The primary outcomes of this secondary analysis of the MOTIVATE-HF study were improvement in caregiver self-efficacy while caring for patients living with heart failure and caregiver contributions to self-care for these patients following motivational interviewing.
A secondary analysis of data from patients living with heart failure and their caregivers, conducted as part of the MOTIVATE-HF (MOTIVATional interviewing to Improve Self-care in Heart Failure Patients) trial, shows that although caregivers were able to improve their how well they provided care following motivational interviewing—via betterment of self-efficacy—the amount of care they provided did not improve concurrently.
“Caregiver self-efficacy, which is caregiver confidence in the ability to help the patient in performing self-care, directly influences caregiver contribution to self-care, while acting as a mediator between predictors of caregiver contribution to self-care and caregiver contribution to self-care itself,” the study authors noted. “Although caregiver self-efficacy is associated with patient and caregiver outcomes, evidence on interventions aiming to improve this variable, as well as CC to self-care, is scarce.”
These findings were presented by Giulia Locatelli, PhD candidate at the University of Rom Tor Vergata and the Australian Catholic University, during a rapid-fire oral abstract session at this year’s ACNAP-EuroHeartCare conference held May 21-24 in Madrid, Spain, and online.
All of the caregivers (n = 235) and patients (n = 238) who participated in this subanalysis were divided into 3 study groups; they were recruited from 3 medical centers in Italy. Arm 1 comprised motivational interviewing for patients, arm 2 was motivational interviewing for patients and caregivers, and arm 3 was usual care. The primary outcomes were improvement in caregiver self-efficacy while caring for patients living with heart failure and caregiver contributions to self-care for these patients following motivational interviewing.
Following assessment of the caregivers via the Caregiver Contribution to Self-Care of HF Index, these results were seen:
A longitudinal mixed linear model that considered time, living with a patient, randomization arm, and interaction confirmed that caregiver self-efficacy significantly improved (β ̂ = 1.39; 95% CI, 0.02-2.75; P = .046) and that their contributions to patient self-care also improved over time. However, the latter was determined to not be statistically significant.
Those receiving the motivational interviewing intervention had 1 in-person session and 3 telephone sessions, and their data were collected at 6 time points: baseline, before the intervention, and at 3, 6, 9, and 12 months post enrollment. Sixty percent of the caregivers lived with their patients, and 61.9% of the patients had a New York Heart Association class II heart failure diagnosis.
“Further studies need to better understand how caregiver self-efficacy affects the caregiver contribution to self-care, how caregiver self-efficacy can be further improved, and the necessary intensity of motivational interviewing to improve caregiver contributions to self-care,” the authors concluded. “Our results show that motivational interviewing was effective in improving caregiver self-efficacy, but not the caregiver contribution to self-care. This may indicate that MI was able to improve how well caregivers were supporting patients, as showed by improvements in self-efficacy, but not how much they were doing it.”
Reference
Locatelli G, Zeffiro V, Occhino G, et al. Motivational interviewing improves caregiver self-efficacy in heart failure: a secondary outcome analysis of the MOTIVATE-HF trial. Presented at: ACNAP-EuroHeartCare 2022; May 21-24, 2022; Madrid, Spain, and online.
Sustaining Compassionate Trauma Care Across Communities
September 30th 2024September is National Recovery Month, and we are bringing you another limited-edition month-long podcast series with our Strategic Alliance Partner, UPMC Health Plan. In our final episode, we speak with Lyndra Bills, MD, and Shari Hutchison, MS.
Listen
New AI Tool Identifies Undiagnosed PNH in Health Records
October 30th 2024The machine learning model shows promise in detecting paroxysmal nocturnal hemoglobinuria (PNH) by assessing electronic health records (EHR) data, potentially transforming the diagnostic landscape for rare diseases.
Read More