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.
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.
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
Related findings · Mixed
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This is one finding among thousands. Every one is graded and traced to its source, so you can see what the evidence actually supports. Browse the research →