A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps

Abstract

Mobile dietary record apps have been increasingly validated by studies with various study designs. This review aims to evaluate the overall accuracy of dietary record apps in measuring the intake of energy, macro- and micronutrients, and food groups in real-life settings and the designs of validation studies. We systematically searched mobile dietary record validation studies published during the period from 2013 to 2019. We identified 14 studies for the systematic review, of which 11 studies were suitable for meta-analyses on energy intake and 8 studies on macronutrient intake. Mean differences and SDs of nutrient estimations between the app and the reference method from studies were pooled using a random-effects model. All apps underestimated energy intake when compared with their reference methods, with a pooled effect of −202 kcal/d (95% CI: −319, −85 kcal/d); the heterogeneity of studies was 72%. After stratification, studies that used the same food-composition table for both the app and the reference method had a lower level of heterogeneity (0%) and a pooled effect of −57 kcal/d (95% CI: −116, 2 kcal/d). The heterogeneity of studies in the differences in carbohydrate, fat, and protein intake was 54%, 73%, and 80%, with the pooled effect of −18.8 g/d, −12.7 g/d, and −12.2 g/d, respectively, after excluding outliers. The intakes of micronutrients and food groups were statistically nonsignificantly underestimated by the apps in most cases. In conclusion, dietary record apps underestimated food consumption compared with traditional dietary assessment methods. Moreover, varying study designs have been found across studies. Recommended practices for conducting validation studies were formulated including considering biomarkers as the reference, testing in a larger and more representative study population for a longer period, avoiding the learning effect of each method, and comparing food group or food item consumption in addition to comparing energy and nutrient intakes.

Relative Validation of an Artificial Intelligence–Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study

“…In addition, other relative validation studies on mobile tools are often performed on the same day of intake (food diary followed by a 24-hour recall the next day), which induces a major training bias. Results from those studies typically show limited systematic bias with wide LOAs [ 52 ], which is comparable with our study’s results even though our tracking periods were different between tools. Finally, some image-assisted methods involve wearable camera devices [ 53 , 54 ], which can also be time-consuming for researchers to collect and analyze data, in addition to potential ethical concerns and issues with participant acceptance because of their invasiveness.…”

Mobile applications for the sport and exercise nutritionist: a narrative review

“…Whilst apps share the portability benefits of traditional dietary records, the additional features of automated time/date-stamped recording of items alongside features such as barcode scanning and image taking provide greater objectivity [ 16 ]. However, current reviews acknowledge the algorithms required for image-assisted apps are still in their infancy, with practitioners and researchers predominantly opting for apps which utilise more textual food input methods [ 17 , 18 ].…”

“…In a more recent review, Zhang et al [ 18 ] conducted a systematic review and meta-analysis on validation studies of dietary apps. Sample sizes ranged from 18 to 362, predominantly including young adults over a 2–7 days collection period, within real-life settings.…”

“…Apps used within these validation studies included MyFitnessPal, Diet-A, EVIDENT II, Eat and Track, FoodNow, Nutrabem, e-CA, MyMealMate, e-DIA, BENECA and Research Food Diary. Findings highlighted that all apps underestimated energy intake when compared to their reference methods, with a pooled effect of − 202 kcal day −1 (95% CI − 319 to 85 kcal day −1 ) [ 18 ]. Wang et al [ 25 ] proposed a difference in energy intake of 110–165 kcal day −1 to be clinically meaningful in a weight loss context; lower than the pooled effect of the underestimation of energy intake when compared to their respective reference method identified in the meta-analysis (− 202 kcal day −1 ).…”

Exercise, diet, and sleep habits of nurses working full-time during the COVID-19 pandemic: An observational study

“…Measuring changes to sleep, diet, and physical activity during times of high stress such as the COVID-19 pandemic is warranted as a first step to inform targeted wellness strategies. Wearable fitness trackers and smartphones allow for objective, valid, and reliable collection of lifestyle behaviors data including sleep duration, quality, and efficiency ( Fuller et al, 2020 ), diet quality and composition ( Zhang et al, 2021 ), and number of steps walked per day ( Guillodo et al, 2020 ; Jin et al, 2022 ). Mobile applications empower individuals to self-report sleep, diet, and physical activity habits daily into electronic repositories ( Jin et al, 2022 ).…”