Nutrition Assessment
Conditions
Keywords
Nutrition Assessment
Brief summary
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
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.
Interventions
DEVICEPortionSize 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.
Sponsors
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Pennington Biomedical Research Center
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 62 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification of Food Plated Using the Openfit Mobile App | One study visit of ~2 hours | 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. |
| Portion Size Estimation (kcal) of Food Plated Using the Openfit Mobile App | One study visit of ~2 hours | 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). |
| User Satisfaction of the Openfit Mobile App for Recording Food Plated | One study visit of ~2 hours | 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. |
| Usability of the Openfit Mobile App for Recording Food Plated | One study visit of ~2 hours | 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. |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| 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
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. | 24 |
| Total | 24 |
Baseline characteristics
| Characteristic | Experimental |
|---|---|
| Age, Continuous | 35.0 years STANDARD_DEVIATION 9.5 |
| Body Mass Index | 24.6 kg/m^2 STANDARD_DEVIATION 4.1 |
| Education Postgraduate degree | 14 Participants |
| Education Some College or Bachelor's Degree | 10 Participants |
| Employment Full time | 22 Participants |
| Employment Part time | 2 Participants |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants |
| Race (NIH/OMB) Asian | 3 Participants |
| Race (NIH/OMB) Black or African American | 4 Participants |
| Race (NIH/OMB) More than one race | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 0 Participants |
| Race (NIH/OMB) White | 17 Participants |
| Region of Enrollment United States | 24 participants |
| Sex: Female, Male Female | 17 Participants |
| Sex: Female, Male Male | 7 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 24 |
| other Total, other adverse events | 0 / 24 |
| serious Total, serious adverse events | 0 / 24 |
Outcome results
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.
Time frame: One study visit of ~2 hours
| Arm | Measure | Group | Value (COUNT_OF_UNITS) |
|---|---|---|---|
| Experimental | Identification of Food Plated Using the Openfit Mobile App | Food items that were automatically identified as an exact match across all participants. | 118 Food items |
| Experimental | Identification of Food Plated Using the Openfit Mobile App | Food items that were semi-automatically identified as an exact match across all participants. | 221 Food items |
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).
Time frame: One study visit of ~2 hours
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Experimental | Portion Size Estimation (kcal) of Food Plated Using the Openfit Mobile App | Energy of weighed meals. | 577 kcal | Standard Deviation 150 |
| Experimental | Portion Size Estimation (kcal) of Food Plated Using the Openfit Mobile App | Energy of automated estimates | 826 kcal | Standard Deviation 490 |
| Experimental | Portion Size Estimation (kcal) of Food Plated Using the Openfit Mobile App | Energy of semi-automated estimates | 769 kcal | Standard Deviation 445 |
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.
Time frame: One study visit of ~2 hours
| Arm | Measure | Value (MEAN) | Dispersion |
|---|---|---|---|
| Experimental | Usability of the Openfit Mobile App for Recording Food Plated | 2.4 score on a scale | Standard Deviation 0.22 |
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.
Time frame: One study visit of ~2 hours
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Experimental | User Satisfaction of the Openfit Mobile App for Recording Food Plated | How satisfied are you with the app for identifying the food provided? | 4.1 score on a scale | Standard Deviation 1.3 |
| Experimental | User Satisfaction of the Openfit Mobile App for Recording Food Plated | How satisfied are you with the app for estimating the amount of food provided? | 4.0 score on a scale | Standard Deviation 1.5 |
| Experimental | User Satisfaction of the Openfit Mobile App for Recording Food Plated | How easy was it to use the app to identify the food provided? | 4.2 score on a scale | Standard Deviation 1.4 |
| Experimental | User Satisfaction of the Openfit Mobile App for Recording Food Plated | How easy was it to use the app for estimating the amount of food provided? | 4.0 score on a scale | Standard Deviation 1.6 |
| Experimental | User Satisfaction of the Openfit Mobile App for Recording Food Plated | How much did the training help prepare you for using the app? | 5.5 score on a scale | Standard Deviation 0.7 |