlled, double-blind, randomized trial, investigated the effects of an oil rich in either DHA or EPA on sleep quality in healthy adults who habitually consumed low amounts of oily fish. Eighty-four participants aged 25–49 years completed the 26-week intervention trial. Compared to placebo, improvements in actigraphy sleep efficiency (p = 0.030) and latency (p = 0.026) were observed following the DHA-rich oil. However, these participants also reported feeling less energetic compared to the placebo (p = 0.041), and less rested (p = 0.017), and there was a trend towards feeling less ready to perform (p = 0.075) than those given EPA-rich oil. A trend towards improved sleep efficiency was identified in the EPA-rich group compared to placebo (p = 0.087), along with a significant decrease in both total time in bed (p = 0.032) and total sleep time (p = 0.019) compared to the DHA-rich oil. No significant effects of either treatment were identified for urinary excretion of the major melatonin metabolite 6-sulfatoxymelatonin. This study was the first to demonstrate some positive effects of dietary supplementation with n-3 PUFAs in healthy adult normal sleepers, and provides novel evidence showing the differential effects of n-3 PUFA supplements rich in either DHA or EPA. Further investigation into the mechanisms underpinning these observations including the effects of n-3 PUFAs on sleep architecture are required.

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