Improving cardiac rehabilitation patient adherence via personalized interventions

Despite documented benefits and physicians' recommendations to participate in cardiac rehabilitation (CR) programs, the average dropout rate remains between 12-56%. This study's goal was to demonstrate that using personalized interventions can significantly increase patient adherence. Nine...

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Bibliographic Details
Published in:PloS one 2022-08, Vol.17 (8), p.e0273815-e0273815
Main Authors: Aharon, Keren B, Gershfeld-Litvin, Avital, Amir, On, Nabutovsky, Irene, Klempfner, Robert
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Behavior
Biology and Life Sciences
Cardiovascular disease
Cardiovascular diseases
Confidentiality
Consent
Customization
Diagnosis
Evaluation
Exercise
Feedback
Health care
Health maintenance organizations
HMOs
Hospitalization
Medicine and Health Sciences
Messages
Participation
Patient compliance
Patients
People and Places
Physical fitness
Physicians
Questionnaires
Rehabilitation
Research and Analysis Methods
Risk factors
Short message service
Smartphones
Social Sciences
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Summary:Despite documented benefits and physicians' recommendations to participate in cardiac rehabilitation (CR) programs, the average dropout rate remains between 12-56%. This study's goal was to demonstrate that using personalized interventions can significantly increase patient adherence. Ninety-five patients (ages 18-90) eligible for the CR program were randomly recruited and received personalized interventions using the Well-Beat system. Adherence levels were compared to those of a historical control group. The Well-Beat system provided Sheba CR Health Care Provider (HCP) guidelines for personalized patient-therapist dialogue. The system also generated ongoing personalized text messages for each patient sent twice a week and related each patient's dynamic profile to their daily behavior, creating continuity, and reinforcing the desired behavior. A significant increase in patient adherence to the CR program: Three months after initiation, 76% remained active compared to the historical average of 24% in the matched control group (log-rank p-value = 0.001). Using an Artificial Intelligence (AI)-based engine that generated recommendations and messages made it possible to improve patient adherence without increasing HCP load, benefiting all. Presenting customized patient insights to the HCP and generating personalized communications along with action motivating text messages can also be useful for remote care.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0273815