Ultra-processed Foods (UP)NCT06165952
Effects of Processed Foods on Brain Reward Circuitry and Food Cue Learning
An observational study of Ultra-processed Foods (UP), run by Stanford University at 1 site in United States.
Trial summary
Examine if ultra-processed (UP) foods are more effective in activating reward, attention, and memory brain regions and in promoting food cue learning than minimally-processed foods. Assess individual differences in neurobehavioral responses to UP foods. Read more about this study
- Who is it for?
- Ages 13 to 15 years
- Conditions the study is for
- Ultra-processed Foods (UP)
- How many people
- 162 participants
- Where
- 1 siteUnited States
- Study dates
- 4 Oct 2024to 28 Feb 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 13 to 15 years
- All sexes
- Child
Study population: Community sample
Who may be able to join
- female and male adolescents 13-15 years of age
- age- and sex- adjusted zBMI scores between the 25th and 75th percentile
- participant and their guardian must be able to read and speak English to gather valid consent
Who cannot join
- current eating disorders or other major psychiatric disorders (e.g., depression, bipolar, schizophrenia, substance use disorder)
- fMRI contra-indicators (e.g., metal implants, braces, claustrophobia, pregnancy)
- serious medical problems (e.g., Type 2 diabetes, cancer)
- history of food allergies or restrictive dietary requirements (e.g., lactose intolerance, vegan)
- use of psychoactive drugs more than once weekly
- medications that impact appetite or reward functioning (e.g., metformin, anti-psychotic medication, insulin)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Stanford University
Stanford, California, 94305
Teena Ambrose, BS, 310-658-6193, tambrose@stanford.edu
Eric Stice, PhD, 541-222-0615, estice@stanford.edu
About this study
Examine if ultra-processed (UP) foods are more effective in activating reward, attention, and memory brain regions and in promoting food cue learning than minimally-processed foods. Assess individual differences in neurobehavioral responses to UP foods.
Read the detailed description
Obesity is the second leading cause of premature death. Consumption of ultra-processed foods is theorized to be a key cause of obesity. Ultra-processed foods are formulations of cheap industrial sources of dietary energy and nutrients plus additives such as fat, sugar, and flavors that enhance acceptability of the foods.
A cross-over experiment with overweight adults found that ad lib access to an ultra-processed diet for 2-weeks resulted in increased caloric intake (508 kcal/day) and more weight gain versus ad lib access to a minimally-processed diet matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. The fact that ad lib access to ultra-processed foods resulted in a large increase in caloric intake and weight gain implies that ultra-processed foods may more effectively activate brain regions implicated in reward processing, attention/salience, and memory that influence eating behavior.
However, no brain imaging study has experimentally tested whether ultra-processed foods are more effective in activating brain regions implicated in reward, attention, and memory than minimally-processed foods or experimentally investigated the relative role of the elevated caloric density versus the flavor enhancers of ultra-processed foods in driving greater activation of these brain regions. Preliminary data showed that tastes of ultra-processed high-calorie chocolate milkshake produced greater activation in regions implicated in reward valuation (caudate, nucleus accumbens), attention/salience (precuneus), and memory retrieval (medial temporal gyrus, dorsomedial prefrontal cortex) than tastes of ultra-processed low-calorie chocolate milkshake.
The investigators propose to evaluate the efficacy of ultra-processed foods to activate reward, attention, and memory regions compared to minimally-processed foods, investigate the relative role of the higher caloric content versus the flavor additives/enhancers of ultra-processed foods to engage this circuitry using a 2 x 2 experimental design, test whether ultra-processed foods are more effective in increasing the incentive salience of food cues than minimally-processed foods, which is important because elevated reward region response to food cues/images increases risk for excess weight gain, and test whether individuals who show the greatest responsivity of reward, attention, and memory regions to ultra-processed foods and stronger food reward cue learning are at risk for greater ad lib intake of ultra-processed foods and future body fat gain.
Aim 1: Test the hypothesis that tastes, anticipated tastes, and images of ultra-processed foods activate reward, attention, and memory brain regions more than tastes, anticipated tastes, and images of minimally-processed foods, and evaluate the relative role of the higher caloric content versus flavor additives/enhancers in activating these regions using a 2 x 2 experimental design.
Aim 2: Test the hypothesis that ultra-processed foods foster stronger learning of cues that predict impending tastes of ultra-processed foods than minimally-processed foods, reflected by greater increases in striatal response over the course of cue exposure and quicker responses to cues for tastes of ultra-processed foods.
