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AI Mobile Application Versus HCP for Bodyweight Squats

Artificial Intelligence (AI) Mobile Application Versus Health Care Provider (HCP) for Bodyweight Squats: A Randomized, Blinded, Controlled Clinical Trial

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT04624594
Enrollment
30
Registered
2020-11-12
Start date
2019-10-15
Completion date
2019-12-30
Last updated
2021-08-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Squat Form

Keywords

Exercise, Squat, Physical therapist, Artificial Intelligence (AI)

Brief summary

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

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.

Interventions

OTHERArtificial Intelligence Feedback

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

OTHERPhysical Therapist Feedback

PT provides feedback to participants randomized to physical therapist group.

Sponsors

National Medical Fellowships
CollaboratorUNKNOWN
Columbia University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
SINGLE (Outcomes Assessor)

Eligibility

Sex/Gender
ALL
Age
20 Years to 35 Years
Healthy volunteers
Yes

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

Design outcomes

Primary

MeasureTime frameDescription
Number of correct squatsUp to 15 minutes or completion of third set of squatsPost-intervention improvement in squats will be determined by the number of correct squats in the third set as compared to the first set of squats.

Secondary

MeasureTime frameDescription
Number of squats that are identified correctly by AIUp to 15 minutes or completion of third set of squatsAI identification of correct and incorrect squats will be determined by the number of squats that are identified correctly by AI as compared with independent evaluators.

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 21, 2026