Search for: All records

The detrimental effects of sleep loss on overall decision‐making have been well described. Due to the complex nature of decisions, there remains a need for studies to identify specific mechanisms of decision‐making vulnerable to sleep loss. Bayesian perspectives of decision‐making posit judgement formation during decision‐making occurs via a process of integrating knowledge gleaned from past experiences (priors) with new information from current observations (likelihoods). We investigated the effects of sleep loss on the ability to integrate multiple sources of information during decision‐making by reporting results from two experiments: the first implementing both sleep restriction (SR) and total sleep deprivation (TSD) protocols, and the second implementing an SR protocol. In both experiments, participants were administered the Bayes Decisions Task on which optimal performance requires the integration of Bayesianpriorandlikelihoodinformation. Participants in Experiment 1 showed reduced reliance on both information sources after SR, while no significant change was observed after TSD. Participants in Experiment 2 showed reduced reliance onlikelihoodafter SR, especially during morning testing sessions. No accuracy‐related impairments resulting from SR and TSD were observed in both experiments. Our findings show SR affects decision‐making through altering the way individuals integrate available sources of information. Additionally, the ability to integrate information during SR may be influenced by time of day. Broadly, our findings carry implications for working professionals who are required to make high‐stakes decisions on the job, yet consistently receive insufficient sleep due to work schedule demands.

 

Abstract Background Cross-sectional studies have found that the coronavirus disease 2019 (COVID-19) pandemic has negatively affected population-level mental health. Longitudinal studies are necessary to examine trajectories of change in mental health over time and identify sociodemographic groups at risk for persistent distress. Purpose To examine the trajectories of mental distress between March 10 and August 4, 2020, a key period during the COVID-19 pandemic. Methods Participants included 6,901 adults from the nationally representative Understanding America Study, surveyed at baseline between March 10 and 31, 2020, with nine follow-up assessments between April 1 and August 4, 2020. Mixed-effects logistic regression was used to examine the association between date and self-reported mental distress (measured with the four-item Patient Health Questionnaire) among U.S. adults overall and among sociodemographic subgroups defined by sex, age, race/ethnicity, household structure, federal poverty line, and census region. Results Compared to March 11, the odds of mental distress among U.S. adults overall were 1.84 (95% confidence interval [CI] = 1.65–2.07) times higher on April 1 and 1.92 (95% CI = 1.62–2.28) times higher on May 1; by August 1, the odds of mental distress had returned to levels comparable to March 11 (odds ratio [OR] = 0.80, 95% CI = 0.66–0.96). Females experienced a sharper increase in mental distress between March and May compared to males (females: OR = 2.29, 95% CI = 1.85–2.82; males: OR = 1.53, 95% CI = 1.15–2.02). Conclusions These findings highlight the trajectory of mental health symptoms during an unprecedented pandemic, including the identification of populations at risk for sustained mental distress. 

Objectives. To assess the impact of the COVID-19 pandemic on mental distress in US adults. Methods. Participants were 5065 adults from the Understanding America Study, a probability-based Internet panel representative of the US adult population. The main exposure was survey completion date (March 10–16, 2020). The outcome was mental distress measured via the 4-item version of the Patient Health Questionnaire. Results. Among states with 50 or more COVID-19 cases as of March 10, each additional day was significantly associated with an 11% increase in the odds of moving up a category of distress (odds ratio = 1.11; 95% confidence interval = 1.01, 1.21; P = .02). Perceptions about the likelihood of getting infected, death from the virus, and steps taken to avoid infecting others were associated with increased mental distress in the model that included all states. Individuals with higher consumption of alcohol or cannabis or with history of depressive symptoms were at significantly higher risk for mental distress. Conclusions. These data suggest that as the COVID-19 pandemic continues, mental distress may continue to increase and should be regularly monitored. Specific populations are at high risk for mental distress, particularly those with preexisting depressive symptoms.