Aim 3: Test the hypothesis that participants who show greater activation in reward/attention/memory regions in response to tastes, anticipated tastes, and images of ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 3a) and show greater future body fat gain (Aim 3b). Exploratory analyses will establish neural fingerprints that predict ad lib intake of ultra-processed foods and body fat gain (Aim 3c).
Aim 4: Test the hypothesis that participants who show the most pronounced reward cue learning in response to ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 4a) and show greater future body fat gain (Aim 4b).
- Conditions
- Ultra-processed Foods (UP)
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Adolescent youth age 13-15 | age- and sex- adjusted body mass index (zBMI) scores between the 25th and 75th percentile |
What is being measured
The primary outcome is the main question the study is designed to answer.
Body Fat
Investigators will use air displacement plethysmography (ADP) via the Bod Pod and bioelectrical impedance (BIA) via the SECA medical body composition analyzer (SECA mBCA) to assess % body fat. The investigators will average the values of both measures. Body density is calculated as body mass divided by body volume; body density is used to calculate % body fat. ADP % body fat shows high test-retest reliability (r = .92-.99) and correlates with dual energy x-ray absorptiometry (DEXA) and hydrostatic weighing estimates (r = .98-.99), with ADP estimate of % body fat falling an average of only 1.7% different relative to DEXA estimates. Height will be measured using a direct reading stadiometer. Weight will be assessed using digital scales with participants wearing light clothing without shoes or coats. Body mass index (BMI)(Kg/M2) will be used to confirm participants are initially at a healthy weight (BMI scores between 25th to 75th age- and sex-adjusted BMI percentile).
Measured: Baseline, 1-year, 2-year, 3-year, 4-year follow-up
Baseline functional magnetic resonance imaging (fMRI) Ultra-Processed Foods Taste, Anticipated Taste, and Picture Paradigm
Participants will complete four rounds of an adapted version of a milkshake paradigm to assess activation in response to receipt (taste) and anticipated receipt of 1) high-calorie, ultra-processed food taste, 2) low-calorie, ultra-processed food taste, 3) high-calorie minimally-processed food taste, 4) low-calorie, minimally-processed food taste, and 5) a tasteless odorless solution containing the main ionic components of saliva. Participants will be cued with a picture of the specific food and label (high-calorie milkshake, low-calorie milkshake, high-calorie smoothie, low-calorie smoothie) before receipt of the tastes.
Measured: Baseline
Baseline fMRI Ultra-Processed Food Picture Paradigm
Participants will complete two rounds of a block version of the food picture paradigm to examine activation in response to 40 high-calorie ultra-processed food pictures, 40 low-calorie ultra-processed food pictures, 40 high-calorie minimally-processed food pictures, 40 low-calorie minimally-processed food pictures, and 40 bottles of water. Fifty percent of each category will be branded foods/bottles to increase ecological validity. Pictures will be matched for complexity, valence and intensity across categories. After the scan, participants will be shown 40 food pictures, including pictures that were and were not used in the paradigm, and will be asked whether they had seen the foods to assess aided recall. Participants will also be asked to rate palatability of 20 food pictures per food category.
Measured: Baseline
Baseline fMRI Ultra-Processed Food Cue Learning Paradigm
Participants will complete two rounds of an adapted version of the food reward learning paradigm wherein four arbitrary fractal cues will signal the delivery of either 0.7 ml of an ultra-processed milkshake, a minimally-processed smoothie, a tasteless solution, and no taste. To make learning more challenging, the paradigm includes both paired cue trials in which the taste is delivered as cued and unpaired cue trials in which the taste is not delivered at an 80:20 ratio. There will be one fractal cue that is not consistently paired with a taste. Participants will be asked to respond with a button press as soon as possible to indicate on which side of the fixation cross the stimuli appeared (providing a behavioral measure of reaction time to each cue).
Measured: Baseline
Baseline Post-fMRI Ad Lib Food Intake
Participants will be presented with a 20-item buffet spread that includes ultra-processed foods (high- and low-calorie), and minimally processed foods (high- and low-calorie). Participants first perform a taste test of 1g of each of the foods. They will then complete perceptual hedonic ratings of the pleasantness, sweetness, savoriness, and desire to consume on generalized labeled magnitude scales. After the taste test, participants will be told that they are free to eat as much as they like because the investigators have to discard the food after each participant. Participants will be alone during the 15-min tasting to minimize demand characteristics. Food will be pre- and post-weighed to determine ad lib intake. Total caloric intake will be calculated and translated to % of calorie needs.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Dec 2023
- Study start4 Oct 2024
- Primary completion1 Feb 2029 (estimated)
- Study completion28 Feb 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Ultra-processed Foods (UP)Studies of the same condition, open and under way.
