Wearable Sensors and Artificial Intelligence for Carbohydrate Counting

NCT05335889 | Completed

• 1 other identifier: 21-01714
• Study Type: observational
• Target: 12
• Locations: 1 country
• Sites: 1

Brief Summary

This is a one-group pilot study where Chinese immigrants who are English speaking with T2D from NYU Langone Health and NYU Brooklyn Family Health Center (Sunset Park) will be recruited.

Conditions

Type 2 Diabetes

Outcome Measures

Primary Outcomes (3)

• Accuracy of Carbohydrate Counting using eButton (absolute error)

  The estimated carb grams using the eButton, gold standard, and participants' food diaries will be compared head to head among each other. The absolute error will be computed: the difference between the estimated value and the gold standard (estimated - gold standard). Bland-Altman plots will be used to examine the level of agreement between eButton and gold standard measurements.

  Day 14

• Accuracy of Carbohydrate Counting using eButton (relative error)

  The estimated carb grams using the eButton, gold standard, and participants' food diaries will be compared head to head among each other. The relative error will be computed: the percentage difference between the estimated value relative to the gold standard. Relative errors will be reported using boxplots to allow visual comparison of the distribution and variability in errors across all methods. Bland-Altman plots will be used to examine the level of agreement between eButton and gold standard measurements.

  Day 14

• Proportion of participants who are fully compliant with eButton use

  Proportion of participants who are fully compliant with eButton use and proportion of meals evaluated using eButton, with 95% confidence intervals will be qualitatively reported. Perceived usefulness and perceived ease of use will be summarized as mean and standard deviation.

  Day 14

Study Arms (1)

Type 2 Diabetes group

Participants with Type 2 Diabetes will wear eButton and Continuous Glucose Monitoring (CGM) for 2 weeks and complete food diaries to record carb grams.

Device: eButton; Device: Continuous Glucose Monitoring (CGM)

Interventions

eButton DEVICE

The eButton is a wearable camera that takes pictures every 6 seconds of whatever is in front of participants. The recorded data are processed by algorithms to determine food names, volumes, and nutrient value of the consumed food (e.g., grams of carbohydrates). The eButton is a wearable device containing a multicore microprocessor, a rechargeable battery capable of 10-15 hours of continuous operation (upon a flexible choice of battery capacity), a miniSD card for data storage.

Type 2 Diabetes group

Continuous Glucose Monitoring (CGM) DEVICE

The use of this device provides ambulatory glucose profiles, giving graphic and quantitative information on 24-hour glucose patterns. It does not require finger-prick testing for calibration. The system consists of a reader and a sensor (35 mm x 5 mm). The sensor is applied to the back of a person's arm. The sensor automatically measures interstitial glucose at 15-minute intervals during daily activities like work, sleep, eating, and exercise. It is able to store blocks of glucose data for 14 days.

Also known as: FreeStyle Libre Pro model

Type 2 Diabetes group

Eligibility Criteria

• Age: 18 Years - 99 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Chinese Americans participants are targeted to test eButton accuracy is that most Chinese Americans live closely within the community (e.g., Chinatown) and use a handful of Asian stores as their main food sources, which reduces the complexity of their food choices. Thus, collecting data from this ethnic group can provide an in-depth validation of eButton closed-loop feedback concept without excessive experimental time.

You may qualify if:

• ≥18 years old
• Diagnosed with T2D at least one year prior
• Self-identified as first- or second-generation Chinese immigrants
• Feel comfortable communicating in English, the reason is that the questionnaires/surveys are validated in English
• Have a computer and internet connection

You may not qualify if:

• Plan frequent travel or vacations or plan to relocate in the next five weeks
• Have serious diabetes-related complications, physical illness, or mental illness (e.g., schizophrenia, bipolar disorder, or substance abuse) that would preclude participation
• Have severe cognitive impairments (e.g., dementia, intellectual disability)

Sponsors & Collaborators

• NYU Langone Health: lead

Study Sites (1)

NYU Langone Health

New York, New York, 10016, United States

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Interventions

Continuous Glucose Monitoring

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Intervention Hierarchy (Ancestors)

Blood Chemical Analysis; Clinical Chemistry Tests; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Techniques, Endocrine; Monitoring, Physiologic; Investigative Techniques

Study Officials

• Yaguang Zheng, PhD, RN

  NYU Langone Health

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 10, 2022
• First Posted: April 20, 2022
• Study Start: July 18, 2022
• Primary Completion: September 22, 2023
• Study Completion: September 22, 2023
• Last Updated: April 12, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP
• Time Frame: Beginning 9 months and ending 36 months following article publication or as required by a condition of awards and agreements supporting the research
• Access Criteria: The investigator who proposed to use the data. Upon reasonable request. Requests should be directed to yaguang.zheng@nyu.edu. To gain access, data requestors will need to sign a data access agreement.

Locations

US (1)