An interventional study of PortionSize AI in Nutrition Assessment, sponsored by Pennington Biomedical Research Center. Completed at 1 site in United States. Open to participants aged 18 Years to 62 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2023-11-18.
Sponsored by Pennington Biomedical Research Center · Not applicable, Interventional, and Other
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.
Pennington Biomedical Research Center is the lead sponsor of 284 studies on the registry; 31 are open to participants now.
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Exclusion Criteria:
* 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
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.
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.
Time frame: 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).
Time frame: 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.
Time frame: 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.
Time frame: One study visit of ~2 hours
| Milestone | Experimental |
|---|---|
| Started | 24 |
| Completed | 24 |
| Not completed | 0 |
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.
| Food items | Experimental |
|---|---|
| Food items that were automatically identified as an exact match across all participants. | 118 |
| Food items that were semi-automatically identified as an exact match across all participants. | 221 |
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).
| kcal | Experimental |
|---|---|
| Energy of weighed meals. | 577 ± 150 |
| Energy of automated estimates | 826 ± 490 |
| Energy of semi-automated estimates | 769 ± 445 |
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.
| score on a scale | Experimental |
|---|---|
| How satisfied are you with the app for identifying the food provided? | 4.1 ± 1.3 |
| How satisfied are you with the app for estimating the amount of food provided? | 4.0 ± 1.5 |
| How easy was it to use the app to identify the food provided? | 4.2 ± 1.4 |
| How easy was it to use the app for estimating the amount of food provided? | 4.0 ± 1.6 |
| How much did the training help prepare you for using the app? | 5.5 ± 0.7 |
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.
| score on a scale | Experimental |
|---|---|
| Usability of the Openfit Mobile App for Recording Food Plated | 2.4 ± 0.22 |
Collected over Over 1 study visit of approximately 2 hours.. Non-serious events are listed at a 0% frequency threshold.
| Group | Deaths | Serious | Other |
|---|---|---|---|
| Experimental | 0/24 (0%) | 0/24 (0%) | 0/24 (0%) |
| Age, Continuous(years) | Experimental |
|---|---|
| Mean | 35.0 ± 9.5 |
| Sex: Female, Male(Participants) | Experimental |
|---|---|
| Female | 17 |
| Male | 7 |
| Race (NIH/OMB)(Participants) | Experimental |
|---|---|
| American Indian or Alaska Native | 0 |
| Asian | 3 |
| Native Hawaiian or Other Pacific Islander | 0 |
| Black or African American | 4 |
| White | 17 |
| More than one race | 0 |
| Unknown or Not Reported | 0 |
| Region of Enrollment(participants) | Experimental |
|---|---|
| United States | 24 |
| Education(Participants) | Experimental |
|---|---|
| Some College or Bachelor's Degree | 10 |
| Postgraduate degree | 14 |
| Employment(Participants) | Experimental |
|---|---|
| Full time | 22 |
| Part time | 2 |
| Body Mass Index(kg/m^2) | Experimental |
|---|---|
| Mean | 24.6 ± 4.1 |
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Plan to share: No — 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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