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Individuals' Intention to Engage in Outpatient Cardiac Rehabilitation Programs: Prediction Based on an Enhanced Model
Journal article   Peer reviewed

Individuals' Intention to Engage in Outpatient Cardiac Rehabilitation Programs: Prediction Based on an Enhanced Model

Sepideh Jahandideh, Mina Jahandideh and Ebrahim Barzegari
Journal of clinical psychology in medical settings, Vol.28(4), pp.798-807
15/03/2021
DOI: https://doi.org/10.1007/s10880-021-09771-7
PMID: 33723685

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Abstract

Outpatient cardiac rehabilitation Patient engagement Barriers to engagement Machine learning Association
Motivation is an important factor in encouraging individuals to attend rehabilitation and underpins many approaches to engagement. The aims of this study were to develop an accurate model able to predict individual intention to engage in outpatient cardiac rehabilitation (CR) programs based on the first stage of the Model of Therapeutic Engagement integrated into a socio-environmental context. The cross-sectional study in the cardiology ward of an Australian hospital included a total of 217 individuals referred to outpatient CR. Through an ordinal logistic regression, the effect of random forest (RF)-selected profile features on individual intention to engage in outpatient CR was explored. The RF based on the conditional inference trees predicted the intention to engage in outpatient CR with high accuracy. The findings highlighted the significant roles of individuals' 'willingness to consider the treatment', 'perceived self-efficacy' and 'perceived need for rehabilitation' in their intention, while the involvement of 'barriers to engagement' and 'demographic and medical factors' was not evident.

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