Low Back PainNCT07733752
Pre-Visit AI Symptom-Checking and Shared Decision-Making in Spine Physical Therapy
An observational study of Low Back Pain and Neck Pain, run by Assiut University at 1 site in Egypt.
Trial summary
Artificial intelligence (AI) symptom-checking tools, including large language models such as ChatGPT, are increasingly used by patients before they seek care. These tools may shape patients' beliefs about their diagnosis, how serious they think their condition is, and when they decide to seek treatment. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Low Back Pain, Neck Pain, Radiculopathy
- What is being tested?
- re-visit AI symptom-checker use
- How many people
- 200 participants
- Where
- 1 siteEgypt
- Study dates
- 10 Jun 2026to 10 Feb 2027
- How long will I take part?
- 6 Weeks
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adults aged 18 years or older presenting for a new evaluation at outpatient physical therapy clinics for a spine-related musculoskeletal complaint - neck, thoracic, or low back pain, with or without radicular symptoms. Consecutive eligible patients are enrolled and grouped by their use of AI-based symptom-checking tools for the current complaint in the 30 days preceding the visit (AI users vs. non-AI users). Patients with recent spinal surgery, serious spinal pathology under active management, cognitive impairment limiting participation, or concurrent enrollment in a study affecting spine-related decision-making are not included.
Who may be able to join
- Age: 18 years or older
- Presenting for a new evaluation at an outpatient physical therapy clinic for a spine-related musculoskeletal complaint, including Neck, Thoracic, and Low back pain With or without radicular symptoms
- Able to provide informed consent.
- Able to read, understand, and complete study questionnaires
Who cannot join
- Recent spinal surgery within the past 3 months, due to differing clinical pathways and management strategies.
- Presence of serious spinal pathology under active medical management, such as:
- Malignancy (e.g., metastatic disease) Spinal infection Acute fracture
- Cognitive impairment or other conditions that limit the ability to provide informed consent or reliably complete study measures.
- Patients currently enrolled in another study that may influence clinical decision-making or outcomes related to spine care.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Egypt1 site, 1 recruiting
- Recruiting
Faculty of Medicine, Assiut University, Egypt
Asyut
Mariam A Ibrahim Principal investigator, +20 10 01539399, Mariam.A.ibrahim@med.aun.edu.eg
About this study
Official title: When AI Is the First Clinician: Impact of Pre-Visit AI Use on Presentation, Diagnostic Expectations, and Shared Decision-Making in Spine Physical Therapy
Artificial intelligence (AI) symptom-checking tools, including large language models such as ChatGPT, are increasingly used by patients before they seek care. These tools may shape patients' beliefs about their diagnosis, how serious they think their condition is, and when they decide to seek treatment. It is not yet known how this pre-visit AI use affects the initial physical therapy encounter for spine-related problems.
This prospective observational cohort study examines whether prior use of AI symptom-checking tools influences the first physical therapy evaluation in adults presenting with spine-related musculoskeletal complaints (neck, thoracic, or low back pain, with or without radicular symptoms). Consecutive patients attending an outpatient physical therapy clinic for a new evaluation are grouped as AI users or non-AI users based on whether they used such a tool for their current complaint in the previous 30 days.
The primary outcome is shared decision-making, measured with the SDM-Q-9 immediately after the initial evaluation. Secondary outcomes include stage of presentation, agreement between the patient's expected diagnosis and the clinician's classification, baseline pain and disability, functional performance, and clinical outcomes at 2 and 6 weeks. The investigators hypothesize that prior AI use is associated with differences in shared decision-making and in how patients present for care.
Read the detailed description
Background and Rationale AI-based symptom-checking tools, including large language models, are increasingly used by patients as a first point of clinical interpretation before seeking care. Available evidence suggests these tools show moderate and variable diagnostic accuracy and can influence patients' diagnostic beliefs, perceived symptom severity, and healthcare-seeking behavior, raising concerns about pre-diagnostic anchoring. In spine and musculoskeletal care, early presentation, accurate classification, and patient engagement are important determinants of outcomes, and current practice relies on history, physical examination, and shared decision-making under the assumption that patients present without strongly preformed diagnostic expectations. The growing use of AI may alter this dynamic. Evidence on the real-world impact of pre-visit AI use on the physical therapy encounter is currently limited.