- More trials run by Stanford UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 162 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Stanford University
- Sponsor type
- University, hospital or other
- Collaborators
- University of North Carolina, Chapel Hill, Oregon Research Institute
- Responsible party
- Principal investigator, Eric Stice, Stanford University
- Investigators
- Eric Stice, PhD, Principal investigator, Stanford University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06165952
- Sponsor's protocol ID
- 71541
- Status last verified
- Apr 2026
- First submitted
- 1 Dec 2023
- Last updated
- 24 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019 Jul 2;30(1):67-77.e3. doi: 10.1016/j.cmet.2019.05.008. Epub 2019 May 16.
PubMed 31105044Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012 Apr 18;32(16):5549-52. doi: 10.1523/JNEUROSCI.5958-11.2012.
PubMed 22514316Stice E, Burger KS, Yokum S. Reward Region Responsivity Predicts Future Weight Gain and Moderating Effects of the TaqIA Allele. J Neurosci. 2015 Jul 15;35(28):10316-24. doi: 10.1523/JNEUROSCI.3607-14.2015.
PubMed 26180206Yokum S, Gearhardt AN, Harris JL, Brownell KD, Stice E. Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity (Silver Spring). 2014 Dec;22(12):2544-51. doi: 10.1002/oby.20882. Epub 2014 Aug 25.
PubMed 25155745Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, Mei Z, Curtin LR, Roche AF, Johnson CL. CDC growth charts: United States. Adv Data. 2000 Jun 8;(314):1-27.
PubMed 11183293Joyner MA, Gearhardt AN, Flagel SB. A Translational Model to Assess Sign-Tracking and Goal-Tracking Behavior in Children. Neuropsychopharmacology. 2018 Jan;43(1):228-229. doi: 10.1038/npp.2017.196. No abstract available.
PubMed 29192653Stice E, Yokum S, Rohde P, Cloud K, Desjardins CD. Comparing healthy adolescent females with and without parental history of eating pathology on neural responsivity to food and thin models and other potential risk factors. J Abnorm Psychol. 2021 Aug;130(6):608-619. doi: 10.1037/abn0000686.
PubMed 34553956O'Doherty JP, Buchanan TW, Seymour B, Dolan RJ. Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron. 2006 Jan 5;49(1):157-66. doi: 10.1016/j.neuron.2005.11.014.
PubMed 16387647
Study at a glance
From the registry record, last updated by the sponsor on 24 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effects of Processed Foods on Brain Reward Circuitry and Food Cue Learning
- ClinicalTrials.gov ID
- NCT06165952
- Acronym / sponsor ID
- 71541
- Conditions
- Ultra-processed Foods (UP)
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 162 participants (estimated)
- Lead sponsor
- Stanford UniversityUniversity, hospital or other
- Collaborators
- University of North Carolina, Chapel Hill, Oregon Research Institute
- Study start
- 4 Oct 2024 (actual)
- Primary completion
- 1 Feb 2029 (estimated)
- Study completion
- 28 Feb 2029 (estimated)
- First posted
- 12 Dec 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Examine if ultra-processed (UP) foods are more effective in activating reward, attention, and memory brain regions and in promoting food cue learning than minimally-processed foods. Assess individual differences in neurobehavioral responses to UP foods.
Detailed description
Obesity is the second leading cause of premature death. Consumption of ultra-processed foods is theorized to be a key cause of obesity. Ultra-processed foods are formulations of cheap industrial sources of dietary energy and nutrients plus additives such as fat, sugar, and flavors that enhance acceptability of the foods.
A cross-over experiment with overweight adults found that ad lib access to an ultra-processed diet for 2-weeks resulted in increased caloric intake (508 kcal/day) and more weight gain versus ad lib access to a minimally-processed diet matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. The fact that ad lib access to ultra-processed foods resulted in a large increase in caloric intake and weight gain implies that ultra-processed foods may more effectively activate brain regions implicated in reward processing, attention/salience, and memory that influence eating behavior.
However, no brain imaging study has experimentally tested whether ultra-processed foods are more effective in activating brain regions implicated in reward, attention, and memory than minimally-processed foods or experimentally investigated the relative role of the elevated caloric density versus the flavor enhancers of ultra-processed foods in driving greater activation of these brain regions. Preliminary data showed that tastes of ultra-processed high-calorie chocolate milkshake produced greater activation in regions implicated in reward valuation (caudate, nucleus accumbens), attention/salience (precuneus), and memory retrieval (medial temporal gyrus, dorsomedial prefrontal cortex) than tastes of ultra-processed low-calorie chocolate milkshake.
