Clinical trial NCT06973915: Be Right! Back: An Artificial Intelligence Enabled Mobile Application for Patients With Low Back Pain
Official title: Be Right! Back: An Artificial Intelligence Enabled Mobile Application for Patients With Low Back Pain
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Low Back Pain
Keywords: Low Back Pain
Artificial Intelligence
Digital Health
Musculoskeletal Pain
Lead sponsor: Singapore General Hospital
Sponsor class: OTHER
Collaborators: name: National Medical Research Council (NMRC), Singapore
name: KK Women's and Children's Hospital
First posted: 2025-05-15
Enrollment: 120
Sex: ALL
Minimum age (years): 21
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Participants will be recruited from LBP patients attending outpatient physiotherapy clinic.

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.

Detailed description: Background: Low back pain (LBP) is a complex condition and its causes are multifactorial, of which the physical, lifestyle, cognitive and emotional factors are potentially modifiable.

Due to the complexity of LBP, Artificial Intelligence (AI) can be used to accurately measure and analyze large amounts of data from different sources to aid in the assessment and management of LBP.

Objective: Development of an AI model that accurately assesses and measures 4 core components that comprise the Physical factor of LBP. The 4 core components are functional activity (measured using the 5 times sit-to-stand task - 5xSTS), trunk range of motion (ROM), gait pattern and pain levels during movement.

Methods: The project aims to recruit 120 LBP patients receiving care at SGH Physiotherapy. For the first (primary) study (n=103), we will compare the measurements (5xSTS, trunk ROM, gait pattern and pain levels during movement) taken by the AI model against that of a trained assessor/physiotherapist.

For the second study (n=17), following integration of the AI model with our industry partner's platform, a pilot study will be conducted to assess the feasibility and usability of a minimum viable product.

Planned Analysis: For the first study, the Bland-Altman plot will be used to compare the measurements taken by the AI model against that of a trained assessor/physiotherapist. If our hypothesis is correct, the results should show narrow limits of agreement between the 2 methods of measurement.

Descriptive statistics will be used for the second study. We anticipate that there will be positive feedback and satisfaction from use of the minimum viable product.

Discussion: Successful development of our solution will allow accurate remote tracking of the progress made by LBP patients. This will support/assist physiotherapists in clinical decision-making, hence allowing for more effective management of LBP.

Eligibility criteria: Inclusion Criteria:

1. Aged 21 to 75 years
2. Referred to physiotherapy for low back pain
3. All genders and races
4. Allow video recording of their facial and body movement
5. Good comprehension of English language
6. Ability to provide informed consent

Exclusion Criteria:

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

Primary outcomes:
measure: 5 times sit-to-stand
timeFrame: Baseline
Secondary outcomes:
measure: Trunk Range Of Motion (ROM)
timeFrame: Baseline
measure: Gait pattern
timeFrame: Baseline
measure: European Quality of Life Questionnaire (EQ-5D-5L)
timeFrame: Baseline, 3 months and 6 months
measure: Pain Catastrophizing Scale (PCS)
timeFrame: Baseline, 3 months and 6 months
measure: Hospital Anxiety and Depression Scale (HADS
timeFrame: Baseline, 3 months and 6 months
measure: Depression Anxiety Stress Scales 21 (DASS21)
timeFrame: Baseline, 3 months and 6 months
measure: Short version of Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ-SF)
timeFrame: Baseline
measure: STarT Back Screening Tool (SBST)
timeFrame: Baseline
measure: Numeric Rating Scale (NRS)
timeFrame: Baseline, 3 months and 6 months
measure: Patient Specific Functional Scale (PSFS)
timeFrame: Baseline, 3 months and 6 months
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: Singapore General Hospital
Study officials:
affiliation: Singapore General Hospital
name: Philip Cheong, DClinPhty
role: PRINCIPAL_INVESTIGATOR
References: