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.