Application of Machine Learning in Multimorbidity Research: Protocol for a Scoping Review.

BACKGROUND: Multimorbidity, defined as the coexistence of multiple chronic conditions, poses significant challenges to health care systems on a global scale. It is associated with increased mortality, reduced quality of life, and increased health care costs. The burden of multimorbidity is expected to worsen if no effective intervention is taken. Machine learning has the potential to assist in addressing these challenges since it offers advanced analysis and decision-making capabilities, such as disease prediction, treatment development, and clinical strategies. OBJECTIVE: This paper represents the protocol of a scoping review that aims to identify and explore the current literature concerning the use of machine learning for patients with multimorbidity. More precisely, the objective is to recognize various machine learning models, the patient groups involved, features considered, types of input data, the maturity of the machine learning algorithms, and the outcomes from these machine learning models. METHODS: The scoping review will be based on the guidelines of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Five databases (PubMed, Embase, IEEE, Web of Science, and Scopus) are chosen to conduct a literature search. Two reviewers will independently screen the titles, abstracts, and full texts of identified studies based on predefined eligibility criteria. Covidence (Veritas Health Innovation Ltd) will be used as a tool for managing and screening papers. Only studies that examine more than 1 chronic disease or individuals with a single chronic condition at risk of developing another will be included in the scoping review. Data from the included studies will be collected using Microsoft Excel (Microsoft Corp). The focus of the data extraction will be on bibliographical information, objectives, study populations, types of input data, types of algorithm, performance, maturity of the algorithms, and outcome. RESULTS: The screening process will be presented in a PRISMA-ScR flow diagram. The findings of the scoping review will be conveyed through a narrative synthesis. Additionally, data extracted from the studies will be presented in more comprehensive formats, such as charts or tables. The results will be presented in a forthcoming scoping review, which will be published in a peer-reviewed journal. CONCLUSIONS: To our knowledge, this may be the first scoping review to investigate the use of machine learning in multimorbidity research. The goal of the scoping review is to summarize the field of literature on machine learning in patients with multiple chronic conditions, highlight different approaches, and potentially discover research gaps. The results will offer insights for future research within this field, contributing to developments that can enhance patient outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53761.

Increased motivation for and use of digital services in heart failure patients participating in a telerehabilitation program: a randomized controlled trial.

Background: eHealth literacy (eHL) may be an important factor in the adoption of telerehabilitation. However, little is known about how telerehabilitation affects patients' eHL. The current study evaluated changes over time in eHL for heart failure (HF) patients in a telerehabilitation program (the Future Patient Program) compared to a traditional rehabilitation program. Methods: As part of a randomized controlled trial comparing telerehabilitation with traditional rehabilitation, 137 HF patients completed the eHealth Literacy Questionnaire (eHLQ) at 6 and 12 months of their respective rehabilitation programs. Results: At 6 months, the telerehabilitation group indicated higher levels of 'using technology to process health information' and 'motivated to engage with digital services'. This difference was consistent over time, and we found no other differences between groups or over time with regard to eHL. Conclusions: Providing a digital toolbox for processing health information to HF patients may aid in increasing their eHL, motivation, and ability to engage with digital services in HF patients. Especially, if the technology is designed to support patient needs in terms of the educational content of the program. Preferably technology should be provided early on in the rehabilitation process to ensure optimal outcome. Trial Registration: The study was registered in ClinicalTrials.gov (NCT03388918).