Top-funded Companies Offering Digital Behavior Change Interventions for the Prevention and Management of Type-2 Diabetes: A Systematic Search on Venture Capital Databases and Content Analysis of Interventions (Preprint)

BACKGROUND Advancements in technology offer new opportunities for the prevention and management of type 2 diabetes. Venture capital companies have been investing in digital diabetes companies that offer digital behavior change interventions (DBCIs). However, little is known about the scientific evidence underpinning such interventions or the degree to which those interventions leverage novel technology-driven automated developments such as conversational agents (CAs) or just-in-time adaptive intervention (JITAI) approaches. OBJECTIVE Our objectives were to identify the top-funded companies offering DBCIs for type 2 diabetes management and prevention, review the level of scientific evidence underpinning the DBCIs, identify which DBCIs are recognized as evidence-based programs by quality assurance authorities, and examine the degree to which these DBCIs include novel automated approaches such as CAs and JITAI mechanisms. METHODS A systematic search was conducted using two venture capital databases (Crunchbase Pro and Pitchbook) to identify the top-funded companies in type 2 diabetes prevention and management. Scientific publications relating to the identified DBCIs were identified via PubMed, Google Scholar, and the DBCI’s website and data regarding intervention effectiveness were extracted. The US CDC’s Diabetes Prevention Recognition Program (DPRP) was used to identify recognition status. The DBCIs’ publications, websites, and mobile applications were reviewed with regards to the intervention characteristics. RESULTS The 16 most-funded companies offering DBCIs for type 2 diabetes received a total funding of 2.4 billion USD as of June 15, 2021. Only four out of 50 identified publications associated with these DBCIs were fully powered randomized controlled trials (RCTs). One of those four RCTs showed a significant difference in HbA1c outcomes between the intervention and control group. However, all of the studies reported HbA1c improvements ranging from 0.2-1.9% over the course of 12 months. Six interventions were fully recognized by the DPRP to deliver evidence-based programs, and two interventions had a pending recognition status. Health professionals were included in the majority of DBCIs (81%, 13/16), whereas only 10% (1/10) of accessible apps involved a CA as part of the intervention delivery. Self-reports represented most of the data sources (62%, 74/119) that could be used to tailor JITAIs. CONCLUSIONS Our findings suggest that the level of funding received by companies offering DBCIs for type 2 diabetes prevention and management does not coincide with the level of evidence on the intervention effectiveness. There a is large variation in the level of evidence underpinning the different DBCIs and an overall need for more rigorous effectiveness trials and transparent reporting by quality assurance authorities. Currently, very few DBCIs use automated approaches such as CAs and JITAIs, limiting the scalability and reach of these solutions. Finally, more research is needed to establish the effectiveness of fully automated DBCIs in comparison to those offering human support.