Journal
JMIR PUBLIC HEALTH AND SURVEILLANCE
Volume 7, Issue 2, Pages -Publisher
JMIR PUBLICATIONS, INC
DOI: 10.2196/25037
Keywords
user-centered design; community; social network analysis; United Kingdom; mobile phone
Categories
Funding
- England European Regional Development Fund as part of the European Structural and Investment Funds Growth Programme 2014-2020
- South West Academic Health Science Network
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This study experimented with creating Smartline Archetypes based on data from surveys, household measurements, sensors, and interviews to support the development of eHealth and eWell-being products, processes, and services. The personas proved to be engaging and accessible to a wider group of stakeholders, enhancing research engagement, innovation, and impact. Through the adoption of widely used tools, research projects can achieve greater policy and practical impact while becoming more transparent and open to the public.
Background: Personas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale. Objective: This study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community. Methods: The project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health-related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes. Results: The Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact. Conclusions: Through the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public.
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