Editorial
Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development – A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens
@ARTICLE{10.4108/eetpht.11.9416, author={Anthony Duffy and Sarah E Bennett and Lucy Yardley and Sylvain Moreno}, title={Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development -- A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens}, journal={EAI Endorsed Transactions of Pervasive Health and Technology}, volume={11}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2025}, month={6}, keywords={mHealth, digital health citizens, co-design, crowdsourcing, user experience, person-based design, sleep, teens}, doi={10.4108/eetpht.11.9416} }- Anthony Duffy
Sarah E Bennett
Lucy Yardley
Sylvain Moreno
Year: 2025
Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development – A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens
PHAT
EAI
DOI: 10.4108/eetpht.11.9416
Abstract
INTRODUCTION: Co-design has become a fundamental pillar of formative digital health research. Typically, this approach involves in–person workshops that involve a rich but limited amount of data. Virtually crowdsourcing co-design, however, provides the promise of rapid and vastly increased data. This is a novel, exploratory approach in mHealth design that may appease common health research concerns surrounding reliability and validity, whilst providing swifter feedback to meet product development timelines. OBJECTIVES: The objective of this exploratory single case study was to explore the virtual, crowdsourced, co-design of Sleep Solved, an educational mHealth sleep app designed with teens. In doing so, we wished to learn which virtual methods were used to engage teens in the co-design and to explore how these virtual co-design methods can be adapted for large-scale ideation and testing. METHODS: We conducted an enquiry-based iterative case study utilising the Bayazit 3-stage model. 85 teens participated over 11 months. Data was thematically analysed over several design iterations. RESULTS: Rapid virtual feedback allowed for quick pivots in a short time frame. Four stages of feedback from teens led to iterative changes to scientific information contextualisation and user experience, from lo-fidelity mock-ups through to a coded app beta. CONCLUSION: The co-design of Sleep Solved exemplified the potential of virtually crowdsourcing teens in mHealth. Key to this evolution will be the ability to leverage big data utilising AI and machine learning approaches to data collation and synthesization, such that meaningful and contextual findings can be applied in line with software development timelines.