The investigators propose to evaluate the efficacy of ultra-processed foods to activate reward, attention, and memory regions compared to minimally-processed foods, investigate the relative role of the higher caloric content versus the flavor additives/enhancers of ultra-processed foods to engage this circuitry using a 2 x 2 experimental design, test whether ultra-processed foods are more effective in increasing the incentive salience of food cues than minimally-processed foods, which is important because elevated reward region response to food cues/images increases risk for excess weight gain, and test whether individuals who show the greatest responsivity of reward, attention, and memory regions to ultra-processed foods and stronger food reward cue learning are at risk for greater ad lib intake of ultra-processed foods and future body fat gain.
Aim 1: Test the hypothesis that tastes, anticipated tastes, and images of ultra-processed foods activate reward, attention, and memory brain regions more than tastes, anticipated tastes, and images of minimally-processed foods, and evaluate the relative role of the higher caloric content versus flavor additives/enhancers in activating these regions using a 2 x 2 experimental design.
Aim 2: Test the hypothesis that ultra-processed foods foster stronger learning of cues that predict impending tastes of ultra-processed foods than minimally-processed foods, reflected by greater increases in striatal response over the course of cue exposure and quicker responses to cues for tastes of ultra-processed foods.
Aim 3: Test the hypothesis that participants who show greater activation in reward/attention/memory regions in response to tastes, anticipated tastes, and images of ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 3a) and show greater future body fat gain (Aim 3b). Exploratory analyses will establish neural fingerprints that predict ad lib intake of ultra-processed foods and body fat gain (Aim 3c).
Aim 4: Test the hypothesis that participants who show the most pronounced reward cue learning in response to ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 4a) and show greater future body fat gain (Aim 4b).
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adolescent youth age 13-15 | – | –age- and sex- adjusted body mass index (zBMI) scores between the 25th and 75th percentile |
Who may qualify
- Ages
- 13 to 15 years
- Sex
- All
- Age groups
- Child
- Study population
- Community sample
Key inclusion criteria (3 listed)
- female and male adolescents 13-15 years of age
- age- and sex- adjusted zBMI scores between the 25th and 75th percentile
- participant and their guardian must be able to read and speak English to gather valid consent
Key exclusion criteria (6 listed)
- current eating disorders or other major psychiatric disorders (e.g., depression, bipolar, schizophrenia, substance use disorder)
- fMRI contra-indicators (e.g., metal implants, braces, claustrophobia, pregnancy)
- serious medical problems (e.g., Type 2 diabetes, cancer)
- history of food allergies or restrictive dietary requirements (e.g., lactose intolerance, vegan)
- use of psychoactive drugs more than once weekly
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Body Fat
Time frame: Baseline, 1-year, 2-year, 3-year, 4-year follow-up
Investigators will use air displacement plethysmography (ADP) via the Bod Pod and bioelectrical impedance (BIA) via the SECA medical body composition analyzer (SECA mBCA) to assess % body fat. The investigators will average the values of both measures. Body density is calculated as body mass divided by body volume; body density is used to calculate % body fat. ADP % body fat shows high test-retest reliability (r = .92-.99) and correlates with dual energy x-ray absorptiometry (DEXA) and hydrostatic weighing estimates (r = .98-.99), with ADP estimate of % body fat falling an average of only 1.7% different relative to DEXA estimates. Height will be measured using a direct reading stadiometer. Weight will be assessed using digital scales with participants wearing light clothing without shoes or coats. Body mass index (BMI)(Kg/M2) will be used to confirm participants are initially at a healthy weight (BMI scores between 25th to 75th age- and sex-adjusted BMI percentile).
Baseline functional magnetic resonance imaging (fMRI) Ultra-Processed Foods Taste, Anticipated Taste, and Picture Paradigm
Time frame: Baseline
Participants will complete four rounds of an adapted version of a milkshake paradigm to assess activation in response to receipt (taste) and anticipated receipt of 1) high-calorie, ultra-processed food taste, 2) low-calorie, ultra-processed food taste, 3) high-calorie minimally-processed food taste, 4) low-calorie, minimally-processed food taste, and 5) a tasteless odorless solution containing the main ionic components of saliva. Participants will be cued with a picture of the specific food and label (high-calorie milkshake, low-calorie milkshake, high-calorie smoothie, low-calorie smoothie) before receipt of the tastes.