Objective The primary objective is to determine whether prior use of AI symptom-checking tools influences shared decision-making during the initial physical therapy evaluation in patients presenting with spine-related musculoskeletal conditions. Secondary objectives examine associations between prior AI use and stage of presentation, agreement between patient-expected and clinician diagnosis, baseline clinical status, functional performance, health-seeking behavior, and short-term clinical outcomes.
Design and Setting This is a prospective observational cohort study conducted in outpatient physical therapy clinics. No study-specific interventions are introduced; all participants receive standard physical therapy care determined by their treating clinician.
Participants and Grouping Consecutive adults (≥18 years) presenting for a new evaluation for a spine-related musculoskeletal complaint (neck, thoracic, or low back pain, with or without radicular symptoms) who can provide informed consent and complete study questionnaires in English are eligible. Patients are excluded for recent spinal surgery within the past 3 months, serious spinal pathology under active medical management (e.g., malignancy, spinal infection, acute fracture), cognitive impairment limiting consent or reliable completion of measures, or current enrollment in another study that may influence clinical decision-making or spine-related outcomes. Participants are classified into two groups based on whether they used an AI symptom-checking tool for their current complaint within the prior 30 days: AI users and non-AI users.
Study Instruments and Measures AI exposure is captured at baseline via a questionnaire assessing AI use (yes/no), tool type, frequency, degree of personalization, and reported influence on care-seeking. Clinical measures include symptom duration, pain intensity (Numeric Pain Rating Scale, 0-10), region-specific disability (Oswestry Disability Index for low back pain or Neck Disability Index for neck pain), and the Five Times Sit-to-Stand test. Diagnostic expectation is assessed by comparing the patient-expected diagnosis with the clinician's classification, categorized as match, partial match, or mismatch. Shared decision-making is measured with the SDM-Q-9 (score range 0-45; higher scores indicate greater patient involvement) immediately after the initial evaluation. Follow-up outcomes at 2 and 6 weeks include pain, disability (ODI/NDI), Global Rating of Change, self-reported adherence, and healthcare utilization.
Procedures At baseline (pre-visit), AI exposure, symptom duration, pain, and disability are collected. During the initial evaluation, the clinician performs a standard physical therapy assessment and classification, and patient expectations are recorded. Immediately post-visit, participants complete the SDM-Q-9. At 2-week and 6-week follow-up, clinical outcomes and adherence are assessed and AI use is re-evaluated. Data are recorded on standardized forms and stored securely using coded identifiers.
Sample Size The primary outcome is SDM-Q-9 following the initial evaluation. Using G*Power (v3.1.9.7), a sample of 128 participants (64 per group) provides 80% power to detect a moderate between-group difference (Cohen's d = 0.5) at a two-sided alpha of 0.05. To account for potential group imbalance and an anticipated attrition of 15-20%, approximately 200 consecutive patients will be recruited.
Statistical Analysis Continuous variables are summarized as mean ± standard deviation and categorical variables as frequencies and percentages. AI users and non-AI users are compared using independent-samples t-tests for continuous variables and chi-square tests for categorical variables. Statistical significance is set at p ≤ 0.05.
- Conditions
- Low Back Pain, Neck Pain, Radiculopathy
- Being tested
- re-visit AI symptom-checker use
- Keywords
- Artificial intelligence, ChatGPT, Shared decision-making, SDM-Q-9, Spine, Patient expectations
- Medical subject headings
- Low Back Pain, Neck Pain, Radiculopathy
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
re-visit AI symptom-checker useOther
Self-reported use of an AI-based symptom-checking tool (e.g., a large language model such as ChatGPT) for the current spine-related complaint during the 30 days before the initial physical therapy evaluation. This exposure occurs naturally prior to presentation and is not assigned by the investigator. Exposure status is ascertained at baseline via a questionnaire capturing whether AI was used (yes/no), the type of tool, frequency of use, degree of personalization, and the reported influence of AI use on care-seeking timing.
| Study group | Role | What they receive |
|---|---|---|
| AI Users | Patients who reported using an AI-based symptom-checking tool (e.g., a large language model such as ChatGPT) for their current spine-related complaint within the 30 days before their initial physical therapy evaluation. Receives: Other: re-visit AI symptom-checker use | |
| Non-AI Users | Patients who reported no use of any AI-based symptom-checking tool for their current spine-related complaint within the 30 days before their initial physical therapy evaluation. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Shared Decision-Making (SDM-Q-9)
Patient-perceived involvement in shared decision-making during the initial physical therapy evaluation, measured with the 9-item Shared Decision-Making Questionnaire (SDM-Q-9). Raw total score ranges from 0 to 45; higher scores indicate greater perceived patient involvement in decision-making.
