[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100656329":3},{"organization":4,"outcomesModule":7,"designInfo":25,"detailedDescription":35,"studyPopulation":24,"armGroups":36,"interventions":49,"overallOfficials":58,"centralContacts":63,"locations":69,"responsibleParty":84,"collaborators":24,"id":87,"slug":88,"hasResults":89,"nctId":90,"briefTitle":91,"officialTitle":92,"acronym":93,"eligibilityCriteria":94,"healthyVolunteers":89,"sex":95,"minAge":96,"maxAge":24,"enrollmentInfo":97,"targetDuration":24,"studyType":100,"phases":101,"briefSummary":103,"conditions":104,"keywords":107,"overallStatus":72,"whyStopped":24,"lastUpdateSubmitDate":112,"lastUpdatePostDateStruct":113,"startDateStruct":116,"completionDateStruct":118,"leadSponsor":120,"locationsCount":121},{"fullName":5,"class":6},"National University of Medical Sciences, Pakistan","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":24},[9],{"measure":10,"description":11,"timeFrame":12},"Heat-Related Illness Symptom Score (HRISS)","The total HRISS score ranges from 0 to 20, based on 10 heat-related illness symptom items assessed for the preceding 7 days; higher scores indicate greater heat-related illness symptom burden. HRISS will be assessed at baseline and Week 6, with the primary analysis comparing Week-6 HRISS between groups after adjustment for baseline HRISS.","Baseline and six weeks after randomization",[14,17,20],{"measure":15,"description":16,"timeFrame":12},"Heat-Health Protective Behavior Checklist (HPBC)","The total score on the 10-item Heat-Health Protective Behavior Checklist (HPBC) ranges from 0 to 10, with higher scores indicating greater adoption of heat-protective behaviors. HPBC will be assessed at baseline and Week 6, with the between-group comparison based on the Week-6 score adjusted for baseline.",{"measure":18,"description":19,"timeFrame":12},"Heat-Health Knowledge, Attitudes and Practices (KAP) Score","Heat-health knowledge, attitudes, and practices will be assessed using the study's 20-item heat-health KAP questionnaire. The questionnaire will be administered at baseline and Week 6, with the prespecified between-group comparison based on the Week 6 assessment, adjusted for baseline values.",{"measure":21,"description":22,"timeFrame":23},"Heat-related hospital admissions","Number of unplanned hospital admissions attributable to heat-related illness during the six-week intervention period.","From randomization through six weeks",null,{"allocation":26,"interventionModel":27,"interventionModelDescription":28,"primaryPurpose":29,"observationalModel":24,"timePerspective":24,"maskingInfo":30},"RANDOMIZED","PARALLEL","Two-arm, parallel-group superiority randomized controlled trial with 1:1 allocation.","SUPPORTIVE_CARE",{"masking":31,"maskingDescription":32,"whoMasked":33},"SINGLE","Statistical analyst: Masked where feasible",[34],"OUTCOMES_ASSESSOR","Extreme heat poses increased health risks for adults living with chronic conditions. Conventional heat-health warning systems generally provide population-level advisories and may not account for individual clinical vulnerability. This study will evaluate an artificial intelligence-assisted personalized heat-risk alert system designed to integrate individual clinical characteristics with environmental exposure information.\n\nThe trial will enroll 120 adults with hypertension, type 2 diabetes, chronic kidney disease, cardiovascular disease, and\u002For obesity from the outpatient department of a selected tertiary-care hospital in Gujranwala, Pakistan. Participants will be randomized 1:1 to an intervention or control group and followed for six weeks.\n\nOn days when the Pakistan Meteorological Department same-day forecast maximum temperature is ≥36°C, the intervention system will process prespecified clinical characteristics and the temperature forecast through a locked AI Prediction Model. Participants will be classified into low, moderate, or high heat-related acute clinical-event risk categories, with corresponding personalized heat-health messaging. The control group will receive a generic PMD heat-health advisory through the same patient-facing mobile application without AI-based risk stratification or clinical personalization.\n\nThe primary outcome is Heat-Related Illness Symptom Score (HRISS) at Week 6. Secondary outcomes include heat-protective behaviors, heat-health knowledge, attitudes and practices, heat-related emergency department visits and hospital admissions, and application engagement. The AI model will be developed and internally validated using a separate historical hospital dataset containing heat-related emergency department visits\u002Fadmissions and will be locked before intervention delivery.",[37,43],{"label":38,"type":39,"description":40,"interventionNames":41},"AI-Assisted Personalized Heat-Risk Alert","EXPERIMENTAL","Participants will receive AI-assisted personalized heat-risk alerts through the patient-facing mobile application on days when the Pakistan Meteorological Department's same-day forecast maximum temperature is ≥36°C. A locked AI prediction model will integrate prespecified clinical characteristics and the same-day temperature forecast to classify heat-related acute clinical-event risk as low, moderate, or high. Risk-category-specific heat-health messaging will then be delivered through the application.",[42],"Behavioral: AI-Assisted Personalized Heat-Risk Alert System",{"label":44,"type":45,"description":46,"interventionNames":47},"Generic