DATACC BY DIME PROJECT

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Measuring what matters in common mental health disorders with a digital measures set

Built on DATAcc by DiMe’s prior work to advance the use of sensor-based digital health technologies (sDHTs) for mental health research and clinical practice, commissioned by Wellcome, DATAcc by DiMe convened leaders across industry, advocacy, research, and regulation to develop a set of digital measures, plus emerging measures that span multiple common mental health disorders. Grounded in clinical practice, current evidence, and expert input, this work sets the stage to measure what matters across mental health conditions, rather than with disease-specific instruments.

“Mental health care has long been constrained by measurement tools that capture only a narrow slice of what patients actually experience between visits, a shared, sensor-based foundation for understanding sleep, activity, and daily patterns gives clinicians and developers a more honest picture of how people are doing and how treatments are working. For the millions of people seeking mental health support virtually, that precision translates directly into better, more personalized care.”

-Aarthi Padmamabhan | PhD, Senior Director of Research | Talkspace

Resources built for you

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Clinical researchers & development leads

Start with the conceptual model to identify which mental health meaningful aspects of health (MAH) can be captured in real-world settings, and which digital measures can capture each one.

Explore the application guides on endpoints and phenotyping, along with the measure ontologies and standards, to support consistent study design and data collection approaches.

Clinician holding a mobile device while analyzing medical data.

Technology developers

Use the conceptual model to identify which real-world measures your technology should capture and why they matter clinically.

Align your data capture with the ontology and data standards so outputs are interoperable across studies, sponsors, and care settings.

Reference the application guides to see how these digital measures translate into endpoints and phenotypes, and what your product needs to support.

Care delivery teams

Start with the conceptual model to identify which digital measures may complement clinical judgement in longitudinal care.

Explore the application guides on endpoints and phenotyping to see which measures are ready to use today, where evidence is still emerging, and how they may support care and value-based payment programs.

Reference partner case studies from Lightfully Behavioral Health, Videra Health, and Healthesphere to see how care teams are using these measures to monitor patients between visits and inform clinical and program decisions.

“For the first time, we can begin to understand what happens between sessions or after discharge, and this is really exciting… if we validate these measures rigorously and we translate them thoughtfully and implement them ethically, we can transform mental health to be… from reactive to proactive, from episodic to continuous, and from generalized [to] personalized, and that is the future that I want to be part of.”

– Nicole Siegfried | PhD, CEDS, Chief Clinical Officer | Lightfully

“One of the most valuable aspects of this project was bringing together clinicians, researchers, technologists, and people with lived experience to create practical, implementation-focused guidance that helps move digital mental health beyond isolated data collection toward more interoperable and clinically actionable models of care.”

– Ozair Bajwa | Founder & CEO | HealthEsphere

“AI-analyzed video and voice data, collected alongside structured clinical assessments, can generate objective behavioral signals that complement traditional rating scales and map to the CMHD core measures. This helps to capture what happens between visits without replacing clinician judgment.”

– Brett Talbot | PhD, Chief Clinical Officer, Co-Founder | Videra Health
View the Videra Health case study to see how

Project partners

Meet the leading industry partners who are bringing clarity to what matters most in mental health assessment

Case studies

Multimodal Digital Phenotyping for Whole-Person Mental Health Monitoring

Linus Health Speech Vitals: Assessing Speech and Language in Schizophrenia

Integrating Wearables and Patient Data into Longitudinal Mental Health Care

Using Digital Education and Longitudinal Measurement to Advance Community-Based Fentanyl Prevention

Get started

The digital measures set is the starting point. It identifies which measures are mature enough to operationalize today, where evidence is still emerging, and where no measure is yet recommended. Use the conceptual model to map measures to the meaningful aspects of health that matter to people living with common mental health disorders.

Go to digital measures