Proposed

Variability in domain-based exercise-intensity prescription: a structured expert judgement study

When researchers receive the same detailed physiological information, do they prescribe the same exercise intensity?

Plain-language summary

Testing agreement before assuming consensus

Researchers can use a range of physiological and performance anchors, together with different analytical methods, to define exercise-intensity domains. The proposed study investigates what happens when independent researchers make a prescription from exactly the same data.

Participants will receive one richly characterised synthetic physiological dataset. They will independently prescribe standardised moderate-, heavy-, and severe-intensity cycling sessions. The session structures will be fixed so that the main decision is the selected work rate and the method used to derive it.

This study will compare prescribed work rates and record the selected physiological or performance anchors, analytical methods, confidence, and methodological rationale. This will help distinguish agreement, disagreement, and genuine methodological pluralism.

Standardised task

Three intended intensity domains

Participants will not alter the sessions’ structure. Their task will be to specify the target work rates and explain the basis for their prescriptions.

Moderate

50 minutes

Continuous, constant-power cycling

Heavy

30 minutes

Continuous, constant-power cycling

Severe

4 × 4 minutes

Work intervals with 3 minutes of active recovery

What the study will examine

Variation in decisions and their methodological basis

The proposal emphasises estimation and description. It does not presume that researchers have already been shown to disagree.

Prescribed work rates

Quantify between-participant variation for each intended intensity domain.

Selected anchors and methods

Record the physiological information and analytical approaches used for each prescription.

Reasons for agreement or disagreement

Examine rationales including validity, reliability, practicality, familiarity, and other methodological considerations.

Dataset and study boundaries

Synthetic data, no physiological testing

The proposed dataset represents a comprehensively assessed trained cyclist and includes incremental, constant-work-rate, and all-out test information. It is synthetic, was designed to be physiologically plausible, and does not derive from or correspond to a real person.

Participants will contribute professional judgements through an online task. The proposed study will not conduct physiological testing of a real individual and will not collect any sensitive health data.

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Relevant methodological expertise?

Early-career researchers can describe how their skills and interests may support this or another developing Consortium project.

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