TY - GEN
T1 - The Virtual Co-design of Sleep Solved – A Case Study of an Educational Sleep App Designed with Teens
AU - Duffy, Anthony
AU - Bennett, Sarah E.
AU - Yardley, Lucy
AU - Moreno, Sylvain
PY - 2025/4/23
Y1 - 2025/4/23
N2 - Background: Sleeplessness is an emerging epidemic amongst young people. Numerous apps exist to mediate sleep problems using a variety of CBT-i workshop design approaches. Virtually crowdsourcing co-design, however, provides the promise of rapid and vastly increased data. The rapid co-design of mHealth apps is an important part of the emerging big data, digital health citizen era. Objective: This exploratory case study explored the virtual, crowdsourced co-design of Sleep Solved—an educational mHealth sleep app designed with teens, to learn which virtual methods were used to engage teen co-designers and how these methods can be scaled up. 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.
AB - Background: Sleeplessness is an emerging epidemic amongst young people. Numerous apps exist to mediate sleep problems using a variety of CBT-i workshop design approaches. Virtually crowdsourcing co-design, however, provides the promise of rapid and vastly increased data. The rapid co-design of mHealth apps is an important part of the emerging big data, digital health citizen era. Objective: This exploratory case study explored the virtual, crowdsourced co-design of Sleep Solved—an educational mHealth sleep app designed with teens, to learn which virtual methods were used to engage teen co-designers and how these methods can be scaled up. 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.
KW - co-design
KW - crowdsourcing
KW - digital health citizens
KW - mHealth
KW - person-based design
KW - sleep
KW - teens
KW - user experience
UR - https://www.scopus.com/pages/publications/105004254420
U2 - 10.1007/978-3-031-85575-7_1
DO - 10.1007/978-3-031-85575-7_1
M3 - Chapter in a published conference proceeding
AN - SCOPUS:105004254420
SN - 9783031855740
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 3
EP - 27
BT - Pervasive Computing Technologies for Healthcare, Pervasive Health 2024, Proceedings
A2 - Kondylakis, Haridimos
A2 - Triantafyllidis, Andreas
PB - Springer
CY - Cham, Switzerland
T2 - 18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024
Y2 - 17 September 2024 through 18 September 2024
ER -