Abstract
Introduction
The Psychomotor Vigilance Test (PVT) is a behavioral attention measure widely used to describe sleep loss deficits. Although there are reported differences in PVT performance ...for various demographic groups, no study has examined the relationship between measures on the 10-minute PVT (PVT10) and the 3-minute PVT (PVT3) within sex, age, and body mass index (BMI) groups throughout a highly controlled sleep deprivation study.
Methods
Forty-one healthy adults (mean±SD ages, 33.9±8.9y) participated in a 13-night experiment 2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Repeated measures correlation (rmcorr) compared PVT10 and PVT3 lapses (reaction time RT >355ms PVT3 and >500ms PVT10) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and time point (e.g., 1000h-2000h) within sex groups (18 females), within age groups defined by a median split (median=32, range=21-49y), and within BMI groups defined by a median split (median=25, range=17-31).
Results
PVT10 and PVT3 1/RT was significantly correlated at all study days and time points excluding at baseline for the younger group and at R2 for the higher BMI group. PVT10 and PVT3 lapses showed overall lower correlations across the study relative to 1/RT. Lapses were not significantly correlated at baseline for any group, for males across recovery (R1-R4), for the high BMI group at R2-R4, for the older group at R2-R3, or for the younger group at SR5 or R3.
Conclusion
Differentiating participants based on age, sex, or BMI revealed important variation in the relationship between PVT10 and PVT3 measures across the study. Surprisingly, lapses were not significantly correlated at baseline for any demographic group or across recovery for males or the high BMI or older group. Thus, PVT10 and PVT3 lapses may be less comparable in certain populations when well-rested. These findings add to a growing literature suggesting demographic factors may be important factors to consider when evaluating the effects of sleep loss.
Support (if any)
ONR Award N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488
Abstract
Introduction
Few studies have examined circadian phase after job loss, an event that upends daily routine. It is common that a daily routine begins with the consumption of breakfast, and ...breakfast behavior may contribute to health status in adults. Therefore, we sought to examine whether a later midpoint of sleep was associated with breakfast skipping among adults whose schedules were no longer dictated by employment.
Methods
Data were obtained from the Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) study. The sample of 155 participants had involuntarily lost their jobs in the last 90 days. Both cross-sectional and 18-month longitudinal analyses assessed the relationship between sleep midpoint after job loss and current and later breakfast skipping. Assessment periods were 14 days. Sleep was measured via actigraphy, and breakfast skipping was measured via daily diary (1 = had breakfast; 0 = did not have breakfast). The midpoint of sleep was calculated as the circular center based on actigraphy sleep onset and offset times.
Results
The midpoint of sleep at baseline was negatively associated with breakfast consumption at baseline (B = -.09, SE = .02, p = .000). Also, a later midpoint was associated with breakfast skipping over the next 18 months (estimate = -.08; SE = .02; p = .000). Prospective findings remained significant when adjusting for gender, ethnicity, age, perceived stress, body mass index (BMI), education, and reemployment over time. Education (estimate = 14.26, SE = 6.23, p < .05) and BMI (estimate = -.51, SE = .25, p < .05) were the only significant covariates. No other sleep indices predicted breakfast behavior cross-sectionally or prospectively.
Conclusion
Consistent with research in adolescents, unemployed adults with a later circadian phase are more likely to skip breakfast more often. Breakfast skipping was also associated with higher BMI. Taken together, these findings provide support for the future testing of sleep/wake scheduling interventions to modify breakfast skipping and potentially mitigate weight gain after job loss.
Support (if any)
#1R01HL117995-01A1
Abstract
Introduction
Polysomnography (PSG) is the gold standard for the diagnosis of obstructive sleep apnea (OSA). Given cost, insurance restrictions and in some cases limited access to sleep ...center testing, the use of home based sleep apnea testing is becoming increasingly more common. A proportion of patients with technically adequate HSAT who are negative end up having significant disease on PSG. The characteristics of patients who are found to have moderate to severe sleep apnea on polysomnogram (PSG) after a negative home sleep apnea test (HSAT) are not known. We aim to phenotype these patients.
