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Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method

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Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method. / Fallaize, Rosalind; Franco, Rodrigo Zenun; Pasang, Jennifer ; Hwang, Faustina; Lovegrove, Julie A.

In: JMIR mHealth and uHealth, Vol. 7, No. 2, e9838, 01.02.2019, p. e9838.

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Fallaize, Rosalind ; Franco, Rodrigo Zenun ; Pasang, Jennifer ; Hwang, Faustina ; Lovegrove, Julie A. / Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method. In: JMIR mHealth and uHealth. 2019 ; Vol. 7, No. 2. pp. e9838.

Bibtex

@article{abc87e75a5e945dd9df5e3c495b0e623,
title = "Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method",
abstract = "Background: Nutrition-related apps are commonly used to provide information about the user{\textquoteright}s dietary intake, but limited research has been performed to assess how well their outputs agree with those from standard methods. Objective: The objective of our study was to evaluate the level of agreement of popular nutrition-related apps for the assessment of energy and available macronutrients and micronutrients against a UK reference method. Methods: We compared dietary analysis of 24-hour weighed food records (n=20) between 5 nutrition-related apps (Samsung Health, MyFitnessPal, FatSecret, Noom Coach, and Lose It!) and Dietplan6 (reference method), using app versions available in the United Kingdom. We compared estimates of energy, macronutrients (carbohydrate, protein, fat, saturated fat, and fiber), and micronutrients (sodium, calcium, iron, vitamin A, and vitamin C) using paired t tests and Wilcoxon signed-rank tests, correlation coefficients, and Bland-Altman plots. We obtained 24-hour weighed food records from 20 participants (15 female, 5 male participants; mean age 36.3 years; mean body mass index 22.9 kg/m2) from previous controlled studies conducted at the Hugh Sinclair Unit of Human Nutrition, University of Reading, Reading, UK. Participants had recorded their food consumption over a 24-hour period using standard protocols. Results: The difference in estimation of energy and saturated fat intake between Dietplan6 and the diet apps was not significant. Estimates of protein and sodium intake were significantly lower using Lose It! and FatSecret than using Dietplan6. Lose It! also gave significantly lower estimates for other reported outputs (carbohydrate, fat, fiber, and sodium) than did Dietplan6. Samsung Health and MyFitnessPal significantly underestimated calcium, iron, and vitamin C compared with Dietplan6, although there was no significant difference for vitamin A. We observed no other significant differences between Dietplan6 and the apps. Correlation coefficients ranged from r=–.12 for iron (Samsung Health vs Dietplan6) to r=.91 for protein (FatSecret vs Dietplan6). Noom Coach was limited to energy output, but it had a high correlation with Dietplan6 (r=.91). Samsung Health had the greatest variation of correlation, with energy at r=.79. Bland-Altman analysis revealed potential proportional bias for vitamin A. Conclusions: The findings suggest that the apps provide estimates of energy and saturated fat intake comparable with estimates by Dietplan6. With the exception of Lose It!, the apps also provided comparable estimates of carbohydrate, total fat, and fiber. FatSecret and Lose It! tended to underestimate protein and sodium. Estimates of micronutrient intake (calcium, iron, vitamin A, and vitamin C) by 2 apps (Samsung Health and MyFitnessPal) were inconsistent and less reliable. Lose It! was the app least comparable with Dietplan6. As the use and availability of apps grows, this study helps clinicians and researchers to make better-informed decisions about using these apps in research and practice.",
keywords = "Diet apps, Diet records, Dietary assessment, Mobile applications, Nutrition apps, Smartphone application, Weighed food records",
author = "Rosalind Fallaize and Franco, {Rodrigo Zenun} and Jennifer Pasang and Faustina Hwang and Lovegrove, {Julie A}",
note = "{\textcopyright}Rosalind Fallaize, Rodrigo Zenun Franco, Jennifer Pasang, Faustina Hwang, Julie A Lovegrove. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.02.2019.",
year = "2019",
month = feb,
day = "1",
doi = "10.2196/mhealth.9838",
language = "English",
volume = "7",
pages = "e9838",
journal = "JMIR mHealth and uHealth",
issn = "2291-5222",
publisher = "JMIR Publications ",
number = "2",

}

RIS

TY - JOUR

T1 - Popular Nutrition-Related Mobile Apps: An Agreement Assessment Against a UK Reference Method

AU - Fallaize, Rosalind

AU - Franco, Rodrigo Zenun

AU - Pasang, Jennifer

AU - Hwang, Faustina

AU - Lovegrove, Julie A

N1 - ©Rosalind Fallaize, Rodrigo Zenun Franco, Jennifer Pasang, Faustina Hwang, Julie A Lovegrove. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.02.2019.