Measured: Immediately after the initial physical therapy evaluation (Day 0)
Secondary outcomes (6)
Stage of Presentation (Symptom Duration)
Self-reported duration of the current spine-related symptoms, in days, recorded at the initial evaluation as an indicator of timeliness/stage of presentation.
Measured: At initial evaluation (Day 0)
Diagnostic Agreement (Patient-Clinician Concordance)
Concordance between the patient's expected diagnosis and the clinician's classification, categorized as 0 = match, 1 = partial match, 2 = mismatch.
Measured: At initial evaluation (Day 0)
Pain Intensity (Numeric Pain Rating Scale)
Self-reported pain intensity on the 11-point Numeric Pain Rating Scale (0 = no pain, 10 = worst imaginable pain); higher scores indicate greater pain.
Measured: Day 0, 2 weeks, 6 weeks
Disability (ODI or NDI)
Region-specific self-reported disability, using the Oswestry Disability Index for low back pain or the Neck Disability Index for neck pain. Both are scored 0-100% (0 = no disability; higher scores indicate greater disability).
Measured: Day 0, 2 weeks, 6 weeks
Health-Seeking Behavior (AI Influence on Care Timing)
Self-reported influence of prior AI symptom-checker use on the timing of seeking care, categorized as earlier, delayed, or no change.
Measured: At initial evaluation (Day 0)
Adherence and Healthcare Utilization
Self-reported adherence to the recommended treatment plan.
Measured: 2 weeks, 6 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Jul 2026
- Study start10 Jun 2026
- Primary completion10 Dec 2026 (estimated)
- Study completion10 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Low Back PainStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
- Target duration
- 6 Weeks
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mariam ibrahim, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07733752
- Sponsor's protocol ID
- 04-2026-300876
- Status last verified
- Jun 2026
- First submitted
- 24 Jul 2026
- Last updated
- 29 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Harvey D, White S, Reid D, Cook C. Patient perspectives of process variables in musculoskeletal care pathways. Musculoskelet Sci Pract. 2025 Apr;76:103287. doi: 10.1016/j.msksp.2025.103287. Epub 2025 Feb 14.
PubMed 39978029Meyer AND, Giardina TD, Spitzmueller C, Shahid U, Scott TMT, Singh H. Patient Perspectives on the Usefulness of an Artificial Intelligence-Assisted Symptom Checker: Cross-Sectional Survey Study. J Med Internet Res. 2020 Jan 30;22(1):e14679. doi: 10.2196/14679.
PubMed 32012052Kuroiwa T, Sarcon A, Ibara T, Yamada E, Yamamoto A, Tsukamoto K, Fujita K. The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study. J Med Internet Res. 2023 Sep 15;25:e47621. doi: 10.2196/47621.
PubMed 37713254Kumar R, Dougherty C, Sporn K, Khanna A, Ravi P, Prabhakar P, Zaman N. Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care. Bioengineering (Basel). 2025 Sep 9;12(9):967. doi: 10.3390/bioengineering12090967.
PubMed 41007212Bensel VA, Habeck A, Brunot MH, Becton EJ, Ray M, Brackett AL, Lisi AJ. Artificial intelligence in spine care: A scoping review of treatment applications. N Am Spine Soc J. 2025 Nov 20;25:100827. doi: 10.1016/j.xnsj.2025.100827. eCollection 2026 Mar.
PubMed 41536317Muelbauer EJ, Alvi MA, Kennedy DJ, Fehlings MG. The future is now: How AI is reshaping spine care. N Am Spine Soc J. 2025 Nov 14;24:100825. doi: 10.1016/j.xnsj.2025.100825. eCollection 2025 Dec.
