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Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study.
Whitton, Clare; Collins, Clare E; Mullan, Barbara A; Rollo, Megan E; Dhaliwal, Satvinder S; Norman, Richard; Boushey, Carol J; Delp, Edward J; Zhu, Fengqing; McCaffrey, Tracy A; Kirkpatrick, Sharon I; Pollard, Christina M; Healy, Janelle D; Hassan, Amira; Garg, Shivangi; Atyeo, Paul; Mukhtar, Syed Aqif; Kerr, Deborah A.
Affiliation
  • Whitton C; 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 Joondalu
  • Collins CE; 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.
  • Mullan BA; 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.
  • Rollo ME; 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.
  • Dhaliwal SS; 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 M
  • Norman R; 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.
  • Boushey CJ; Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA. Electronic address: cjboushey@cc.hawaii.edu.
  • Delp EJ; School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: ace@ecn.purdue.edu.
  • Zhu F; School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States. Electronic address: zhu0@purdue.edu.
  • McCaffrey TA; Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Australia. Electronic address: tracy.mccaffrey@monash.edu.
  • Kirkpatrick SI; School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada. Electronic address: sharon.kirkpatrick@uwaterloo.ca.
  • Pollard CM; 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:
  • Healy JD; 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.
  • Hassan A; 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.
  • Garg S; Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia. Electronic address: shivangi.garg@curtin.edu.au.
  • Atyeo P; Health Section, Health and Disability Branch, Australian Bureau of Statistics, Canberra, Australia. Electronic address: paul.atyeo@abs.gov.au.
  • Mukhtar SA; Curtin School of Population Health, Curtin University, Kent Street, GPO Box U1987, Perth 6845, WA, Australia. Electronic address: Aqif.Mukhtar@curtin.edu.au.
  • Kerr DA; 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.
Am J Clin Nutr ; 120(1): 196-210, 2024 07.
Article in En | MEDLINE | ID: mdl-38710447
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Energy Intake / Diet Records / Nutrition Assessment / Cross-Over Studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: Am J Clin Nutr Year: 2024 Document type: Article Country of publication: United States
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Energy Intake / Diet Records / Nutrition Assessment / Cross-Over Studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: Am J Clin Nutr Year: 2024 Document type: Article Country of publication: United States