Niurka Suero-Tejeda

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BACKGROUND AND OBJECTIVES Cardiac caregivers may represent a novel low-cost strategy to improve patient adherence to medical follow-up and guidelines and, ultimately, patient outcomes. Prior work on caregiving has been conducted primarily in mental health and cancer research; few data have systematically evaluated caregivers of cardiac patients. The purpose(More)
Caregivers might represent an opportunity to improve cardiovascular disease outcomes, but prospective data are limited. We studied 3,188 consecutive patients (41% minority, 39% women) admitted to a university hospital medical cardiovascular service to evaluate the association between having a caregiver and rehospitalization/death at 1 year. The clinical(More)
OBJECTIVE To collaborate with community members to develop tailored infographics that support comprehension of health information, engage the viewer, and may have the potential to motivate health-promoting behaviors. METHODS The authors conducted participatory design sessions with community members, who were purposively sampled and grouped by preferred(More)
Within the context of patient participation in a Learning Health System, this study examined consent rates and factors associated with consent for linking survey data with electronic clinical data in a sample of 2,271 Latinos. Consent rate was 96.3%. Government insurance status and health literacy significantly influenced the odds of consent.
We applied data mining techniques to a community-based behavioral dataset to build prediction models to gain insights about physical activity levels as the foundation for future interventions for urban Latinos. Our application of data mining strategies identified environment factors including having a convenient location for physical activity and(More)
The current study applied innovative data mining techniques to a community survey dataset to develop prediction models for two aspects of physical activity (i.e., active transport and screen time) in a sample of urban, primarily Hispanic, older adults (N=2,514). Main predictors for active transport (accuracy=69.29%, precision=0.67, recall=0.69) were(More)
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