PubMed 41458002Rossettini G, Bargeri S, Cook C, Guida S, Palese A, Rodeghiero L, Pillastrini P, Turolla A, Castellini G, Gianola S. Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in advising on lumbosacral radicular pain against clinical practice guidelines: cross-sectional study. Front Digit Health. 2025 Jun 27;7:1574287. doi: 10.3389/fdgth.2025.1574287. eCollection 2025.
PubMed 40657647Basharat A, Shah R, Wilcox N, Tur G, Tripati S, Kansal P, Gandhi N, Pokuri S, Chong G, Odonkor CA, Varhabhatla N, Chow R. ChatGPT and low back pain - Evaluating AI-driven patient education in the context of interventional pain medicine. Interv Pain Med. 2025 Sep 2;4(3):100636. doi: 10.1016/j.inpm.2025.100636. eCollection 2025 Sep.
PubMed 40978326Li YH, Li N, Liu ZX, Du S, Shuai Y, Yang R, Xu L, Li X, Jiang Y, Li W. The effectiveness of artificial intelligence health education accurately linking system on self-management in non-specific lower back pain patients. Front Public Health. 2025 Sep 11;13:1630329. doi: 10.3389/fpubh.2025.1630329. eCollection 2025.
PubMed 41018793Baldus SG, Wiesmann M, Habel U, Gerhards A, Hasan D, Weyland CS, Truhn D, Hasl MM, Clemens B, Nikoubashman O. Patients' views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey. Insights Imaging. 2026 Jan 5;17(1):6. doi: 10.1186/s13244-025-02159-3.
PubMed 41491377Alzubaidi H, Hussein A, Mc Namara K, Scholl I. Psychometric properties of the Arabic version of the 9-item Shared Decision-Making Questionnaire: the entire process from translation to validation. BMJ Open. 2019 Apr 4;9(4):e026672. doi: 10.1136/bmjopen-2018-026672.
PubMed 30948608Algarni AS, Ghorbel S, Jones JG, Guermazi M. Validation of an Arabic version of the Oswestry index in Saudi Arabia. Ann Phys Rehabil Med. 2014 Dec;57(9-10):653-63. doi: 10.1016/j.rehab.2014.06.006. Epub 2014 Aug 4.
PubMed 25262247Zhou M, Pan Y, Zhang Y, Song X, Zhou Y. Evaluating AI-generated patient education materials for spinal surgeries: Comparative analysis of readability and DISCERN quality across ChatGPT and deepseek models. Int J Med Inform. 2025 Jun;198:105871. doi: 10.1016/j.ijmedinf.2025.105871. Epub 2025 Mar 13.
PubMed 40107040
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- When AI Is the First Clinician: Impact of Pre-Visit AI Use on Presentation, Diagnostic Expectations, and Shared Decision-Making in Spine Physical Therapy
- ClinicalTrials.gov ID
- NCT07733752
- Acronym / sponsor ID
- 04-2026-300876
- Conditions
- Low Back Pain, Neck Pain, Radiculopathy
- Keywords
- Artificial intelligence, ChatGPT, Shared decision-making, SDM-Q-9, Spine, Patient expectations
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- 10 Jun 2026 (actual)
- Primary completion
- 10 Dec 2026 (estimated)
- Study completion
- 10 Feb 2027 (estimated)
- First posted
- 29 Jul 2026 (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.
Artificial intelligence (AI) symptom-checking tools, including large language models such as ChatGPT, are increasingly used by patients before they seek care. These tools may shape patients' beliefs about their diagnosis, how serious they think their condition is, and when they decide to seek treatment. It is not yet known how this pre-visit AI use affects the initial physical therapy encounter for spine-related problems.
This prospective observational cohort study examines whether prior use of AI symptom-checking tools influences the first physical therapy evaluation in adults presenting with spine-related musculoskeletal complaints (neck, thoracic, or low back pain, with or without radicular symptoms). Consecutive patients attending an outpatient physical therapy clinic for a new evaluation are grouped as AI users or non-AI users based on whether they used such a tool for their current complaint in the previous 30 days.
The primary outcome is shared decision-making, measured with the SDM-Q-9 immediately after the initial evaluation. Secondary outcomes include stage of presentation, agreement between the patient's expected diagnosis and the clinician's classification, baseline pain and disability, functional performance, and clinical outcomes at 2 and 6 weeks. The investigators hypothesize that prior AI use is associated with differences in shared decision-making and in how patients present for care.
