NCT03246490

A Machine Learning Approach for Inferring Alcohol Intoxication Levels From Gait Data

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 141 (actual)
• Sponsor: Butler Hospital · Academic / Other
• Sex: All
• Age: 21 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

This study aims to develop a phone app to assess gait differences at different levels of alcohol intoxication.

Detailed description

250 adult volunteers who will each participate in a single laboratory-based visit. At the orientation, each participant will provide informed consent, be weighed, and undergo a medical history to confirm eligibility. A urine drug quick-screen will be given. Female participants will take a urine pregnancy test. Baseline questionnaires will be administered. After giving participants the phone on which the AlcoGait 2.0 app is installed, baseline assessment of gait will be performed with the participant walking a distance of 50 yards ten times. Drinking will then commence. The total amount will be consumed over 30 minutes. Participants will have their BrAC assessed multiple times during and after drinking has commenced and finished. At BrAC levels of .02, .04, .06 and .08 g%, will perform the gait task. After the last gait task, participant data will be transmitted to a secure server. Participants will receive a meal, and will be escorted to a sitting area with a DVR and videos, and allowed to use their own electronic devices. Their BrAC will be periodically tested until it reaches .02 g% or below, then a taxi will be called to take them home.

Conditions

• Alcohol Drinking

Interventions

Type	Name	Description
OTHER	All Participants	all participants will participate in the same protocol involving drinking alcohol to a .08 g% blood alcohol level

Timeline

• Start date: 2017-12-12
• Primary completion: 2019-07-31
• Completion: 2019-07-31
• First posted: 2017-08-11
• Last updated: 2020-04-07

Locations

1 site across 1 country: United States