# Evaluation of the validity of the PortionSize app in controlled and free-living conditions: Tests of an app that calculates food intake and provides immediate feedback to users

> **NIH NIH R01** · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · 2020 · $415,276

## Abstract

Project Summary / Abstract
 Accurately quantifying food intake is vital to promoting health and reducing chronic disease risk. Food
intake encompasses energy intake, nutrient intake, and intake of various food groups (e.g., fruits, vegetables),
and thus reflects the nutritional status of individuals. Nutrition affects disease risk, including risk of developing
obesity, diabetes, and cancer, all of which negatively affect the United States (U.S). Despite its importance,
accurately quantifying food intake has challenged researchers and clinicians for decades. Self-report methods
(e.g., food records and diet recall) are a mainstay of nutritional epidemiology research, but their accuracy has
been questioned, due, in part, to missing data and people inaccurately estimating portion size and recalling
what they ate. Advances in assessing food intake over the past 15 years include technology-assisted
approaches, including those that rely on food photography. Our group previously developed the Remote Food
Photography Method (RFPM) and SmartIntake app, which quantifies food intake based on food images that
users capture before and after they eat. Accurate estimates of food intake are obtained with this method in
most study populations and settings, yet analysis of the images takes time and resources, requires a human
rater, and users do not receive immediate feedback about their food intake. We developed the PortionSize
smartphone app to overcome these limitations. The PortionSize app relies on users capturing images of their
food selection and waste, but it immediately provides users with food intake data. The PortionSize app
includes innovative technology to minimize missing data and to help users accurately estimate portion size.
Preliminary data supports the validity of the PortionSize app, and during the proposed research the reliability
and validity of PortionSize and MyFitnessPal, a commonly used smartphone-based food record, will be tested
against `gold-standard' criterion measures. Specifically, the apps will be tested in healthy adults under the
following three conditions: 1) laboratory-based test meals (Study 1), 2) free-living conditions, where
participants will consume pre-weighed food from a cooler, which provides a test of energy and nutrient intake
in free-living conditions (Study 2), and 3) free-living conditions, where energy intake is also assessed by doubly
labeled water (Study 3). If found to be valid, the PortionSize app will move the field forward by providing a
method that could widely and affordably be disseminated to assess food intake and foster/track adherence to
personalized diets in real time.

## Key facts

- **NIH application ID:** 9943316
- **Project number:** 1R01DK124558-01
- **Recipient organization:** LSU PENNINGTON BIOMEDICAL RESEARCH CTR
- **Principal Investigator:** John William Apolzan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $415,276
- **Award type:** 1
- **Project period:** 2020-04-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9943316

## Citation

> US National Institutes of Health, RePORTER application 9943316, Evaluation of the validity of the PortionSize app in controlled and free-living conditions: Tests of an app that calculates food intake and provides immediate feedback to users (1R01DK124558-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9943316. Licensed CC0.

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