Person-Specific Analyses of Smartphone Use and Mental Health: An Intensive Longitudinal Study Over One Year.

Background: Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly found null or small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health.

Objective: This exploratory empirical demonstration aimed to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of one entire year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders.

Binding the Person-Specific Approach to Modern AI in the Human Screenome Project: Moving past Generalizability to Transferability.

Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about of results might be productively reframed with respect to of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto - "In short, the concerns about commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of .

Lymphocyte-to-C-Reactive Protein (LCR) Ratio Is Not Accurate to Predict Severity and Mortality in Patients with COVID-19 Admitted to the ED.

Health care systems worldwide have been battling the ongoing COVID-19 pandemic. Since the beginning of the COVID-19 pandemic, Lymphocytes and CRP have been reported as markers of interest. We chose to investigate the prognostic value of the LCR ratio as a marker of severity and mortality in COVID-19 infection.

Communication Skills Training Using Remote Augmented Reality Medical Simulation: a Feasibility and Acceptability Qualitative Study.

Introduction: Augmented reality (AR) has promise as a clinical teaching tool, particularly for remote learning. The Chariot Augmented Reality Medical (CHARM) simulator integrates real-time communication into a portable medical simulator with a holographic patient and monitor. The primary aim was to analyze feedback from medical and physician assistant students regarding acceptability and feasibility of the simulator.

Causal and Associational Language in Observational Health Research: A Systematic Evaluation.

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations.

Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.

Background/introduction: Emotion detection classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle compound and ambiguous labels. We explore the feasibility of using crowdsourcing to acquire reliable soft-target labels and evaluate an emotion detection classifier trained with these labels.

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study.

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces.

Objective: We designed a strategy to gamify the collection and labeling of child emotion-enriched images to boost the performance of automatic child emotion recognition models to a level closer to what will be needed for digital health care approaches.

DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs.

Directed acyclic graphs (DAGs) are frequently used in epidemiology as a method to encode causal inference assumptions. We propose the DAGWOOD framework to bring many of those encoded assumptions to the forefront. DAGWOOD combines a root DAG (the DAG in the proposed analysis) and a set of branch DAGs (alternative hidden assumptions to the root DAG).

Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength.

Introduction: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment.

Methods: We included studies that were primarily designed to estimate the quantitative impact of one or more implemented COVID-19 policies on direct SARS-CoV-2 and COVID-19 outcomes.

Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials.

Background: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.

[Emergency medical assessment of the patient with psychiatric conditions].

The evaluation by the emergency doctor of the patient presenting with a psychiatric symptom before being taken care of by the psychiatric team is described by the term medical clearance. There is little work on the performance of complementary examinations on these patients. A retrospective multicentre study conducted at the Metz-Thionville regional hospital (57) shows that at least one complementary examination was carried out in 61% of hospitalised patients, compared with 28% of non-hospitalised patients.

Much ado about something: a response to "COVID-19: underpowered randomised trials, or no randomised trials?".

Non-pharmaceutical interventions (NPI) for infectious diseases such as COVID-19 are particularly challenging given the complexities of what is both practical and ethical to randomize. We are often faced with the difficult decision between having weak trials or not having a trial at all. In a recent article, Dr.

The impact of the SARS-CoV-2 pandemic on the mental health of hemodialysis patients in Lebanon.

Hemodialysis is a necessary treatment for end-stage kidney disease patients. It imposes undergoing three sessions of dialysis per week in a specialized center. Amid the SARS-CoV-2 pandemic, precautionary measures were mandatory in all dialysis facilities and may have negatively impacted patients' well-being.

Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.

Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107.

Association of Convalescent Plasma Treatment With Clinical Outcomes in Patients With COVID-19: A Systematic Review and Meta-analysis.

Importance: Convalescent plasma is a proposed treatment for COVID-19.

Objective: To assess clinical outcomes with convalescent plasma treatment vs placebo or standard of care in peer-reviewed and preprint publications or press releases of randomized clinical trials (RCTs).

Data Sources: PubMed, the Cochrane COVID-19 trial registry, and the Living Overview of Evidence platform were searched until January 29, 2021.

Integrated eye tracking on Magic Leap One during augmented reality medical simulation: a technical report.

Augmented reality (AR) has been studied as a clinical teaching tool, however eye-tracking capabilities integrated within an AR medical simulator have limited research. The recently developed Chariot Augmented Reality Medical (CHARM) simulator integrates real-time communication into a portable medical simulator. The purpose of this project was to refine the gaze-tracking capabilities of the CHARM simulator on the Magic Leap One (ML1).

Problems with Evidence Assessment in COVID-19 Health Policy Impact Evaluation: A systematic review of study design and evidence strength.

Introduction: Assessing the impact of COVID-19 policy is critical for informing future policies. However, there are concerns about the overall strength of COVID-19 impact evaluation studies given the circumstances for evaluation and concerns about the publication environment. This study systematically reviewed the strength of evidence in the published COVID-19 policy impact evaluation literature.

The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days.

: Never before have clinical trials drawn as much public attention as those testing interventions for COVID-19. We aimed to describe the worldwide COVID-19 clinical research response and its evolution over the first 100 days of the pandemic. Descriptive analysis of planned, ongoing or completed trials by April 9, 2020 testing any intervention to treat or prevent COVID-19, systematically identified in trial registries, preprint servers, and literature databases.

Precision Telemedicine through Crowdsourced Machine Learning: Testing Variability of Crowd Workers for Video-Based Autism Feature Recognition.

Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls.

HIV self-testing among young women in rural South Africa: A randomized controlled trial comparing clinic-based HIV testing to the choice of either clinic testing or HIV self-testing with secondary distribution to peers and partners.

Background: HIV testing rates in many hyper-endemic areas are lower than needed to curtail the HIV epidemic. New HIV testing strategies are needed to overcome barriers to traditional clinic based testing; HIV self-testing is one modality that offers promise in reaching individuals who experience barriers to clinic-based testing.

Methods: We conducted a randomized control trial among young women ages 18-26 living in rural Mpumalanga, South Africa where they were randomized in a 1:1 allocation to either the: (1) HIV Counseling and Testing (HCT) arm: an invitation to test at one of the 9 local government clinics where free HCT is provided and is standard of care (SOC), or (2) choice arm: choice of either a clinic-based HCT invitation or oral HIV Self-Testing (HIVST) kits.