Wearables in Clinical Trials: What Fit for Purpose Really Requires

A wearable result deserves your trust only when the sensor and the endpoint built on it have been shown to be fit for purpose for the specific question a trial is asking. That phrase is the load-bearing standard in the U.S. Food and Drug Administration's final guidance, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, issued in December 2023 and announced in the Federal Register on December 22 of that year. Fit for purpose means three things must hold together: the device measures what it claims to measure, the measurement corresponds to a clinical event or characteristic that matters, and the intended participants can actually use it as designed. When a company reports that its product improved "sleep quality" or "activity" as captured by a wrist sensor, the right question is not whether the number moved. It is whether that chain of evidence exists at all.

This article is educational and not medical advice. My aim is to give you a framework for reading these claims, not to endorse or criticize any device.

What a digital health technology is asked to do

A digital health technology, in the FDA's usage, is a system that uses hardware and software to measure or otherwise capture data for a health purpose. In a trial, a wearable or sensor is standing in for a trained observer or a clinic instrument. That substitution is only legitimate if the digital tool produces data of comparable meaning. The guidance finalizes a draft issued in December 2021 and was completed partly to fulfill a mandate Congress set in the Food and Drug Omnibus Reform Act, so its expectations now sit inside a statutory frame rather than floating as suggestions.

The central discipline is separating three distinct claims that marketing tends to blur into one.

Verification: does the hardware do what the spec says

Verification asks whether the device and its software capture and process the raw signal correctly. A photoplethysmography sensor estimating heart rate, an accelerometer counting steps, a smartphone camera reading a test strip: each has a technical specification, and verification is the evidence that a given unit meets it across the conditions the trial will encounter. The guidance flags a detail that is easy to overlook. When multiple device models or firmware versions appear in one trial, consistency across them has to be demonstrated, because a step counted on one watch should mean the same thing as a step counted on another. Skin tone, motion, and fit are not footnotes here. They are the conditions under which a sensor either performs to spec or quietly does not.

Validation: does the measurement mean something clinically

Verification can pass while the endpoint remains meaningless. Validation is the harder claim, and it has two layers. Analytical validation asks whether the algorithm's output accurately reflects the physiological signal it processes, judged against a reference standard. Clinical validation asks whether that output corresponds to a clinical event or characteristic that matters to how a patient feels, functions, or survives. A wrist device can track movement with impressive precision and still tell you nothing reliable about whether a person's disease is better. The gap between measuring motion accurately and showing that a patient improved is exactly where weak evidence lives.

Usability: can the intended patient actually use it

The guidance treats usability as part of fit for purpose, not a courtesy. If participants cannot operate the device as designed, the data degrade no matter how good the sensor is. That means evaluating whether the proposed population, including older adults or people with the very impairment under study, can wear, charge, and interact with the technology over the trial's duration. The guidance also notes that lack of personal device access should not by itself exclude someone from a study, which is both an equity point and a data quality point. A sample that only includes the technologically fluent may not represent the patients a treatment is meant to serve.

Reading a claim: the questions that separate evidence from marketing

Fit for purpose is contextual. A sensor validated to measure resting heart rate is not thereby validated to measure sleep stages or stress. When you encounter a wearable result, a few questions do most of the work.

First, what exactly was measured, and against what reference was it validated? A named comparator, such as polysomnography for sleep or a supervised walk test for mobility, is a good sign. Silence about the reference is a warning.

Second, is the endpoint established or novel? The guidance draws a sharp line. Using a digital tool to replace manual capture of an accepted endpoint is one thing. Using it to define a new endpoint is a heavier lift, and the FDA expects additional justification: how the novel measure relates to other endpoints, how reliable the data are, and how a known effect would even be detectable through it. Novel digital endpoints can be valuable, but they carry a larger burden of proof, and a marketing claim rarely acknowledges that.

Third, does the population studied resemble the people the product targets? A device validated in young, healthy volunteers tells you little about performance in the frail or the acutely ill.

Fourth, who was responsible for data integrity when things went wrong? The guidance expects sponsors to plan for firmware updates, data loss, and device errors, and to restrict trial devices to participants and caregivers. Trials that describe these procedures are taking the data seriously.

Why the distinction matters beyond trials

The same wearable hardware often powers both a regulated trial endpoint and a consumer wellness feature, and the two are not held to the same standard. A consumer metric can be directional and still be useful for personal curiosity. A trial endpoint that will inform whether a medical product works has to survive verification, validation, and usability scrutiny before it counts as evidence. When a marketing page borrows the credibility of "clinically validated" without showing that chain, it is trading on a word the FDA reserves for a specific and demanding process. Knowing what that process requires lets you tell the difference.