Personalized Diet Information for Healthy Habits

(PATTERN Trial)

Overseen By Veronica Carrion, B.S

Age: < 18

Sex: Any

Trial Phase: Academic

Sponsor: Duke University

Disqualifiers: Health conditions impacting feeding

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

600 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It seems focused on diet and stool samples, so it's unlikely that your medications will be affected, but it's best to check with the study team.

What data supports the effectiveness of the treatment Personalized Diet Information for healthy habits?

Research shows that personalized diet advice, which considers individual health and lifestyle factors, leads to better dietary choices and improved health outcomes compared to generic advice. Participants receiving personalized nutrition advice improved their intake of healthy foods like fruits and whole grains and reduced unhealthy choices. ^{1 2 3 4 5}

Is Personalized Diet Information generally safe for humans?

There is no specific safety data available for Personalized Diet Information, but personalized nutrition approaches are generally considered safe as they use individual-specific information to promote dietary behavior change based on evidence-based science. ^{6 7 8 9 10}

How does the treatment 'Personalized Diet Information' differ from other treatments for healthy habits?

Personalized Diet Information is unique because it tailors dietary advice based on individual characteristics like diet, lifestyle, and genetic information, unlike generic guidelines that apply to everyone. This personalized approach has been shown to be more effective in promoting lasting dietary changes and improving health outcomes. ^{1 2 4 9 11}

What is the purpose of this trial?

This study is designed to test the effects of diet data return on human dietary patterns. The investigators' working hypothesis is that returning diet data, collected via an empirical biomarker-based assessment method, will promote participant eating behavior changes towards a more healthful diet, and that these dietary patterns may also be reflected in the structure and function of the gut microbiome and in participant health outcomes. Here, the investigators will explore this hypothesis with participants supplying stool (poop) samples that will be used to assess their diet using the investigators' DNA-biomarker based method. Participants will have their diet data returned to them, and will then supply post-intervention stool samples for dietary assessment to investigate the efficacy of diet data return in impacting subject dietary patterns and health. It is anticipated that the investigators will enroll infants, children, and adults across the age span, including infants that are currently participating in Project HOPE 1000, a longitudinal observational study of mothers and infants from pregnancy through early childhood (Pro00100000). This study represents a minimal risk to subjects; namely, the potential loss of privacy involved with study participation.

Eligibility Criteria

This trial is for parents or guardians over 18 who understand English, plan to stay local for two years, and care for an infant under 12 months. They must be able to collect and ship their infant's stool samples.

Inclusion Criteria

Primary caregiver for infant less than 12 months old

Plans to remain in the area for the next two years

I can collect and send the required baby stool samples.

Exclusion Criteria

There will be no specific exclusions for this study. However, the following biospecimens will not be collected for research purposes from individuals who have a condition that, in the opinion of the site investigators and/or providers, would place the subject at unacceptable risk of injury or confound data interpretation; including but not limited to: Health conditions that impact the infant feeding in a way that may confound interpretation of study results.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants receive dietary guidance and their own dietary DNA metabarcoding data from stool samples

12 months

Regular visits for sample collection and data return

Follow-up

Participants are monitored for changes in dietary patterns and gut microbiome composition

4 weeks

Treatment Details

Interventions

• Personalized Diet Information

Trial Overview The study tests if returning personalized diet data based on DNA markers in stool can lead people to eat healthier. It looks at how this might change the gut microbiome and overall health.

Participant Groups

Group I: Intervention Experimental Treatment 1 Intervention

Participants in this group will receive the standard infant dietary guidance packet via email or mail and will additionally be presented with their infant's own dietary DNA metabarcoding data from stool samples collected as part of Project HOPE 1000 and this study. Diet data will be returned via electronic report or a paper report mailed to the participant following the visit.

Group II: Control Experimental Treatment 1 Intervention

Participants in this group will receive a dietary information packet via mail or email, which will consist of the sections of the DGA relevant to 12-24 month old children (Included as a supplemental document, "Standard dietary guidance"). This dietary guidance will also be provided at the 18- and 24-month visits. Please note that participants in this arm will receive their personalized diet information at the conclusion of the study.