Baseline fMRI Ultra-Processed Food Picture Paradigm
Time frame: Baseline
Participants will complete two rounds of a block version of the food picture paradigm to examine activation in response to 40 high-calorie ultra-processed food pictures, 40 low-calorie ultra-processed food pictures, 40 high-calorie minimally-processed food pictures, 40 low-calorie minimally-processed food pictures, and 40 bottles of water. Fifty percent of each category will be branded foods/bottles to increase ecological validity. Pictures will be matched for complexity, valence and intensity across categories. After the scan, participants will be shown 40 food pictures, including pictures that were and were not used in the paradigm, and will be asked whether they had seen the foods to assess aided recall. Participants will also be asked to rate palatability of 20 food pictures per food category.
Baseline fMRI Ultra-Processed Food Cue Learning Paradigm
Time frame: Baseline
Participants will complete two rounds of an adapted version of the food reward learning paradigm wherein four arbitrary fractal cues will signal the delivery of either 0.7 ml of an ultra-processed milkshake, a minimally-processed smoothie, a tasteless solution, and no taste. To make learning more challenging, the paradigm includes both paired cue trials in which the taste is delivered as cued and unpaired cue trials in which the taste is not delivered at an 80:20 ratio. There will be one fractal cue that is not consistently paired with a taste. Participants will be asked to respond with a button press as soon as possible to indicate on which side of the fixation cross the stimuli appeared (providing a behavioral measure of reaction time to each cue).
Baseline Post-fMRI Ad Lib Food Intake
Time frame: Baseline
Participants will be presented with a 20-item buffet spread that includes ultra-processed foods (high- and low-calorie), and minimally processed foods (high- and low-calorie). Participants first perform a taste test of 1g of each of the foods. They will then complete perceptual hedonic ratings of the pleasantness, sweetness, savoriness, and desire to consume on generalized labeled magnitude scales. After the taste test, participants will be told that they are free to eat as much as they like because the investigators have to discard the food after each participant. Participants will be alone during the 15-min tasting to minimize demand characteristics. Food will be pre- and post-weighed to determine ad lib intake. Total caloric intake will be calculated and translated to % of calorie needs.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Apr 2026.
- Background: PubMed 31105044Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019 Jul 2;30(1):67-77.e3. doi: 10.1016/j.cmet.2019.05.008. Epub 2019 May 16.
- Background: PubMed 22514316Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012 Apr 18;32(16):5549-52. doi: 10.1523/JNEUROSCI.5958-11.2012.
- Background: PubMed 26180206Stice E, Burger KS, Yokum S. Reward Region Responsivity Predicts Future Weight Gain and Moderating Effects of the TaqIA Allele. J Neurosci. 2015 Jul 15;35(28):10316-24. doi: 10.1523/JNEUROSCI.3607-14.2015.
- Background: PubMed 25155745Yokum S, Gearhardt AN, Harris JL, Brownell KD, Stice E. Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity (Silver Spring). 2014 Dec;22(12):2544-51. doi: 10.1002/oby.20882. Epub 2014 Aug 25.
- Background: PubMed 11183293Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, Mei Z, Curtin LR, Roche AF, Johnson CL. CDC growth charts: United States. Adv Data. 2000 Jun 8;(314):1-27.
- Background: PubMed 29192653Joyner MA, Gearhardt AN, Flagel SB. A Translational Model to Assess Sign-Tracking and Goal-Tracking Behavior in Children. Neuropsychopharmacology. 2018 Jan;43(1):228-229. doi: 10.1038/npp.2017.196. No abstract available.
- Background: PubMed 34553956Stice E, Yokum S, Rohde P, Cloud K, Desjardins CD. Comparing healthy adolescent females with and without parental history of eating pathology on neural responsivity to food and thin models and other potential risk factors. J Abnorm Psychol. 2021 Aug;130(6):608-619. doi: 10.1037/abn0000686.
- Background: PubMed 16387647O'Doherty JP, Buchanan TW, Seymour B, Dolan RJ. Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron. 2006 Jan 5;49(1):157-66. doi: 10.1016/j.neuron.2005.11.014.
Related trials for Ultra-processed Foods (UP)
Not yet recruiting
Sustainable and AI-Enabled Adolescents and Youth-Centred Interventions to Upgrade Food Choices and Promote Healthy, Sustainable Diets
Ultra-Processed Food Consumption, Obesity Prevention
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Eric Stice, PhD principal investigator
Stanford University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ultra-processed Foods (UP), run by Stanford University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 24 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Eric Stice, PhD (541-222-0615, estice@stanford.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Body Fat (time frame: Baseline, 1-year, 2-year, 3-year, 4-year follow-up). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06165952, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Apr 2026.