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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Feb 5, 2023
Open Peer Review Period: Feb 5, 2023 - Apr 2, 2023
Date Accepted: Jan 29, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

Sheng Y, Doyle J, Bond R, Jaiswal R, Dinsmore J

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

J Med Internet Res 2024;26:e46287

DOI: 10.2196/46287

PMID: 38546724

PMCID: 11009852

Augmenting K-means Clustering with Qualitative Data to Discover Engagement Patterns of Older Adults with Multimorbidity when using Digital Health Technologies: Findings from a Proof-of-Concept Trial

  • Yiyang Sheng; 
  • Julie Doyle; 
  • Raymond Bond; 
  • Rajesh Jaiswal; 
  • John Dinsmore

ABSTRACT

Background:

Multiple chronic conditions (multimorbidity) are becoming more prevalent among ageing 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 behaviour change.

Methods:

The aim of this study is to analyse how 60 older adults (average age=74 ± 6.4 [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. Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics such as age, gender and chronic health conditions, engagement outcomes and symptom outcomes of the different clusters that were discovered.

Results:

Three clusters were identified; the typical user, the least engaged user, and the highly engaged user. Our findings show that gender and the types of chronic conditions do not influence engagement. Even though all participants are older adults, the highly engaged user group still has the youngest average age (74.1) when compared to the other two groups (74.6 & 74.8). Whether the same device was used to submit different health and/or well-being parameters; the number of manual operations required to take a reading; and the daily routine of the participants were the three primary factors influencing engagement. Findings also indicate that higher levels of engagement may improve the participants’ outcomes (e.g., reduce symptom exacerbation, 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.


 Citation

Please cite as:

Sheng Y, Doyle J, Bond R, Jaiswal R, Dinsmore J

Augmenting K-Means Clustering With Qualitative Data to Discover the Engagement Patterns of Older Adults With Multimorbidity When Using Digital Health Technologies: Proof-of-Concept Trial

J Med Internet Res 2024;26:e46287

DOI: 10.2196/46287

PMID: 38546724

PMCID: 11009852

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