Rapid antigen detection tests (Ag-RDT) for SARS-CoV-2 with emergency use authorization generally include a condition of authorization to evaluate the test’s performance in asymptomatic individuals when used serially. We aim to describe a novel study design that was used to generate regulatory-quality data to evaluate the serial use of Ag-RDT in detecting SARS-CoV-2 virus among asymptomatic individuals.

This prospective cohort study used a siteless, digital approach to assess longitudinal performance of Ag-RDT. Individuals over 2 years old from across the USA with no reported COVID-19 symptoms in the 14 days prior to study enrollment were eligible to enroll in this study. Participants throughout the mainland USA were enrolled through a digital platform between October 18, 2021 and February 15, 2022. Participants were asked to test using Ag-RDT and molecular comparators every 48 hours for 15 days. Enrollment demographics, geographic distribution, and SARS-CoV-2 infection rates are reported.

A total of 7361 participants enrolled in the study, and 492 participants tested positive for SARS-CoV-2, including 154 who were asymptomatic and tested negative to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 US states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide.

The digital site-less approach employed in the “Test Us At Home” study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19 and can be adapted across research disciplines to optimize study enrollment and accessibility.

It is unknown if the COVID-19 pandemic and public health measures had an immediate impact on stroke subtypes and etiologies in patients not infected with COVID-19. We aimed to evaluate if the proportion of non-COVID-19-related stroke subtypes (ischemic vs. hemorrhagic) and etiologies (cardioembolic, atherosclerosis, small vessel disease, and others) during the pandemic’s first wave were different from prepandemic.

For this retrospective cohort study, we included patients without COVID-19 with ischemic or hemorrhagic stroke at two large Canadian stroke centers between March–May 2019 (prepandemic cohort) and March–May 2020 (pandemic cohort). Proportions of stroke subtypes and etiologies were compared between cohorts using chi-square tests.

The prepandemic cohort consisted of 234 stroke patients and the pandemic cohort of 207 stroke patients. There were no major differences in baseline characteristics. The proportions of ischemic versus hemorrhagic stroke were similar (ischemic stroke: 77% prepandemic vs. 75% pandemic; hemorrhagic stroke:12% prepandemic vs. 14% pandemic; p > 0.05). There were no differences in etiologies, except for a decreased proportion of ischemic stroke due to atherosclerosis in the pandemic cohort (26% prepandemic vs. 15% pandemic; difference: 10.6%, 95%CI: 1.4-19.7; p = 0.03). Notably, during the pandemic, the cause of ischemic stroke was more often unknown because of incomplete work-up (13.3% prepandemic vs. 28.2% pandemic, difference: 14.9%, 95%-CI: 5.7–24.2; p = <0.01).

In this study, the pandemic had no clear effect on stroke subtypes and etiologies suggesting a limited impact of the pandemic on stroke triggers. However, the shift from atherosclerosis toward other causes warrants further exploration.

Subjective cognitive difficulties (SCDs) are associated with factors commonly reported in older adults and former contact sport athletes, regardless of objective cognitive decline. We investigated the relative contribution of these factors to SCD in former National Football League (NFL)-players with and without a diagnosis of mild cognitive impairment (MCI).

Former NFL players (n = 907) aged ≥ 50 years (mean = 64.7 ± 8.9), with (n = 165) and without (n = 742) a diagnosis of MCI completed health questionnaires. Multivariable regression and dominance analyses determined the relative importance of SCD factors on SCD: 1) depression, 2) anxiety, 3) sleep disturbance, 4) pain interference, 5) ability to participate in social roles and activities, 6) stress-related events, 7) fatigue, 8) concussion history, and 9) education. SCD outcomes included Neuro-QoL Emotional-Behavioral Dyscontrol and the PROMIS Cognitive Function. Fisher’s z-transformation compared comorbid contributing factors to SCD across MCI and non-MCI groups.

Complete dominance of anxiety was established over most comorbid factors across the MCI and non-MCI groups. Fatigue also exhibited complete dominance over most comorbid factors, though its influence in the MCI group was less robust (general dominance). Average contributions to variance accounted for by comorbid factors to ratings of SCD across MCI and non-MCI groups did not statistically differ (Z-statistics <1.96, ps>.05).

Anxiety and fatigue are the most robust factors associated with SCD in former professional football players across various combinations of clinical presentations (different combinations of comorbid factors), regardless of documented cognitive impairment. Self-reported deficits may be less reliable in detecting objective impairment in the presence of these factors, with multidimensional assessment being ideal.

The rapid spread of coronavirus disease 2019 (COVID-19) required swift preparation to protect healthcare personnel (HCP) and patients, especially considering shortages of personal protective equipment (PPE). Due to the lack of a pre-existing biocontainment unit, we needed to develop a novel approach to placing patients in isolation cohorts while working with the pre-existing physical space.

To prevent disease transmission to non–COVID-19 patients and HCP caring for COVID-19 patients, to optimize PPE usage, and to provide a comfortable and safe working environment.

An interdisciplinary workgroup developed a combination of approaches to convert existing spaces into COVID-19 containment units with high-risk zones (HRZs). We developed standard workflow and visual management in conjunction with updated staff training and workflows. The infection prevention team created PPE standard practices for ease of use, conservation, and staff safety.

The interventions resulted in 1 possible case of patient-to-HCP transmission and zero cases of patient-to-patient transmission. PPE usage decreased with the HRZ model while maintaining a safe environment of care. Staff on the COVID-19 units were extremely satisfied with PPE availability (76.7%) and efforts to protect them from COVID-19 (72.7%). Moreover, 54.8% of HCP working in the COVID-19 unit agreed that PPE monitors played an essential role in staff safety.

The HRZ model of containment unit is an effective method to prevent the spread of COVID-19 with several benefits. It is easily implemented and scaled to accommodate census changes. Our experience suggests that other institutions do not need to modify existing physical structures to create similarly protective spaces.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.

Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.

Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.

All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.

The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.