CI

At a glance

ClinicalIndex Comparison Record
N/ACompleted· 39 enrolled
Drug / intervention
Ecological Momentary Interventionbehavioral
Likely dose
Not stated in record
Structured eligibility isn't available for this trial yet — see the full criteria in the Eligibility tab below.

Standardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.

Search/NCT04507464
NCT04507464N/ACompleted
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A Physical Activity Program to Disrupt Sedentary Time in Older Latinos

University of Illinois at Chicago·interventional·Posted Aug 11, 2020·Updated Jul 25, 2023

In Brief

A clinical study evaluating Ecological Momentary Intervention for Sedentary Behavior and Cognitive Impairment. Completed, enrolled 39 participants across 1 site.

Detailed Summary

To test the feasibility of an intervention designed to replace sedentary time with physical activity in older Latinos, delivered in their homes to improve cognitive function and brain connectivity.

Study Details

Study Typeinterventional
Allocation--
Masking--
Primary Purpose--
ConditionsSedentary Behavior, Cognitive Impairment
CountriesUnited States
CollaboratorsNational Institute on Aging (NIA)

Timeline

N/ACompletedFinished
202120222023202420252026
First PostedAug 11, 2020
Enrollment StartNov 2, 2020
Primary CompletionApr 2, 2022
TodayJul 2, 2026
Enrollment to primary: 1.4 yearsPosted 5.9 years ago

Interventions

Ecological Momentary Interventionbehavioral

Participants will wear a Fitbit that has been programmed to deliver a notification (a buzz) when activity has ceased for more than 10 minutes. They will receive suggestions on their Illumivu mEMA smartphone app on how to replace sitting time with PA such as standing up 5 times or taking 20 steps, or even performing a short (20 seconds) preferred dance routine. Fitbit data will be captured in real-time via the iCardia platform. Participants will receive reminders from their smartphones to enter real-time feedback on activity options selected and how successful they were in adopting the option. These data will allow tracking of underlying preferences for behaviors and to tailor the program accordingly.