Evaluation of PIQNIQ, a Novel Mobile Application for Capturing Dietary Intake

Abstract

Background Accurate measurement of dietary intake is vital for providing nutrition interventions and understanding the complex role of diet in health. Traditional dietary assessment methods are very resource intensive and burdensome to participants. Technology may help mitigate these limitations and improve dietary data capture. Objective Our objective was to evaluate the accuracy of a novel mobile application (PIQNIQ) in capturing dietary intake by self-report. Our secondary objective was to assess whether food capture using PIQNIQ was comparable with an interviewer-assisted 24-h recall (24HR). Methods This study was a single-center randomized clinical trial enrolling 132 adults aged 18 to 65 y from the general population. Under a provided-food protocol with 3 menus designed to include a variety of foods, participants were randomly assigned to 1 of 3 food capture methods: simultaneous entry using PIQNIQ, photo-assisted recall using PIQNIQ, and 24HR. Primary outcomes were energy and nutrient content (calories, total fat, carbohydrates, protein, added sugars, calcium, dietary fiber, folate, iron, magnesium, potassium, saturated fat, sodium, and vitamins A, C, D, and E) captured by the 3 methods. Results The majority of nutrients reported were within 30% of consumed intake in all 3 food capture methods (n = 129 completers). Reported intake was highly (>30%) overestimated for added sugars in both PIQNIQ groups and underestimated for calcium in the photo-assisted recall group only (P < 0.001 for all). However, in general, both PIQNIQ methods had similar levels of accuracy and were comparable to the 24HR except in their overestimation (>30%) of added sugars and total fat (P < 0.001 for both). Conclusions Our results suggest that intuitive, technology-based methods of dietary data capture are well suited to modern users and, with proper execution, can provide data that are comparable to data obtained with traditional methods. This trial was registered at clinicaltrials.gov as NCT03578458.

Performance of the Digital Dietary Assessment Tool MyFoodRepo

“…Unlike MFR, many digital dietary assessment tools used in studies rely on user participation for portion size estimation, either via a portion size selector [2,19] or a complementary portion size booklet [22]. Portion size estimation relying on the capture of a single image was proven to reduce user burden as automated estimations are not affected by the user's lack of knowledge about quantities [36,37].…”

“…Digital measurement devices can help overcome the limitations of conventional dietary assessment tools and provide a cost-effective way to simplify and scale up nutritional data collection. Such devices were shown to increase user acceptance while providing valuable real-time food intake data [2][3][4], and have the potential to eliminate participant burden linked to portion size estimation [5]. Digital image capture of foods is further facilitated by the distribution of mobile phones and the population's familiarity with this technology.…”

“…The majority of available mobile dieting apps are designed to support behavioral changes and either have not been validated for use in research or lack accuracy [7][8][9][10]. Although digital dietary assessment tools employed in research overcome these limitations, they often rely on more cumbersome participation from users; for example, necessitating specific experimental settings [11], requiring participants to wear impractical gear (e.g., chest-worn camera) [12][13][14] or manually select dishes and estimate portion sizes [2,[15][16][17]. The accuracy of these tools relies on the comprehensiveness and quality of their underlying nutrition databases, whose continuous update is a challenge [3,10].…”

“…Whereas some researchers investigating digital tools mainly focus on the segmentation and classification of food components-as is the case for DietCam [18]-other validation studies-e.g., for e-Ca [19] or mFR [20]-concentrate on determining weight error and related energy and macronutrient intakes. In most validation studies-amongst which the performance analysis of the digital dietary assessment tools Keenoa [9], PIQNIQ [2], EaT [21] and Bridge2U [22]-the accuracy of energy and macronutrient content is the sole endpoint investigated. One exception is Snap-n-Eat, a mobile phone recognition system able to perform automatic segmentation and classification of foods and allowing subsequent weight estimation and energy and macronutrient content calculation, but this app has not yet been validated [23].…”

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

“…To improve the limitations of app usage for accurate dietary assessment, the newly developed PIQNIQ app incorporates features such as text entry with dropdown menus and a comprehensive database, as well as portion size selector combined with slider aid for visual estimation of food and drink items. In a study investigating the accuracy of the PIQNIQ app results indicate that, with the exception of added sugars and total fats, macro and micronutrient profiles were comparable to interviewer administered multiple-pass 24 h recalls [ 23 ]. Findings are encouraging, suggesting apps which are carefully designed to incorporate user-friendly portion size selection tools combined with image assistance, and links to comprehensive databases, may produce similar accuracy as traditional validated methods, whilst reducing the burden of such methods.…”