Effect of sampling rate on acceleration and counts of hip- and wrist-worn ActiGraph accelerometers in children
Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mgs), when using wrist-worn accelerometers and during non-locomotive activities. Obj...
Gespeichert in:
Veröffentlicht in: | Physiological measurement 2019-09, Vol.40 (9), p.095008-095008 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mgs), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15 s and mean acceleration and to compare differences due to sampling rate between accelerometer wear locations and across different types of activities. Approach: Children (n = 29) wore a hip- and wrist-worn accelerometer (sampled at 100 Hz, downsampled in MATLAB to 30 Hz) during rest/transition periods, active video games, and a treadmill test to volitional exhaustion. Mean acceleration and counts/15 s were computed for each axis and as vector magnitude. Main results: There were mostly no significant differences in mean acceleration. However, 100 Hz data resulted in significantly more total counts/15 s (mean bias 4-43 counts/15 s across axes) for both the hip- and wrist-worn monitor when compared to 30 Hz data. Absolute differences increased with activity intensity (hip: r = 0.46-0.63; wrist: r = 0.26-0.55) and were greater for hip- versus wrist-worn monitors. Percent agreement between 100 and 30 Hz data was high (97.4%-99.7%) when cut-points or machine learning algorithms were used to classify activity intensity. Significance: Our findings support that sampling rate affects the generation of counts but adds that differences increase with intensity and when using hip-worn monitors. We recommend researchers be consistent and vigilantly report the sampling rate used, but note that classifying data into activity intensities resulted in agreement despite differences in sampling rate. |
---|---|
ISSN: | 0967-3334 1361-6579 1361-6579 |
DOI: | 10.1088/1361-6579/ab444b |