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| Name | Class |
|---|---|
| Hospital San Carlos, Madrid | OTHER |
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NutriTrack is a digital health application designed to support nutritional and behavioral tracking in patients with obesity followed in an outpatient obesity clinic. The application allows patients to record food intake using food photographs, barcode scanning, or manual search, and to register behavioral variables related to eating episodes.
This prospective, single-center, observational pilot study will evaluate the feasibility and usability of NutriTrack in 20 to 50 adult patients with obesity or overweight with comorbidities followed at the Obesity Clinic of Hospital Clínico San Carlos. Participants will use the application for 4 weeks as a complementary tool. The information generated by NutriTrack will be available to healthcare professionals as supportive information and will not replace clinical judgment or modify usual care decisions.
The main outcome is usability measured using the System Usability Scale. Secondary and exploratory outcomes include agreement between artificial intelligence-based nutritional estimates and standard dietitian assessment, adherence to daily food logging, professional perceived clinical utility, changes in eating behavior and emotional regulation scales, and technical feasibility of data export.
NutriTrack is a digital health application developed within Hospital Clínico San Carlos and Universidad Complutense de Madrid to support nutritional and behavioral tracking in patients with obesity. The application integrates artificial intelligence-based food image recognition, the Spanish BEDCA nutritional database, and a rule-based engine for the detection of clinically relevant eating patterns.
This is a prospective, single-center, observational pilot study with complementary clinical use. The application will be used by adult patients followed at the Obesity Clinic for 4 weeks. Participants will record daily food intake and behavioral variables related to eating episodes, including hunger, satiety, emotional state, eating context, eating speed, perceived control, and cravings.
Healthcare professionals may review the NutriTrack clinical panel as supportive information. The application output will not replace clinical judgment, will not trigger automated clinical decisions, will not modify usual care, and will not be integrated into the hospital electronic health record during the pilot phase.
The study will assess usability, nutritional estimation accuracy, adherence to food logging, professional perceived clinical utility, exploratory changes in emotional regulation and eating behavior, and technical feasibility of data export. The study is intended to generate preliminary evidence to support future larger-scale validation.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| NutriTrack Observational Cohort | Adult patients with obesity or overweight with comorbidities followed at the Obesity Clinic of Hospital Clínico San Carlos who will use the NutriTrack application for 4 weeks as a complementary nutritional and behavioral tracking tool. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| NutriTrack digital health application | Other | NutriTrack is a digital health application used for nutritional and behavioral tracking. Patients record food intake using food photographs, barcode scanning, or manual search, and report behavioral variables such as hunger, satiety, emotional state, eating context, eating speed, perceived control, and cravings. The application output is informational, requires healthcare professional supervision, and does not replace clinical judgment or modify usual care. |
| Measure | Description | Time Frame |
|---|---|---|
| System Usability Scale score | Usability of the NutriTrack application measured using the System Usability Scale. The score ranges from 0 to 100, with higher scores indicating better usability. A score of 70 or higher will be considered acceptable. | Week 4 |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation between AI-based caloric estimation and dietitian assessment | Correlation between caloric estimation obtained using NutriTrack artificial intelligence-based food image recognition and standard dietitian-nutritionist assessment. | Week 4 |
| Adherence to daily food logging |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Difficulties in Emotion Regulation Scale score | Change in emotional regulation from baseline to Week 4 measured using the Difficulties in Emotion Regulation Scale. Higher scores indicate greater difficulties in emotion regulation. | Baseline to Week 4 |
| Change in Eating Disorder Examination Questionnaire score |
Inclusion Criteria:
Exclusion Criteria:
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Adult patients with obesity or overweight with comorbidities who are in active follow-up at the Obesity Clinic of Hospital Clínico San Carlos and who have regular access to and use of a smartphone with iOS or Android operating system.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Francisco José García González, PhD, RN | Contact | +34657675111 | franciscojose.garcia@salud.madrid.org |
| Name | Affiliation | Role |
|---|---|---|
| Francisco José García González, PhD, RN | Hospital Clínico San Carlos / Universidad Complutense de Madrid | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Clínico San Carlos | Madrid | Madrid | 28040 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | Ministerio de Sanidad. Base de Datos Española de Composición de Alimentos (BEDCA). 2023. | ||
| Background | Gratz KL, Roemer L. Multidimensional assessment of emotion regulation and dysregulation. Journal of Psychopathology and Behavioral Assessment. 2004;26(1):41-54. | ||
| Background | Fairburn CG, Beglin SJ. Eating Disorder Examination Questionnaire (EDE-Q 6.0). Guilford Press; 2008. | ||
| Background | Brooke J. SUS: A quick and dirty usability scale. Usability Evaluation In Industry. 1996;189(194):4-7. |
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| ID | Term |
|---|---|
| D009765 | Obesity |
| D050177 | Overweight |
| D005247 | Feeding Behavior |
| ID | Term |
|---|---|
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D001835 | Body Weight |
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Percentage of days with complete food intake records during the 4-week follow-up period. |
| From baseline to Week 4 |
| Healthcare professional perceived clinical utility score | Perceived clinical utility of NutriTrack assessed by the healthcare professional using an ad hoc questionnaire. | Week 4 |
Change in eating behavior from baseline to Week 4 measured using the Eating Disorder Examination Questionnaire. Higher scores indicate greater severity of eating disorder-related psychopathology. |
| Baseline to Week 4 |
| Digital competence score | Baseline digital competence assessed using the DigComp-ES questionnaire. The score will be explored as a potential factor associated with usability of the NutriTrack application. | Baseline |
| Frequency and type of rule-based eating risk patterns detected by NutriTrack | Frequency and type of eating risk patterns detected by the NutriTrack rule-based engine during the 4-week follow-up period. Patterns may include emotional eating, rapid eating, night eating, repeated meal omission, and possible binge-eating episodes. These alerts are informational and visible only to healthcare professionals. | From baseline to Week 4 |
| Technical feasibility of HL7 FHIR R4 data export | Qualitative assessment of the technical feasibility of exporting NutriTrack study data using the HL7 FHIR R4 format for research data archiving. The export will not be used for clinical integration with the hospital electronic health record during the pilot study. | Week 4 |
| D012816 |
| Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D001522 | Behavior, Animal |
| D001519 | Behavior |