An estimated 970 million people worldwide are affected by a mental health condition, with anxiety and depression being the most prevalent. This staggering number highlights the urgent need to improve health outcomes for these individuals.
The Digital Health Measurement Collaborative Community (DATAcc) by the Digital Medicine Society (DiMe) – in collaboration with the UCLA Depression Grand Challenge and with funding support from Wellcome – is assessing how sDHTs may be leveraged to address this pressing issue.
We thank Wellcome and the UCLA Grand Depression Challenge for their support and collaboration. This research will also be informed by individuals with mental health conditions, their care partners, and healthcare providers.
We’re identifying the key early symptoms of depression, anxiety, and psychosis and the most effective sDHTs to monitor these signals. These capabilities will enable more precise timing and selection of treatments, thus facilitating personalized care strategies that enhance outcomes and patient care.
The research
We aim to identify the main challenges that must be addressed to realize the potential utility of sDHTs in mental health research. Through interviews with individuals affected by mental health conditions, literature reviews, and expert consensus, we will:
This project will clarify the potential role of sDHTs in mental health research and clinical settings and, specifically, how they can be applied to the early detection of symptoms.
We aim to help individuals with mental health conditions, their care partners, and healthcare providers determine which interventions will work best and when to administer them.
Our funder
We are immensely grateful to be Wellcome’s trusted organization for this crucial work.
Our collaborator
We are honored to spearhead this work alongside experts from the UCLA Depression Grand Challenge.
DATAcc’s recently announced projects, Advancing Digital Capabilities to Enable Digital Risk Prediction for Cytokine Release Syndrome (CRS) and Developing a Risk Prediction Engine for Relapse in Opioid Use Disorder (OUD) projects are also among this portfolio of work to actively embrace the progress and evolution of sDHTs to serve all of those in which our industry exists to care for.
