Validity of Electronic Diet Recording Nutrient Estimates Compared to Dietitian Analysis of Diet Records: Randomized Controlled Trial

Raatz, Susan K.; Scheett, Angela; Johnson, LuAnn K.; Jahns, Lisa

doi:10.2196/jmir.3744

Cited by 23 publications

(49 citation statements)

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“…The cost and practicality associated with the direct observation of intake or the collection of biological samples for the analysis of biomarkers of intake can also be limiting factors. In the absence of biomarkers or direct observation, dietary records (preferably weighed food records) are often considered the 'gold standard' for the measurement of food and nutrient intake and are typically used as a reference comparison method (37) . De Keyzer et al (38) notes that the 24HDR method is currently the most commonly used tool to collect dietary intake data in national surveys, usually taking multiple recalls to assess habitual intake and, in some instances, used in conjunction with an FFQ (for example, Canada and USA) (39,40) .…”

Section: Introductionmentioning

confidence: 99%

A review of the design and validation of web- and computer-based 24-h dietary recall tools

et al. 2016

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Access to the full text of the published version may require a subscription. Rights © The Authors 2016. Published by Cambridge University Press(CUP) on behalf of The Nutrition Society.Embargo information Access to this item is restricted until 12 months after publication by the request of the publisher. (24HDR) method is currently the most utilised method for the collection of dietary intake data at a national level. Recently there have been many developments using web-based platforms to collect food intake data using the principles of the 24HDR method. This review identifies web-and computer-based 24HDR tools that have been developed for both children and adult population groups, and examines common design features and the methods used to investigate the performance and validity of these tools. Overall, there is generally good to strong agreement between web-based 24HDR and respective reference measures for intakes of macro-and micronutrients. Embargo lift date

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Section: Introductionmentioning

confidence: 99%

A review of the design and validation of web- and computer-based 24-h dietary recall tools

et al. 2016

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“…Estimates of carbohydrate and total fat intake were also highly comparable between DietPlan6 and S Health, MyFitnessPal and FatSecret, with no significant differences between the outputs and similarly small mean differences (5.3 to-13.8g for carbohydrate and -4.6 to -15.9g for total fat). Raatz et al also found no significant differences in estimates of energy, carbohydrate and total fat when comparing nutrient analysis of a 3-day WFR using the Tap & Track app and US Department of Agriculture (USDA) nutrient analysis program (mean differences 85kcal, 15.4g and 2.6g respectively) [9]. However, in contrast to our study, users (n=19) entered the WFR into Tap & Track (n=19) and a registered dietitian into the USDA nutrient analysis program [9].…”

Section: Discussionmentioning

confidence: 99%

“…Raatz et al also found no significant differences in estimates of energy, carbohydrate and total fat when comparing nutrient analysis of a 3-day WFR using the Tap & Track app and US Department of Agriculture (USDA) nutrient analysis program (mean differences 85kcal, 15.4g and 2.6g respectively) [9]. However, in contrast to our study, users (n=19) entered the WFR into Tap & Track (n=19) and a registered dietitian into the USDA nutrient analysis program [9]. The authors suggested that the observed variability in BlandAltman plots between methods may be associated with the ability of the users to pick appropriate food items and ensure all data is recorded [9], [13], which would not have been an issue in our study as a single researcher entered data into all apps and reference programme.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, with the growing number of commercial nutrition-related apps, the difficulty of creating criteria to assess them increases with varying features and databases. Previous studies by Carter et al [6] and Raatz et al [9] comparing commercial nutrition-related apps with reference methods have used 24-hour recalls and weighed food records (WFR) respectively. Both studies found no significant differences in mean energy and macronutrients between the nutritionrelated app and reference method.…”

Section: Introductionmentioning

confidence: 99%

“…Both studies found no significant differences in mean energy and macronutrients between the nutritionrelated app and reference method. Carter et al used an app for smartphones called My Meal Mate (MMM) [6] whereas Raatz, et al used Tap & Track [9]. However, MMM is not considered a 'popular' smartphone app (with less than 500 thousand installs), as identified by Franco et al [10], and Tap & Track is only available in the Apple Store, limiting its use.…”

Section: Introductionmentioning

confidence: 99%

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

Fallaize¹,

Franco²,

Pasang³

et al. 2018

Preprint

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Background: Nutrition-related apps are commonly used to provide information about the users' dietary intake, but limited research has been performed on the evaluation of their reliability. Objective: To evaluate the relative reliability of popular nutrition-related apps for the assessment of energy and available macro-and micronutrients against a standard method. Methods: Dietary analysis of 24-hour weighed food records (n = 20) were compared between five nutrition-related apps: S Health, MyFitnessPal, FatSecret, Noom Coach and Lose It!, and DietPlan6 (reference method). Estimates of energy, macronutrients (carbohydrate, protein, fat, saturated fat and fibre) and micronutrients (sodium, calcium, iron, vitamin A and vitamin C) were compared using t-tests and Wilcoxon signed-rank tests, correlation coefficients and Bland-Altman plots. 24-hour weighed food records from 20 participants (female/male, 15/5; mean age, 36.3 years; mean body mass index, 22.9 kg/m 2 ) recruited from previous controlled studies conducted at the Hugh Sinclair Unit of Human Nutrition, University of Reading, UK. Results: No significant difference in estimation of energy and saturated fat intake between DietPlan6 and the diet apps was observed. Estimates of protein and sodium intake were significantly lower using Lose It! and FatSecret than DietPlan6. Lose It! also gave significantly lower estimates for other reported outputs: carbohydrate, fat, fibre and sodium, compared with DietPlan6. For S Health and MyFitnessPal, calcium, iron and vitamin C were all significantly under-estimated compared with DietPlan6, although there was no significant difference for vitamin A. No other significant differences were observed between DietPlan6 and the apps. Correlation coefficients ranged from -0.12 for iron (S Health vs. DietPlan6) to 0.91 for protein (FatSecret vs DietPlan6). Noom Coach was limited to energy output but it had a high correlation with DietPlan6 (r=0.91) and S Health had the greatest variation of correlation, with energy at 0.79. Bland-Altman analysis revealed potential proportional bias for vitamin A. Conclusions: The findings suggest that the apps provide comparable estimates of energy and saturated fat intake compared with DietPlan6. With the exception of Lose It!, the apps also provided comparable estimates of carbohydrate, total fat and fibre. Two apps displayed a tendency to underestimate protein and sodium (FatSecret and Lose It!). Apart from vitamin A, the estimates of micronutrient intake (calcium, iron and vitamin C) by the two apps (S Health and MyFitnessPal) were inconsistent and less reliable. Lose It! was the less comparable app in relation to DietPlan6. As the use and availability of apps grows, this study helps clinicians and researchers to make betterinformed decisions about using these apps in research and practice.

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Developing a Chinese Food Nutrient Data Analysis System for Precise Dietary Intake Management

Hou

Guo

et al. 2018

Big Data – BigData 2018

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Validity of Electronic Diet Recording Nutrient Estimates Compared to Dietitian Analysis of Diet Records: Randomized Controlled Trial

Cited by 23 publications

References 24 publications

A review of the design and validation of web- and computer-based 24-h dietary recall tools

A review of the design and validation of web- and computer-based 24-h dietary recall tools

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

Developing a Chinese Food Nutrient Data Analysis System for Precise Dietary Intake Management

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