PAIN (Pain AI iNtervention) Platform for Patients at Home

December 15, 2025 updated by: Antonio J. Forte, Mayo Clinic

Development of the PAIN (Pain AI iNtervention) Platform for Patients at Home

The purpose of this research is to identify physiological markers to determine pain intensity and build an Artificial Intelligence (AI) enabled system to objectively measure pain intensity. Researchers hope to personalize pain medication regimens to help prevent medication over-use.

Study Overview

Status

Enrolling by invitation

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

70

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • Florida
      • Jacksonville, Florida, United States, 32224
        • Mayo Clinic Florida

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

14 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Patients aged 18 or older undergoing low-risk outpatient plastic surgery procedures with expected pain intensities ranging from mild to severe will be included in the study.

Description

Inclusion Criteria:

  • Patients undergoing low-risk outpatient plastic surgery procedures with expected pain intensities ranging from mild to severe.

Exclusion Criteria:

  • Patients with treated or untreated cardiopulmonary syndromes.
  • Patients with treated or untreated ophthalmologic pathologies.
  • Patients with skin pathologies that prevent us from using the TENS device.
  • Patients with pathologies or conditions preventing them from appropriately using their voice.
  • Patients with barriers to effective communication.
  • Patients with poor digital literacy.
  • Patients incapable of taking oral medication.
  • Patients who are currently taking medical therapy for chronic pain.
  • Patients with a previous diagnosis of severe anxiety disorders.
  • Patients who are immobile at baseline.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Post-Surgery data collection system
Subjects undergoing standard of care low-risk plastic surgery be provided with wearable sensors to take home and start recording heart rate, body temperature and body movements
Other: Machine learning algorithms
Machine learning techniques to rank order physiologic variables obtained via the wearable and handheld devices as well as remove low-importance and redundant variables to accurately determine postoperative pain intensity in outpatients
Other Names:
  • Artificial Intelligence (AI) enabled system

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Using machine Learning for Postoperative Pain Pain Prediction
Time Frame: 8 months
The primary outcome will be the accuracy of machine learning algorithms for postoperative pain prediction using root mean square errors.
8 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Physiologic variable %Δ defining the physiologic biomarker's change in measurements after pain medication
Time Frame: 8 months
The secondary outcome will be the physiologic variable's use to define the physiologic biomarker's change in measurements after pain medication (%Δ in signal's respective units).
8 months
Physiologic variable absolute Δ defining the physiologic biomarker's change in measurements after pain medication
Time Frame: 8 months
The secondary outcome will be the physiologic variable's use to define the physiologic biomarker's change in measurements after pain medication (absolute Δ in signal's respective units).
8 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mayo Clinic

Investigators

  • Principal Investigator: Antonio Forte, MD, PhD, Mayo Clinic

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 23, 2022

Primary Completion (Estimated)

November 1, 2026

Study Completion (Estimated)

November 1, 2027

Study Registration Dates

First Submitted

July 22, 2022

First Submitted That Met QC Criteria

July 25, 2022

First Posted (Actual)

July 26, 2022

Study Record Updates

Last Update Posted (Actual)

December 22, 2025

Last Update Submitted That Met QC Criteria

December 15, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 21-013443

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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