Using the SenseCam to improve classifications of sedentary behavior in free-living settings
<p>Background: Studies have shown relationships between important health outcomes and sedentary behavior, independent of physical activity. There are known errors in tools employed to assess sedentary behavior. Studies of accelerometers have been limited to laboratory environments.</p>&l...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2013
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| Subjects: |
| _version_ | 1797095785264840704 |
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| author | Kerr, J Marshall, S Godbole, S Chen, J Legge, A Doherty, A Kelly, P Oliver, M Badland, H Foster, C |
| author2 | American College of Preventive Medicine |
| author_facet | American College of Preventive Medicine Kerr, J Marshall, S Godbole, S Chen, J Legge, A Doherty, A Kelly, P Oliver, M Badland, H Foster, C |
| author_sort | Kerr, J |
| collection | OXFORD |
| description | <p>Background: Studies have shown relationships between important health outcomes and sedentary behavior, independent of physical activity. There are known errors in tools employed to assess sedentary behavior. Studies of accelerometers have been limited to laboratory environments.</p><p>Purpose: To assess a broad range of sedentary behaviors in free-living adults using accelerometers and a Microsoft SenseCam that can provide an objective observation of sedentary behaviors through first person–view images.</p><p>Methods: Participants were 40 university employees who wore a SenseCam and Actigraph accelerometer for 3–5 days. Images were coded for sitting and standing posture and 12 activity types. Data were merged and aggregated to a 60-second epoch. Accelerometer counts per minute (cpm) of <100 were compared with coded behaviors. Sensitivity and specificity analyses were performed. Data were collected in June and July 2011 and analyzed in April 2012.</p><p>Results: TV viewing, other screen use, and administrative activities were correctly classified by the 100-cpm cutpoint. However, standing behaviors also fell under this threshold, and driving behaviors exceeded it. Multiple behaviors occurred simultaneously. A nearly 30-minute per day difference was found in sedentary behavior estimates based on the accelerometer versus the SenseCam.</p><p>Conclusions: Researchers should be aware of the strengths and weaknesses of the 100-cpm accelerometer cutpoint for identifying sedentary behavior. The SenseCam may be a useful tool in free-living conditions to better understand health behaviors such as sitting.</p> |
| first_indexed | 2024-03-07T04:32:53Z |
| format | Journal article |
| id | oxford-uuid:ceecb27c-6e16-4832-ac11-dd8024fc9fe7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:32:53Z |
| publishDate | 2013 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:ceecb27c-6e16-4832-ac11-dd8024fc9fe72022-03-27T07:38:56ZUsing the SenseCam to improve classifications of sedentary behavior in free-living settingsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ceecb27c-6e16-4832-ac11-dd8024fc9fe7Cardiovascular diseaseEpidemiologyDisease preventionEnglishOxford University Research Archive - ValetElsevier2013Kerr, JMarshall, SGodbole, SChen, JLegge, ADoherty, AKelly, POliver, MBadland, HFoster, CAmerican College of Preventive MedicineAssociation for Prevention Teaching and Research<p>Background: Studies have shown relationships between important health outcomes and sedentary behavior, independent of physical activity. There are known errors in tools employed to assess sedentary behavior. Studies of accelerometers have been limited to laboratory environments.</p><p>Purpose: To assess a broad range of sedentary behaviors in free-living adults using accelerometers and a Microsoft SenseCam that can provide an objective observation of sedentary behaviors through first person–view images.</p><p>Methods: Participants were 40 university employees who wore a SenseCam and Actigraph accelerometer for 3–5 days. Images were coded for sitting and standing posture and 12 activity types. Data were merged and aggregated to a 60-second epoch. Accelerometer counts per minute (cpm) of <100 were compared with coded behaviors. Sensitivity and specificity analyses were performed. Data were collected in June and July 2011 and analyzed in April 2012.</p><p>Results: TV viewing, other screen use, and administrative activities were correctly classified by the 100-cpm cutpoint. However, standing behaviors also fell under this threshold, and driving behaviors exceeded it. Multiple behaviors occurred simultaneously. A nearly 30-minute per day difference was found in sedentary behavior estimates based on the accelerometer versus the SenseCam.</p><p>Conclusions: Researchers should be aware of the strengths and weaknesses of the 100-cpm accelerometer cutpoint for identifying sedentary behavior. The SenseCam may be a useful tool in free-living conditions to better understand health behaviors such as sitting.</p> |
| spellingShingle | Cardiovascular disease Epidemiology Disease prevention Kerr, J Marshall, S Godbole, S Chen, J Legge, A Doherty, A Kelly, P Oliver, M Badland, H Foster, C Using the SenseCam to improve classifications of sedentary behavior in free-living settings |
| title | Using the SenseCam to improve classifications of sedentary behavior in free-living settings |
| title_full | Using the SenseCam to improve classifications of sedentary behavior in free-living settings |
| title_fullStr | Using the SenseCam to improve classifications of sedentary behavior in free-living settings |
| title_full_unstemmed | Using the SenseCam to improve classifications of sedentary behavior in free-living settings |
| title_short | Using the SenseCam to improve classifications of sedentary behavior in free-living settings |
| title_sort | using the sensecam to improve classifications of sedentary behavior in free living settings |
| topic | Cardiovascular disease Epidemiology Disease prevention |
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