Automated Ecological Assessment of Physical Activity: Advancing Direct Observation
| Autor: | Jordan A. Carlson; Bo Liu; James F. Sallis; Jacqueline Kerr; J. Aaron Hipp; Vincent S. Staggs; Amy Papa; Kelsey Dean; Nuno M. Vasconcelos |
|---|---|
| Sprache: | Englisch |
| Veröffentlicht: |
2017 |
| Quelle: | Directory of Open Access Journals: DOAJ Articles |
| Online Zugang: |
https://www.mdpi.com/1660-4601/14/12/1487 https://doaj.org/toc/1660-4601 1660-4601 doi:10.3390/ijerph14121487 https://doaj.org/article/357072162ae04b128df5b9f2d31b998f https://doi.org/10.3390/ijerph14121487 https://doaj.org/article/357072162ae04b128df5b9f2d31b998f |
| Erfassungsnummer: | ftdoajarticles:oai:doaj.org/article:357072162ae04b128df5b9f2d31b998f |
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bispbase |
| url |
https://www.mdpi.com/1660-4601/14/12/1487 https://doaj.org/toc/1660-4601 1660-4601 doi:10.3390/ijerph14121487 https://doaj.org/article/357072162ae04b128df5b9f2d31b998f https://doi.org/10.3390/ijerph14121487 https://doaj.org/article/357072162ae04b128df5b9f2d31b998f |
| url-type |
primary primary primary primary primary info info |
| spelling |
accelerometry exercise measurement parks public health Medicine R Automated Ecological Assessment of Physical Activity: Advancing Direct Observation |
| publishDate |
2017 |
| publishDate_facet |
2017 |
| baseCollectionName |
Directory of Open Access Journals: DOAJ Articles |
| baseCountry |
org |
| title |
Automated Ecological Assessment of Physical Activity: Advancing Direct Observation |
| spellingShingle |
Automated Ecological Assessment of Physical Activity: Advancing Direct Observation |
| title_short |
Automated Ecological Assessment of Physical Activity: Advancing Direct Observation |
| title_sort |
Automated Ecological Assessment of Physical Activity: Advancing Direct Observation |
| author2 |
Jordan A. Carlson Bo Liu James F. Sallis Jacqueline Kerr J. Aaron Hipp Vincent S. Staggs Amy Papa Kelsey Dean Nuno M. Vasconcelos |
| author_facet |
Jordan A. Carlson Bo Liu James F. Sallis Jacqueline Kerr J. Aaron Hipp Vincent S. Staggs Amy Papa Kelsey Dean Nuno M. Vasconcelos |
| author2-role |
Autor Autor Autor Autor Autor Autor Autor Autor Autor |
| abstract |
Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82–0.98). Total MET-minutes were slightly underestimated by 9.3–17.1% and the ICCs were good (0.68–0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings. |
| abstract_type |
general |
| abstract_lang |
eng |
| language |
eng |
| publisher |
MDPI AG |
| _version_ |
1793496373657600000 |
| score |
13,561382 |