PY - 2019/2/1

Y1 - 2019/2/1

N2 - Background: Nutrition-related apps are commonly used to provide information about the user’s dietary intake, but limited research has been performed to assess how well their outputs agree with those from standard methods. Objective: The objective of our study was to evaluate the level of agreement of popular nutrition-related apps for the assessment of energy and available macronutrients and micronutrients against a UK reference method. Methods: We compared dietary analysis of 24-hour weighed food records (n=20) between 5 nutrition-related apps (Samsung Health, MyFitnessPal, FatSecret, Noom Coach, and Lose It!) and Dietplan6 (reference method), using app versions available in the United Kingdom. We compared estimates of energy, macronutrients (carbohydrate, protein, fat, saturated fat, and fiber), and micronutrients (sodium, calcium, iron, vitamin A, and vitamin C) using paired t tests and Wilcoxon signed-rank tests, correlation coefficients, and Bland-Altman plots. We obtained 24-hour weighed food records from 20 participants (15 female, 5 male participants; mean age 36.3 years; mean body mass index 22.9 kg/m2) from previous controlled studies conducted at the Hugh Sinclair Unit of Human Nutrition, University of Reading, Reading, UK. Participants had recorded their food consumption over a 24-hour period using standard protocols. Results: The difference in estimation of energy and saturated fat intake between Dietplan6 and the diet apps was not significant. Estimates of protein and sodium intake were significantly lower using Lose It! and FatSecret than using Dietplan6. Lose It! also gave significantly lower estimates for other reported outputs (carbohydrate, fat, fiber, and sodium) than did Dietplan6. Samsung Health and MyFitnessPal significantly underestimated calcium, iron, and vitamin C compared with Dietplan6, although there was no significant difference for vitamin A. We observed no other significant differences between Dietplan6 and the apps. Correlation coefficients ranged from r=–.12 for iron (Samsung Health vs Dietplan6) to r=.91 for protein (FatSecret vs Dietplan6). Noom Coach was limited to energy output, but it had a high correlation with Dietplan6 (r=.91). Samsung Health had the greatest variation of correlation, with energy at r=.79. Bland-Altman analysis revealed potential proportional bias for vitamin A. Conclusions: The findings suggest that the apps provide estimates of energy and saturated fat intake comparable with estimates by Dietplan6. With the exception of Lose It!, the apps also provided comparable estimates of carbohydrate, total fat, and fiber. FatSecret and Lose It! tended to underestimate protein and sodium. Estimates of micronutrient intake (calcium, iron, vitamin A, and vitamin C) by 2 apps (Samsung Health and MyFitnessPal) were inconsistent and less reliable. Lose It! was the app least comparable with Dietplan6. As the use and availability of apps grows, this study helps clinicians and researchers to make better-informed decisions about using these apps in research and practice.

AB - Background: Nutrition-related apps are commonly used to provide information about the user’s dietary intake, but limited research has been performed to assess how well their outputs agree with those from standard methods. Objective: The objective of our study was to evaluate the level of agreement of popular nutrition-related apps for the assessment of energy and available macronutrients and micronutrients against a UK reference method. Methods: We compared dietary analysis of 24-hour weighed food records (n=20) between 5 nutrition-related apps (Samsung Health, MyFitnessPal, FatSecret, Noom Coach, and Lose It!) and Dietplan6 (reference method), using app versions available in the United Kingdom. We compared estimates of energy, macronutrients (carbohydrate, protein, fat, saturated fat, and fiber), and micronutrients (sodium, calcium, iron, vitamin A, and vitamin C) using paired t tests and Wilcoxon signed-rank tests, correlation coefficients, and Bland-Altman plots. We obtained 24-hour weighed food records from 20 participants (15 female, 5 male participants; mean age 36.3 years; mean body mass index 22.9 kg/m2) from previous controlled studies conducted at the Hugh Sinclair Unit of Human Nutrition, University of Reading, Reading, UK. Participants had recorded their food consumption over a 24-hour period using standard protocols. Results: The difference in estimation of energy and saturated fat intake between Dietplan6 and the diet apps was not significant. Estimates of protein and sodium intake were significantly lower using Lose It! and FatSecret than using Dietplan6. Lose It! also gave significantly lower estimates for other reported outputs (carbohydrate, fat, fiber, and sodium) than did Dietplan6. Samsung Health and MyFitnessPal significantly underestimated calcium, iron, and vitamin C compared with Dietplan6, although there was no significant difference for vitamin A. We observed no other significant differences between Dietplan6 and the apps. Correlation coefficients ranged from r=–.12 for iron (Samsung Health vs Dietplan6) to r=.91 for protein (FatSecret vs Dietplan6). Noom Coach was limited to energy output, but it had a high correlation with Dietplan6 (r=.91). Samsung Health had the greatest variation of correlation, with energy at r=.79. Bland-Altman analysis revealed potential proportional bias for vitamin A. Conclusions: The findings suggest that the apps provide estimates of energy and saturated fat intake comparable with estimates by Dietplan6. With the exception of Lose It!, the apps also provided comparable estimates of carbohydrate, total fat, and fiber. FatSecret and Lose It! tended to underestimate protein and sodium. Estimates of micronutrient intake (calcium, iron, vitamin A, and vitamin C) by 2 apps (Samsung Health and MyFitnessPal) were inconsistent and less reliable. Lose It! was the app least comparable with Dietplan6. As the use and availability of apps grows, this study helps clinicians and researchers to make better-informed decisions about using these apps in research and practice.

KW - Diet apps

KW - Diet records

KW - Dietary assessment

KW - Mobile applications

KW - Nutrition apps

KW - Smartphone application

KW - Weighed food records

UR - http://www.scopus.com/inward/record.url?scp=85064522340&partnerID=8YFLogxK

U2 - 10.2196/mhealth.9838

DO - 10.2196/mhealth.9838

M3 - Article

VL - 7

SP - e9838

JO - JMIR mHealth and uHealth

JF - JMIR mHealth and uHealth

SN - 2291-5222

IS - 2

M1 - e9838

ER -