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Digital applications to support self-management of multimorbidity: A scoping review
Lucy Smith1, Glenn Simpson1, Sian Holt1
1Primary Care Research Centre, University of Southampton, Southampton, UK.
Introduction:
Multimorbidity, defined as the co-occurrence of two or more long-term conditions, is increasing rapidly and poses challenges for healthcare systems. Advances in digital technologies offer solutions by facilitating personalised, scalable care interventions that empower individuals to manage their conditions more effectively. These applications have potential to improve access to care, enhance patient engagement, and support tailored approaches to self-management.
Objectives:
This scoping review aims to synthesise current evidence on the use of digital applications for self-management in adults with multimorbidity.
Methods:
A scoping review was conducted, systematically searching PubMed, Web of Science, OVID, CINAHL, EMBASE, and additional manual searches. Boolean operators and targeted key terms were employed to retrieve relevant studies from database inception to 16th January 2024.
Results:
The search yielded 1,974 articles, of which 31 met the inclusion criteria. Digital applications for self-management in multimorbidity demonstrated high acceptability and varying efficacy. Key benefits included improved communication, symptom tracking, and autonomy. Barriers included privacy concerns, additional patient burden, and engagement challenges. Socio-demographics, self-efficacy, and digital literacy influenced both barriers and facilitators to tool usage. Theoretical models underpinning digital applications were limited. Older adults and the working-age population were rarely included.
Conclusion:
The current evidence base does not fully address the needs of older adults with low digital literacy or working-age populations with multimorbidity. Our model highlights the importance of broader contextual mechanisms in digital tool adoption. Future research should prioritise theory-driven tool development tailored to disease clusters and aligned with sociodemographic profiles, health risks, and social care needs. Addressing these gaps could improve self-management and health outcomes for high-risk populations.
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