Pre-Visit AI Symptom-Checking and Shared Decision-Making in Spine Physical Therapy

NCT07733752 | Recruiting

• 1 other identifier: 04-2026-300876
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Low Back Pain; Neck Pain; Radiculopathy

Keywords

Artificial intelligence; ChatGPT; Shared decision-making; SDM-Q-9; Spine; Patient expectations

Outcome Measures

Primary Outcomes (1)

• 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.

  Immediately after the initial physical therapy evaluation (Day 0)

Secondary Outcomes (6)

• Stage of Presentation (Symptom Duration)

  At initial evaluation (Day 0)

• Diagnostic Agreement (Patient-Clinician Concordance)

  At initial evaluation (Day 0)

• Pain Intensity (Numeric Pain Rating Scale)

  Day 0, 2 weeks, 6 weeks

• Disability (ODI or NDI)

  Day 0, 2 weeks, 6 weeks

• Health-Seeking Behavior (AI Influence on Care Timing)

  At initial evaluation (Day 0)

Study Arms (2)

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.

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.

Interventions

re-visit AI symptom-checker use OTHER

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.

AI Users

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: Males, and Females
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Assiut University: lead

Study Sites (1)

Faculty of Medicine, Assiut University, Egypt

Asyut, Egypt

RECRUITING

Related Publications (13)

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  PMID: 41007212 BACKGROUND

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• Muelbauer 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.

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• Rossettini 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.

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  PMID: 41491377 BACKGROUND

• Alzubaidi 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.

  PMID: 30948608 BACKGROUND

• Algarni 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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• Zhou 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.

  PMID: 40107040 BACKGROUND

MeSH Terms

Conditions

Low Back Pain; Neck Pain; Radiculopathy

Condition Hierarchy (Ancestors)

Back Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Peripheral Nervous System Diseases; Neuromuscular Diseases; Nervous System Diseases

Central Study Contacts

Mariam A Ibrahim Principal investigator

CONTACT

+20 10 01539399; Mariam.A.ibrahim@med.aun.edu.eg

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 6 Weeks
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Researcher

Study Record Dates

• First Submitted: July 24, 2026
• First Posted: July 29, 2026
• Study Start: June 10, 2026
• Primary Completion (Estimated): December 10, 2026
• Study Completion (Estimated): February 10, 2027
• Last Updated: July 29, 2026

Record last verified: 2026-06

Locations

EG (1)