Low Back PainNCT06973915
Be Right! Back: An Artificial Intelligence Enabled Mobile Application for Patients With Low Back Pain
An observational study of Low Back Pain, run by Singapore General Hospital at 1 site in Singapore.
Trial 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. Read more about this study
- Who is it for?
- Ages 21 to 75 years
- Conditions the study is for
- Low Back Pain
- What is being tested?
- AI model for movement and pain assessment in low back pain
- How many people
- 120 participants
- Where
- 1 siteSingapore
- Study dates
- 1 Jun 2025to 31 Mar 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Participants will be recruited from LBP patients attending outpatient physiotherapy clinic.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Singapore1 site
Singapore General Hospital
Singapore, 168582
Philip Cheong, DClinPhty, +6563214130, philip.cheong.k.c@sgh.com.sg
Philip Cheong, DClinPhty
About this study
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.
Read more
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.
Read the 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.
- Conditions
- Low Back Pain
- Being tested
- AI model for movement and pain assessment in low back pain
- Keywords
- Low Back Pain, Artificial Intelligence, Digital Health, Musculoskeletal Pain
- Medical subject headings
- Low Back Pain, Musculoskeletal Pain
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI model for movement and pain assessment in low back painOther
Also called Be Right! Back app, AI-based movement and pain assessment tool
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.
| Study group | Role | What they receive |
|---|---|---|
| Low Back Pain | Patients with Low Back Pain Receives: Other: AI model for movement and pain assessment in low back pain |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline
Secondary outcomes (10)
Trunk Range Of Motion (ROM)
Measurement of trunk (lumbar spine) ROM for:
- Flexion (forward bending) and Extension (backward bending) in the sagittal plane
- Right Lateral Flexion (right tilt) and Left Lateral Flexion (left tilt) in the frontal plane
- Right Rotation (right turn) and Left Rotation (left turn) in the transverse plane
Measured: Baseline
Gait pattern
Walking pattern of patients with low back pain
Measured: Baseline
European Quality of Life Questionnaire (EQ-5D-5L)
Patient Reported Outcome Measure (PROM) that represents an estimation of the patient's perceived quality of life and state of health.
Measured: Baseline, 3 months and 6 months
Pain Catastrophizing Scale (PCS)
Patient Reported Outcome Measure (PROM) describing thoughts and feelings that individuals might experience when in pain.
Measured: Baseline, 3 months and 6 months
Hospital Anxiety and Depression Scale (HADS
Patient Reported Outcome Measure (PROM) used to evaluate depression and anxiety. It has 14 items over 2 subscales: depression and anxiety.
Measured: Baseline, 3 months and 6 months
Depression Anxiety Stress Scales 21 (DASS21)
Patient Reported Outcome Measure (PROM) designed to measure the emotional states of depression, anxiety and stress. It has 21 items over 3 subscales: depression, anxiety and stress.
Measured: Baseline, 3 months and 6 months
Short version of Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ-SF)
Patient Reported Outcome Measure (PROM). 10 item questionnaire that was developed from the Örebro Musculoskeletal Pain Screening Questionnaire (ÖMSPQ). The ÖMSPQ "was developed as a tool to assist in the early identification of yellow flags and patients risking the development of work disability due to the pain".
Measured: Baseline
STarT Back Screening Tool (SBST)
9 item screening tool that is designed to subgroup low back pain patients for prognostic indicators that are relevant to their care.
Measured: Baseline
Numeric Rating Scale (NRS)
NRS is a single 11-point numeric scale for rating pain intensity.
Measured: Baseline, 3 months and 6 months
Patient Specific Functional Scale (PSFS)
PSFS is a self-reported, patient specific measure, that is designed to assess functional change in patients with musculoskeletal disorders. Patients are asked to identify up to five functional activities that are important to them and of which they have difficulty performing, after which the patients then rate each activity on an 11-point scale on the current level of difficulty they have performing the activity.
Measured: Baseline, 3 months and 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 May 2025
- Study start1 Jun 2025 (estimated)
- Primary completion30 Sep 2026 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Low Back PainStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Singapore General Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- National Medical Research Council (NMRC), Singapore, KK Women's and Children's Hospital
- Responsible party
- Principal investigator, Philip Cheong Kwok Chee, Singapore General Hospital
- Investigators
- Philip Cheong, DClinPhty, Principal investigator, Singapore General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06973915
- Sponsor's protocol ID
- 2025-0641
- Status last verified
- May 2025
- First submitted
- 7 May 2025
- Last updated
- 15 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Due to considerations of intellectual property rights and patient privacy, only anonymized individual participant data will be shared. This will include de-identified patient demographics and key point data extracted from the patient video recordings (e.g., joint and facial landmark coordinates), with no facial identifiers or video footage being shared.
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Be Right! Back: An Artificial Intelligence Enabled Mobile Application for Patients With Low Back Pain
- ClinicalTrials.gov ID
- NCT06973915
- Acronym / sponsor ID
- 2025-0641
- Conditions
- Low Back Pain
- Keywords
- Low Back Pain, Artificial Intelligence, Digital Health, Musculoskeletal Pain
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Singapore General HospitalUniversity, hospital or other
- Collaborators
- National Medical Research Council (NMRC), Singapore, KK Women's and Children's Hospital
- Study start
- 1 Jun 2025 (estimated)
- Primary completion
- 30 Sep 2026 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 15 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Low Back Pain | – | Other: AI model for movement and pain assessment in low back painPatients with Low Back Pain |
Who may qualify
- Ages
- 21 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants will be recruited from LBP patients attending outpatient physiotherapy clinic.
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (6 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI model for movement and pain assessment in low back painAlso: Be Right! Back app, AI-based movement and pain assessment toolThis 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. | Other | Low Back Pain |
Primary outcome
5 times sit-to-stand
Time frame: Baseline
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.
10 secondary outcomes
Trunk Range Of Motion (ROM)
Time frame: Baseline
Gait pattern
Time frame: Baseline
European Quality of Life Questionnaire (EQ-5D-5L)
Time frame: Baseline, 3 months and 6 months
Pain Catastrophizing Scale (PCS)
Time frame: Baseline, 3 months and 6 months
Hospital Anxiety and Depression Scale (HADS
Time frame: Baseline, 3 months and 6 months
Depression Anxiety Stress Scales 21 (DASS21)
Time frame: Baseline, 3 months and 6 months
Short version of Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ-SF)
Time frame: Baseline
STarT Back Screening Tool (SBST)
Time frame: Baseline
Numeric Rating Scale (NRS)
Time frame: Baseline, 3 months and 6 months
Patient Specific Functional Scale (PSFS)
Time frame: Baseline, 3 months and 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2025.
- Individual participant dataSharing planned: Yes. Due to considerations of intellectual property rights and patient privacy, only anonymized individual participant data will be shared. This will include de-identified patient demographics and key point data extracted from the patient video recordings (e.g., joint and facial landmark coordinates), with no facial identifiers or video footage being shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Philip Cheong, DClinPhty principal investigator
Singapore General Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Low Back Pain, run by Singapore General Hospital at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 15 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Philip Cheong, DClinPhty (+6563214130, philip.cheong.k.c@sgh.com.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
5 times sit-to-stand (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 May 2025.