At age 38 years, the Munich Chronotype Questionnaire was used to assess social jetlag as well as sleep duration and chronotype.29 (link) Social jetlag, the discrepancy between our internal timing and external timing, was measured by subtracting each participant’s midpoint of sleep on work days from their midpoint of sleep on free days (MSF). Sleep duration was calculated by averaging the sleep duration on work days and free days, assuming five work days and two free days a week as standard. Chronotype, the preference in sleep timing, was assessed using sleep-debt-corrected MSF (MSFsc) (see Ronneberg et al.17 (link)). A detailed protocol for calculating the complete set of Munich Chronotype Questionnaire variables can be found elsewhere.14 (link) Social jetlag was significantly correlated with chronotype (r = 0.40, P < 0.01), but not with sleep duration (r = −0.04, P = 0.28). The mean social jetlag among participants in our cohort was 0.88 h, with a standard deviation of 0.96 (n = 815) (see Supplementary Figure 1 ). All analyses were conducted using the absolute value of social jetlag.
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Jet Lag Syndrome
Jet Lag Syndrome
Jet Lag Syndrome is a temporary disruption of the body's circadian rhythms that occurs when individuals rapidly travel across multiple time zones.
This disorder is characterized by fatigue, disorientation, insomnia, and other sleep disturbances that can negatively impact daily functioning.
PubCompare.ai, an AI-driven platform, offers a solution by helping researchers optimize protocols to overcome jet lag.
Users can easily locate relevant protocols from literature, preprints, and patents, and leverage AI-driven comparisons to identify the best strategies and products to combat this syndrome.
With PubCompare.ai, researchers can take control of their jet lag research and develop more effective interventions.
This disorder is characterized by fatigue, disorientation, insomnia, and other sleep disturbances that can negatively impact daily functioning.
PubCompare.ai, an AI-driven platform, offers a solution by helping researchers optimize protocols to overcome jet lag.
Users can easily locate relevant protocols from literature, preprints, and patents, and leverage AI-driven comparisons to identify the best strategies and products to combat this syndrome.
With PubCompare.ai, researchers can take control of their jet lag research and develop more effective interventions.
Most cited protocols related to «Jet Lag Syndrome»
Chronotype
Jet Lag Syndrome
Sleep
The Pittsburgh Sleep Quality Index (PSQI) (19 (link)) was used to assess sleep quality over the previous month (with scores >5 indicating poor sleep quality). Participants were asked to report usual bedtime, wake-up time, sleep-onset latency, and actual sleep duration on weekdays and weekends over the previous month. Participants were also asked whether they had a diagnosis of obstructive sleep apnea (OSA), and those without a previous diagnosis completed the Berlin questionnaire to assess the risk of OSA, which categorizes respondents as high or low risk of having OSA (18 (link)). Participants who had a diagnosis of or were at high risk for OSA were identified together as a group with presence or high risk of OSA (OSA/risk).
The following circadian and sleep parameters were calculated. Mid-sleep time was calculated as the midpoint between sleep onset (bedtime plus sleep latency) and wake time. The primary outcome measure of mid-sleep time on free days (MSF), a metric of chronotype, was derived from mid-sleep time on weekend nights with further correction for calculated sleep debt as previously described (20 (link),21 (link)). Specifically, the MSF equals the mid-sleep time on weekend nights subtracting 0.5 times the sleep debt, which is calculated as the difference between sleep duration (duration from sleep onset time until wake time) on weekends minus the weekly average sleep duration. This metric was first proposed by Roenneberg and colleagues with the assumption being that sleep timing on days when unconstrained by the social clock would more accurately reflect the underlying phase of the circadian system (22 (link)). Social jetlag, a behavioral indicator of circadian misalignment, was calculated based on the absolute difference between mid-sleep time on weekdays and weekends (9 (link)).
Sleep duration was computed using a weighted average of self-reported actual sleep duration between weekdays and weekends [(reported weekday actual sleep duration × 5 + reported weekend actual sleep duration × 2)/7]. In addition, because the perception of not getting enough sleep on weekdays has been shown to be better correlated with HbA1c than the reported sleep duration itself (15 (link)), perceived sleep debt was calculated using the difference between participants’ preferred weekday sleep duration (i.e., how many hours they would choose to sleep if their job, family, or other responsibilities did not limit the number of hours they slept) and their self-reported actual weekday sleep duration. Perceived sleep debt is a subjective variable that is likely to combine insufficient sleep duration and poor sleep quality.
The following circadian and sleep parameters were calculated. Mid-sleep time was calculated as the midpoint between sleep onset (bedtime plus sleep latency) and wake time. The primary outcome measure of mid-sleep time on free days (MSF), a metric of chronotype, was derived from mid-sleep time on weekend nights with further correction for calculated sleep debt as previously described (20 (link),21 (link)). Specifically, the MSF equals the mid-sleep time on weekend nights subtracting 0.5 times the sleep debt, which is calculated as the difference between sleep duration (duration from sleep onset time until wake time) on weekends minus the weekly average sleep duration. This metric was first proposed by Roenneberg and colleagues with the assumption being that sleep timing on days when unconstrained by the social clock would more accurately reflect the underlying phase of the circadian system (22 (link)). Social jetlag, a behavioral indicator of circadian misalignment, was calculated based on the absolute difference between mid-sleep time on weekdays and weekends (9 (link)).
Sleep duration was computed using a weighted average of self-reported actual sleep duration between weekdays and weekends [(reported weekday actual sleep duration × 5 + reported weekend actual sleep duration × 2)/7]. In addition, because the perception of not getting enough sleep on weekdays has been shown to be better correlated with HbA1c than the reported sleep duration itself (15 (link)), perceived sleep debt was calculated using the difference between participants’ preferred weekday sleep duration (i.e., how many hours they would choose to sleep if their job, family, or other responsibilities did not limit the number of hours they slept) and their self-reported actual weekday sleep duration. Perceived sleep debt is a subjective variable that is likely to combine insufficient sleep duration and poor sleep quality.
Chronotype
Diagnosis
Jet Lag Syndrome
Sleep
Sleep Apnea, Obstructive
Adult
Bipolar Disorder
Blindness, Bilateral
Cardiovascular Diseases
Child
Diabetes Mellitus
Disabled Persons
Jet Lag Syndrome
Malignant Neoplasms
Medical Devices
Menopause
Mental Disorders
Nervous System Disorder
Obesity
Physiological Phenomena
Schizophrenia
Sleep
Sleep Disorders
Substance Abuse
Weightlessness
Each subject was studied near sea level (SL) (130 m, average PB = 749 mmHg, Figure 1 ), and over three study periods at Mt Chacaltaya, Bolivia; 5260 m; average PB = 406 mmHg); on the first/second and sixteenth/seventeenth days at 5260 m (ALT1, ALT16), and again upon reascent to 5260 m, after either seven (n = 14) or 21 (n = 7) days at low altitude (POST7 or POST21). Baseline studies at SL, including laboratory (physiologic and OMICS) and field (3.2-km uphill run) tests, were conducted over a two-week period in Eugene, OR, USA. Approximately one month after the SL studies, subjects traveled to Bolivia in pairs on successive days. Upon arrival at El Alto (4050 m) after an overnight flight, subjects immediately descended to Coroico, Bolivia (1525 m; PB = 639 mmHg). Subjects rested for 48 hrs in Coroico to limit the effects of jet lag and were then driven over three hrs to 5260 m. To provide an acute change in inspired PO2 from 1525 m to 5260 m, subjects breathed supplemental oxygen (2 L/min, nasal cannula or mask) during the drive. On arrival at 5260 m, the first subject immediately began the experimental protocol described below. The second subject rested while continuing to breathe supplemental oxygen for ∼ two hrs until the first subject had completed the arterial/venous catheterization and cognitive testing portion of the protocol. Then the second subject began the protocol as described for the first subject. Two subjects were studied per day for ALT1, ALT16, POST7, and POST21. After completing laboratory testing and AMS scoring on ALT1, subjects slept overnight on supplemental oxygen to minimize the risk of developing severe high-altitude illness. The next morning, subjects completed a 3.2-km uphill run (305 m elevation gain) before descending by car to La Paz, Bolivia (3800 m; average PB = 487 mmHg) to continue acclimatizing at a lower altitude over three nights (ALT2-ALT4). On ALT4 subjects visited 5260 m for four to six hrs. On ALT5, they returned to 5260 m, where they remained for an additional 13 days. On ALT16/17 subjects were tested, as on ALT1/2 prior to descending by car to 1525 m. To test physiological retention of acclimatization after living for seven (n = 14) or 21 (n = 7) days at low altitude (1525 m), subjects returned to 5260 m by car, as they did on ALT1 but this time without supplemental oxygen, and completed the POST7/21 testing (detailed below). After completion of a 3.2-km uphill run on POST7/21, the subjects returned home. Assignment to POST7 or POST21 was determined by each subject based on their desire to stay in the field an extra seven or 21 days. While in Bolivia, subjects were housed and fed as a group. Meals and snacks were kept similar to the subjects' typical ad libitum diet. Subjects were instructed to ingest at least three liters of water each day and to remain physically active.
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Adaptation, Physiological
Altitude Sickness
Arteries
Catheterization
Diet
Jet Lag Syndrome
Nasal Cannula
Oxygen
physiology
Retention (Psychology)
Sleep
Snacks
Veins
Social jetlag status was determined using questions about typical bedtime, sleep latency, and wake times on weekdays and weekends. The midpoint of sleep was assessed by the sleep onset time (adding sleep latency time to bedtime) and wake time. It was then calculated for each weekday (MSW) and weekend days or free days (MSF). Following the formula established by Wittman et al. [11 (link)], social jetlag was estimated as the absolute value of the differences (in hours) in the midpoint of sleep times between weekdays and weekends (MSF − MSW). In this study, social jetlag ranged from 0 to 4.5 hours. We thus classified participants into three groups namely, less than 1 hour of social jetlag, 1 to less than 2 hours of social jetlag, and at least 2 hours of social jetlag. The reference category was less than 1 hour as done in previous studies [15 (link), 19 ].
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Jet Lag Syndrome
Sleep
Most recents protocols related to «Jet Lag Syndrome»
These experiments were conducted in Davis, CA, June-August, 2022. Seeds were germinated and grown for 3 weeks in a PGV36 growth chambers (Conviron, Winnipeg, MB, Canada) as described above, at which point they were transferred to a field site. Before the start of anthesis plants were transferred to either a PGV36 or a PGR15 growth chamber (Conviron, Winnipeg, MB, Canada) for either 2 days for LL|28°C and Control A plants, or 1 week for jet lag and Control B plants. Controls were maintained in chambers with light, temperature, and humidity cycling in coordination with the local average daily forecast for the following week. Constant light plants were maintained at a constant 28°C, with 300 μmol m–2 s–1 provided by metal halide and incandescent lamps. Jet lagged plants were entrained to the same conditions as the control plants but with a 3 hr phase delay. Plants were transferred to the field just before anthesis of the second or third pseudowhorl, just after dawn in the case of the constant light experiment and 1 hour before dawn for the jet lag experiment. In the jet lag experiments, florets that had opened on previous days were removed with forceps to limit the pollinator recruitment signals to florets developing on the day of the experiment. Pots were arranged so the capitula faced east and stems were secured to bamboo poles for imaging. Images were taken at 5 min intervals using BirdCam 2.0 cameras (Wingscapes). Pollinator visits were scored using ImageGlass (https://imageglass.org/ ). Any insect large enough to be seen in the images was counted. New visits were counted when an insect landed on or changed location on the disk florets. The first image in which pollen was visible on the tip of the stamen was scored for time of pollen presentation. All data was plotted in R using ggplot (Wickham, 2016 (link)) and tidyverse (Wickham et al., 2019 (link)).
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Forceps
Humidity
Incandescence
Insecta
Jet Lag Syndrome
Light
Marijuana Abuse
Metals
Plant Embryos
Plants
Pollen
Stem, Plant
Vision
Fighter jet pilots were recruited via the Belgian Air Force. Inclusion criteria were age between 18 and 65. Exclusion criteria were: neurological disease, medication with effects on the CNS, excessive alcohol- and/or drug use, vestibular problems, jetlag (at least 1 week after transcontinental flight/mission) and at least 24 h since last exposure to high g-levels. A total of 10 male fighter pilots were included (mean age (SD) = 29 (3.2) years, range 23–32 years), who had on average 1,025 h of flight experience in an F16 fighter jet (SD = 595; range from 200 to 2,116 h). A control group (mean age (SD) = 29 (3.2) years, range 23–32 years) of 10 adults with no experience in flying was included, matched for age, gender, and educational level. Additionally, controls were also matched for handedness (9 right- and 1 left-handed in each group). All participants signed an informed consent form. The study was approved by the local ethics committee of the Antwerp University Hospital (13/38/357).
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Adult
Ethanol
Ethics Committees, Clinical
Jet Lag Syndrome
Males
Nervous System Disorder
Pharmaceutical Preparations
Vestibular Diseases
A structured web questionnaire consisting of questions about demographics, health habits, current sleep patterns, history of COVID-19 and COVID-19 vaccination, modified Insomnia Severity Index (ISI), and Epworth Sleepiness Scale (ESS) was utilized.
All participants were asked if they had ever had COVID-19 and whether they had been vaccinated. Participants who received two or more doses of the COVID-19 vaccine were asked if they had any adverse reactions to the vaccine. We used the following questions: “Have you ever had COVID-19?”, “Have you received two or more doses of the COVID-19 vaccine?”, and “Have you experienced any side effects from the COVID-19 vaccination?” (Figure 1 ).
We investigated usual sleep times on workdays and free days. The average sleep time was calculated as follows: (weekday sleep time × 5 + weekend sleep time × 2)/7. Sleep efficiency was defined as the ratio of average sleep duration to average time in bed.
To measure chronotype, we used mid-sleep on free days corrected for sleep debt on workdays (MSFsc), using the formula: MSFsc = midsleep point on free days − (sleep duration on free days − average weekly sleep duration)/2. A higher MSFsc value reflects a stronger eveningness tendency. Based on the MSFsc distribution of their sample, with 2.5% at each end of the distribution as the extreme chronotypes, Kühnle et al. [17 ] suggested that an MSFsc value less than 2.17 should be defined as extreme morningness and that an MSFsc value of 7.25 or greater should be defined as extreme eveningness. We used the midsleep point on free days (MSF) and the midsleep point on workdays (MSW) to quantify social jet lag using the formula: Social jetlag = |MSF − MSW|. The definition and measurement refer to the theory and process presented by Wittmann et al. [18 (link)].
We used the Korean version of the ISI to investigate insomnia symptoms [19 (link)]. The optimal cut-off score of the ISI was 15.5, and the sensitivity and specificity at that score were 0.92 and 0.82, respectively [19 (link)]. The reliability was confirmed by Cronbach’s alpha of 0.92, and the item-to-total-score correlations (item–total correlations) ranged from 0.65 to 0.84 [19 (link)]. The ISI is a brief screening questionnaire that measures insomnia severity. The total score ranges from 0 to 28, with a higher score indicating greater insomnia severity. If a participant’s ISI score was 15 or more, they were classified as having moderate to severe insomnia. In this study, difficulty initiating sleep, difficulty maintaining sleep, and waking up too early were evaluated using the criteria that these issues were experienced five or more times per week.
The Korean version of the ESS is a reliable and valid instrument for screening patients with daytime sleepiness [20 (link)]. The ESS consists of eight sleep-related situations, and participants were asked to assess the likelihood of falling asleep in each situation. The total score ranges from 0 to 24, with a higher score indicating a high level of daytime sleepiness. An ESS score of 11 or more was classified as excessive daytime sleepiness.
All participants were asked if they had ever had COVID-19 and whether they had been vaccinated. Participants who received two or more doses of the COVID-19 vaccine were asked if they had any adverse reactions to the vaccine. We used the following questions: “Have you ever had COVID-19?”, “Have you received two or more doses of the COVID-19 vaccine?”, and “Have you experienced any side effects from the COVID-19 vaccination?” (
We investigated usual sleep times on workdays and free days. The average sleep time was calculated as follows: (weekday sleep time × 5 + weekend sleep time × 2)/7. Sleep efficiency was defined as the ratio of average sleep duration to average time in bed.
To measure chronotype, we used mid-sleep on free days corrected for sleep debt on workdays (MSFsc), using the formula: MSFsc = midsleep point on free days − (sleep duration on free days − average weekly sleep duration)/2. A higher MSFsc value reflects a stronger eveningness tendency. Based on the MSFsc distribution of their sample, with 2.5% at each end of the distribution as the extreme chronotypes, Kühnle et al. [17 ] suggested that an MSFsc value less than 2.17 should be defined as extreme morningness and that an MSFsc value of 7.25 or greater should be defined as extreme eveningness. We used the midsleep point on free days (MSF) and the midsleep point on workdays (MSW) to quantify social jet lag using the formula: Social jetlag = |MSF − MSW|. The definition and measurement refer to the theory and process presented by Wittmann et al. [18 (link)].
We used the Korean version of the ISI to investigate insomnia symptoms [19 (link)]. The optimal cut-off score of the ISI was 15.5, and the sensitivity and specificity at that score were 0.92 and 0.82, respectively [19 (link)]. The reliability was confirmed by Cronbach’s alpha of 0.92, and the item-to-total-score correlations (item–total correlations) ranged from 0.65 to 0.84 [19 (link)]. The ISI is a brief screening questionnaire that measures insomnia severity. The total score ranges from 0 to 28, with a higher score indicating greater insomnia severity. If a participant’s ISI score was 15 or more, they were classified as having moderate to severe insomnia. In this study, difficulty initiating sleep, difficulty maintaining sleep, and waking up too early were evaluated using the criteria that these issues were experienced five or more times per week.
The Korean version of the ESS is a reliable and valid instrument for screening patients with daytime sleepiness [20 (link)]. The ESS consists of eight sleep-related situations, and participants were asked to assess the likelihood of falling asleep in each situation. The total score ranges from 0 to 24, with a higher score indicating a high level of daytime sleepiness. An ESS score of 11 or more was classified as excessive daytime sleepiness.
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Chronotype
COVID-19 Vaccines
COVID 19
Excessive Daytime Sleepiness
Jet Lag Syndrome
Koreans
Patients
Sleep
Sleeplessness
Somnolence
Vaccination
Vaccines
Data are described as the mean ± standard deviation (SD) or number (%). We compared sleep and circadian rhythms according to the history of COVID-19 or the self-reported side effects of the COVID-19 vaccination. An analysis of covariance (ANCOVA) adjusted for age, sex, residential area, marital status, employment, education level, monthly income level, body mass index (BMI), alcohol consumption, smoking, and coffee consumption was used.
Logistic regression analyses were performed to explore the association between sleep and circadian patterns with COVID-19 or the self-reported side effects of the COVID-19 vaccination. The average sleep latency, average sleep duration, social jet lag, sleep efficiency, MSFsc, ESS, and ISI were each used as independent variables. To analyze the independent effect of each sleep parameter, a multivariable analysis was performed after adjusting for age, sex, BMI, alcohol consumption, and smoking.
Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 19.0, Chicago, IL, USA). For all analyses, the significance threshold was set at a p-value of less than 0.05.
Logistic regression analyses were performed to explore the association between sleep and circadian patterns with COVID-19 or the self-reported side effects of the COVID-19 vaccination. The average sleep latency, average sleep duration, social jet lag, sleep efficiency, MSFsc, ESS, and ISI were each used as independent variables. To analyze the independent effect of each sleep parameter, a multivariable analysis was performed after adjusting for age, sex, BMI, alcohol consumption, and smoking.
Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 19.0, Chicago, IL, USA). For all analyses, the significance threshold was set at a p-value of less than 0.05.
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Circadian Rhythms
Coffee
COVID 19
Index, Body Mass
Jet Lag Syndrome
Sleep
Vaccination
Demographics, habit characteristics, and the measurement of anthropometric parameters were collected as part of the medical examination. Chronotype was assessed by the “Morningness–Eveningness Questionnaire” (MEQ) [43 (link)]—a questionnaire with 19 items and a total score ranging from 16 to 86 that is widely used in adults and workers [44 (link),45 (link),46 (link)]. Social jet lag has been investigated and computed as the absolute difference between midsleep on free days and midsleep on workdays [47 (link)]. All workers were sampled between 1 November and 15 December 2019 (local sunrise time is 6:40 A.M.–7:32 A.M., and local sunset time is 4:29 P.M.–5:00 P.M.) to limit the possible differences in time of sunlight exposure during the year. Evening exposure to blue light has been estimated considering the minutes of use of video display devices after dinner. Wake-up time on the day of blood sampling was investigated.
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Adult
Chronotype
Jet Lag Syndrome
Light
Medical Devices
Sunlight
Workers
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More about "Jet Lag Syndrome"
Jet Lag Syndrome, also known as jetlag or jet lag disorder, is a temporary disruption of the body's circadian rhythms that occurs when individuals rapidly travel across multiple time zones.
This condition is characterized by a range of symptoms, including fatigue, disorientation, insomnia, and other sleep disturbances, which can negatively impact an individual's daily functioning.
To overcome the challenges posed by jet lag, researchers can utilize an AI-driven platform like PubCompare.ai.
This solution helps optimize research protocols and identify the most effective interventions to combat jet lag syndrome.
Users can easily locate relevant protocols from literature, preprints, and patents, and leverage AI-driven comparisons to pinpoint the best strategies and products to alleviate jet lag symptoms.
Circadian rhythms, which are the internal 24-hour cycles that regulate various physiological processes, are a key factor in jet lag syndrome.
When individuals rapidly travel across time zones, their body's internal clock becomes misaligned with the new local time, leading to disruptions in sleep-wake cycles, hormone production, and other bodily functions.
To study circadian rhythms, researchers may utilize tools like ClockLab software, Actiwatch 2, and C3(1)-TAg mice.
These tools provide insights into the mechanisms underlying jet lag and help researchers develop more effective interventions.
Additionally, statistical software like SPSS, Stata, and RNATI can be used to analyze data and identify patterns related to jet lag.
By leveraging PubCompare.ai and the wealth of research tools available, researchers can take control of their jet lag research and develop more effective strategies to overcome this syndrome.
With the ability to easily locate and compare protocols, researchers can optimize their approaches and deliver better outcomes for individuals affected by jet lag.
This condition is characterized by a range of symptoms, including fatigue, disorientation, insomnia, and other sleep disturbances, which can negatively impact an individual's daily functioning.
To overcome the challenges posed by jet lag, researchers can utilize an AI-driven platform like PubCompare.ai.
This solution helps optimize research protocols and identify the most effective interventions to combat jet lag syndrome.
Users can easily locate relevant protocols from literature, preprints, and patents, and leverage AI-driven comparisons to pinpoint the best strategies and products to alleviate jet lag symptoms.
Circadian rhythms, which are the internal 24-hour cycles that regulate various physiological processes, are a key factor in jet lag syndrome.
When individuals rapidly travel across time zones, their body's internal clock becomes misaligned with the new local time, leading to disruptions in sleep-wake cycles, hormone production, and other bodily functions.
To study circadian rhythms, researchers may utilize tools like ClockLab software, Actiwatch 2, and C3(1)-TAg mice.
These tools provide insights into the mechanisms underlying jet lag and help researchers develop more effective interventions.
Additionally, statistical software like SPSS, Stata, and RNATI can be used to analyze data and identify patterns related to jet lag.
By leveraging PubCompare.ai and the wealth of research tools available, researchers can take control of their jet lag research and develop more effective strategies to overcome this syndrome.
With the ability to easily locate and compare protocols, researchers can optimize their approaches and deliver better outcomes for individuals affected by jet lag.