DATACC BY DIME PROJECT

The digital measures of common mental health disorders

This conceptual model defines meaningful aspects of health, associated concepts of interest, and a set of digital measures for common mental health disorders (CMHD).* The eight CMHD included in this model were selected based on their high prevalence and global disease burden.

*Informed by DiMe’s Digital Measures that Matter Framework.

The eight common mental health disorders:

Depression
Anxiety
Bipolar disorder
Schizophrenia
Post-traumatic stress disorder (PTSD)
Attention-deficit/hyperactivity disorder (ADHD)
Autism spectrum disorder (ASD)
Obsessive-compulsive disorder (OCD)

CONCEPTUAL MODEL

This conceptual model is a practical map of where digital measurement can meaningfully capture symptoms, functioning, and lived experience in mental health. Built through a Delphi process with 26 experts and a structured literature synthesis spanning clinical research, patient perspectives, and disorder-specific expertise.

Download Conceptual Model (pdf)

Measure ontologies

Consistent data standards are essential for advancing research and care in common mental health disorders. To ensure consistent data naming and structure across collection, processing, and sharing, the table below provides you with a curated set of established ontologies for each of the digital measures to standardize your data coding.

The table below aggregates relevant ontologies, data collection methods and established interoperability standards for the CMHD digital measures that keep research rigorous, comparable, and scalable.

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
Diminished mental health (HPO) Atypical behavior (HPO) Finding of a physical location of a patient (SNOMED) Instrumental activity of daily living (SNOMED)	DiMe: Sleep regularity index	IEEE 11073 Personal Health Device (PHD) OASIS Standard: Classification of Everyday Living	GLOBEM Dataset

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
Cognitive impairment (Human Phenotype Ontology) activity of daily living (SNOMED)	Cognitive Atlas (COGAT)Cognitive Paradigm Ontology (CogPO)NeuroPsychological Testing Ontology (NPT)Neurocognitive Integrated Ontology (NIO)		NIH toolbox

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
Physical activity finding (SNOMED)Lack of sufficient daily physical activity (Human Phenotype Ontology)	DiMe: Core measures of physical activity Physical Activity Ontology (PACO)	IEEE Standard for Open Mobile Health Data—Representation of Metadata, Sleep, and Physical Activity Measures	PROMPT Study

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
Sleep disturbances (Human Phenotype Ontology)Findings related to sleep (SNOMED)	DiMe: Core measures of sleep AASM Manual for the Scoring of Sleep and Associated Events	IEEE Standard for Open Mobile Health Data—Representation of Metadata, Sleep, and Physical Activity Measures	World Sleep Society Recommendations consumer devices NSRR (National Sleep Research Resource)PROMPT Study

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
	OLiA (Ontologies of Linguistic Annotation	ISO 24612:2012 Language resource management — Linguistic annotation framework (LAF)Emotion Markup Language (EmotionML)ISO/IEC 14496‑2 (MPEG‑4 Part 2)	Consensus-Based Definitions for Vocal Biomarkers: The International VOCAL Initiative The Process to Identify a Vocal Biomarker Methodological framework for collection and analysis of repeated speech samples TalkBank Bridge2AI

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
	Heart Rate VariabilityHeart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use Publication guidelines for HRV Guideline for Electrodermal Activity Vital sign ontology	IEEE 11073 Personal Health Device (PHD)	WESAD PROMPT Study

Clinical documentation & phenotype ontologies	Digital measurement & domain specific ontologies	Data interoperability	Databases, tools & resources
	Facial Action Coding System (FACS)ibug 68 facial point standard	ISO/IEC 14496‑2 (MPEG‑4 Part 2)	OpenFace Minimal reporting guideline for research involving eye tracking (2023 edition)

We welcome your expertise. If you have a resource or ontology to recommend for inclusion, please reach out to our team.

Real-world applications of digital measures for CMHD

Meaningful aspects of health and digital measures of CMHD provide clinical development programs with a strong foundation by enabling the capture of treatment effects and patients’ experiences more continuously and at a finer level of detail across shared symptoms. They can reduce technical failure rates in clinical development programs and shorten the path to market. The digital measures set can establish detailed digital phenotypes that move beyond the disease-based model and may enable more precise subtyping of mental health conditions in pre- and post-market settings.

These examples detail how the digital measures can be applied across different common mental health disorders.

CASE STUDY

Proposed protocol to capture speech and language changes in a schizophrenia clinical trial, including data and metadata collection strategies

→ Go to Case Study

PUBLICATION

Physical activity and sleep measures used for longitudinal assessment of depression and anxiety symptoms

→ Go to Publication

Built on evidence

The digital measures for CMHDs build on the foundation of traditional patient- and clinician-reported outcomes, enhancing well-validated yet cumbersome periodic assessments with objective, passively collected, high-frequency data. When fit-for-purpose, they can enable new insights into mental health disorders that are difficult to observe using conventional methods alone.

The conceptual model is underpinned by multiple sources of evidence, including a structured literature review and a Delphi-informed expert process.

QUALITATIVE UMBRELLA REVIEW

Download dataset (xlxs)

We extracted, coded, and synthesized patient and caregiver quotes from published reviews across CMHD to derive meaningful aspects of health (MAH). This dataset can support further patient-centered research and product development as a comprehensive synthesis of lived experiences across the literature.

33

Reviews from

690

Assessed articles

38

Countries

Age range: 9-80 years old

Study sample size: 52 – 1695 participants

76.5% Lived-experienced individuals

2.9% Caregiver

20.6% Both

SURVEY

A Delphi-informed process used iterative rounds of consensus-building to define the set of digital measures for CMHD.

Delphi survey results (PDF)

REPORT

This report’s foundational research established the technical groundwork for sDHT development and use in mental health.

Read report

Ready to apply these measures?

Explore our resources to build defensible endpoints for clinical trials, define patient phenotypes by symptoms and behaviors rather than diagnosis, and apply digital measures in care delivery and post-market settings.

Go to Application Guides