Comparing Step Counting Algorithms for High-Resolution Wrist Accelerometry Data in NHANES 2011–2014.
Purpose: To quantify the relative performance of step counting algorithms in studies that collect free-living high-resolution wrist accelerometry data and to highlight the implications of using these algorithms in translational research. Methods: Five step counting algorithms (four open source and o...
| Published in: | Medicine & Science in Sports & Exercise Vol. 57; no. 4; pp. 746 - 756 |
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| Main Authors: | , , |
| Format: | research tables/charts Journal Article |
| Published: |
Lippincott Williams & Wilkins
Apr2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183689556&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183689556 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Apr2025 vid: 57 iid: 4 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 183689556 183689556 183689556 10.1249/MSS.0000000000003616 183689556 ppf: 746 ppct: 10 formats: tig: atl: Comparing Step Counting Algorithms for High-Resolution Wrist Accelerometry Data in NHANES 2011–2014. aug: au: KOFFMAN, LILY CRAINICEANU, CIPRIAN MUSCHELLI, JOHN affil: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD sug: subj: Accelerometry Methods Step Evaluation Algorithms Wrist Physiology Age Factors Monitoring, Physiologic Human Male Female Adult Middle Age Aged Correlational Studies Comparative Studies Surveys Prediction Models Physical Activity Predictive Value of Tests Mortality Risk Factors Risk Assessment Accelerometers Walking Physiology Nutrition Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose: To quantify the relative performance of step counting algorithms in studies that collect free-living high-resolution wrist accelerometry data and to highlight the implications of using these algorithms in translational research. Methods: Five step counting algorithms (four open source and one proprietary) were applied to the publicly available, free-living, high-resolution wrist accelerometry data collected by the National Health and Nutrition Examination Survey (NHANES) in 2011–2014. The mean daily total step counts were compared in terms of correlation, predictive performance, and estimated hazard ratios of mortality. Results: The estimated number of steps were highly correlated (median, 0.91; range, 0.77–0.98), had high and comparable predictive performance of mortality (median concordance, 0.72; range, 0.70–0.73). The distributions of the number of steps in the population varied widely (mean step counts range from 2453 to 12,169). Hazard ratios of mortality associated with a 500-step increase per day varied among step counting algorithms between HR = 0.88 and 0.96, corresponding to a 300% difference in mortality risk reduction ([1–0.88] / [1–0.96] = 3). Conclusions: Different step counting algorithms provide correlated step estimates and have similar predictive performance that is better than traditional predictors of mortality. However, they provide widely different distributions of step counts and estimated reductions in mortality risk for a 500-step increase. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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