Personas for Better Targeted eHealth Technologies: User-Centered Design Approach

Iris Ten Klooster; Jobke Wentzel; Floor Sieverink; Gerard Linssen; Robin Wesselink; Lisette van Gemert-Pijnen

doi:10.2196/24172

Personas for Better Targeted eHealth Technologies: User-Centered Design Approach

JMIR Hum Factors. 2022 Mar 15;9(1):e24172. doi: 10.2196/24172.

Authors

Iris Ten Klooster¹, Jobke Wentzel^{1

2

3}, Floor Sieverink⁴, Gerard Linssen⁵, Robin Wesselink⁶, Lisette van Gemert-Pijnen¹

Affiliations

¹ Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands.
² Department of Health and Social Studies, Windesheim University of Applied Sciences, Zwolle, Netherlands.
³ Research Group IT Innovations in Health Care, Windesheim University of Applied Sciences, Zwolle, Netherlands.
⁴ Carintreggeland, Hengelo, Netherlands.
⁵ Hospital Group Twente, Almelo and Hengelo, Netherlands.
⁶ Location AMC, Amsterdam University Medical Centers, Amsterdam, Netherlands.

PMID: 35289759
PMCID: PMC8965674
DOI: 10.2196/24172

Abstract

Background: The full potential of eHealth technologies to support self-management and disease management for patients with chronic diseases is not being reached. A possible explanation for these lacking results is that during the development process, insufficient attention is paid to the needs, wishes, and context of the prospective end users. To overcome such issues, the user-centered design practice of creating personas is widely accepted to ensure the fit between a technology and the target group or end users throughout all phases of development.

Objective: In this study, we integrate several approaches to persona development into the Persona Approach Twente to attain a more holistic and structured approach that aligns with the iterative process of eHealth development.

Methods: In 3 steps, a secondary analysis was carried out on different parts of the data set using the Partitioning Around Medoids clustering method. First, we used health-related electronic patient record data only. Second, we added person-related data that were gathered through interviews and questionnaires. Third, we added log data.

Results: In the first step, 2 clusters were found, with average silhouette widths of 0.12 and 0.27. In the second step, again 2 clusters were found, with average silhouette widths of 0.08 and 0.12. In the third step, 3 clusters were identified, with average silhouette widths of 0.09, 0.12, and 0.04.

Conclusions: The Persona Approach Twente is applicable for mixed types of data and allows alignment of this user-centered design method to the iterative approach of eHealth development. A variety of characteristics can be used that stretches beyond (standardized) medical and demographic measurements. Challenges lie in data quality and fitness for (quantitative) clustering.

Keywords: clustering; eHealth; heart failure; personas; user-centered design.

©Iris ten Klooster, Jobke Wentzel, Floor Sieverink, Gerard Linssen, Robin Wesselink, Lisette van Gemert-Pijnen. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 15.03.2022.