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dc.contributor.authorKlous, Gijs
dc.contributor.authorKretzschmar, Mirjam E E
dc.contributor.authorCoutinho, Roel A
dc.contributor.authorHeederik, Dick J J
dc.contributor.authorHuss, Anke
dc.date.accessioned2020-02-11T12:51:25Z
dc.date.available2020-02-11T12:51:25Z
dc.date.issued2019-11-26
dc.identifier.issn1559-064X
dc.identifier.pmid31772295
dc.identifier.doi10.1038/s41370-019-0194-6
dc.identifier.urihttp://hdl.handle.net/10029/623667
dc.description.abstractEstimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R2 of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility.en_US
dc.language.isoenen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectActive mobilityen_US
dc.subjectAssessmenten_US
dc.subjectBikingen_US
dc.subjectExposureen_US
dc.subjectGPS validationen_US
dc.subjectMobility estimation methoden_US
dc.subjectWalkingen_US
dc.titlePrediction of human active mobility in rural areas: development and validity tests of three different approaches.en_US
dc.typeArticleen_US
dc.identifier.journalJ Expo Sci Environ Epidemiol 2019en_US
dc.source.journaltitleJournal of exposure science & environmental epidemiology


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