How Digital Health Technologies are Reshaping Clinical Development

Despite rising trial costs and increasingly complex development timelines, one trend is clear: digital health technologies are rapidly moving from pilot programs to core components of global clinical trial strategy. With regulators now actively encouraging patient-centric designs and real-world data, sponsors are shifting focus from if to how DHTs should be integrated. 

DHTs are being embraced by sponsors, regulators, and investigators for their potential to streamline operations, reduce patient burden, and generate richer, real-world data. With growing support from agencies like the FDA and EMA, the pressure is mounting for clinical teams to modernize their approach. But what does this look like in practice? And where do the biggest gains, and risks, lie? 

Why are DHTs gaining ground? 

The push toward DHTs is a direct response to well-known pain points in clinical development: protocol amendments, participant dropout rates, slow enrollment, and high operational costs. 

The COVID-19 pandemic accelerated the use of DHTs, as sponsors had to find alternatives to in-clinic visits. However, this shift uncovered a longer-term opportunity: trials could successfully be run remotely, opening a new means of data capture and patient engagement. 

Thus, regulators began issuing guidance encouraging the use of DHTs to improve access, inclusion, and data quality. The FDA’s Digital Health Center of Excellence and the EMA’s support of digital endpoints like SV95C in Duchenne trials are signals that they expect sponsors to leverage digital endpoints. 

What can DHTs measure? 

DHTs include wearables, smartphone apps, connected medical devices, ambient sensors, and ingestible or implantable trackers. These tools collect digital measures, which are objective data that can be captured continuously in the real world, rather than at isolated clinic visits. 

Common data captured by DHTs include: 

• Cardiac data: heart rate, heart rate variability, arrhythmia detection 

• Mobility data: gait speed, stride length, daily step counts 

• Sleep data: duration, efficiency, wake frequency 

• Respiratory data: breathing rate, cough detection 

• Cognitive or behavioural data: touchscreen patterns, voice changes, reaction times 

These data start as digital measures, and once properly validated, can be either digital biomarkers or passively-collected clinical outcome assessments (COAs), both of which can then be translated into digital endpoints. 

Difference between digital biomarkers, passively collected COAs, and digital endpoints 

Digital biomarkers are objective, quantifiable indicators of biological processes. They’re often used to monitor disease progression, identify subtypes, or stratify patients. For example, changes in gait speed could signal the onset of a neurodegenerative condition. 

Passively-collected COAs are clinical outcomes that measure how a person feels, functions or survives. These digitally-derived COAs are often used to obtain objective measures that compliment more traditional actively-collected COAs such as questionnaires. For example, a wrist-worn monitor may track number of nighttime awakenings, which can be paired with a PRO asking the patient about their perceived sleep quality. 

Digital endpoints are formal outcomes used to evaluate the effect of a treatment. They must be defined, validated, and statistically analysed to support regulatory decisions. For example, reduction in nocturnal waking captured by a wearable device as evidence of treatment efficacy in an insomnia trial is a digital endpoint. In sum, biomarkers and COAs help explain what’s happening while endpoints help prove that the intervention worked. 

Digital endpoint validation 

The V3 framework+, which stands for verification, analytical validation, and clinical validation, provides a structured path to confirm that: 

1. The sensor or tool accurately captures the raw signal (Verification) 

2. The algorithm processes that signal into a meaningful measure (Analytical Validation) 

3. The measure is relevant to the disease and responds to treatment (Clinical Validation) 

4. The sensor or tool demonstrates the ability to capture the measure of interest, taking into consideration critical factors such as ease of use, clinical trial feasibility and user experience and satisfaction 

Without this level of rigor, digital endpoints may be deemed as exploratory or dismissed during regulatory review. 

Example: stride velocity in Duchenne muscular dystrophy (DMD) 

One of the clearest examples of a digital endpoint advancing the field is SV95C: stride velocity 95th centile, the first wearable-derived primary digital endpoint for use in Duchenne Muscular Dystrophy trials. Captured via a fit-for-purpose ankle-worn sensor, this measure reflects a patient’s best natural walking performance in everyday life. 

Traditional assessments like the six-minute walk test (6MWT) are captured only at a moment in time, and typically, during a clinic visit. The SV95C, by contrast, offers high-frequency, real-world data that’s more sensitive to change and aligned with how patients actually function. 

The 2023 EMA acceptance of SV95C as an outcome measure was a milestone in the regulatory acceptance of digital endpoints. 

What is the value of DHT in trials? 

According to new analysis, trials that integrate DHTs can reduce timelines by 3-5 months, cut enrolment sizes by 11-16%, and lower the need for protocol amendments. These efficiencies add up to a real and measurable financial benefit: 

• Phase II trials using digital endpoints can see an increase of $2.1M–$3.3M in expected net present value (eNPV) 

• Phase III trials can see ROI gains of 5–20x the original investment, depending on implementation costs 

Beyond financial metrics, DHTs support broader access, higher retention, and greater diversity, which are all outcomes increasingly prioritized by both regulators and sponsors. 

However, despite the upside, implementation is not without challenges. Many organizations underestimate the infrastructure required to support DHTs: from data governance and interoperability to patient onboarding and site training. 

Sponsors also face a crowded and fast-moving vendor landscape. Without clear guidance, there’s a risk of selecting tools that are not fit-for-purpose. Here, strategic partnerships can make the difference between successful deployment and costly missteps. 

What comes next? 

As digital measures continue to gain regulatory traction, the burden of proof is shifting. Sponsors who treat DHTs as a side experiment may find themselves behind. Those who invest in endpoint strategy, validation, and operational readiness will be better positioned to run faster, more inclusive, and more efficient trials. The future of clinical development won’t just be site-based or decentralized. Rather, it will be digital-first.

Authors
Caprice Sassano, MPH - Senior Outcomes Researcher, Outcome Measures, ICON
Keith Thomas - Senior Director, Clinical Systems, Mapi Research Trust

Topic: Next-Gen Tools