Be Right! Back: An Artificial Intelligence Enabled Mobile Application for Patients With Low Back Pain

NCT06973915 | Not Yet Recruiting

• 1 other identifier: 2025-0641
• Study Type: observational
• Target: 120
• Locations: 1 country
• Sites: 1

Brief Summary

Low back pain (LBP) is a common problem with complex causes, of which some are modifiable. Physical factors like strength, movement, and pain play a big role, but measuring all these factors accurately is tricky. This is where Artificial Intelligence (AI) comes in. This projects aims to develop an AI solution (in the form of a mobile application) that can measure four key components of the physical factor of LBP, such as how quickly you can stand up five times, your spine's flexibility, how you walk, and your pain levels while moving. The measurements taken by the mobile application will be compared against those of trained physiotherapists to ensure its accuracy. If successful, this AI solution will be a game-changer. Physiotherapists will be able to remotely track the progress of their LBP patients. The data gained from the remote tracking will allow physiotherapists to have a better understanding of the individual profile of each LBP patient and adjust their treatment accordingly, hence allowing for better care and more effective LBP management. In short, this project aims to harness the power of AI to make managing LBP easier for both patients and physiotherapists.

Conditions

Low Back Pain

Keywords

Low Back Pain; Artificial Intelligence; Digital Health; Musculoskeletal Pain

Outcome Measures

Primary Outcomes (1)

• 5 times sit-to-stand

  The test provides a method to quantify functional lower extremity strength and/or identify movement strategies a patient uses to complete transitional movements. The score is the amount of time (to the nearest decimal in seconds) it takes a patient to transfer from a seated to a standing position and back to sitting five times.

  Baseline

Secondary Outcomes (10)

• Trunk Range Of Motion (ROM)

  Baseline

• Gait pattern

  Baseline

• European Quality of Life Questionnaire (EQ-5D-5L)

  Baseline, 3 months and 6 months

• Pain Catastrophizing Scale (PCS)

  Baseline, 3 months and 6 months

• Hospital Anxiety and Depression Scale (HADS

  Baseline, 3 months and 6 months

Study Arms (1)

Low Back Pain

Patients with Low Back Pain

Other: AI model for movement and pain assessment in low back pain

Interventions

AI model for movement and pain assessment in low back pain OTHER

This intervention involves developing an artificial intelligence (AI) model to objectively assess four physical parameters relevant to low back pain (LBP): 1) sit-to-stand performance, 2) trunk range of motion, 3) gait pattern, and 4) facial expression-based pain levels during movement. The AI model processes video recordings of participants performing these tasks to extract movement and facial data, providing standardized measurements. The tool is designed to assist physiotherapists in clinical decision-making by offering consistent and accurate assessments compared to traditional observational methods.

Also known as: Be Right! Back app, AI-based movement and pain assessment tool

Low Back Pain

Eligibility Criteria

• Age: 21 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Participants will be recruited from LBP patients attending outpatient physiotherapy clinic.

You may qualify if:

• Aged 21 to 75 years
• Referred to physiotherapy for low back pain
• All genders and races
• Allow video recording of their facial and body movement
• Good comprehension of English language
• Ability to provide informed consent

You may not qualify if:

• Psychiatric disorders (e.g. anxiety, depression)
• Any cognitive impairment
• Neurological disorders (e.g. CVA, Parkinson's Disease)
• Musculoskeletal limitations that result in gait abnormalities/limitations
• Previous thoracic and/or lumbar spine surgery with instrumentation
• Inability to provide informed consent

Sponsors & Collaborators

• Singapore General Hospital: lead
• National Medical Research Council (NMRC), Singapore: collaborator
• KK Women's and Children's Hospital: collaborator

Study Sites (1)

Singapore General Hospital

Singapore, 168582, Singapore

Location

MeSH Terms

Conditions

Low Back Pain; Musculoskeletal Pain

Interventions

Movement

Condition Hierarchy (Ancestors)

Back Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Muscular Diseases; Musculoskeletal Diseases

Intervention Hierarchy (Ancestors)

Physiological Phenomena; Musculoskeletal Physiological Phenomena; Musculoskeletal and Neural Physiological Phenomena

Study Officials

• Philip Cheong, DClinPhty

  Singapore General Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Philip Cheong, DClinPhty

CONTACT

+6563214130; philip.cheong.k.c@sgh.com.sg

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Senior Principal Physiotherapist (Clinical)

Study Record Dates

• First Submitted: May 7, 2025
• First Posted: May 15, 2025
• Study Start: June 1, 2025
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): March 31, 2027
• Last Updated: May 15, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Beginning 12 months and ending 10 years after the publication of results.
• Access Criteria: Data will be stored in the NMRC Research Data Repository. The project information and metadata of the final research data will be made openly available in the NMRC Research Data Repository to serve as data catalogue and inform the prospective data requestors the data available for sharing. Only PIs and their affiliates with primary appointment in local public institution is allowed to submit the Data Access Request for the data stored in the NMRC Research Data Repository.

Locations

SG (1)