The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults

Timothy Reistetter, Kimberly Hreha, Julianna M. Dean, Monique R. Pappadis, Rachel R. Deer, Chih Ying Li, Ickpyo Hong, Annalisa Na, Sara Nowakowski, Hashem M. Shaltoni, Suresh K. Bhavnani

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: Managing multimorbidity as aging stroke patients is complex; standard self-management programs necessitate adaptations. We used visual analytics to examine complex relationships among aging stroke survivors’ comorbidities. These findings informed pre-adaptation of a component of the Chronic Disease Self-Management Program. Methods: Secondary analysis of 2013–2014 Medicare claims with stroke as an index condition, hospital readmission within 90 days (n = 42,938), and 72 comorbidities. Visual analytics identified patient subgroups and co-occurring comorbidities. Guided by the framework for reporting adaptations and modifications to evidence-based interventions, an interdisciplinary team developed vignettes that highlighted multimorbidity to customize the self-management program. Results: There were five significant subgroups (z = 6.19, p <.001) of comorbidities such as obesity and cancer. We constructed 6 vignettes based on the 5 subgroups. Discussion: Aging stroke patients often face substantial disease-management hurdles. We used visual analytics to inform pre-adaptation of a self-management program to fit the needs of older adult stroke survivors.

Original languageEnglish (US)
Pages (from-to)632-642
Number of pages11
JournalJournal of Aging and Health
Volume35
Issue number9
DOIs
StatePublished - Oct 2023

Keywords

  • adaptation
  • self-management
  • stroke
  • visual analytics

ASJC Scopus subject areas

  • Health(social science)
  • Life-span and Life-course Studies
  • Sociology and Political Science

Fingerprint

Dive into the research topics of 'The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults'. Together they form a unique fingerprint.

Cite this