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Abstract Prior studies have shown that sleep duration peri-vaccination influences an individual’s antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P

Abstract Patients in the ICU often sleep poorly for various reasons, which may predispose to delirium. We previously conducted a clinical trial in which we tested the efficacy of ramelteon, a melatonin-receptor agonist used to treat insomnia, versus placebo, in preventing ICU delirium in patients who underwent elective pulmonary thromboendarterectomy (PTE) surgery. Here we examine sleep, activity, and circadian patterns, measured with actigraphy, to understand changes in these metrics with our intervention and in those with and without delirium. Participants wore wrist actigraphy devices while recovering post-operatively in the ICU. For sleep analysis, we extracted total sleep time and sleep fragmentation metrics over the 22:00 to 06:00 period nightly, and daytime nap duration from the daytime period (0:600 to 22:00) for each participant. For activity analyses, we extracted the following metrics: total daytime activity count (AC), maximum daytime AC, total nighttime AC, and maximum nighttime AC. Next, we performed a nonparametric circadian analysis on ACs over each 24-h day and extracted the following: interdaily stability (IS), intra-daily variability (IV), relative amplitude (RA), and low and high periods of activity (L5 and M10) as well as their start times. These metrics were compared between patients who received ramelteon versus placebo, and between patients who became delirious versus those who did not develop delirium. We additionally made comparisons between groups for daytime and nighttime light levels. No differences in sleep, activity, circadian metrics or light levels were found between drug groups. Delirious patients, when compared to those who were never delirious, had a lower IS (0.35 ± 0.16 vs. 0.47 ± 0.23; P = 0.006). Otherewise, no differences in IV, L5, M10, or RA were found between groups. L5 and M10 activity values increased significantly over the post-extubation for the whole cohort. No differences were found for daytime or nighttime light levels between groups. Overall, ramelteon did not impact sleep or circadian metrics in this cohort. Consistent with clinical experience, delirious patients had less inter-daily stability in their rest-activity rhythms. These data suggest that actigraphy might have value for individual assessment of sleep in the ICU, and for determining and detecting the impact of interventions directed at improving sleep and circadian activity rhythms in the ICU. Trial registration: REGISTERED at CLINICALTRIALS.GOV: NCT02691013. Registered on February 24, 2016 by principal investigator, Dr. Robert L. Owens.

Objective: Examine how changes in sleep duration, objectively measured by activity trackers, impact weight gain in incoming college freshman. Participants: Incoming college freshmen, age [greater than equal to] 18. Methods: We measured weight and daily sleep duration before college entry and through the 1st college quarter. Additionally, we examined changes in sleep variability, activity levels and smartphone screen time use as possible predictors of weight gain. Results: 75 participants completed the study. Total sleep duration decreased from 437.9 ± SD 57.3 minutes at baseline to 416.5 ± SD 68.6 minutes by the end of the first quarter (p = 6.6 × 10[superscript -3]). (BMI) did not change significantly in this cohort. Higher sleep variability at baseline and an increase in sleep variability were associated with increases in BMI. Smartphone screen use was note to be high (235.2 ± SD 110.3 minutes/day) at the end of the first quarter. Conclusions: College weight gain may be affected by factors other than sleep duration, including sleep variability.