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

NCT ID: NCT05343585

Last Updated: 2023-11-18

Study Results

Results available

Outcome measurements, participant flow, baseline characteristics, and adverse events have been published for this study.

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Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

24 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-04-27

Study Completion Date

2022-06-03

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.

Conditions

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Nutrition Assessment

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

OTHER

Blinding Strategy

NONE

Study Groups

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Experimental

* 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

Group Type EXPERIMENTAL

PortionSize AI

Intervention Type DEVICE

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.

Interventions

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PortionSize AI

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.

Intervention Type DEVICE

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
Minimum Eligible Age

18 Years

Maximum Eligible Age

62 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

NIH

Sponsor Role collaborator

Pennington Biomedical Research Center

OTHER

Sponsor Role lead

Responsible Party

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Chloe

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Chloe P Lozano, PhD

Role: PRINCIPAL_INVESTIGATOR

Pennington Biomedical Research Center

Locations

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Pennington Biomedical Research Center

Baton Rouge, Louisiana, United States

Site Status

Countries

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United States

References

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Lozano CP, Canty EN, Saha S, Broyles ST, Beyl RA, Apolzan JW, Martin CK. Validity of an Artificial Intelligence-Based Application to Identify Foods and Estimate Energy Intake Among Adults: A Pilot Study. Curr Dev Nutr. 2023 Sep 29;7(11):102009. doi: 10.1016/j.cdnut.2023.102009. eCollection 2023 Nov.

Reference Type DERIVED
PMID: 38026571 (View on PubMed)

Provided Documents

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Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

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T32DK064584

Identifier Type: NIH

Identifier Source: secondary_id

View Link

PBRC 2022-011

Identifier Type: -

Identifier Source: org_study_id

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