The illusion of completeness

Health is increasingly perceived as measurable. Blood values, apps, wearables, scores, and dashboards create the impression that more data inevitably leads to better decisions. In practice, the opposite often occurs. Many people have access to extensive measurements but struggle to understand:

• which values truly matter
• how to interpret change
• which data requires action and which does not

Completeness creates a sense of security—at least superficially. Orientation rarely follows.

Data is not the same as information

Not every measurement contributes to meaningful decisions. Individual values fluctuate, react to short-term influences, or lack relevance without context. Health-related relevance only emerges when data:

• is observed over time
• is interpreted in relation to other data
• reveals a direction rather than a snapshot

An isolated value may attract attention. A pattern over time creates understanding. Many diagnostic systems deliver momentary snapshots. For prevention and long-term guidance, their value is limited.

Why too many values complicate decisions

Decision science shows a consistent pattern: the more information and options people must evaluate simultaneously, the worse decisions tend to become. This principle applies equally to health. Large volumes of data often lead to:

• uncertainty instead of clarity
• overreaction to minor deviations
• focus on secondary details
• neglect of slow but relevant changes

Rather than increasing agency, excessive data can undermine it. More data does not improve decisions by default — it increases the need for interpretation.

Relevance depends on the question being asked

Health data is never neutral. Its meaning depends on the question behind it. “Is this value normal?” is a different question from “What does this value say about my resilience?” “Is something outside the reference range?” differs fundamentally from “Which functional system is stable or under strain?” Preventive health focuses less on thresholds and more on function. Not whether a value is technically acceptable, but whether a system remains resilient over time.

Trends are more informative than single values

Many meaningful health changes occur gradually. Metabolism, recovery capacity, mental resilience, and physical performance evolve over weeks or months. Single measurements are poorly suited to capture this. Repeated, comparable measurements reveal:

• direction
• stability
• response to changes in daily life

Trends reduce random variation and enable realistic interpretation. They show whether interventions have an effect — or none at all.

Measurement without interpretation remains ineffective

Data alone rarely improves decisions. Without interpretation, it remains abstract. Interpretation involves:

• explaining relationships
• setting priorities
• identifying realistic courses of action

Especially in preventive contexts, interpretation is essential. It prevents both inaction and overcorrection. Without interpretation, measurement becomes an end in itself. With interpretation, it becomes a tool.

Why fewer measurements often lead to more sustainable outcomes

A reduced, focused approach offers several advantages:

• lower cognitive load
• better comparability over time
• clearer priorities
• greater feasibility in everyday life

Fewer measurements do not mean less knowledge. They mean higher information density per value. Prevention does not benefit from maximal data collection, but from targeted observation of relevant functions.

Health cannot be monitored into existence — it must be understood

Health is not a system that can be optimized through constant surveillance. It responds to behavior, stress, and recovery — often with delay. Those who attempt to track every fluctuation lose sight of what matters. Those who understand relevant functions can interpret change more effectively. Measurement is a means of orientation. Not of control.

Summary

More measurements do not automatically lead to better health. Often, they lead to greater uncertainty. Orientation emerges where data is:

• limited
• comparable
• contextualized
• interpreted

Fewer, well-chosen measurements can provide more clarity than extensive datasets without direction. Health does not require maximum transparency. It requires meaningful interpretation.

Scientific background and context

• Gigerenzer G. (2014): Risk Savvy – How to Make Good Decisions, Penguin
• Redelmeier & Tversky (1992): On the belief that arthritis pain is related to the weather, PNAS
• Ioannidis JPA (2016): Why Most Clinical Research Is Not Useful, PLOS Medicine
• Kahneman D. (2011): Thinking, Fast and Slow, Farrar, Straus and Giroux
• OECD (2023): Health Data Governance and Digital Health