Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study

Clare Whitton¹, Clare E Collins², Barbara A Mullan³, Megan E Rollo⁴, Satvinder S Dhaliwal⁵, Richard Norman⁶, Carol J Boushey⁷, Edward J Delp⁸, Fengqing Zhu⁹, Tracy A McCaffrey¹⁰, Sharon I Kirkpatrick¹¹, Christina M Pollard¹², Janelle D Healy¹³, Amira Hassan¹⁴, Shivangi Garg¹⁵, Paul Atyeo¹⁶, Syed Aqif Mukhtar¹⁷, Deborah A Kerr¹⁸

Affiliations

¹ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Joondalup WA 6027, Australia. Electronic address: clare.whitton@curtin.edu.au.
² School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, Newcastle, Australia. Electronic address: clare.collins@newcastle.edu.au.
³ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Enable Institute, Curtin University, Perth, Australia. Electronic address: barbara.mullan@curtin.edu.au.
⁴ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia. Electronic address: megan.rollo@curtin.edu.au.
⁵ Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; Obstetrics & Gynaecology Academic Clinical Program, Duke-NUS Medical School, National University of Singapore, 8 College Rd, 169857, Singapore; Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Pulau Pinang, Malaysia; Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore. Electronic address: S.Dhaliwal@curtin.edu.au.
⁶ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Enable Institute, Curtin University, Perth, Australia. Electronic address: richard.norman@curtin.edu.au.
⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA. Electronic address: cjboushey@cc.hawaii.edu.
⁸ School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: ace@ecn.purdue.edu.
⁹ School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: zhu0@purdue.edu.
¹⁰ Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Australia. Electronic address: tracy.mccaffrey@monash.edu.
¹¹ School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada. Electronic address: sharon.kirkpatrick@uwaterloo.ca.
¹² Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; Enable Institute, Curtin University, Perth, Australia. Electronic address: C.Pollard@curtin.edu.au.
¹³ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia. Electronic address: janelle.healy@curtin.edu.au.
¹⁴ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia. Electronic address: amira.hassan@curtin.edu.au.
¹⁵ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia. Electronic address: shivangi.garg@curtin.edu.au.
¹⁶ Health Section, Health and Disability Branch, Australian Bureau of Statistics, Canberra, Australia. Electronic address: paul.atyeo@abs.gov.au.
¹⁷ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia. Electronic address: Aqif.Mukhtar@curtin.edu.au.
¹⁸ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia. Electronic address: d.kerr@curtin.edu.au.

PMID: 38710447
PMCID: PMC11347807
DOI: 10.1016/j.ajcnut.2024.04.030

Randomized Controlled Trial

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study

Clare Whitton et al. Am J Clin Nutr. 2024 Jul.

. 2024 Jul;120(1):196-210.

doi: 10.1016/j.ajcnut.2024.04.030. Epub 2024 May 6.

Authors

Affiliations

¹ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Joondalup WA 6027, Australia. Electronic address: clare.whitton@curtin.edu.au.
² School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia; Food and Nutrition Research Program, Hunter Medical Research Institute, New Lambton Heights, Newcastle, Australia. Electronic address: clare.collins@newcastle.edu.au.
³ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Enable Institute, Curtin University, Perth, Australia. Electronic address: barbara.mullan@curtin.edu.au.
⁴ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia. Electronic address: megan.rollo@curtin.edu.au.
⁵ Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; Obstetrics & Gynaecology Academic Clinical Program, Duke-NUS Medical School, National University of Singapore, 8 College Rd, 169857, Singapore; Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Pulau Pinang, Malaysia; Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore. Electronic address: S.Dhaliwal@curtin.edu.au.
⁶ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Enable Institute, Curtin University, Perth, Australia. Electronic address: richard.norman@curtin.edu.au.
⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA. Electronic address: cjboushey@cc.hawaii.edu.
⁸ School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: ace@ecn.purdue.edu.
⁹ School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: zhu0@purdue.edu.
¹⁰ Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Australia. Electronic address: tracy.mccaffrey@monash.edu.
¹¹ School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada. Electronic address: sharon.kirkpatrick@uwaterloo.ca.
¹² Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia; Enable Institute, Curtin University, Perth, Australia. Electronic address: C.Pollard@curtin.edu.au.
¹³ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia. Electronic address: janelle.healy@curtin.edu.au.
¹⁴ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia. Electronic address: amira.hassan@curtin.edu.au.
¹⁵ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia. Electronic address: shivangi.garg@curtin.edu.au.
¹⁶ Health Section, Health and Disability Branch, Australian Bureau of Statistics, Canberra, Australia. Electronic address: paul.atyeo@abs.gov.au.
¹⁷ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia. Electronic address: Aqif.Mukhtar@curtin.edu.au.
¹⁸ Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia; Curtin Health Innovation Research Institute, Curtin University, Kent Street, GPO Box U1987, Perth 6845, Australia. Electronic address: d.kerr@curtin.edu.au.

