• Phase I, II, III: Remote pre-screening to identify eligible participants and enrich the target population based on real-world behaviors/ symptoms

Faster recruitment leads to reduced recruitment costs and trial delays due to efficient, remote identification of eligible participants

Increased access to representative patient populations due to decentralized recruitment

Better identification of target population due to increased sensitivity enabled by frequent, objective, and continuous measurement

• Digital measures are valid for inclusion/exclusion criteria
  
  
• Seamless integration with recruitment workflows
  
  
• High participant adherence during screening

For digital endpoints at all stages, the following must be met: 

• High participant adherence to device use and the V3+ framework

• Modality.AI – Multimodal digital health technologies (DHTs) allow for more efficient and less costly recruitment and reduced patient burden with decentralized clinical trials: View case study

• Roche – Estimating the benefits of digital and plasma-based pre-screening in Alzheimer’s Disease trial recruitment: View case study

• Phase I & II: Early detection of adverse events
• Phase IV: Long-term safety surveillance

Near real-time safety monitoring 

Comprehensive and earlier detection of adverse events (subtle, cumulative, or transient events)

Enhanced participant protection and improved adverse event resolution due to earlier detection and intervention

Improved patient experience of monitoring at home as compared to in-clinic

• Identify appropriate safety signals 
  
  
• Establish feasibility and validity of this signal to be collected remotely using digital health technologies (DHTs)
  
  
• Clear safety thresholds and event notifications
  
  
• A triage protocol to review safety signals and weed out false positives
  
  
• Robust data monitoring infrastructure

For digital endpoints at all stages, the following must be met: 

• High participant adherence to device use and the V3+ framework

• Next-generation implementation of chimeric antigen receptor T-cell therapy using digital health: View publication

• Smart devices to measure and monitor QT intervals: View publication

• Phase I: Disease extension cohorts
• Phase II POC: Early efficacy proof in smaller populations
• Phase III: Confirmatory evidence of efficacy 
• Phase IV: Real-world insights

Higher frequency, continuous, and ecologically valid measurement of treatment effects

Earlier go/no-go decisions due to improved data-driven decision-making

Reduced recall bias through objective, passive, and real-time data collection

Improved ability to detect treatment effects via more frequent, more precise, and/or less variable objective measures 

Remotely collected, objective measures of additional treatment benefits

Reduced sample size due to improved ability to detect treatment effect

Reduced trial time due to the ability to detect treatment effects sooner

• Efficacy requirement is pre-specified in the protocol and statistical analysis plan
  
  
• Regulatory alignment on endpoint relevance and use for phase III studies

For digital endpoints at all stages, the following must be met: 

• High participant adherence to device use and the V3+ framework

• Impacts on study design when implementing digital measures in Parkinson’s disease-modifying therapy trials: View publication
  
  
• Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball: View publication
  
  
• Ponsegromab for the treatment of cancer cachexia: View publication

• Roche – Estimating the benefits of digital and plasma-based pre-screening in Alzheimer’s Disease trial recruitment: View case study

• Merck – Advancing digital biomarkers to accelerate Parkinson’s disease drug development: View case study

• ActiGraph – Enhancing clinical insights: De-risking traditional outcome measures in Bellerophon Therapeutics REBUILD study: View case study

• AbbVie – Value of novel digital endpoints in late-stage trials: View case study

• Koneksa – Simulator of study power for Parkinson’s Disease trials: View case study

• Modality.AI – Multimodal digital measures demonstrate greater responsiveness and sensitivity to disease progression and treatment effects than traditional clinical scales: View case study

• Roche – Driving innovation in Huntington’s Disease by tracking progression through at-home digital measurements: View case study

• Sysnav – Advancing nano-rare disease treatment with Syde technology in clinical trials: View case study

• VivoSense – Accelerated trial setup & operations: A case study in Cystic Fibrosis to illustrate the ROI of improved data availability: View case study

• Phase IV: Real-world evidence generation for safety, effectiveness, and new insights

Potential for additional label claims through the collection of more comprehensive and continuous data allowing sponsors to investigate additional therapeutic benefits on measures meaningful to patients

Informs post-market recommendations, such as off-label use and new indications

Informs market access and reimbursement by generating objective, real-world data

• Long-term participant retention and digital health technology adherence
  
  
• Integration with healthcare data systems (EHRs, registries)
  
  
• Scalable infrastructure for global data collection
  
  
• Ongoing regulatory engagement to align real-world evidence use

For digital endpoints at all stages, the following must be met: 

• High participant adherence to device use and the V3+ framework

• Developing a novel measurement of sleep in rheumatoid arthritis: Study proposal for approach and considerations: View publication