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The COVID-19 pandemic had a disruptive effect on higher education. A critical question is whether these changes affected students' learning outcomes. Knowledge gaps have consequences for future learning and may—in health professionals' education—also pose a threat to patient safety. Current research has shortcomings and does not allow for clear-cut interpretation. Our context is instruction in human physiology in an undergraduate medical program from high stakes end of term examinations. The sequence of imposed measures to slow the COVID-19 pandemic created a natural experiment, allowing for comparisons in performance during in-person versus remote instruction. In a two-factorial design, mode of instruction (in-person vs. remote) and mode of assessment (in-person vs. remote) were analyzed using both basic (non-parametric statistics, T-tests) and advanced statistical methods (linear mixed-effects model; resampling techniques). Test results from a total of N = 1095 s-year medical students were included in the study. We did not find empirical evidence of knowledge gaps; rather, students received comparable or higher scores during remote teaching. We interpret these findings as empirical evidence that both students and teachers adapted to pandemic disruption in a way that did not lead to knowledge gaps. We conclude that highly motivated students had no reduction in academic achievement. Moreover, we have developed an accessible digital exam system for secure, fair, and effective assessments which is sufficiently defensible for making pass/fail decisions. [ABSTRACT FROM AUTHOR]

Background: Depression is a common condition among cancer patients, across several points in the disease trajectory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technologies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal prospective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual federated learning framework, enabling to build machine learning models for the prediction and monitoring of depression without direct access to user's personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is effective, it will provide healthcare systems with a novel and innovative method to screen depressive symptoms in oncological settings. Trial registration: Trial ID: ISRCTN10423782. Date registered: 21/03/2022. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to describe and assess a remote height and weight protocol that was developed for an ongoing trial conducted during the SARS COV-2 pandemic.Methods: Thirty-eight rural families (children 8.3 ± 0.7 years; 68% female; and caregivers 38.2 ± 6.1 years) were provided detailed instructions on how to measure height and weight. Families obtained measures via remote data collection (caregiver weight, child height and weight) and also by trained staff. Differences between data collection methods were examined.Results: Per absolute mean difference analyses, slightly larger differences were found for child weight (0.21 ± 0.21 kg), child height (1.53 ± 1.29 cm), and caregiver weight (0.48 ± 0.42 kg) between school and home measurements. Both analyses indicate differences had only minor impact on child BMI percentile (- 0.12, 0.68) and parent BMI (0.05, 0.13). Intraclass coefficients ranged from 0.98 to 1.00 indicating that almost all of the variance was due to between person differences and not measurement differences within a person.Conclusion: Results suggest that remote height and weight collection is feasible for caregivers and children and that there are minimal differences in the various measurement methods studied here when assessing group differences. These differences did not have clinically meaningful impacts on BMI. This is promising for the use of remote height and weight measurement in clinical trials, especially for hard-to reach-populations.Trial Registration: Clinical. Registered in clinicaltrials.gov ( NCT03304249 ) on 06/10/2017. [ABSTRACT FROM AUTHOR]

Recent advances in technology and expanding therapeutic opportunities in neuromuscular disorders has resulted in greater interest in and development of remote assessments. Over the past year, the rapid and abrupt COVID-19 shutdowns and stay-at-home orders imposed challenges to routine clinical management and clinical trials. As in-person services were severely limited, clinicians turned to remote assessments through telehealth to allow for continued care. Typically, disease-specific clinical outcome assessments (COAs) for neuromuscular disorders (NMD) are developed over many years through rigorous and iterative processes to fully understand their psychometric properties. While efforts were underway towards developing remote assessments for NMD before the pandemic, few if any were fully developed or validated. These included assessments of strength, respiratory function and patient-reported outcomes, as well as wearable technology and other devices to quantify physical activity and function. Without many choices, clinicians modified COAs for a virtual environment recognizing it was not yet known how they compared to standard in-person administration. Despite being able to quickly adapt to the demands of the COVID-19 pandemic, these experiences with remote assessments uncovered limitations and opportunities. It became clear that existing COAs required modifications for use in a virtual environment limiting the interpretation of the information gathered. Still, the opportunity for real-world evaluation and reduced patient burden were clear benefits to remote assessment and may provide a more robust understanding and characterization of disease impact in NMD. Hence, we propose a roadmap navigating an informed post-pandemic path toward development and implementation of safe and successful use of remote assessments for patients with NMD. [ABSTRACT FROM AUTHOR]

