The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method
Open accessAbstract
The aim of the present study was to report the first reliability and validity tests of the remote food photography method (RFPM), which consists of camera-enabled cell phones with data transfer capability. Participants take and transmit photographs of food selection and plate waste to researchers/clinicians for analysis. Following two pilot studies, adult participants (n 52; BMI 20–35 kg/m 2 inclusive) were randomly assigned to the dine-in or take-out group. Energy intake (EI) was measured for 3 d. The dine-in group ate lunch and dinner in the laboratory. The take-out group ate lunch in the laboratory and dinner in free-living conditions (participants received a cooler with pre-weighed food that they returned the following morning). EI was measured with the RFPM and by directly weighing foods. The RFPM was tested in laboratory and free-living conditions. Reliability was tested over 3 d and validity was tested by comparing directly weighed EI to EI estimated with the RFPM using Bland–Altman analysis. The RFPM produced reliable EI estimates over 3 d in laboratory (r 0·62;P < 0·0001) and free-living (r 0·68;P < 0·0001) conditions. Weighed EI correlated highly with EI estimated with the RFPM in laboratory and free-living conditions (r>0·93;P < 0·0001). In two laboratory-based validity tests, the RFPM underestimated EI by − 4·7 % (P = 0·046) and − 5·5 % (P = 0·076). In free-living conditions, the RFPM underestimated EI by − 6·6 % (P = 0·017). Bias did not differ by body weight or age. The RFPM is a promising new method for accurately measuring the EI of free-living individuals. Error associated with the method is small compared with self-report methods.
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STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
Martin Bland, J., & Altman, D. (1986) · The Lancet · doi:10.1016/s0140-6736(86)90837-8 · Cited by 47,965
Inaccuracies in self-reported intake identified by comparison with the doubly labelled water method
Schoeller, D. A., Bandini, L. G., & Dietz, W. H. (1990) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/y90-143 · Cited by 266
Yon, B. A., Johnson, R. K., Harvey-Berino, J., Gold, B. C., & Howard, A. B. (2007) · Journal of Behavioral Medicine · doi:10.1007/s10865-006-9092-1 · Cited by 175
Measuring individual plate waste in school lunches
Comstock, E. M., St.Pierre, R. G., & Mackiernan, Y. D. (1981) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(21)39368-3 · Cited by 144
Assessing dietary intake in the management of obesity
O'Neil, P. M. (2001) · Obesity Research · doi:10.1002/oby.2001.9.s5.361 · Cited by 48 · cited 2×
“These prompts utilised EMA((29)) principles and consisted of emails and text messages; (3) Participants were provided with a telescoping pen to standardise the distance of the camera from the food and were instructed to take photographs at a 458 angle; (4) Participants were instructed to record their EI using traditio…”
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Plate waste of adults in the United States measured in free-living conditions
Roe, B. E., Apolzan, J. W., Qi, D., Allen, H. R., & Martin, C. K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191813 · Cited by 43
Turner-McGrievy, G. M., Yang, C.-H., Monroe, C., Pellegrini, C., & West, D. S. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-021-00203-9 · Cited by 22
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Metadata
- Authors
- Martin, C. K., Han, H., Coulon, S. M., Allen, H. R., Champagne, C. M., & Anton, S. D.
- Venue
- British Journal of Nutrition
- Publisher
- Cambridge University Press (CUP)
- Published
- 2008-07-11
- Volume
- 101
- Issue
- 3
- Pages
- 446-456
- ISSN
- 0007-1145 (ISSN-L)
- 1475-2662
- OpenAlex
- W2156323397
- Cited by
- 328
- References
- 5
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- bronze
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