NEAT! Technology to Increase Breaks in Sedentary Behavior in Adults With Diabetes

NCT ID: NCT01754467

Last Updated: 2017-04-28

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 9 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2013-02-28
• Study Completion Date: 2013-10-31

Brief Summary

Sedentary behavior is associated with an increased risk of mortality and many health conditions including type 2 diabetes, obesity, cardiovascular disease, and metabolic syndrome, independent of the time spent in moderate-to-vigorous intensity physical activity (MVPA). Furthermore, independent of total sedentary time and MVPA, Healy et al. observed that individuals who had more breaks in sedentary time had lower 2-h plasma glucose. Recent experimental findings also suggests that breaking up prolonged bouts of sedentary behavior (≥ 20 minutes) with either light or moderate intensity activity for 2 minutes reduces postprandial glucose and insulin responses.10 Replacing sedentary time with light-intensity activity or nonexercise activity thermogenesis (NEAT) may help to reduce the health consequences of sedentary behavior. The purpose of this study is to develop a smartphone application (NEAT!) to encourage sedentary adults with diabetes to increase breaks in prolonged bouts of sedentary behavior objectively measured by an accelerometer. NEAT! will be refined by modifying technology created for the ENGAGED trial (RC1DK087126) and will work in conjunction with a Bluetooth enabled accelerometer that can detect bouts of sedentary behavior. When a sedentary bout (≥ 20 minutes) is detected, the smartphone application will trigger a reminder prompt to the user encouraging him/her to participate in NEAT for at least 2 minutes. Following the development, testing, and refining of the application, a sample of 10 sedentary adults with type 2 diabetes will be recruited to participate in a one month trial to examine the feasibility and acceptability of NEAT!. To our knowledge, this is the first study to design and examine the acceptability of a smartphone application that will target interrupting sedentary behavior with NEAT using objectively measured sedentary time in a diabetic population.

Conditions

Diabetes Mellitus

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: NONE

Study Groups

NEAT!

Participants will use the NEAT! smartphone application and accelerometer over a 1 month period.

Group Type: EXPERIMENTAL

NEAT!

Intervention Type: BEHAVIORAL

Participants will wear the accelerometer and use the NEAT! application during waking hours for 1 month. The NEAT! app will prompt participants to stand up when they have been sitting for a prolonged period.

Interventions

NEAT!

Participants will wear the accelerometer and use the NEAT! application during waking hours for 1 month. The NEAT! app will prompt participants to stand up when they have been sitting for a prolonged period.

Intervention Type: BEHAVIORAL

Eligibility Criteria

Inclusion Criteria

• 21-70 years of age
• have physician diagnosed type 2 diabetes that is currently being treated by dietary modification, oral agents, or insulin
• currently and plan on having an Android smartphone for the next 2 months
• be willing to wear an accelerometer and use the NEAT! application
• spend the majority of the day sitting.

Exclusion Criteria

• unable to ambulate without assistance

• Minimum Eligible Age: 21 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Northwestern University

OTHER

Sponsor Role: lead

Responsible Party

Christine Pellegrini

Research Assistant Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Christine Pellegrini, Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Northwestern University

Locations

Northwestern University

Chicago, Illinois, United States

Countries

United States

Other Identifiers

CCDTR01

Identifier Type: -

Identifier Source: org_study_id