Detailed description
Background and Rationale AI-based symptom-checking tools, including large language models, are increasingly used by patients as a first point of clinical interpretation before seeking care. Available evidence suggests these tools show moderate and variable diagnostic accuracy and can influence patients' diagnostic beliefs, perceived symptom severity, and healthcare-seeking behavior, raising concerns about pre-diagnostic anchoring. In spine and musculoskeletal care, early presentation, accurate classification, and patient engagement are important determinants of outcomes, and current practice relies on history, physical examination, and shared decision-making under the assumption that patients present without strongly preformed diagnostic expectations. The growing use of AI may alter this dynamic. Evidence on the real-world impact of pre-visit AI use on the physical therapy encounter is currently limited.
Objective The primary objective is to determine whether prior use of AI symptom-checking tools influences shared decision-making during the initial physical therapy evaluation in patients presenting with spine-related musculoskeletal conditions. Secondary objectives examine associations between prior AI use and stage of presentation, agreement between patient-expected and clinician diagnosis, baseline clinical status, functional performance, health-seeking behavior, and short-term clinical outcomes.
Design and Setting This is a prospective observational cohort study conducted in outpatient physical therapy clinics. No study-specific interventions are introduced; all participants receive standard physical therapy care determined by their treating clinician.
Participants and Grouping Consecutive adults (≥18 years) presenting for a new evaluation for a spine-related musculoskeletal complaint (neck, thoracic, or low back pain, with or without radicular symptoms) who can provide informed consent and complete study questionnaires in English are eligible. Patients are excluded for recent spinal surgery within the past 3 months, serious spinal pathology under active medical management (e.g., malignancy, spinal infection, acute fracture), cognitive impairment limiting consent or reliable completion of measures, or current enrollment in another study that may influence clinical decision-making or spine-related outcomes. Participants are classified into two groups based on whether they used an AI symptom-checking tool for their current complaint within the prior 30 days: AI users and non-AI users.
Study Instruments and Measures AI exposure is captured at baseline via a questionnaire assessing AI use (yes/no), tool type, frequency, degree of personalization, and reported influence on care-seeking. Clinical measures include symptom duration, pain intensity (Numeric Pain Rating Scale, 0-10), region-specific disability (Oswestry Disability Index for low back pain or Neck Disability Index for neck pain), and the Five Times Sit-to-Stand test. Diagnostic expectation is assessed by comparing the patient-expected diagnosis with the clinician's classification, categorized as match, partial match, or mismatch. Shared decision-making is measured with the SDM-Q-9 (score range 0-45; higher scores indicate greater patient involvement) immediately after the initial evaluation. Follow-up outcomes at 2 and 6 weeks include pain, disability (ODI/NDI), Global Rating of Change, self-reported adherence, and healthcare utilization.
Procedures At baseline (pre-visit), AI exposure, symptom duration, pain, and disability are collected. During the initial evaluation, the clinician performs a standard physical therapy assessment and classification, and patient expectations are recorded. Immediately post-visit, participants complete the SDM-Q-9. At 2-week and 6-week follow-up, clinical outcomes and adherence are assessed and AI use is re-evaluated. Data are recorded on standardized forms and stored securely using coded identifiers.
Sample Size The primary outcome is SDM-Q-9 following the initial evaluation. Using G*Power (v3.1.9.7), a sample of 128 participants (64 per group) provides 80% power to detect a moderate between-group difference (Cohen's d = 0.5) at a two-sided alpha of 0.05. To account for potential group imbalance and an anticipated attrition of 15-20%, approximately 200 consecutive patients will be recruited.
