[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657479":3},{"organization":4,"outcomesModule":7,"designInfo":24,"detailedDescription":27,"studyPopulation":28,"armGroups":29,"interventions":34,"overallOfficials":23,"centralContacts":38,"locations":44,"responsibleParty":60,"collaborators":23,"id":64,"slug":65,"hasResults":66,"nctId":67,"briefTitle":68,"officialTitle":69,"acronym":23,"eligibilityCriteria":70,"healthyVolunteers":71,"sex":72,"minAge":73,"maxAge":74,"enrollmentInfo":75,"targetDuration":23,"studyType":78,"phases":23,"briefSummary":79,"conditions":80,"keywords":82,"overallStatus":87,"whyStopped":23,"lastUpdateSubmitDate":88,"lastUpdatePostDateStruct":89,"startDateStruct":92,"completionDateStruct":94,"leadSponsor":96,"locationsCount":97},{"fullName":5,"class":6},"Amasya University","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":23,"otherOutcomes":23},[9,13,17,20],{"measure":10,"description":11,"timeFrame":12},"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\u002Fhands, upper back, lower back, hips\u002Fthighs, knees, and ankles\u002Ffeet).","baseline",{"measure":14,"description":15,"timeFrame":16},"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\u002FNo) for overall pain and for specific body regions.","1 week post-baseline",{"measure":18,"description":19,"timeFrame":12},"Electrical Sensory, Motor, and Pain Thresholds","Quantitative evaluation of somatosensory processing using a standardized monochromatic combined electrotherapy device (Intelect Advanced Monochromatic Combo). Electrodes 5x5 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\u002Fs). 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.",{"measure":21,"description":22,"timeFrame":12},"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.\n\nSubscale scores are calculated as the mean of their respective items (range: 0 to 5), with higher scores indicating higher levels of interoceptive awareness.",null,{"allocation":23,"interventionModel":23,"interventionModelDescription":23,"primaryPurpose":23,"observationalModel":25,"timePerspective":26,"maskingInfo":23},"COHORT","PROSPECTIVE","The aim of this study is to investigate the relationship between musculoskeletal pain, electrical threshold values, and interoception in healthy university students, and to evaluate the predictive accuracy of machine learning models for both retrospective and prospective musculoskeletal pain.\n\nDesigned as an observational, analytical, and cross-sectional study with a prospective follow-up component, this study will include approximately 550 healthy, right-hand dominant students aged 18-25 who provide written informed consent.\n\nParticipants' demographic data and healthy lifestyle behaviors (physical activity, sitting time, nutrition, and tobacco\u002Falcohol\u002Fsubstance use via ASSIST) will be recorded. Musculoskeletal complaints will be evaluated using the Nordic Musculoskeletal Questionnaire, and interoception will be assessed using the Multidimensional Assessment of Interoceptive Awareness-II. Finally, electrical sensory, motor, and pain thresholds will be measured using a combined electrotherapy device with electrodes placed over the flexor muscles of the forearm. One week later, participants will be contacted again to re-evaluate their pain complaints during the preceding 7 days.\n\nIn statistical analyses, in addition to correlation and regression analyses among parameters, the performance of three machine learning models-Logistic Regression, Random Forest, and XGBoost-in predicting participants' retrospective and prospective pain will be evaluated.\n\nThe research results will demonstrate several key outcomes. First, they will elucidate the relationship between musculoskeletal pain, electrical threshold values, and interoception in university students, while testing the utility of electrical threshold values as an objective tool in musculoskeletal pain assessment. Furthermore, the study will evaluate the accuracy of machine learning models in predicting both retrospective and prospective pain within this population. By leveraging objective measurements obtained from widely accessible clinical devices, the findings will contribute to digital health approaches and applications that guide researchers and clinicians throughout pain evaluation and treatment processes. Ultimately, this work will provide critical insights into the efficacy of the proposed research methodology before its implementation in diverse patient populations.","University students with healthy lifestyle",[30],{"label":31,"type":23,"description":23,"interventionNames":32},"university students with healthy lifestyle",[33],"Other: No Intervention: Observational Cohort",[35],{"type":6,"name":36,"description":36,"armGroupLabels":37,"otherNames":23},"No Intervention: Observational Cohort",[31],[39],{"name":40,"role":41,"phone":42,"phoneExt":23,"email":43},"Aybike Şenel, Assistant Professor","CONTACT","+90 531 583 99 24","aybikesenel@hotmail.com",[45],{"facility":5,"status":23,"city":46,"state":46,"zip":47,"country":48,"countryCode":23,"cosmosGeoPoint":49,"geoPoint":54,"contacts":55},"Amasya","05100","Turkey (Türkiye)",{"type":50,"coordinates":51},"Point",[52,53],35.83306,40.65333,{"lat":53,"lon":52},[56],{"name":57,"role":41,"phone":58,"phoneExt":59,"email":43},"Kübra Okuyucu","0(358)2115005","4879",{"type":61,"investigatorFullName":62,"investigatorTitle":63,"investigatorAffiliation":5,"oldNameTitle":23,"oldOrganization":23},"PRINCIPAL_INVESTIGATOR","Aybike Senel","Assistant Professor","100657479","pain-prediction-via-machine-learning-electrical-thresholds-and-interoception-100657479",false,"NCT07839520","Pain Prediction Via Machine Learning: Electrical Thresholds and Interoception","Machine Learning-Based Pain Prediction in University Students: Electrical Threshold Measurements and Interoception","Inclusion criteria:\n\n* Being healthy and aged 18-25\n* Having the right hand as the dominant extremity\n* Being able to communicate effectively and understand the study procedure\n* Being willing to participate in the study and having signed the written informed consent\n\nExclusion criteria:\n\n* Presence of continuous or periodic pain in the same region over the last 3 months (presence of chronic pain)\n* Presence of sleep problems\n* History of severe menstrual or abdominal pain\n* Diagnosis of an endocrinological, psychiatric, urogenital, neurological, dermatological, cardiorespiratory, or musculoskeletal disease and\u002For sleep disorder that could be related to the study outcomes\n* Being pregnant or the possibility of pregnancy\n\nCriteria for withdrawal from the study:\n\n* Deciding not to participate in the study and not wishing to complete the assessments\n* Experiencing discomfort due to the assessments and measurements and not wishing to complete them",true,"ALL","18 Years","25 Years",{"count":76,"type":77},550,"ESTIMATED","OBSERVATIONAL","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:\n\nCan electrical sensory, motor, and pain thresholds, along with interoceptive awareness scores, predict musculoskeletal pain status in young adults using machine learning algorithms?\n\nParticipants will:\n\nComplete demographic, healthy lifestyle (physical activity, sitting time, meal frequency), and substance use (ASSIST v3.0) questionnaires.\n\nComplete self-report surveys including the Nordic Musculoskeletal Questionnaire (NMQ) and the Multidimensional Assessment of Interoceptive Awareness (MAIA-2).\n\nUndergo 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).",[81],"University Students",[83,84,85,86],"pain","machine learning","interoception","electrical threshold tests","NOT_YET_RECRUITING","2026-10-07",{"date":90,"type":91},"2026-10-13","ACTUAL",{"date":93,"type":77},"2026-10-01",{"date":95,"type":77},"2028-01-01",{"name":5,"class":6},1]