Pain Prediction Via Machine Learning: Electrical Thresholds and Interoception

NCT07839520 | Not Yet Recruiting

• 1 other identifier: 19092026-AS
• Study Type: observational
• Target: 550
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this observational study is to predict (retrospective and prospective) musculoskeletal pain in university students using machine learning models based on electrical threshold measurements and interoceptive awareness. The main question it aims to answer is: Can electrical sensory, motor, and pain thresholds, along with interoceptive awareness scores, predict musculoskeletal pain status in young adults using machine learning algorithms? Participants will: Complete demographic, healthy lifestyle (physical activity, sitting time, meal frequency), and substance use (ASSIST v3.0) questionnaires. Complete self-report surveys including the Nordic Musculoskeletal Questionnaire (NMQ) and the Multidimensional Assessment of Interoceptive Awareness (MAIA-2). Undergo quantitative electrical threshold testing on their dominant hand, which includes 3 consecutive measurements of sensory threshold, motor threshold (after a 10-minute rest), and pain threshold (after another 10-minute rest).

Conditions

University Students

Keywords

pain; machine learning; interoception; electrical threshold tests

Outcome Measures

Primary Outcomes (4)

• Retrospective Pain

  Self-reported musculoskeletal pain presence and anatomical localization over the preceding 12 months and past 7 days, assessed using the Nordic Musculoskeletal Questionnaire (NMQ). The NMQ is a standardized screening instrument that evaluates musculoskeletal symptoms (pain, ache, or discomfort) across 9 distinct body regions (neck, shoulders, elbows, wrists/hands, upper back, lower back, hips/thighs, knees, and ankles/feet).

  baseline

• Prospective Pain

  Presence and anatomical site of self-reported prospective musculoskeletal pain or discomfort experienced during the 1-week follow-up period. Participants will be re-evaluated via a structured telephone or digital questionnaire inquiring about any newly developed or ongoing musculoskeletal pain symptoms in the preceding 7 days. Pain presence is recorded as a binary outcome (Yes/No) for overall pain and for specific body regions.

  1 week post-baseline

• Electrical Sensory, Motor, and Pain Thresholds

  Quantitative evaluation of somatosensory processing using a standardized monochromatic combined electrotherapy device (Intelect Advanced Monochromatic Combo). Electrodes ($5\\times5$ cm) will be placed over the wrist flexor muscles on the anterior aspect of the dominant forearm using a symmetric biphasic square wave (100 Hz frequency, 100 µs phase duration, current increased at a rate of 1 mA/s). Three threshold parameters will be measured in milliamperes (mA):Electrical Sensory Threshold (EST): The minimum current intensity at which the participant first perceives a tingling or sensory stimulus.Electrical Motor Threshold (EMT): The minimum current intensity required to elicit a visible muscle contraction.Electrical Pain Threshold (EPT): The minimum current intensity at which the electrical sensation first becomes painful.

  baseline

• Interoceptive Awareness

  Multidimensional self-reported interoceptive bodily awareness assessed using the Multidimensional Assessment of Interoceptive Awareness-2 (MAIA-2) scale. The MAIA-2 is a 37-item questionnaire evaluated on a 6-point Likert scale (0= Never to 5=Always) across 8 distinct subscales: Noticing, Not-Distracting, Not-Worrying, Attention Regulation, Emotional Awareness, Self-Regulation, Body Listening, Trust. Subscale scores are calculated as the mean of their respective items (range: 0 to 5), with higher scores indicating higher levels of interoceptive awareness.

  baseline

Study Arms (1)

university students with healthy lifestyle

Other: No Intervention: Observational Cohort

Interventions

No Intervention: Observational Cohort OTHER

No Intervention: Observational Cohort

university students with healthy lifestyle

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

University students with healthy lifestyle

You may qualify if:

• Being healthy and aged 18-25
• Having the right hand as the dominant extremity
• Being able to communicate effectively and understand the study procedure
• Being willing to participate in the study and having signed the written informed consent

You may not qualify if:

• Presence of continuous or periodic pain in the same region over the last 3 months (presence of chronic pain)
• Presence of sleep problems
• History of severe menstrual or abdominal pain
• Diagnosis of an endocrinological, psychiatric, urogenital, neurological, dermatological, cardiorespiratory, or musculoskeletal disease and/or sleep disorder that could be related to the study outcomes
• Being pregnant or the possibility of pregnancy
• Criteria for withdrawal from the study:
• Deciding not to participate in the study and not wishing to complete the assessments
• Experiencing discomfort due to the assessments and measurements and not wishing to complete them

Sponsors & Collaborators

• Amasya University: lead

Study Sites (1)

Amasya University

Amasya, Amasya, 05100, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Pain

Condition Hierarchy (Ancestors)

Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Aybike Şenel, Assistant Professor

CONTACT

+90 531 583 99 24; aybikesenel@hotmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: September 19, 2026
• First Posted: September 24, 2026
• Study Start: October 1, 2026
• Primary Completion (Estimated): January 1, 2028
• Study Completion (Estimated): January 1, 2028
• Last Updated: September 25, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will share

Locations

TU (1)