Statistical Analysis Continuous variables are summarized as mean ± standard deviation and categorical variables as frequencies and percentages. AI users and non-AI users are compared using independent-samples t-tests for continuous variables and chi-square tests for categorical variables. Statistical significance is set at p ≤ 0.05.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Weeks
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI Users | – | Other: re-visit AI symptom-checker usePatients who reported using an AI-based symptom-checking tool (e.g., a large language model such as ChatGPT) for their current spine-related complaint within the 30 days before their initial physical therapy evaluation. |
| Non-AI Users | – | –Patients who reported no use of any AI-based symptom-checking tool for their current spine-related complaint within the 30 days before their initial physical therapy evaluation. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adults aged 18 years or older presenting for a new evaluation at outpatient physical therapy clinics for a spine-related musculoskeletal complaint - neck, thoracic, or low back pain, with or without radicular symptoms. Consecutive eligible patients are enrolled and grouped by their use of AI-based symptom-checking tools for the current complaint in the 30 days preceding the visit (AI users vs. non-AI users). Patients with recent spinal surgery, serious spinal pathology under active management, cognitive impairment limiting participation, or concurrent enrollment in a study affecting spine-related decision-making are not included.
Key inclusion criteria (4 listed)
- Age: 18 years or older
- Presenting for a new evaluation at an outpatient physical therapy clinic for a spine-related musculoskeletal complaint, including Neck, Thoracic, and Low back pain With or without radicular symptoms
- Able to provide informed consent.
- Able to read, understand, and complete study questionnaires
Key exclusion criteria (5 listed)
- Recent spinal surgery within the past 3 months, due to differing clinical pathways and management strategies.
- Presence of serious spinal pathology under active medical management, such as:
- Malignancy (e.g., metastatic disease) Spinal infection Acute fracture
- Cognitive impairment or other conditions that limit the ability to provide informed consent or reliably complete study measures.
- Patients currently enrolled in another study that may influence clinical decision-making or outcomes related to spine care.
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 |
|---|---|---|
| re-visit AI symptom-checker useSelf-reported use of an AI-based symptom-checking tool (e.g., a large language model such as ChatGPT) for the current spine-related complaint during the 30 days before the initial physical therapy evaluation. This exposure occurs naturally prior to presentation and is not assigned by the investigator. Exposure status is ascertained at baseline via a questionnaire capturing whether AI was used (yes/no), the type of tool, frequency of use, degree of personalization, and the reported influence of AI use on care-seeking timing. | Other | AI Users |
Primary outcome
Shared Decision-Making (SDM-Q-9)
Time frame: Immediately after the initial physical therapy evaluation (Day 0)
Patient-perceived involvement in shared decision-making during the initial physical therapy evaluation, measured with the 9-item Shared Decision-Making Questionnaire (SDM-Q-9). Raw total score ranges from 0 to 45; higher scores indicate greater perceived patient involvement in decision-making.
6 secondary outcomes
Stage of Presentation (Symptom Duration)
Time frame: At initial evaluation (Day 0)
Diagnostic Agreement (Patient-Clinician Concordance)
Time frame: At initial evaluation (Day 0)
Pain Intensity (Numeric Pain Rating Scale)
Time frame: Day 0, 2 weeks, 6 weeks
Disability (ODI or NDI)
Time frame: Day 0, 2 weeks, 6 weeks
Health-Seeking Behavior (AI Influence on Care Timing)
Time frame: At initial evaluation (Day 0)
Adherence and Healthcare Utilization
Time frame: 2 weeks, 6 weeks
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 29 Jul 2026.
- Background: PubMed 39978029Harvey D, White S, Reid D, Cook C. Patient perspectives of process variables in musculoskeletal care pathways. Musculoskelet Sci Pract. 2025 Apr;76:103287. doi: 10.1016/j.msksp.2025.103287. Epub 2025 Feb 14.
- Background: PubMed 32012052Meyer AND, Giardina TD, Spitzmueller C, Shahid U, Scott TMT, Singh H. Patient Perspectives on the Usefulness of an Artificial Intelligence-Assisted Symptom Checker: Cross-Sectional Survey Study. J Med Internet Res. 2020 Jan 30;22(1):e14679. doi: 10.2196/14679.
- Background: PubMed 37713254Kuroiwa T, Sarcon A, Ibara T, Yamada E, Yamamoto A, Tsukamoto K, Fujita K. The Potential of ChatGPT as a Self-Diagnostic Tool in Common Orthopedic Diseases: Exploratory Study. J Med Internet Res. 2023 Sep 15;25:e47621. doi: 10.2196/47621.
- Background: PubMed 41007212Kumar R, Dougherty C, Sporn K, Khanna A, Ravi P, Prabhakar P, Zaman N. Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care. Bioengineering (Basel). 2025 Sep 9;12(9):967. doi: 10.3390/bioengineering12090967.
