Lessons Learned
Our multi-institutional/multi-disciplinary team has learned 3 key lessons conducting the m-RESIST project. We summarize them aiming to inform and prepare researchers who are planning an mHealth study.
First, we have to consider that levels of familiarity with technology vary, particularly for caregivers. This has a major impact on the engagement with the mHealth resource. Data that guide the decision about the devices should be collected ahead of time, in order to answer questions regarding using owned phones or not, choosing the best support model, programming operating system etc. Furthermore, project plan must include time and effort needed to train participants on using the devices.
Second, the process of translating clinical procedures and interventions into technological forms is a challenge. The goals of each intervention and the chain of actions needed to achieve them, should be clearly defined. Thus, technologists should have an idea about the aim of each feature. Focused weekly meetings and intensive sessions can be helpful in order to work in alignment.
Finally, the transition from idea to a product-as-a-service business model is more complicated than expected. The implementation of a new mHealth solution in different clinical environments requires a predefined planning to detail resources and time effort. Healthcare system is not still well prepared for mHealth services that enable communication between professionals, shared decision-making processes and a tailored patient-centered plan.
The limitations that m-RESIST project met, refer to the sample’s size, its non-randomised design and the short follow-up period that, although, it was adequate to accommodate feasibility studies, yet, it was not enough to generalize results and analyse effectiveness. The current status of the healthcare technological systems have also been a barrier in m-RESIST solution deployment. We consider that the next steps in this project should be to perform a cost-effectiveness clinical randomised trial, that includes the most relevant variables accounting for complexity in treatment-resistant schizophrenia according to m-RESIST predictive model results, and to provide a communication tool in healthcare technological system where professionals can communicate, share decisions making plan and set tailored patient-centered interventions.