ArticlePloS one2020
A pragmatic approach to the comparison of wrist-based cutpoints of physical activity intensity for the MotionWatch8 accelerometer in children.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Behaviour-based movement cut-off points in 3-year old children comparing wrist- with hip-worn actigraphs MW8 and GT3X.PloS one · 2025Article
- Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.Journal of medical Internet research · 2024Article
- Intake of L-serine before bedtime prevents the delay of the circadian phase in real life.Journal of physiological anthropology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundA variety of wearable monitors are available for objectively assessing physical activity but there is a lack of established values for the activity intensity of MotionWatch8 (MW8) and a similar lack of studies on comparability across devices. Our study aimed to establish activity intensity cutpoints for the MW8 accelerometer in children, which are necessary to determine whether they are meeting physical activity guidelines.
methodsChildren (n = 39, ages 9-13 years) were asked to wear two different accelerometers (MW8 and ActiGraph) simultaneously on the same dominant wrist as they performed different activities designed to mimic activities of variable intensity that a child might perform in a free-living environment. Linear regression and receiver operating characteristic (ROC) curves were performed to assess sensitivity and specificity of the identified MW8 intensity cutpoints compared to established ActiGraph cutpoints.
resultsMean values for each activity were positively correlated using the MW8 and ActiGraph monitors (r = 0.85, p<0.001). The optimal cutpoints for differentiating sedentary from light physical activity, light from moderate, and moderate from vigorous activity were ≤32 counts, ≥ 371.5 counts, and ≥ 859.5 counts per 30 seconds, respectively.
conclusionsOur study demonstrated the ability of MW8 to discriminate different intensity activities and provided the first cutoff values for researchers using the MW8 to measure physical activity patterns among children.
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Registered trials
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