BackgroundMind-body practices such as yoga have been studied for their generally positive effects on health-related quality of life (HRQOL). The association between how a person practices yoga and the person’s HRQOL is not known.Materials and methodsYoga practitioners were sent invitations to participate in an online survey via email. Yoga characteristics, HRQOL, and other sociodemographics were collected. Analyses of data from 309 consenting responders evaluated associations between yoga practice characteristics (use of yoga tools, length of practice, location, method, etc.) and the 10-item PROMIS Global Health scale for both physical and mental health components.ResultsMultivariable regression models demonstrated higher mental health scores were associated with regular meditation practice, higher income, and the method of practicing in a community group class (versus one-on-one). Higher physical health scores were associated with length of lifetime practice, teacher status, Krishnamacharya yoga style, and practicing in a yoga school/studio (versus at home).ConclusionsMeditation practice in yoga is positively associated with mental health. Length of lifetime yoga practice was significantly associated with better physical health, suggesting yoga has a potential cumulative benefit over time. Different locations and methods of practice may be associated with varying effects on health outcomes. Comparative cross-sectional and longitudinal studies on the variations in yoga practice are needed to further characterize health benefits of yoga.

Purpose PCORnet, the National Patient‐Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. Methods We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012‐2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD‐9/ICD‐10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ≥6.5%, or (CP3) an antidiabetic prescription and HbA1c ≥6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t0. Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident‐T2DM CP using electronic health record (EHR) review as reference. Results The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% (n = 1572; CI:95.1‐97.0) and was consistently high across sites. The PPV for the incident‐T2DM CP was 5.8% (CI:4.5‐7.5). Conclusions The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.

Background: Although proven effective interventions for childhood obesity exist, there remains a substantial gap in the adoption of recommended practices by clinicians.Objective: The aims are to: (1) package implementation and training supports to facilitate the adoption of the evidence-based Healthy Weight Clinic Pediatric Weight Management Intervention (PWMI) (based on three previous effectiveness trials); (2) pilot and evaluate the packaged Healthy Weight Clinic PWMI; and (3) develop a sustainability and dissemination plan.Design/Methods: We used the Consolidated Framework of Implementation Research constructs to create an Implementation Research Logic Model that defined the facilitators and barriers of the Healthy Weight Clinic PWMI. We linked these constructs to implementation strategies and mechanisms. Packaging and design will be informed by the core essential components and functions of the PWMI along with stakeholder engagement. Once the package is complete, we will pilot the PWMI by using a Type III effectiveness-implementation hybrid design. Implementation outcomes will be evaluated by using the RE-AIM framework.Results: We will create an integrated, multisystems level package for national dissemination. The package will include training and a suite of resources for primary care physicians and healthy weight clinic staff, including: patient and caregiver facing videos, patient and caregiver handouts, group curriculum guide, online provider trainings, and access to a virtual learning collaborative. Conclusion:The results will highlight the extent to which the package of the Healthy Weight Clinic PWMI facilitates the adoption of effective strategies for treating childhood obesity. Lessons learned will inform modifications to the Healthy Weight Clinic PWMI and strategies for future scaling.

Yoga is a globally popular mind-body practice used for health. The objective of this study was to characterize yoga practice and factors associated with frequency of practice. Yoga practitioners were sent invitations via email to participate in an online survey. Yoga characteristics and other sociodemographics were collected. Data from 309 consenting respondents were analyzed for patterns in practice characteristics (yoga techniques, location of practice, method of instruction, teacher status, and yoga type). Associations between characteristics of practice and yoga practice frequency were computed. The following characteristics were then analyzed as predictors of practice frequency in a regression model: location of practice, method of instruction, teacher status, yoga type, and techniques. Most respondents reported use of all three yoga techniques (movements, breathing, and meditation) and practiced an average of 4.5 ± 1.9 days a week. Key characteristics such as location of practice, method of instruction, and frequency significantly differed by teacher status and by yoga type (Viniyoga practitioners vs. other yoga styles). In our regression model, being a yoga teacher, being taught a one-on-one yoga practice by someone else, and regular use of movement were positively associated with increased practice frequency. Our analyses provide insight into how yoga factors such as teacher status, method of instruction, and use of tools relate to practice frequency. Understanding the relationships between practice characteristics and practice frequency may allow for the improved implementation of yoga for health.