- Background: PubMed 41536317Bensel VA, Habeck A, Brunot MH, Becton EJ, Ray M, Brackett AL, Lisi AJ. Artificial intelligence in spine care: A scoping review of treatment applications. N Am Spine Soc J. 2025 Nov 20;25:100827. doi: 10.1016/j.xnsj.2025.100827. eCollection 2026 Mar.
- Background: PubMed 41458002Muelbauer EJ, Alvi MA, Kennedy DJ, Fehlings MG. The future is now: How AI is reshaping spine care. N Am Spine Soc J. 2025 Nov 14;24:100825. doi: 10.1016/j.xnsj.2025.100825. eCollection 2025 Dec.
- Background: PubMed 40657647Rossettini G, Bargeri S, Cook C, Guida S, Palese A, Rodeghiero L, Pillastrini P, Turolla A, Castellini G, Gianola S. Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in advising on lumbosacral radicular pain against clinical practice guidelines: cross-sectional study. Front Digit Health. 2025 Jun 27;7:1574287. doi: 10.3389/fdgth.2025.1574287. eCollection 2025.
- Background: PubMed 40978326Basharat A, Shah R, Wilcox N, Tur G, Tripati S, Kansal P, Gandhi N, Pokuri S, Chong G, Odonkor CA, Varhabhatla N, Chow R. ChatGPT and low back pain - Evaluating AI-driven patient education in the context of interventional pain medicine. Interv Pain Med. 2025 Sep 2;4(3):100636. doi: 10.1016/j.inpm.2025.100636. eCollection 2025 Sep.
- Background: PubMed 41018793Li YH, Li N, Liu ZX, Du S, Shuai Y, Yang R, Xu L, Li X, Jiang Y, Li W. The effectiveness of artificial intelligence health education accurately linking system on self-management in non-specific lower back pain patients. Front Public Health. 2025 Sep 11;13:1630329. doi: 10.3389/fpubh.2025.1630329. eCollection 2025.
- Background: PubMed 41491377Baldus SG, Wiesmann M, Habel U, Gerhards A, Hasan D, Weyland CS, Truhn D, Hasl MM, Clemens B, Nikoubashman O. Patients' views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey. Insights Imaging. 2026 Jan 5;17(1):6. doi: 10.1186/s13244-025-02159-3.
- Background: PubMed 30948608Alzubaidi H, Hussein A, Mc Namara K, Scholl I. Psychometric properties of the Arabic version of the 9-item Shared Decision-Making Questionnaire: the entire process from translation to validation. BMJ Open. 2019 Apr 4;9(4):e026672. doi: 10.1136/bmjopen-2018-026672.
- Background: PubMed 25262247Algarni AS, Ghorbel S, Jones JG, Guermazi M. Validation of an Arabic version of the Oswestry index in Saudi Arabia. Ann Phys Rehabil Med. 2014 Dec;57(9-10):653-63. doi: 10.1016/j.rehab.2014.06.006. Epub 2014 Aug 4.
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Empowering Pain Psychology Access and Collaboration (EPPAC) to Improve Treatment Engagement Among Medicaid-insured Adults With Chronic Low Back Pain
Chronic Low Back Pain (CLBP)
Not yet recruiting
Gamified Home-Based Core Neuromuscular Training for Chronic Low Back Pain
Low Back Pain, Core Muscles
Not yet recruiting
Dual-Task Core Stabilization Training in Chronic Nonspecific Low Back Pain
Chronic Nonspecific Low Back Pain
Not yet recruitingPhase 2
Psilocybin-Assisted Physical Therapy in Chronic Low Back Pain
Chronic Low Back Pain (CLBP), Physical Therapy
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.
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 and Neck Pain, run by Assiut University at 1 site in Egypt.
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 4 inclusion and 5 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: looking for participants now, as of the sponsor's last update on 29 Jul 2026. 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 Egypt. 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 Mariam A Ibrahim Principal investigator (+20 10 01539399, Mariam.A.ibrahim@med.aun.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Shared Decision-Making (SDM-Q-9) (time frame: Immediately after the initial physical therapy evaluation (Day 0)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07733752, which also lists 13 linked publications.
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 29 Jul 2026.