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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

P

Performance outcome (PerfO)

A performance outcome (PerfO) is a type of clinical outcome assessment based on a standardised task actively performed by the patient according to instructions, and administered and evaluated by an appropriately trained individual or independently completed. Examples include the six-minute walk test, timed up-and-go, grip strength, the Nine-Hole Peg Test in multiple sclerosis, and cognitive tests such as the Digit Symbol Substitution Test or word recall tasks in Alzheimer's disease trials.

PerfOs measure what a patient can do rather than what they report or what a clinician judges, and are therefore valued for objectivity. Their limitations are learning effects, dependence on motivation and testing conditions, and the need for standardised administration. Digital versions, using smartphone sensors, wearables or gamified apps to record gait, speech, fine motor control or reaction time, are transforming PerfOs into continuous, home-based digital health technology measures, for which the FDA and EMA have issued qualification frameworks.

Digital PerfOs generate large volumes of raw sensor data, voice recordings and video, which are health data and may also constitute biometric data if processed to identify the individual. Voice and video are particularly hard to pseudonymise, algorithms deriving scores from raw data may be software as a medical device and high-risk AI systems, and continuous collection tests the minimisation principle. The DPIA should address raw-data retention, on-device processing options, vendor roles and the information given to participants about passive monitoring.