Measurement Strategy First: Designing Fit-for-Purpose Digital Endpoints

Clinical development has never had more tools at its disposal. Wearables, sensors, mobile apps, imaging, biomarkers, and real-world data streams are increasingly used to generate digital endpoints in clinical trials, enabling high-frequency and longitudinal data collection at unprecedented scale.

Yet despite this expansion in data collection, development outcomes have not improved at the same pace. Late-stage trial failure rates remain stubbornly high, and many programs still struggle to demonstrate clear treatment effects even when underlying biology suggests a drug is doing something meaningful.

This gap points to a less visible but fundamental issue: how measurement decisions are made.

Too often, trials fail not because a therapy lacks efficacy, but because the measurement strategy was never designed to detect the effect in the first place.

The Hidden Problem With Tool-First Endpoint Design

A familiar pattern still shapes many endpoint strategies:

• A new technology becomes available. 

• A digital signal looks promising or convenient. 

• A device or platform is selected. 

• Endpoints are then defined around what that tool can capture.

This tool-first approach is understandable. Technologies are tangible. They come with validation packages, dashboards, and a sense of progress. But starting with the tool introduces a structural risk: measurement strategy becomes constrained by availability rather than guided by biology.

When endpoints are chosen because they are easy to deploy or widely used, several downstream problems emerge:

• Measures may not reflect how the disease actually progresses

• Signals may be insensitive to early or subtle treatment effects, undermining signal detection

• Variability and confounding factors can overwhelm true signal

• Data accumulates without becoming interpretable evidence

The result is often more data, but less clarity.

Measurement Strategy First: Designing Endpoints Around Disease Biology

A measurement-first approach reverses this order. Instead of asking “What technology should we use?”, the starting question becomes: What needs to be measured to answer the clinical question with confidence?

A measurement strategy first approach begins by defining what should be measured based on:

• Disease biology and pathophysiology

• Progression dynamics over time

• Therapeutic mechanism of action

• The concept of meaningful clinical change

Only after these elements are defined does technology enter the conversation, selected specifically to serve the measurement strategy rather than define it. This disease-centric endpoint design is foundational to measurement science. Endpoints are not administrative requirements or data outputs. They are scientific instruments that determine whether a trial can reveal true treatment effects.

Where Digital Endpoints Add Value in Clinical Trials (and Where They Don’t)

Digital measurement can be powerful when applied with intent. High-frequency data can improve precision and help characterize within-subject variability over time. Real-world assessment can capture aspects of disease that episodic clinic visits miss. Passive or low-burden tools can improve sustainability and inclusivity across digital endpoint use cases.

But digital endpoints are enablers, not the strategy. Without a clearly defined measurement approach, digital signals can become noisy, disconnected, or difficult to interpret. Industry and regulatory analyses have repeatedly highlighted endpoint sensitivity and endpoint interpretability as persistent challenges in clinical development. Reviews of trial outcomes and regulatory feedback show that programs often struggle to demonstrate efficacy not because biological activity is absent, but because endpoints lack the sensitivity or clarity needed to detect meaningful change. Recent analyses of FDA regulatory decisions and industry trial design trends underscore that deficiencies in efficacy data, often linked to endpoint selection and interpretation, remain a common reason for delayed approvals or development setbacks.

Digital tools do not solve this problem automatically. Measurement strategy does. When digital endpoints are selected because they align with disease biology and therapeutic intent, they amplify signal. When they are selected because they are available or novel, they often add complexity without clarity.

From Measurement to Learning: A Model-Driven Loop

A measurement-first strategy does not end once the protocol is finalized. When designed correctly, measurement becomes part of a continuous learning system, not a static trial artifact. Measurement strategies are designed around a therapeutic hypothesis, data is captured with reliability and longitudinal consistency, and signals are interpreted through models that account for variability and context.

Insights then feed back into refined measurement, endpoints, and future study design. This model-driven loop enables structured learning during execution, not only after database lock. It allows evidence to compound across studies rather than resetting with each trial. Importantly, models do not replace measurement. They strengthen it. Better measurement enables better models, and better models sharpen measurement over time.

What This Looks Like Across Therapeutic Areas

In CNS and movement disorders, high-frequency, real-world measurements used to derive digital endpoints in clinical trials can reveal progression patterns and treatment responses that episodic rating scales often obscure.

In oncology, aligning measurement with biological response dynamics helps distinguish treatment effect from background variability in heterogeneous populations, reinforcing disease-centric endpoint design.

In respiratory and cardio-metabolic disease, integrating functional, physiological, and biological signals into unified concepts improves endpoint sensitivity and endpoint interpretability compared with isolated clinic-based endpoints.

Across these areas, the principle is consistent: measurement strategies grounded in disease biology improve signal detection and decision confidence.

Designing Trials That Learn

Clinical development is moving toward a future where learning happens continuously, models guide decisions in flight, and evidence compounds across programs. That future will not be enabled by more tools. It will be enabled by better measurement design.

Starting with a measurement strategy grounded in disease biology and therapeutic intent creates the conditions for clearer signal, stronger evidence, and faster, more confident decisions. Digital endpoints play a critical role, but only when they serve a strategy designed with purpose from the outset.

Measurement Science as a Strategic Partnership

Designing measurement strategy is not a transactional task. It requires disease expertise, scientific ownership, and integration across disciplines.

This is the distinction between a data collection vendor and a measurement-science partner. Koneksa Health approaches clinical development by designing biologically grounded measurement strategies that integrate physiological, clinical, and digital signals into coherent evidence systems. Measurement is treated as a strategic driver of decision quality, not a downstream operational detail.

If you are rethinking how endpoints are selected, interpreted, or integrated in your clinical program, we welcome the conversation. Designing the right measurement strategy early can fundamentally change what your trial is able to show later.

Contact us to discuss designing a measurement strategy for your program.

Contact Koneksa