Methods
We conducted a retrospective chart review from March 2018 to February 2020. A total of 953 adult patients (18 years old and older) underwent HSAT, 248 tests resulted negative (apnea-hypopnea index <5/h). Out of the negative HSAT, 17 patients had moderate to severe obstructive sleep apnea on PSG. Those were included for analysis. Data on patient characteristics such as age, body mass index (BMI), gender, STOP-BANG, ESS and comorbidities was gathered. Respiratory disturbance index, recording time, flow time, oximetry time on HSAT was recorded. PSG recording time, baseline AHI, supine AHI and non-supine AHI were also noted. Technically inadequate HSAT were excluded from analysis.
Results
The percentage of patients with negative HSAT who were found to have moderate to severe sleep apnea on PSG and were included for analysis was 6.85% (n17). Mean age was 41 years. Mean BMI was 33 kg/m2. Common comorbidities were hypertension (29%), asthma (17.6 %), depression (17.6%), anxiety (11.7%) and reflux (5.9%). Average ESS was 11.7 and STOP-BANG was 3.8. The mean recording time was 477 minutes, flow time 391 minutes and oximetry time was 426 minutes on HSAT. Average PSG recording time was 433 minutes. Average AHI was 24 with supine being 33.2/h and non-supine 17.9/h.
Conclusion
A proportion of patients with negative HSAT have moderate to severe OSA on follow-up polysomnogram. These patients were young, with lower-class obesity, more positional OSA, and no associated complex comorbidities. Re-evaluation of current diagnostic algorithms and further research is needed to phenotype this at-risk group, as first-line PSG may be more cost-effective and efficient.
Support (if any):
The coronavirus disease 2019 (COVID-19) has become a worst pandemic. The clinical characteristics vary from asymptomatic to fatal. This study aims to examine the association between body mass index ...(BMI) levels and the severity of COVID-19. A cohort study included 147 adult patients with confirmed COVID-19 were categorized into 4 groups by BMI levels on admission: <18.5 (underweight), 18.5-22.9 (normal weight), 23.0-24.9 (overweight), and greater than or equal to25.0 kg/m.sup.2 (obese). Rates of pneumonia, severe pneumonia, acute kidney injury (AKI), and ICU stay during hospitalization across BMI group was determined. Logistic regression analysis was used to determine the association between BMI and severe pneumonia. Of the totals, patients having a BMI <18.5, 18.5-22.9, 23.0-24.9, and greater than or equal to25.0 kg/m.sup.2 were 12.9%, 38.1%, 17.7%, and 31.3%, respectively. The rates of pneumonia and severe pneumonia tended to be higher in patients with higher BMI, whereas the rates of AKI and ICU stay were higher in patients with BMI <18.5 kg/m.sup.2 and greater than or equal to 25 kg/m.sup.2, when compared to patients with normal BMI. After controlling for age, sex, diabetes, hypertension and dyslipidemia in the logistic regression analysis, having a BMI greater than or equal to25.0 kg/m.sup.2 was associated with higher risk of severe pneumonia (OR 4.73; 95% CI, 1.50-14.94; p = 0.003) compared to having a BMI 18.5-22.9 kg/m.sup.2 . During admission, elevated hemoglobin and alanine aminotransferase levels on day 7 and 14 of illness were associated with higher BMI levels. In contrast, rising of serum creatinine levels was observed in underweight patients on days 12 and 14 of illness. Obesity in patients with COVID-19 was associated with severe pneumonia and adverse outcomes such as AKI, transaminitis and ICU stay. Underweight patients should be closely monitored for AKI. Further studies in body composition are warranted to explore the links between adiposity and COVID-19 pathogenesis.
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