Home-based Digital Health Technologies for Older Adults to Self-Manage Multiple Chronic Conditions: A Data-Informed Analysis of User Engagement from a Longitudinal Trial

Yiyang Sheng, Julie Doyle, RR Bond, Rajesh Jaiswal, Shane Gavin, John Dinsmore

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
94 Downloads (Pure)

Abstract

Background: Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions. Methods: The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement. Results: Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements. Conclusions: The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalDigital Health
Volume8
Early online date22 Sept 2022
DOIs
Publication statusPublished online - 22 Sept 2022

Bibliographical note

Funding Information:
The authors disclosed receipt of the follwoing financial support for the research, authorship, and/or publicayion of this article: This work was part-funded by the ProACT project and has received funding from the European Union's Horizon 2020 research and innovation programme under grant No. 689996. This work was part-funded by the EU's INTERREG VA program, managed by the Special EU Programs Body (SEUPB), through the ECME project. This work was part-funded by the Higher Education Authority of Ireland's COVID-19 Relief for Researchers Scheme.

Funding Information:
The authors would like to sincerely thank all of the participants of this research for their valuable time. The authors disclosed receipt of the follwoing financial support for the research, authorship, and/or publicayion of this article: This work was part-funded by the ProACT project and has received funding from the European Union's Horizon 2020 research and innovation programme under grant No. 689996. This work was part-funded by the EU's INTERREG VA program, managed by the Special EU Programs Body (SEUPB), through the ECME project. This work was part-funded by the Higher Education Authority of Ireland's COVID-19 Relief for Researchers Scheme.

Publisher Copyright:
© The Author(s) 2022.

Keywords

  • Digital health
  • self management
  • data analysis
  • user log analysis
  • digital health
  • engagement
  • Ageing
  • chronic disease
  • multimorbidity
  • self-management

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