Abstract
Background: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital
health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring
of health and well-being, supporting a better understanding of the disease, and encouraging behavior change.
Objective: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity
engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately
12 months.
Methods: Principal component analysis and clustering analysis were used to group participants based on their levels of
engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes,
and symptom outcomes of the different clusters that were discovered.
Results: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group.
Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors
influencing engagement were whether the same device was used to submit different health and well-being parameters, the number
of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher
levels of engagement may improve the participants’ outcomes (eg, reduce symptom exacerbation and increase physical activity).
Conclusions: The findings indicate potential factors that influence older adult engagement with digital health technologies for
home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes
related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of
home-based digital health technologies may improve symptom management and physical activity outcomes for older adults
self-managing multimorbidity.
Original language | English |
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Article number | e46287 |
Pages (from-to) | 1-23 |
Number of pages | 23 |
Journal | Journal of Medical Internet Research |
Volume | 26 |
Issue number | 1 |
Early online date | 28 Mar 2024 |
DOIs | |
Publication status | Published (in print/issue) - 28 Mar 2024 |
Bibliographical note
©Yiyang Sheng, Raymond Bond, Rajesh Jaiswal, John Dinsmore, Julie Doyle. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.03.2024.Keywords
- Digital health
- clustering
- k-means clustering
- digital health
- engagement
- chronic disease
- multimorbidity
- aging
- Data Accuracy
- Humans
- Digital Health
- Multimorbidity
- Aging
- Aged
- Cluster Analysis