AI Mobile Application Versus HCP for Bodyweight Squats

NCT ID: NCT04624594

Last Updated: 2021-08-16

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

30 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-10-15

Study Completion Date

2019-12-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

To assess if an artificial intelligence (AI) mobile application can identify and improve bodyweight squat form in adult participants when compared to a Physical Therapist (PT).

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Artificial intelligence (AI) is changing the way people can address their health needs. One such way related to physical exercise is AI-enabled exercise mobile application (digital coach), which uses motion tracking technology to monitor and provide real-time audio feedback on a person's exercise form. However, this AI technology has yet to be independently tested against an in-person evaluator (human coach) for its ability to improve exercise form. This study is a blinded randomized controlled trial comparing the ability of the digital coach (n=15) and a Physical Therapist (PT) human coach (n=15) to improve bodyweight squat form in 30 able-bodied volunteers age 20 - 35. Each volunteer performs 10 unassisted control squats, then 10 squats with assistive vocal feedback from either coach after each repetition, and finally 10 more unassisted test squats, all squats video-recorded. Three independent video evaluators count the number of correct squat repetitions completed by volunteers before and after intervention by the different coaches. This project is important to validate the digital coach compared to a PT human coach in a small population using a bodyweight squat for its wide applicability to daily movement patterns.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Squat Form

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

OTHER

Blinding Strategy

SINGLE

Outcome Assessors

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Artificial Intelligence (AI) Group

To determine baseline ability and serve as their own control, participants in both groups performed 10 bodyweight squat "control" repetitions without feedback followed by one minute of rest. Those in the AI group then performed 10 more "practice" repetitions with real-time audiovisual feedback from the app followed by one minute of rest. The AI's design provided one piece of feedback, if necessary, with a vocal statement and on-screen video per repetition (e.g. when a participant performed a squat repetition with their neck flexed downward, AI suggested keeping their head up with on-screen instruction). Participants in both groups then performed 10 "test" repetitions without feedback followed by one minute of rest.

Group Type EXPERIMENTAL

Artificial Intelligence Feedback

Intervention Type OTHER

AI mobile application provides feedback to participants randomized to artificial intelligence group.

Physical Therapist Group

To determine baseline ability and serve as their own control, participants in both groups performed 10 bodyweight squat "control" repetitions without feedback followed by one minute of rest. Those in the PT group (n=15) also performed 10 "practice" repetitions with one piece of feedback per repetition, if necessary, from the PT followed by one minute of rest. Participants in both groups then performed 10 "test" repetitions without feedback followed by one minute of rest.

Group Type ACTIVE_COMPARATOR

Physical Therapist Feedback

Intervention Type OTHER

PT provides feedback to participants randomized to physical therapist group.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Artificial Intelligence Feedback

AI mobile application provides feedback to participants randomized to artificial intelligence group.

Intervention Type OTHER

Physical Therapist Feedback

PT provides feedback to participants randomized to physical therapist group.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Columbia University affiliate
* Aged 20 to 35 years
* Able to perform moderate bodyweight exercise for 10 minutes

Exclusion Criteria

* Unable to provide consent
Minimum Eligible Age

20 Years

Maximum Eligible Age

35 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

National Medical Fellowships

UNKNOWN

Sponsor Role collaborator

Columbia University

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Sunil K. Agrawal, PhD

Role: PRINCIPAL_INVESTIGATOR

Columbia University

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Columbia University Medical Center

New York, New York, United States

Site Status

Countries

Review the countries where the study has at least one active or historical site.

United States

References

Explore related publications, articles, or registry entries linked to this study.

Luna A, Casertano L, Timmerberg J, O'Neil M, Machowsky J, Leu CS, Lin J, Fang Z, Douglas W, Agrawal S. Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial. Sci Rep. 2021 Sep 13;11(1):18109. doi: 10.1038/s41598-021-97343-y.

Reference Type DERIVED
PMID: 34518568 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

AAAS7301

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.