Advancing the use of sensor-based digital health technologies (sDHTs) for the early detection and monitoring of mental health symptoms

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.

Together, we are identifying the key aspects of early symptom development in people with depression, anxiety, and psychosis and provide insight into which sDHTs are the most appropriate for capturing these signals.

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:

Identify which behavioral and physiological signs and symptoms are the most crucial to monitor for individuals with depression, anxiety, and psychosis through sDHTs

Define the characteristics of effective sDHTs to measure symptoms, ensuring they are clinically relevant and capable of improving mental health outcomes, regardless of location or socioeconomic status

Develop a report with important factors, data elements, and research questions for future studies to develop and validate the performance of algorithms based on sensor data to aid in the early detection of mental health

Recommend strategies for sharing the findings with clinical and digital innovation communities to improve the use and development of sDHTs for mental health

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.

Funded by Wellcome

Our collaborator

We are honored to spearhead this work alongside experts from the UCLA Depression Grand Challenge.

Driving innovation with an expanded portfolio

This project is part of DATAcc’s new and growing portfolio of work to address underserved areas of healthcare in the digital era. This portfolio of work strategically leverages the advancing maturity of sDHTs to elevate the ambition and impact of digitally derived measures.

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.

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