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dc.contributor.authorParadis, Timothy
dc.contributor.authorCarbone, Elena
dc.contributor.authorMcCollough, Jeffrey
dc.contributor.authorPuleo, Elaine M.
dc.date2022-08-11T08:08:14.000
dc.date.accessioned2022-08-23T15:47:32Z
dc.date.available2022-08-23T15:47:32Z
dc.date.issued2014-05-20
dc.date.submitted2014-10-10
dc.identifier.doi10.13028/e0j0-nv27
dc.identifier.urihttp://hdl.handle.net/20.500.14038/28010
dc.description<p>Abstract of poster presented at the 2014 UMass Center for Clinical and Translational Science Research Retreat, held on May 20, 2014 at the University of Massachusetts Medical School, Worcester, Mass.</p>
dc.description.abstractOBJECTIVE: To measure RW usage prior to implementation of a targeted LWS intervention. METHODS: Users were automatically counted by TRAFx infrared trail counters, which were installed at three locations along the RW – Brightwood, Boathouse, and Depot. Data are expressed in counts, not in number of people, because the counters cannot determine user identity. Data represent counts from August through October 2013. RESULTS: The median daily counts for the Brightwood, Boathouse, and Depot locations were 70, 96, 181, respectively; mean counts were 69, 97, 189, respectively; and the range in counts were 39-133, 18-209, and 52-374, respectively. Hourly distributions varied. DISCUSSION: Brightwood had relatively high counts during the 6 PM hour (6:00 to 6:59), suggesting nearby residents using the trail after work. Boathouse counts showed no sharp hourly peaks, suggesting usage is less related to a typical work schedule. Depot counts peaked sharply during the 12 PM and 1 PM hours, suggesting employees from downtown using the trail on their lunch break. The range in counts at each location suggests that weather affected usage overall, while differences between locations suggest that characteristics of each location played a large role in determining counts. CONCLUSION: Results of this study will inform LWS programming and lay the foundation for post-intervention comparisons.
dc.formatyoutube
dc.language.isoen_US
dc.rightsCopyright the Author(s)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subjectCommunity Health and Preventive Medicine
dc.subjectTranslational Medical Research
dc.titleLive Well Springfield (LWS): Measuring Baseline Usage of the Springfield River Walk
dc.typePoster Abstract
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1312&amp;context=cts_retreat&amp;unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/cts_retreat/2014/posters/92
dc.identifier.contextkey6226137
refterms.dateFOA2022-08-23T15:47:32Z
html.description.abstract<p>OBJECTIVE: To measure RW usage prior to implementation of a targeted LWS intervention.</p> <p>METHODS: Users were automatically counted by TRAFx infrared trail counters, which were installed at three locations along the RW – Brightwood, Boathouse, and Depot. Data are expressed in counts, not in number of people, because the counters cannot determine user identity. Data represent counts from August through October 2013.</p> <p>RESULTS: The median daily counts for the Brightwood, Boathouse, and Depot locations were 70, 96, 181, respectively; mean counts were 69, 97, 189, respectively; and the range in counts were 39-133, 18-209, and 52-374, respectively. Hourly distributions varied.</p> <p>DISCUSSION: Brightwood had relatively high counts during the 6 PM hour (6:00 to 6:59), suggesting nearby residents using the trail after work. Boathouse counts showed no sharp hourly peaks, suggesting usage is less related to a typical work schedule. Depot counts peaked sharply during the 12 PM and 1 PM hours, suggesting employees from downtown using the trail on their lunch break. The range in counts at each location suggests that weather affected usage overall, while differences between locations suggest that characteristics of each location played a large role in determining counts.</p> <p>CONCLUSION: Results of this study will inform LWS programming and lay the foundation for post-intervention comparisons.</p>
dc.identifier.submissionpathcts_retreat/2014/posters/92


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