Validity of an AI-based Program to Identify Foods and Estimate Food Portion Size

Study Overview
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Brief Summary
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The purpose of this study is to test the accuracy of the Nutrition Artificial Intelligence in the Openfit app during meals in a controlled laboratory setting

Detailed Description
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For this pilot study, using a convenience sample, the investigators will recruit up to 25 adults to use the Nutrition AI technology in Openfit to identify and estimate portion size of foods plated in a laboratory setting at Pennington Biomedical Research Center (PBRC) and/or Louisiana State University (LSU). Laboratory members within the Ingestive Behavioral Laboratory will also test the ability of Nutrition AI to identify foods and to quantify foods provided in the laboratory. Meals will be simulated, and participants will not consume the foods provided.

Official Title
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Testing the Validity of an Artificial Intelligence-based Program to Identify Foods and Estimate Food Portion Size Among Adults, a Pilot Study

Conditions
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Nutrition Assessment

Intervention / Treatment
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* Device: PortionSize AI


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Male or female Aged 18-62 years Self-reported body mass index (BMI) 18.5-50 kg/m2 Exclusion Criteria: Any condition or circumstance that could impede study completion Unfamiliar with or not able to use an iPhone

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 62 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Experimental<br>Training and use of Openfit Using the app to estimate food intake from simulated meals in a laboratory at PBRC or LSU (participants will not eat food during the meals) Rating the usability and satisfaction of the app | Device: PortionSize AI<br>* For this pilot study, using a convenience sample, the investigators will recruit up to 25 adults to use the Nutrition AI technology in Openfit to identify and estimate portion size of foods provided in a laboratory setting at Pennington Biomedical Research Center (PBRC) and/or Louisiana State University (LSU). Laboratory members within the Ingestive Behavioral Laboratory will also test the ability of Nutrition AI to identify foods and to quantify foods provided in the laboratory. Meals will be simulated, and participants will not consume the foods provided.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification of Food Plated Using the Openfit Mobile App | Agreement surrounding identification of food and beverages provided compared with known identification, at the item level, and across all items where identification is determined by: 1) Nutrition AI without correction (automated), 2) Nutrition AI with user correction (semi-automated) For a food identified through the Nutrition AI to be considered an exact food match, the name of the food identified must match or be a close match to the food served. For example, a fruit cocktail identified as a fruit salad is an acceptable match. Proportions will be used to assess whether the percentage of food items plated that were correctly identified by Nutrition AI is different to the percentage of foods correctly identified by a criterion method (human rater). Descriptive data will also be used to describe the frequency at which food plated was correctly identified for all food items across all participants. In total there was 255 food items tested across all participants. | One study visit of  2 hours | 
| Portion Size Estimation (kcal) of Food Plated Using the Openfit Mobile App | Error between mean estimates of food plated (kcal) and known food plated (kcal), determined by: 1) Nutrition AI without user correction (automated), 2) Nutrition AI with user correction (semi-automated) Mean error and Bland-Altman analysis will be performed to determine errors in estimation of food plated from the Nutrition AI compared to estimations from the criterion measure (weighed food). | One study visit of  2 hours | 
| User Satisfaction of the Openfit Mobile App for Recording Food Plated | After completing assessment of food plated, participants will complete a user satisfaction survey (USS). The USS was adapted from a previous version used to assess the usability of a mobile application for dietary assessment. The USS includes five quantitative questions and three open response questions. The quantitative questions will each be scored using a 6-point Likert scale, with 1 being the lowest and worst score, and 6 being the highest and best score. Data for each of the five quantitative responses in the USS will be averaged across participants and presented separately as mean (SD). Open responses will be evaluated using qualitative methods to identify common themes. | One study visit of  2 hours | 
| Usability of the Openfit Mobile App for Recording Food Plated | Participants will complete the Computer Usability Satisfaction Questionnaire (CSUQ). The CSUQ is frequently used to assess the usability of mobile applications. The CSUQ consists of 19 questions, each scored using a 7-point Likert scale (with 1 being the lowest and best score and 7 being the highest and worst score) and participants will rate satisfaction, usefulness, information quality, and interface quality of the Openfit app. The average of these 19 questions (1 being the best average score and 7 being the worst average score) provides an overall usability score. | One study visit of  2 hours | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Nutrition Assessment