BACKGROUND: Automated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes. OBJECTIVES: We evaluated the effects of modifications made during manual data cleaning on nutrient intakes of interest: energy, carbohydrate, total fat, protein, and fiber. METHODS: Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. The Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined. RESULTS: After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (P < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (P < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared with supervised recalls (P = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared with raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (P < 0.001). CONCLUSIONS: Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications. Published by Oxford University Press on behalf of the American Society for Nutrition 2021.
BACKGROUND: Automated dietary assessment tools such as ASA24® are useful for collecting 24-hour recall data in large-scale studies. Modifications made during manual data cleaning may affect nutrient intakes. OBJECTIVES: We evaluated the effects of modifications made during manual data cleaning on nutrient intakes of interest: energy, carbohydrate, total fat, protein, and fiber. METHODS: Differences in mean intake before and after data cleaning modifications for all recalls and average intakes per subject were analyzed by paired t-tests. The Chi-squared test was used to determine whether unsupervised recalls had more open-ended text responses that required modification than supervised recalls. We characterized food types of text response modifications. Correlations between predictive energy requirements, measured total energy expenditure (TEE), and mean energy intake from raw and modified data were examined. RESULTS: After excluding 11 recalls with invalidating technical errors, 1499 valid recalls completed by 393 subjects were included in this analysis. We found significant differences before and after modifications for energy, carbohydrate, total fat, and protein intakes for all recalls (P < 0.05). Limiting to modified recalls, there were significant differences for all nutrients of interest, including fiber (P < 0.02). There was not a significantly greater proportion of text responses requiring modification for home compared with supervised recalls (P = 0.271). Predicted energy requirements correlated highly with TEE. There was no significant difference in correlation of mean energy intake with TEE for modified compared with raw data. Mean intake for individual subjects was significantly different for energy, protein, and fat intakes following cleaning modifications (P < 0.001). CONCLUSIONS: Manual modifications can change mean nutrient intakes for an entire cohort and individuals. However, modifications did not significantly affect the correlation of energy intake with predictive requirements and measured expenditure. Investigators can consider their research question and nutrients of interest when deciding to make cleaning modifications. Published by Oxford University Press on behalf of the American Society for Nutrition 2021.
Entities:
Keywords:
24-hour recall; ASA24; data cleaning; dietary assessment; total energy expenditure
Authors: Maria Carlota Dao; Amy F Subar; Marisol Warthon-Medina; Janet E Cade; Tracy Burrows; Rebecca K Golley; Nita G Forouhi; Matthew Pearce; Bridget A Holmes Journal: Public Health Nutr Date: 2018-11-15 Impact factor: 4.022
Authors: Sharon I Kirkpatrick; Amy F Subar; Deirdre Douglass; Thea P Zimmerman; Frances E Thompson; Lisa L Kahle; Stephanie M George; Kevin W Dodd; Nancy Potischman Journal: Am J Clin Nutr Date: 2014-04-30 Impact factor: 7.045
Authors: Sharon I Kirkpatrick; Patricia M Guenther; Deirdre Douglass; Thea Zimmerman; Lisa L Kahle; Abiodun Atoloye; Michelle Marcinow; Mateja R Savoie-Roskos; Kevin W Dodd; Carrie Durward Journal: J Nutr Date: 2019-01-01 Impact factor: 4.798
Authors: Amy F Subar; Victor Kipnis; Richard P Troiano; Douglas Midthune; Dale A Schoeller; Sheila Bingham; Carolyn O Sharbaugh; Jillian Trabulsi; Shirley Runswick; Rachel Ballard-Barbash; Joel Sunshine; Arthur Schatzkin Journal: Am J Epidemiol Date: 2003-07-01 Impact factor: 4.897
Authors: Lacey M Baldiviez; Nancy L Keim; Kevin D Laugero; Daniel H Hwang; Liping Huang; Leslie R Woodhouse; Dustin J Burnett; Melissa S Zerofsky; Ellen L Bonnel; Lindsay H Allen; John W Newman; Charles B Stephensen Journal: BMC Nutr Date: 2017-10-19
Authors: Andrew Oliver; Zhengyao Xue; Yirui T Villanueva; Blythe Durbin-Johnson; Zeynep Alkan; Diana H Taft; Jinxin Liu; Ian Korf; Kevin D Laugero; Charles B Stephensen; David A Mills; Mary E Kable; Danielle G Lemay Journal: mBio Date: 2022-05-10 Impact factor: 7.786
Authors: Kristen L James; Erik R Gertz; Eduardo Cervantes; Ellen L Bonnel; Charles B Stephensen; Mary E Kable; Brian J Bennett Journal: Nutrients Date: 2022-03-25 Impact factor: 5.717
Authors: Niknaz Riazati; Mary E Kable; John W Newman; Yuriko Adkins; Tammy Freytag; Xiaowen Jiang; Charles B Stephensen Journal: Front Immunol Date: 2022-09-29 Impact factor: 8.786