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American Heart Association

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Final ID: WP76

Leveraging Digital Health to Improve the Cardiovascular Health of Stroke Survivors: A Nurse-Led Approach

Abstract Body: Background: The aim of this study was to determine the impact of a nurse-led digital health intervention targeting cardiovascular risk in patients with cryptogenic stroke, as well as to identify factors associated with whether or not cardiovascular health improves. Methods: This is a secondary analysis of an ongoing randomized controlled trial of an digital health intervention that integrates advanced nursing practice to improve cardiovascular health in cryptogenic stroke patients aged 55 years or older, recruited in Spain (SMART THUNDER NCT03169257). The intervention consists of using an activity tracker and a mHealth application. The follow-up period was 12 months. The Life’s Simple 7 (LS7 score) is use to determine cardiovascular risk. Results: Out of 116 subjects, 59 participated in the digital health intervention group and 56 received usual care. Among all participants, higher self-reported health literacy (OR = 1.32, 95% CI: 1.166 - 1.501, p = 0.000) and higher self-reported attitude toward technology (OR = 1.26, 95% CI: 1.091 - 1.455, p = 0.002) were associated with and ideal LS7 score at 3 months. Compared with those receiving usual care, participants in the digital health intervention group showed significant improvement of LS7 score from 3 to 12 months (p = 0.013). Conclusion: The nurse-led digital health intervention was associated with greater self-improvement of cardiovascular health in stroke patients over time. This study also demonstrated that higher health literacy and a positive attitude towards technology contribute to improved cardiovascular outcomes, allowing us to identify a vulnerable subset of patients that might benefit from a more comprehensive digital health intervention.
  • Meza Burgos, Claudia Patricia  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Molina, Carlos  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Pagola, Jorge  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Juega, Jesus  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Francisco Pascual, Jaume  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Santos Ortega, Alba  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Duran, Laura  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Alvarez-sabin, Jose  ( Vall d'Hebron University Hospital , Barcelona , Spain )
  • Sero, Laia  ( Hospital Universitari Joan XXIII , Tarragona , Spain )
  • Ustrell, Xavier  ( Hospital Universitari Joan XXIII , Tarragona , Spain )
  • Bashir, Saima  ( Hospital Universitari Dr. Josep Trueta , Girona , Spain )
  • Author Disclosures:
    Claudia Patricia Meza Burgos: DO NOT have relevant financial relationships | Carlos Molina: No Answer | Jorge Pagola: DO NOT have relevant financial relationships | Jesus Juega: No Answer | Jaume Francisco Pascual: No Answer | Alba Santos Ortega: DO NOT have relevant financial relationships | Laura Duran: No Answer | Jose Alvarez-Sabin: No Answer | LAIA SERO: No Answer | Xavier Ustrell: DO NOT have relevant financial relationships | Saima Bashir: No Answer
Meeting Info:
Session Info:

Cerebrovascular Nursing Posters I

Wednesday, 02/05/2025 , 07:00PM - 07:30PM

Poster Abstract Session

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PREDICT-POOR_COMP: An artificial intelligence-based tool to predict poor medication compliance after stroke

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Initial Core Volume Assessment-based Machine Learning on Non-Contrast CT could Discriminate Outcomes According to ASPECTS score and Stroke Elapsed Time in Reperfusion Treatment Patients

Flores Alan, Sero Laia, Ustrell Xavier, Avivar Ylenia, Olive-gadea Marta, Canals Pere, Ribo Marc

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