Research

Mixed

Accelerometer-derived summary metrics (MIMS-unit, AAI, MAD, ENMO) derived from raw data provide greater comparability across different device manufacturers and models than traditional proprietary activity counts.

If you are a researcher, stop relying on proprietary 'counts' to compare studies. Use open-source raw data algorithms (like MIMS-unit or AAI) to ensure your findings are comparable across different device brands. If you are a consumer, understand that 'steps' or 'activity scores' from different brands are not mathematically equivalent, even if they look similar.

GoodSupportsHIGH confidence
To overcome this lack of standardization, several methods have been proposed that yield summary measures comparable across devices from different manufacturers and devices of the same manufacturer over time, using high-resolution raw accelerometer data which are now available from many research-grade accelerometers.
I‐Min Lee et al. · Journal for the Measurement of Physical Behaviour · 2023

Why this rating

Based on a pilot study of 100 women and theoretical algorithmic validation, not a large-scale clinical outcome trial.

Source

Maximizing the Utility and Comparability of Accelerometer Data From Large-Scale Epidemiologic Studies

I‐Min Lee et al. · Journal for the Measurement of Physical Behaviour · 2023

DOI 10.1123/jmpb.2022-0035

narrative_reviewCited 10×
Read the paper
DOI resolved against Crossref · corpus check 2026-06-10

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