Background: Optimal application of the recently updated World Health Organization (WHO) guidelines for exercise in advanced age necessitates an accurate adjustment for the age-related increasing variability in biological age and fitness levels, alongside detailed recommendations across a range of motor fitness components, including balance, strength, and flexibility. We previously developed and validated a novel tool, designed to both remotely assess these fitness components, and subsequently deliver a personalized exercise program via smartphone. We describe the design of a prospective randomized control trial, comparing the effectiveness of the remotely delivered personalized multicomponent exercise program to either WHO exercise guidelines or no intervention.Methods: Participants (n = 300) are community dwelling, healthy, functionally independent, cognitively intact volunteers aged ≥65 at low risk for serious fall injuries, assigned using permuted block randomization (age/gender) to intervention, active-control, or control group. The intervention is an 8-week program including individually tailored exercises for upper/lower body, flexibility, strength, and balance (dynamic, static, vestibular); active-controls receive exercising counselling according to WHO guidelines; controls receive no guidance. Primary outcome is participant fitness level, operationalized as 42 digital markers generated from 10 motor fitness measures (balance, strength, flexibility); measured at baseline, mid-trial (4-weeks), trial-end (8-weeks), and follow-up (12-weeks). Target sample size is 300 participants to provide 99% power for moderate and high effect sizes (Cohen's f = 0.25, 0.40 respectively).Discussion: The study will help understand the value of individualized motor fitness assessment used to generate personalized multicomponent exercise programs, delivered remotely among older adults.Trial Registration: ClinicalTrials.gov Identifier: NCT04181983. [ABSTRACT FROM AUTHOR]

Background: Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that has been neuropathologically diagnosed in brain donors exposed to repetitive head impacts, including boxers and American football, soccer, ice hockey, and rugby players. CTE cannot yet be diagnosed during life. In December 2015, the National Institute of Neurological Disorders and Stroke awarded a seven-year grant (U01NS093334) to fund the "Diagnostics, Imaging, and Genetics Network for the Objective Study and Evaluation of Chronic Traumatic Encephalopathy (DIAGNOSE CTE) Research Project." The objectives of this multicenter project are to: develop in vivo fluid and neuroimaging biomarkers for CTE; characterize its clinical presentation; refine and validate clinical research diagnostic criteria (i.e., traumatic encephalopathy syndrome [TES]); examine repetitive head impact exposure, genetic, and other risk factors; and provide shared resources of anonymized data and biological samples to the research community. In this paper, we provide a detailed overview of the rationale, design, and methods for the DIAGNOSE CTE Research Project. Methods: The targeted sample and sample size was 240 male participants, ages 45–74, including 120 former professional football players, 60 former collegiate football players, and 60 asymptomatic participants without a history of head trauma or participation in organized contact sports. Participants were evaluated at one of four U.S. sites and underwent the following baseline procedures: neurological and neuropsychological examinations; tau and amyloid positron emission tomography; magnetic resonance imaging and spectroscopy; lumbar puncture; blood and saliva collection; and standardized self-report measures of neuropsychiatric, cognitive, and daily functioning. Study partners completed similar informant-report measures. Follow-up evaluations were intended to be in-person and at 3 years post-baseline. Multidisciplinary diagnostic consensus conferences are held, and the reliability and validity of TES diagnostic criteria are examined. Results: Participant enrollment and all baseline evaluations were completed in February 2020. Three-year follow-up evaluations began in October 2019. However, in-person evaluation ceased with the COVID-19 pandemic, and resumed as remote, 4-year follow-up evaluations (including telephone-, online-, and videoconference-based cognitive, neuropsychiatric, and neurologic examinations, as well as in-home blood draw) in February 2021. Conclusions: Findings from the DIAGNOSE CTE Research Project should facilitate detection and diagnosis of CTE during life, and thereby accelerate research on risk factors, mechanisms, epidemiology, treatment, and prevention of CTE. Trial registration: NCT02798185 [ABSTRACT FROM AUTHOR]