PMD Heat-Health Advisory","ACTIVE_COMPARATOR","Participants will receive a generic Pakistan Meteorological Department heat-health advisory through the same patient-facing mobile application on days when the same-day forecast maximum temperature is ≥36°C. The control condition will not include AI-based risk stratification, individualized risk classification, or disease-specific personalization.",[48],"Behavioral: Generic PMD Heat-Health Advisory",[50,55],{"type":51,"name":52,"description":53,"armGroupLabels":54,"otherNames":24},"BEHAVIORAL","AI-Assisted Personalized Heat-Risk Alert System","A mobile application-based heat-health alert system that uses a locked AI prediction engine to integrate prespecified individual clinical characteristics with the Pakistan Meteorological Department's same-day forecast maximum temperature (≥36°C) and classify participants into low, moderate, or high heat-related acute clinical-event risk categories. The application delivers corresponding personalized heat-health messages.",[38],{"type":51,"name":44,"description":56,"armGroupLabels":57,"otherNames":24},"Generic Pakistan Meteorological Department heat-health advisory delivered through the same patient-facing mobile application when the same-day forecast maximum temperature is ≥36°C, without AI-based risk stratification or individualized clinical personalization.",[44],[59],{"name":60,"affiliation":61,"role":62},"Shamaila Mohsin, PhD Public Health","Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi","PRINCIPAL_INVESTIGATOR",[64],{"name":65,"role":66,"phone":67,"phoneExt":24,"email":68},"Mubra Noor, MS Public Health","CONTACT","+92- 321-6143378","mubranoor111@gmail.com",[70],{"facility":71,"status":72,"city":73,"state":74,"zip":75,"country":76,"countryCode":77,"cosmosGeoPoint":78,"geoPoint":83,"contacts":24},"Gondal Medical Complex","RECRUITING","Gujranwala","Punjab Province","52250","Pakistan","PK",{"type":79,"coordinates":80},"Point",[81,82],74.18705,32.15567,{"lat":82,"lon":81},{"type":62,"investigatorFullName":85,"investigatorTitle":86,"investigatorAffiliation":5,"oldNameTitle":24,"oldOrganization":24},"Prof. Dr. Shamaila Mohsin, PhD","Head of Department (HoD), Public Health Department","100656329","ai-assisted-personalized-heat-risk-alerts-100656329",false,"NCT07825831","AI-Assisted Personalized Heat-Risk Alerts","Effectiveness of an Artificial Intelligence-Assisted Personalized Heat-Risk Alert System in Reducing Heat-Related Illness Among Adults With Chronic Conditions: A Randomized Controlled Trial in Pakistan","HEAT-CARE","Inclusion Criteria:\n\n* Adults aged 18 years or older attending the outpatient department of the selected tertiary-care hospital during the recruitment period.\n* Have a documented diagnosis of at least one chronic non-communicable disease associated with increased susceptibility to heat-related illness, including hypertension, type 2 diabetes mellitus, chronic kidney disease, cardiovascular disease, or obesity (BMI ≥30 kg\u002Fm²).\n* Have access to a personal smartphone capable of receiving study heat-risk alert notifications.\n* Be able to read Urdu or English, or have a household member\u002Fcaregiver available to read and explain study alerts when required.\n* Be willing and able to provide written informed consent.\n* Intend to remain within the study catchment area for the duration of the six-week study period to facilitate follow-up.\n\nExclusion Criteria:\n\n* Patients requiring immediate emergency treatment or hospital admission at the time of recruitment.\n* Individuals with severe cognitive impairment, dementia, psychotic illness, or another medical condition that limits their ability to understand study procedures or provide informed consent.\n* Patients with terminal illness or those receiving palliative care.\n* Individuals with severe visual, hearing, or communication impairments that prevent effective receipt of the study alert intervention and outcome assessment without a reliable caregiver.\n* Pregnant women, because pregnancy has distinct physiological responses to heat exposure and would require separate clinical risk stratification beyond the scope of this study.\n* Participants currently enrolled in another clinical trial or structured behavioral intervention related to heat-health, climate adaptation, or chronic disease self-management.\n* Participants who are unable or unwilling to comply with study procedures or complete the required follow-up assessment.","ALL","18 Years",{"count":98,"type":99},120,"ESTIMATED","INTERVENTIONAL",[102],"NA","This two-arm randomized controlled trial will evaluate whether an artificial intelligence-assisted personalized heat-risk alert system reduces heat-related illness symptom burden among adults with chronic conditions. The intervention will integrate prespecified clinical characteristics with the Pakistan Meteorological Department's same-day forecast maximum temperature to classify individual heat-related acute clinical-event risk and deliver personalized alerts through a mobile application. The control group will receive a generic PMD heat-health advisory through the same application.",[105,106],"Heat-related Illness","Chronic Disease",[108,109,110,111],"Heat-Health Warning Systems","Heat-Related Illness","Chronic Diseases","Artificial Intelligence","2026-09-12",{"date":114,"type":115},"2026-09-17","ACTUAL",{"date":117,"type":115},"2026-09-01",{"date":119,"type":99},"2026-10-30",{"name":5,"class":6},1]