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| ID | Type | Description | Link |
|---|---|---|---|
| T32DK064584 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) | NIH |
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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
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experimental | Experimental |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| PortionSize AI | 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. |
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Chloe P Lozano, PhD | Pennington Biomedical Research Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Pennington Biomedical Research Center | Baton Rouge | Louisiana | 70808 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38026571 | Derived | 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. |
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Any identifiers might be removed from the participants identifiable information and after such removal, the information could be used for future research studies or given to another investigator for future research without additional informed consent from the subject or legally authorized representative.
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| ID | Title | Description |
|---|---|---|
| FG000 | Experimental |
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 and simulated plate waste, and food intake 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, plate waste and food intake, in the laboratory. Meals will be simulated, and participants will not consume the foods provided. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | Experimental |
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 and simulated plate waste, and food intake 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, plate waste and food intake, in the laboratory. Meals will be simulated, and participants will not consume the foods provided. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | Posted | Count of Units | Food items | One study visit of ~2 hours | Food items | Food items |
|
Over 1 study visit of approximately 2 hours.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Experimental |
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 and simulated plate waste, and food intake 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, plate waste and food intake, in the laboratory. Meals will be simulated, and participants will not consume the foods provided. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Chloe Panizza Lozano | Pennington Biomedical Research Center | (225) 763-2500 | cpanizza@hawaii.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 8, 2022 | Aug 24, 2023 | Prot_SAP_000.pdf |
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|
| 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 |
| years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
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| Education | Count of Participants | Participants |
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| Employment | Count of Participants | Participants |
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| Body Mass Index | Mean | Standard Deviation | kg/m^2 |
|
| OG000 |
| Experimental |
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 and simulated plate waste, and food intake 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, plate waste and food intake, in the laboratory. Meals will be simulated, and participants will not consume the foods provided. |
|
|
| Primary | 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). | Posted | Mean | Standard Deviation | kcal | One study visit of ~2 hours |
|
|
|
| Primary | 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. | Posted | Mean | Standard Deviation | score on a scale | One study visit of ~2 hours |
|
|
|
| Primary | 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. | Posted | Mean | Standard Deviation | score on a scale | One study visit of ~2 hours |
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| Title | Measurements |
|---|---|
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| How easy was it to use the app for estimating the amount of food provided? |
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| How much did the training help prepare you for using the app? |
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