Before the score, understand the measure.
A reading, a questionnaire, a recovery curve. Explore what each can tell us about adaptation—and which questions to ask next.
THE QUESTIONS BEHIND THE NUMBERS
Can HRV tell me whether I’ve recovered?
Start with the signal, the recording conditions and the claim being made.
Explore the question →How is allostatic load measured?
Look inside the index: selected systems, biomarkers, thresholds and population.
Explore the question →Is feeling recovered the same as recovering function?
Compare what people report with the functions researchers follow over time.
Explore the question →What would it mean to measure resilience?
Define the disturbance, the function of interest and the observation period.
Explore the question →Four questions to carry into any study.
Use the same questions when reading about a wearable recovery score: what does it measure, how was it tested, and does that evidence fit your question?
The question
Recovery from what, in whom, and over what period?
The measure
A biological signal, reported experience, functional test or combined score?
The context
Under which conditions, and compared with which reference?
The interpretation
What evidence connects this measure to the conclusion being drawn?
Read the methods behind the measures.
Understand what a signal, questionnaire or composite index actually measures.
Open the reading guide- 2017Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting
- 2013The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance
- 2007The Recovery Experience Questionnaire: development and validation of a measure for assessing recuperation and unwinding from work
- 2022Allostatic Load Measurement: A Systematic Review of Reviews, Database Inventory, and Considerations for Neighborhood Research
- 2023Towards a consensus definition of allostatic load: a multi-cohort, multi-system, multi-biomarker individual participant data (IPD) meta-analysis