Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries.
Purpose: This study developed and tested an algorithm to classify accelerometer data as walking or nonwalking using either GPS or travel diary data within a large sample of adults under free-living conditions. Methods: Participants wore an accelerometer and a GPS unit and concurrently completed a tr...
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 45; no. 7; pp. 1419 - 1429 |
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| Autores principales: | , , , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jul2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=107952399&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107952399 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Jul2013 vid: 45 iid: 7 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 107952399 88340620 10.1249/MSS.0b013e318285f202 NLM23439414 107952399 ppf: 1419 ppct: 10 formats: tig: atl: Walking Objectively Measured: Classifying Accelerometer Data with GPS and Travel Diaries. aug: au: Bumjoon Kang Moudon, Anne V. Hurvitz, Philip M. Reichley, Lucas Saelens, Brian E. affil: Urban Form Lab and Department of Urban Design and Planning, University of Washington, Seattle, WA sug: subj: Accelerometry Geographic Information Systems Travel Diaries Walking Evaluation Human Middle Age Descriptive Statistics Male Female Algorithms Decision Trees Funding Source Middle Aged: 45-64 years Male Female ab: Purpose: This study developed and tested an algorithm to classify accelerometer data as walking or nonwalking using either GPS or travel diary data within a large sample of adults under free-living conditions. Methods: Participants wore an accelerometer and a GPS unit and concurrently completed a travel diary for seven consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or nonwalking based on a decision-tree algorithm consisting of seven classification scenarios. Algorithm reliability was examined relative to two independent analysts' classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts. Results: The 706 participants' (mean age = 51 yr, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified PA into 8170 walking bouts (58.5 %) and 5337 nonwalking bouts (38.2%); 464 bouts (3.3%) were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the nonwalking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean ± SD duration of PA bouts classified as walking was 15.2 + 12.9 min. On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day. Conclusions: GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or nonwalking behavior. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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