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
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
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.
co-design, crowdsourcing, digital health citizens, mHealth, person-based design, sleep, teens, user experince, user experience
Duffy, Anthony
75c141ac-593c-48a3-8277-08110259d4f2
Bennett, Sarah E.
409e3275-0202-46b1-9535-086e42b2f76c
Yardley, Lucy
64be42c4-511d-484d-abaa-f8813452a22e
Moreno, Sylvain
e26542f8-7600-4358-a80d-b2f9651fce19
5 June 2025
Duffy, Anthony
75c141ac-593c-48a3-8277-08110259d4f2
Bennett, Sarah E.
409e3275-0202-46b1-9535-086e42b2f76c
Yardley, Lucy
64be42c4-511d-484d-abaa-f8813452a22e
Moreno, Sylvain
e26542f8-7600-4358-a80d-b2f9651fce19
Duffy, Anthony, Bennett, Sarah E., Yardley, Lucy and Moreno, Sylvain
(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.
EAI Endorsed Transactions on Pervasive Health and Technology, 11.
(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.
Text
9416-PHAT
- Version of Record
More information
Accepted/In Press date: 17 April 2025
e-pub ahead of print date: 2 June 2025
Published date: 5 June 2025
Keywords:
co-design, crowdsourcing, digital health citizens, mHealth, person-based design, sleep, teens, user experince, user experience
Identifiers
Local EPrints ID: 504305
URI: http://eprints.soton.ac.uk/id/eprint/504305
ISSN: 2411-7145
PURE UUID: f0cefa7b-0fd1-402d-a362-a760d7c24f06
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Date deposited: 04 Sep 2025 16:33
Last modified: 05 Sep 2025 01:36
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Contributors
Author:
Anthony Duffy
Author:
Sarah E. Bennett
Author:
Sylvain Moreno
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