Social determinants of health, the effects of colonialism, and systemic injustices result in some groups being at disproportionately higher risk for developing type 2 diabetes (T2D). Many T2D prevention programs have not been designed to provide equitable and inclusive care to everyone. This paper presents an example of the steps taken in an evidence-based community T2D prevention program, Small Steps for Big Changes (SSBC), to improve equitable access and inclusivity based on input from a stakeholder advisory group and the ConNECT Framework. To improve reach to those most at risk for T2D, SSBC has changed both eligibility criteria and program delivery. To ensure that all testing is done in an inclusive manner, changes have been made to measurements, and to training for those delivering the program. This paper also provides actionable recommendations for other researchers to incorporate into their own health programs to promote inclusivity and ensure that they reach those most at risk of T2D.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/2752535X231189932 | DOI Listing |
BMC Geriatr
January 2025
James P. Wilmot Cancer Institute, Rochester, New York, USA.
Background: Older adults with cancer are vulnerable to declines in muscle performance (e.g., strength, speed, duration of muscular contraction), which are associated with worse cancer-related outcomes.
View Article and Find Full Text PDFAnal Chim Acta
January 2025
Christian Doppler Laboratory for Innovative Gut Health Concepts of Livestock, Austria; BOKU University, Vienna, Dept. IFA-Tulln, Institute of Bioanalytics and Agro-Metabolomics, Tulln, Austria.
Background: Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine.
View Article and Find Full Text PDFAppl Clin Inform
January 2025
Pediatrics, Children's Healthcare of Atlanta Egleston Hospital, Atlanta, United States.
Background: Engagement of clinicians who understand clinical workflows and technology constraints can accelerate the development and implementation of better electronic health record (EHR) designs that improve quality and reduce burnout. Provider builder programs can accelerate clinical informatics education for a broader coalition of clinical specialties.
Objective: In this State of the Art / Best Practice paper, we aim to (1) propose a provider builder maturity model informed by the experience of three institutions using a single EHR vendor (Epic Systems©) and (2) describe the program elements and relationships necessary to advance along this model to yield organizational benefits.
J Neuroimaging
January 2025
Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
Background And Purpose: Peak width of skeletonized mean diffusivity (PSMD) is a novel marker of white matter damage, which may be related to small vessel disease. This study aimed to investigate the presence of white matter damage in patients with isolated rapid eye movement sleep behavior disorder (RBD) using PSMD.
Methods: We enrolled patients with newly diagnosed isolated RBD confirmed by polysomnography and age- and sex-matched healthy controls.
Alzheimers Dement
December 2024
Mayo Clinic, Rochester, MN, USA
Background: Recent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.
Method: An ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!