Who Is Running the Clinical Trial?

Duke University

Lead Sponsor

Trials

2,495

Recruited

5,912,000+

Findings from Research

In a study involving 155 participants aged 25 to 70, personalized nutrition advice (PNA) led to significant improvements in dietary intake, including increased consumption of fruits, whole grains, and fish, compared to those receiving generic nutrition advice (GNA) or no advice.

Setting personal dietary goals within the PNA group further enhanced positive changes in food choices, demonstrating that tailored advice and goal-setting can effectively promote healthier eating behaviors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Evaluation of Food-Intake Behavior in a Healthy Population: Personalized vs. One-Size-Fits-All. Hoevenaars, FPM., Berendsen, CMM., Pasman, WJ., et al.[2021]

Personalized nutrition (PN) advice delivered through an internet intervention led to significant improvements in dietary behavior among 1269 European adults, including reduced intake of red meat, salt, and saturated fat, and increased folate consumption over a 6-month period.

The study found that while PN was more effective than conventional dietary advice, adding phenotypic or genotypic information did not further enhance the effectiveness of the dietary changes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. Celis-Morales, C., Livingstone, KM., Marsaux, CF., et al.[2022]

In a 6-month randomized controlled trial with 1,125 participants, those receiving higher-frequency personalized nutritional feedback showed significantly greater improvements in diet quality and reductions in body weight and waist circumference at 3 months compared to those receiving lower-frequency feedback.

While the benefits in diet quality were not sustained, the reductions in body weight and BMI remained significant at 6 months, indicating that more frequent feedback can lead to meaningful changes in obesity-related traits over time.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Frequent Nutritional Feedback, Personalized Advice, and Behavioral Changes: Findings from the European Food4Me Internet-Based RCT. Celis-Morales, C., Livingstone, KM., Petermann-Rocha, F., et al.[2021]

A 10-week personalized systems nutrition (PSN) program involving 82 healthy participants led to significant reductions in calorie intake and various unhealthy dietary components, resulting in decreased body weight, BMI, and body fat.

The program was particularly effective for individuals with lower phenotypic flexibility, showing notable reductions in LDL and total cholesterol levels, suggesting that personalized nutrition can effectively target behavior change in health-compromised groups.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Novel Personalized Systems Nutrition Program Improves Dietary Patterns, Lifestyle Behaviors and Health-Related Outcomes: Results from the Habit Study. de Hoogh, IM., Winters, BL., Nieman, KM., et al.[2021]

A personalized diet tailored to patients with comorbidities like obesity and hypertension led to significant improvements in metabolic control indicators, including reduced body weight, waist circumference, and lower fasting glucose levels in a study of 39 patients over three months.

The dietary intervention also positively affected renal function, as evidenced by decreased urine albumin excretion and increased glomerular filtration rate in both microalbuminuria and macroalbuminuria groups.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Effect of a personalized diet in the metabolic control and renal function of patients with type 2 diabetes]. Velázquez-López, L., Sil-Acosta, MJ., Goycochea-Robles, MV., et al.[2015]

Personalized nutrition (PN) can effectively promote dietary behavior changes and improve health outcomes, but a clear definition and guiding principles are necessary for its credibility and efficacy.

The article proposes a definition for PN and outlines 10 guiding principles to ensure responsible and evidence-based practices, highlighting the need for further research to address challenges in implementation and measurement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function. Adams, SH., Anthony, JC., Carvajal, R., et al.[2023]

The study evaluated the accuracy of food product databases in seven popular diet apps, finding that while 99% of scanned products had energy values available, only 79% of these values were within 5% of the true values, indicating a generally high accuracy for energy reporting.

MyFitnessPal was the most effective app, correctly identifying 96% of products and providing comprehensive nutrient data, while other apps like SparkPeople had significantly lower identification rates, suggesting that not all diet apps are equally reliable for detailed nutrient assessments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Food identification by barcode scanning in the Netherlands: a quality assessment of labelled food product databases underlying popular nutrition applications. Maringer, M., Wisse-Voorwinden, N., Veer, PV'., et al.[2023]

There is a growing recognition that food impacts bodily functions beyond just providing calories, yet much of the information available to consumers about food and diets is unverified for safety and efficacy.

The paper suggests a framework for improving research and development in food safety and health benefits, emphasizing the need for regulatory oversight and addressing patent issues to support long-term investment in this area.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Nutritional products as drugs or food implications for development. Richards, DJ.[2008]

Computer-tailored nutrition education is more effective than general nutrition information in motivating dietary changes, particularly in reducing dietary fat, as it provides personalized feedback that resonates more with individuals.

The use of interactive technology, like the Internet, enhances the delivery of this tailored education, although it also introduces new challenges that need to be addressed for optimal effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Past, present, and future of computer-tailored nutrition education. Brug, J., Oenema, A., Campbell, M.[2022]

The use of electronic health records (EHR) among nutrition professionals increased from 86% in 2012 to 94% in 2014, indicating a growing reliance on digital systems for managing nutrition care.

Despite the increased usage of EHRs, there was no overall improvement in the safety and efficacy of nutrition content areas, with notable declines in the ordering of oral nutrition supplements and parenteral nutrition, highlighting the need for better collaboration between clinicians and EHR vendors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Follow-Up Survey on Functionality of Nutrition Documentation and Ordering Nutrition Therapy in Currently Available Electronic Health Record Systems. Vanek, VW., Ayers, P., Charney, P., et al.[2017]

A web-based personalized nutrition system was developed to improve dietary quality, showing that tailored dietary advice may be more effective than general recommendations.

In a pilot study with 255 dietitians, the personalized dietary feedback report received a high acceptability score of 4.2 out of 5, indicating strong support for this approach among professionals in the field.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Web-Based Personalized Nutrition System for Delivering Dietary Feedback Based on Behavior Change Techniques: Development and Pilot Study among Dietitians. Murakami, K., Shinozaki, N., Masayasu, S., et al.[2021]

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

Evaluation of Food-Intake Behavior in a Healthy Population: Personalized vs. One-Size-Fits-All. [2021]

2. England pubmed.ncbi.nlm.nih.gov

Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial. [2022]

3. Netherlands pubmed.ncbi.nlm.nih.gov

Frequent Nutritional Feedback, Personalized Advice, and Behavioral Changes: Findings from the European Food4Me Internet-Based RCT. [2021]

4. Switzerland pubmed.ncbi.nlm.nih.gov

A Novel Personalized Systems Nutrition Program Improves Dietary Patterns, Lifestyle Behaviors and Health-Related Outcomes: Results from the Habit Study. [2021]

5. Mexico pubmed.ncbi.nlm.nih.gov

[Effect of a personalized diet in the metabolic control and renal function of patients with type 2 diabetes]. [2015]

6. United States pubmed.ncbi.nlm.nih.gov

Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function. [2023]

7. England pubmed.ncbi.nlm.nih.gov

Food identification by barcode scanning in the Netherlands: a quality assessment of labelled food product databases underlying popular nutrition applications. [2023]

8. Italy pubmed.ncbi.nlm.nih.gov

Nutritional products as drugs or food implications for development. [2008]

9. United States pubmed.ncbi.nlm.nih.gov

Past, present, and future of computer-tailored nutrition education. [2022]

10. United States pubmed.ncbi.nlm.nih.gov

Follow-Up Survey on Functionality of Nutrition Documentation and Ordering Nutrition Therapy in Currently Available Electronic Health Record Systems. [2017]

11. Switzerland pubmed.ncbi.nlm.nih.gov

Web-Based Personalized Nutrition System for Delivering Dietary Feedback Based on Behavior Change Techniques: Development and Pilot Study among Dietitians. [2021]