Digital Nutrition Intervention for Malnutrition

Overseen By Tianou Zhang, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: The University of Texas at San Antonio

Disqualifiers: Blind, Terminal illness, Dementia, others

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

Compensation: Varies

Number of Visits: Unknown

369 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.

What data supports the effectiveness of the Digital Nutrition Intervention treatment for malnutrition?

Research shows that using technology like the MyFood decision support system and NUTRI-TEC can help improve patients' nutritional status by making it easier for them to track their food intake and engage in their nutrition care, which can lead to better health outcomes during hospital stays. ^{1 2 3 4 5}

Is the Digital Nutrition Intervention safe for humans?

The research does not provide specific safety data for Digital Nutrition Interventions, but these technologies are generally used to improve dietary assessments and support nutritional care in hospitals, suggesting they are considered safe for use in these settings. ^{1 6 7 8 9}

How is the Digital Nutrition Intervention treatment unique for addressing malnutrition?

The Digital Nutrition Intervention is unique because it uses a digital decision support system, like the MyFood app, to simplify dietary recording and provide personalized nutritional recommendations, making it more efficient and tailored compared to traditional paper-based methods. ^{1 9 10 11 12}

What is the purpose of this trial?

This trial aims to help older adults in high-poverty areas by teaching them how to use technology and then providing online nutrition education. The goal is to improve their diet and reduce social isolation by bridging the digital divide.

Research Team

Sarah L Ullevig, PhD

Principal Investigator

University of Texas at San Antonio

Eligibility Criteria

This trial is for older adults aged 60 or above who are facing malnutrition, have a sedentary lifestyle, and feel socially isolated. Participants should lack technology like computers or smartphones, have poor internet access at home, or not know how to use these technologies. They must be able to read and write in English or Spanish and cannot have dementia, Alzheimer's disease, blindness, or any terminal illness.

Inclusion Criteria

Inadequate or no working technology device (computer, smart-phone, tablet), no or poor internet connectivity at home, or lack of knowledge and usage of technology

You do not have regular access to enough nutritious food.

60 years of age

Exclusion Criteria

You have been diagnosed with dementia or Alzheimer's disease.

Unable to read or write in English or Spanish

You have a visual impairment that limits your ability to see.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Technology Training

Participants undergo 5 weeks of technology training, including internet access and devices

5 weeks

Online sessions

Nutrition Intervention

Participants receive a 15-week culturally tailored nutrition intervention via online sessions

15 weeks

Online sessions

Follow-up

Participants are monitored for changes in diet quality, food security, physical activity, technology use, social isolation, and loneliness

6 months

Treatment Details

Interventions

• Digital Nutrition Intervention
• Technology intervention

Trial Overview The study tests a digital nutrition intervention aimed at improving food security and diet quality among older adults. It will also assess the impact on their tech knowledge and usage, physical activity levels, as well as feelings of social isolation. The trial uses a stepped-wedge cluster design involving community partners for implementation.

Participant Groups

Group I: Cohort 1 Experimental Treatment 2 Interventions

Cohort 1 will start the intervention directly after randomization.

Group II: Cohort 2 Active Control 2 Interventions

Cohort 2 will serve as the control while cohort 1 is in the intervention stage. Cohort 2 will start the intervention after cohort 1 concludes the intervention.

Who Is Running the Clinical Trial?

The University of Texas at San Antonio

Lead Sponsor

Trials

24

Recruited

7,600+

Older Adult Technology Services

Collaborator

Trials

1

Recruited

370+

City of San Antonio Department of Human Services

Collaborator

Trials

1

Recruited

370+

Agile Analytics, LLC

Collaborator

Trials

1

Recruited

370+

National Institute of Nursing Research (NINR)

Collaborator

Trials

623

Recruited

10,400,000+

Findings from Research

Between 2005 and 2009, about 75% of studies showed that e-health technologies effectively helped reduce fat intake and increase fruit and vegetable consumption in behavioral nutrition interventions.

By 2010, interventions began to focus more on body weight management and included personalized features like self-monitoring, but there has been limited progress in using objective dietary behavior measures instead of self-reports.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Behavioral Nutrition Interventions Using e- and m-Health Communication Technologies: A Narrative Review. Olson, CM.[2019]

Digital health interventions using Computerized Decision Support (CDS) and Machine Learning (ML) have shown promise in the prevention and treatment of childhood obesity, with all identified studies reporting statistically significant outcomes.

CDS tools, particularly those utilizing Electronic Health Records and BMI alerts, can aid in self-management of obesity, while ML algorithms like decision trees and artificial neural networks are effective for predicting obesity risk, highlighting the potential for smart interventions in childhood obesity care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature. Triantafyllidis, A., Polychronidou, E., Alexiadis, A., et al.[2021]

References

1. England pubmed.ncbi.nlm.nih.gov

Effects of using the MyFood decision support system on hospitalized patients' nutritional status and treatment: A randomized controlled trial. [2021]

2. Switzerland pubmed.ncbi.nlm.nih.gov

Using Technology to Promote Patient Engagement in Nutrition Care: A Feasibility Study. [2021]

3. Spain pubmed.ncbi.nlm.nih.gov

[Development of integrated support software for clinical nutrition]. [2017]

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

Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition Consensus Malnutrition Characteristics: Usability and Association With Outcomes. [2020]

5. Canada pubmed.ncbi.nlm.nih.gov

The Use of Technology in Identifying Hospital Malnutrition: Scoping Review. [2020]

6. Ireland pubmed.ncbi.nlm.nih.gov

Comparing the web-based and traditional self-reported 24-hour dietary recall data in the PakNutriStudy. [2023]

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

Using an interactive nutrition technology platform to predict malnutrition risk. [2023]

8. England pubmed.ncbi.nlm.nih.gov

Implementation of an electronic solution to improve malnutrition identification and support clinical best practice. [2022]

9. Canada pubmed.ncbi.nlm.nih.gov

Popular Nutrition-Related Mobile Apps: A Feature Assessment. [2022]

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

Technology Interventions to Manage Food Intake: Where Are We Now? [2018]

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

Behavioral Nutrition Interventions Using e- and m-Health Communication Technologies: A Narrative Review. [2019]

12. Netherlands pubmed.ncbi.nlm.nih.gov

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature. [2021]