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

doi:10.1177/08982643231152520

. 2023 Oct;35(9):632-642.

doi: 10.1177/08982643231152520. Epub 2023 Jan 31.

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

Affiliations

¹ Department of Occupational Therapy, School of Health Professions, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
² Department of Orthopaedic Surgery, School of Medicine, Duke University, Durham, NC, USA.
³ Department of Clinical, Health, and Applied Sciences, College of Human Sciences and Humanities, University of Houston-Clear Lake, Houston, TX, USA.
⁴ Department of Population Health and Health Disparities, School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA.
⁵ Cardiovascular, Renal, Metabolism, Novartis Pharmaceuticals Field Medical Division, Houston, TX, USA.
⁶ Department of Occupational Therapy, University of Texas Medical Branch, Galveston, TX, USA.
⁷ Department of Occupational Therapy, Yonsei University, Wonju, Republic of Korea.
⁸ Department of Physical Therapy and Rehabilitation Sciences, Drexel University, Philadelphia, PA, USA.
⁹ Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
¹⁰ Department of Neurology, University of Texas Medical Branch, Galveston, TX, USA.

PMID: 36719035
PMCID: PMC10387498
DOI: 10.1177/08982643231152520

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

Timothy Reistetter et al. J Aging Health. 2023 Oct.

. 2023 Oct;35(9):632-642.

doi: 10.1177/08982643231152520. Epub 2023 Jan 31.

Authors

Affiliations

¹ Department of Occupational Therapy, School of Health Professions, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
² Department of Orthopaedic Surgery, School of Medicine, Duke University, Durham, NC, USA.
³ Department of Clinical, Health, and Applied Sciences, College of Human Sciences and Humanities, University of Houston-Clear Lake, Houston, TX, USA.
⁴ Department of Population Health and Health Disparities, School of Public and Population Health, University of Texas Medical Branch, Galveston, TX, USA.
⁵ Cardiovascular, Renal, Metabolism, Novartis Pharmaceuticals Field Medical Division, Houston, TX, USA.
⁶ Department of Occupational Therapy, University of Texas Medical Branch, Galveston, TX, USA.
⁷ Department of Occupational Therapy, Yonsei University, Wonju, Republic of Korea.
⁸ Department of Physical Therapy and Rehabilitation Sciences, Drexel University, Philadelphia, PA, USA.
⁹ Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
¹⁰ Department of Neurology, University of Texas Medical Branch, Galveston, TX, USA.

PMID: 36719035
PMCID: PMC10387498
DOI: 10.1177/08982643231152520

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.

Keywords: adaptation; self-management; stroke; visual analytics.

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Conflict of interest statement

Declaration of Conflicting Interests: The authors declare that there is no conflict of interest.

Figures

**Figure 1.**
The five subgroups from the visual analytics output.

See this image and copyright information in PMC

References

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1. Bhavnani SK, Chen T, Ayyaswamy A, Visweswaran S, Bellala G, Rohit D, & Kevin E, B. (2017). Enabling Comprehension of Patient Subgroups and Characteristics in Large Bipartite Networks: Implications for Precision Medicine. AMIA Summits on Translational Science Proceedings, 2017, 21–29. - PMC - PubMed

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[1] Adepoju OE, Bolin JN, Phillips CD, Zhao H, Ohsfeldt RL, McMaughan DK, Helduser JW, & Forjuoh SN (2014). Effects of diabetes self-management programs on time-to-hospitalization among patients with type 2 diabetes: A survival analysis model. Patient Education and Counseling, 95(1), 111–117. 10.1016/j.pec.2014.01.001 - DOI - PMC - PubMed

[2] Adepoju OE, Bolin JN, Phillips CD, Zhao H, Ohsfeldt RL, McMaughan DK, Helduser JW, & Forjuoh SN (2014). Effects of diabetes self-management programs on time-to-hospitalization among patients with type 2 diabetes: A survival analysis model. Patient Education and Counseling, 95(1), 111–117. 10.1016/j.pec.2014.01.001 - DOI - PMC - PubMed

[3] Benjamin SE (2020). Sleep in Patients With Neurologic Disease. Continuum (Minneapolis, Minn.), 26(4), 1016–1033. 10.1212/CON.0000000000000887 - DOI - PubMed

[4] Benjamin SE (2020). Sleep in Patients With Neurologic Disease. Continuum (Minneapolis, Minn.), 26(4), 1016–1033. 10.1212/CON.0000000000000887 - DOI - PubMed

[5] Bhavnani S, Dang B, Caro M, Saade G, & Visweswaran S (2014). 698: Genetic differences reveal heterogeneity in spontaneous preterm birth pathophysiology: a visual analytical approach. American Journal of Obstetrics and Gynecology, 210, S343–S344. 10.1016/j.ajog.2013.10.731 - DOI

[6] Bhavnani S, Dang B, Caro M, Saade G, & Visweswaran S (2014). 698: Genetic differences reveal heterogeneity in spontaneous preterm birth pathophysiology: a visual analytical approach. American Journal of Obstetrics and Gynecology, 210, S343–S344. 10.1016/j.ajog.2013.10.731 - DOI

[7] Bhavnani SK, Bellala G, Victor S, Bassler KE, & Visweswaran S (2012). The role of complementary bipartite visual analytical representations in the analysis of SNPs: A case study in ancestral informative markers. Journal of the American Medical Informatics Association: JAMIA, 19(e1), e5–e12. 10.1136/amiajnl-2011-000745 - DOI - PMC - PubMed

[8] Bhavnani SK, Bellala G, Victor S, Bassler KE, & Visweswaran S (2012). The role of complementary bipartite visual analytical representations in the analysis of SNPs: A case study in ancestral informative markers. Journal of the American Medical Informatics Association: JAMIA, 19(e1), e5–e12. 10.1136/amiajnl-2011-000745 - DOI - PMC - PubMed

[9] Bhavnani SK, Chen T, Ayyaswamy A, Visweswaran S, Bellala G, Rohit D, & Kevin E, B. (2017). Enabling Comprehension of Patient Subgroups and Characteristics in Large Bipartite Networks: Implications for Precision Medicine. AMIA Summits on Translational Science Proceedings, 2017, 21–29. - PMC - PubMed

[10] Bhavnani SK, Chen T, Ayyaswamy A, Visweswaran S, Bellala G, Rohit D, & Kevin E, B. (2017). Enabling Comprehension of Patient Subgroups and Characteristics in Large Bipartite Networks: Implications for Precision Medicine. AMIA Summits on Translational Science Proceedings, 2017, 21–29. - PMC - PubMed

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

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Abstract

Conflict of interest statement

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