PMID: 38710447
PMCID: PMC11347807
DOI: 10.1016/j.ajcnut.2024.04.030

Abstract

Background: Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population.

Objectives: To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner.

Methods: In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m², SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models.

Results: The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods.

Conclusions: Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

Keywords: Automated Self-Administered Dietary Assessment Tool; Intake24; controlled feeding; dietary assessment; dietary measurement error; image-assisted; interview-administered dietary recall; mobile food record; mobile technology; validation.

PubMed Disclaimer

Figures

**FIGURE 1**
Study flow chart on enrollment, randomization, and study design. ASA24, Feeding day followed by completion of Automated Self-Administered Dietary Assessment Tool (ASA24)-Australia; Intake24, Feeding day followed by completion of Intake24-Australia; mFR-TA & IA-24HR, Feeding day including capture of images of meals using mobile Food Record app, followed by completion of Image-Assisted Interviewer-Administered 24-Hour Recall.

**FIGURE 2**
Bland–Altman scatterplots of percentage error in estimated energy intake against true energy intake, showing mean difference and upper and lower limits of agreement (± 1.96 standard deviation) (solid lines), and locally weighted scatterplot smoothing lines (dotted lines). (A) ASA24-Australia; (B) Intake24-Australia; (C) mFR-TA; (D) IA-24HR. ASA24, Feeding day followed by completion of Automated Self-Administered Dietary Assessment Tool (ASA24)-Australia; Intake24, Feeding day followed by completion of Intake24-Australia; mFR-TA & IA-24HR, Feeding day including capture of images of meals using mobile Food Record app, followed by completion of Image-Assisted Interviewer-Administered 24-Hour Recall.

See this image and copyright information in PMC

References

1. Byers T., Sedjo R. In: Nutritional Epidemiology. 3rd ed. Willet W., editor. Oxford University Press; Oxford, United Kingdom: 2013. Nutrition monitoring and surveillance.
1. Australian Bureau of Statistics Australian Health Survey: Users’ Guide, 2011-13, cat. no. 4363.0.55.001. 2013 https://www.abs.gov.au/ausstats/abs@.nsf/Lookup/0D6B1FE95EAB8FF3CA257CD2... [Internet] Available from:
1. Food Surveys Research Group Agricultural Research Service, US Department of Agriculture. AMPM - USDA Automated Multiple-Pass Method. 2019 https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-h... [Internet] Available from:
1. De Keyzer W., Bracke T., McNaughton S.A., Parnell W., Moshfegh A.J., Pereira R.A., et al. Cross-continental comparison of national food consumption survey methods--a narrative review. Nutrients. 2015;7(3):3587–3620. doi: 10.3390/nu7053587. - DOI - PMC - PubMed
1. Huybrechts I., Aglago E.K., Mullee A., De Keyzer W., Leclercq C., Allemand P., et al. Global comparison of national individual food consumption surveys as a basis for health research and integration in national health surveillance programmes. Proc. Nutr. Soc. 2017;76(4):549–567. doi: 10.1017/s0029665117001161. - DOI - PubMed

Publication types

Actions
Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
- Elsevier Science
- PubMed Central

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study

Affiliations

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources