An observational study in Low Back Pain, Neck Pain and Radiculopathy, sponsored by Assiut University. Recruiting at 1 site in Egypt. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-07-29.
Sponsored by Assiut University · Observational
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.
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.
2,922 studies on the registry are indexed under Low Back Pain; 580 are open to participants now.
This study's planned enrollment of 200 is above the median of 100 across 481 observational studies indexed under Low Back Pain.
Browse Low Back Pain studies →Assiut University is the lead sponsor of 5,078 studies on the registry; 2,178 are open to participants now.
Of its 14 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
(e.g., disease) Spinal infection Acute fracture
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Cancer that has spread from where it began to other parts of the body. “Metastatic” cancer is cancer that has spread this way.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Cancer (malignancy)
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Metastases
Cancer that has spread from where it began to other parts of the body. “Metastatic” cancer is cancer that has spread this way.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
Other: re-visit AI symptom-checker use
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.
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.
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.
Time frame: Immediately after the initial physical therapy evaluation (Day 0)
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.
Time frame: 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.
Time frame: 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.
Time frame: 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).
Time frame: 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.
Time frame: At initial evaluation (Day 0)
Adherence and Healthcare Utilization
Self-reported adherence to the recommended treatment plan.
Time frame: 2 weeks, 6 weeks
Started Jun 2026 (actual). Primary completion is estimated for Dec 2026, 6 months after the start.
Of 175 non-industry observational studies under Low Back Pain, half reached primary completion within 24 months of starting (the median), counting those still running; a quarter within 10 and three quarters within 48. This study's planned 6 months is below that.
Counting every sponsor, half of 185 studies reached it within 24 months; a quarter within 10 and three quarters within 48.
How these figures are worked out, and which studies are left out of them.
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