Resourcefulness Among Initial Ischemic Stroke Patients: A Longitudinal Study of 12 Months

Lina Guo; Jaclene A Zauszniewski; Gege Zhang; Xiaoyu Lei; Mengyu Zhang; Miao Wei; Keke Ma; Caixia Yang; Yanjin Liu; Yuanli Guo

doi:10.2147/PPA.S448647

Resourcefulness Among Initial Ischemic Stroke Patients: A Longitudinal Study of 12 Months

Patient Prefer Adherence. 2024 Mar 5:18:565-577. doi: 10.2147/PPA.S448647. eCollection 2024.

Authors

Affiliations

¹ Department of Neurology, National Advanced Stroke Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
² Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA.
³ Department of Nursing, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, People's Republic of China.

Abstract

Objective: To explore distinct longitudinal trajectories of resourcefulness among initial ischemic stroke patients from diagnosis to 12 months, and to identify whether sociodemographic factors, disease-related factors, self-efficacy, family function, and social support can predict patterns in the trajectories of resourcefulness.

Methods: A prospective longitudinal study was conducted. Initial ischemic stroke patients who met inclusion and exclusion criteria were followed up when still in hospital (Preparing for discharge, Baseline, T1), at 1 month (T2), at 3 months (T3), at 6 months (T4), at 9 months (T5) and 12 months (T6) (±1 week) after discharge. General information, National Institute of Health Stroke Scale (NIHSS), Modified Rankin Scale (mRS), General Self-Efficacy Scale (GSES), General Family Functioning Subscale (FAD-GF), and Social Support Rate Scale (SSRS) were used in T1. The Resourcefulness Scale^© was evaluated at 6 time points. Growth mixture modeling was used to identify trajectory patterns of resourcefulness. Logistic regression was used to identify predictors of resourcefulness trajectories.

Results: Three longitudinal trajectories of resourcefulness were identified and named as the high-stable class (38.9%, n=71), fluctuation class (41.2%, n=75), and low-stable class (19.9%, n=36), respectively. Dwelling areas (x²=6.805, P=0.009), education (x²=44.865, P=0.000), monthly income (x²=13.063, P=0.001), NIHSS scores (x²=44.730, P=0.000), mRS scores (x²=51.788, P=0.000), Hcy (x²=9.345, P=0.002), GSES (x²=56.933, P=0.000), FAD-GF (x²=41.305, P=0.000) and SSRS (x²=52.373, P=0.000) were found to be statistically significant for distinguishing between different resourcefulness trajectory patterns. Lower education (OR=0.404), higher NIHSS(OR=6.672) scores, and higher mRS(OR=21.418) scores were found to be risk factors for lower resourcefulness, whereas higher education(OR=0.404), GSES(OR=0.276), FAD-GF(OR=0.344), and SSRS(OR=0.358) scores were identified as protective factors enhancing resourcefulness.

Conclusion: This study obtained three patterns of trajectories and identified their predictive factors in initial ischemic stroke. The findings will assist health care professionals in identifying subgroups of patients and when they may be at risk of low resourcefulness and provide timely targeted intervention to promote resourcefulness.

Keywords: initial ischemic stroke; longitudinal study; nursing care; predictive factors; resourcefulness; social ecology theory.

Grants and funding

This study was funded by the National Natural Science Foundation of China (72204225, 72274179); China Postdoctoral Science Foundation (2023M733234); Medical Science and Technology Project of Henan Province (SBGJ202102076, 222102310246, LHGJ20220429); Nursing Science Foundation of the First Affiliated Hospital of Zhengzhou University (HLKY2023001).