Association Between Video-Based Telemedicine Visits and Medication Adherence Among Patients With Heart Failure: Retrospective Cross-Sectional Study.

Background: Despite the exponential growth in telemedicine visits in clinical practice due to the COVID-19 pandemic, it remains unknown if telemedicine visits achieved similar adherence to prescribed medications as in-person office visits for patients with heart failure.

Objective: Our study examined the association between telemedicine visits (vs in-person visits) and medication adherence in patients with heart failure.

Methods: This was a retrospective cross-sectional study of adult patients with a diagnosis of heart failure or an ejection fraction of ≤40% using data between April 1 and October 1, 2020.

Approach to Estimating Adherence to Heart Failure Medications Using Linked Electronic Health Record and Pharmacy Data.

Background: Medication non-adherence, which is common in chronic diseases such as heart failure, is often estimated using proportion of days covered (PDC). PDC is typically calculated using medication fill information from pharmacy or insurance claims data, which lack information on when medications are prescribed. Many electronic health records (EHRs) have prescription and pharmacy fill data available, enabling enhanced PDC assessment that can be utilized in routine clinical care.

Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach.

Purpose: Researchers often use model-based multiple imputation to handle missing at random data to minimize bias. However, constraints within the data may sometimes result in implausible values, making model-based imputation infeasible. In these contexts, we illustrate how random hot deck imputation can allow for plausible multiple imputation in longitudinal studies.

Investigating the Effect of Return-to-Play Timing After Injury on Performance: Does the Analysis Answer the Research Objective?

Return-to-play decision making should be based on all the advantages and disadvantages of return to play for athletes, not just the risk of injury. For competitive athletes, this includes the effect of early versus delayed return to sport on performance. In this paper, we address the questions "How can I estimate the effect of injury on the individual's performance at return to play?" and "What is the effect of delaying return to sport on the individual's performance?".

Causal effects, workload and injury risk: The importance of specifying the research question.

Objectives: To illustrate why the research question determines whether and how sport medicine investigators should adjust for workload when interested in interventions or causal risk factors for injury.

Design: Theoretical conceptualization.

Methods: We use current concepts of causal inference to demonstrate the advantages and disadvantages of adjusting for workload through different analytic approaches when evaluating causal effects on injury risk.

Predicting Injury Risk Over Changes in Physical Activity in Children Using the Acute:Chronic Workload Ratio.

Limited research exists on the relationship between changes in physical activity levels and injury in children. In this study, we investigated the prognostic relationship between changes in activity, measured by the acute:chronic workload ratio (ACWR), and injury in children. We used data from the Childhood Health, Activity, and Motor Performance School Study Denmark (2008-2014), a prospective cohort study of 1,660 children aged 6-17 years.

Causal simulation experiments: Lessons from bias amplification.

Recent theoretical work in causal inference has explored an important class of variables which, when conditioned on, may further amplify existing unmeasured confounding bias (bias amplification). Despite this theoretical work, existing simulations of bias amplification in clinical settings have suggested bias amplification may not be as important in many practical cases as suggested in the theoretical literature. We resolve this tension by using tools from the semi-parametric regression literature leading to a general characterization in terms of the geometry of OLS estimators which allows us to extend current results to a larger class of DAGs, functional forms, and distributional assumptions.

Analyzing Activity and Injury: Lessons Learned from the Acute:Chronic Workload Ratio.

Injuries occur when an athlete performs a greater amount of activity than what their body can withstand. To maximize the positive effects of training while avoiding injuries, athletes and coaches need to determine safe activity levels. The International Olympic Committee has recommended using the acute:chronic workload ratio (ACWR) to monitor injury risk and has provided thresholds to minimize risk when designing training programs.