AI-Assisted Personalized Heat-Risk Alerts (HEAT-CARE)
12 de septiembre de 2026 actualizado por: Prof. Dr. Shamaila Mohsin, PhD, National University of Medical Sciences, Pakistan
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
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.
Descripción general del estudio
Estado
Reclutamiento
Intervención / Tratamiento
Descripción detallada
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.
The trial will enroll 120 adults with hypertension, type 2 diabetes, chronic kidney disease, cardiovascular disease, and/or 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.
On 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.
The 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/admissions and will be locked before intervention delivery.
Tipo de estudio
Intervencionista
Inscripción (Estimado)
120
Fase
- No aplica
Contactos y Ubicaciones
Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.
Estudio Contacto
- Nombre: Mubra Noor, MS Public Health
- Número de teléfono: +92- 321-6143378
- Correo electrónico: mubranoor111@gmail.com
Ubicaciones de estudio
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Punjab Province
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Gujranwala, Punjab Province, Pakistán, 52250
- Reclutamiento
- Gondal Medical Complex
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Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
No
Descripción
Inclusion Criteria:
- Adults aged 18 years or older attending the outpatient department of the selected tertiary-care hospital during the recruitment period.
- 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/m²).
- Have access to a personal smartphone capable of receiving study heat-risk alert notifications.
- Be able to read Urdu or English, or have a household member/caregiver available to read and explain study alerts when required.
- Be willing and able to provide written informed consent.
- Intend to remain within the study catchment area for the duration of the six-week study period to facilitate follow-up.
Exclusion Criteria:
- Patients requiring immediate emergency treatment or hospital admission at the time of recruitment.
- Individuals with severe cognitive impairment, dementia, psychotic illness, or another medical condition that limits their ability to understand study procedures or provide informed consent.
- Patients with terminal illness or those receiving palliative care.
- Individuals with severe visual, hearing, or communication impairments that prevent effective receipt of the study alert intervention and outcome assessment without a reliable caregiver.
- Pregnant women, because pregnancy has distinct physiological responses to heat exposure and would require separate clinical risk stratification beyond the scope of this study.
- Participants currently enrolled in another clinical trial or structured behavioral intervention related to heat-health, climate adaptation, or chronic disease self-management.
- Participants who are unable or unwilling to comply with study procedures or complete the required follow-up assessment.
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Cuidados de apoyo
- Asignación: Aleatorizado
- Modelo Intervencionista: Asignación paralela
- Enmascaramiento: Único
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
|---|---|
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Experimental: AI-Assisted Personalized Heat-Risk Alert
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.
|
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.
|
|
Comparador activo: Generic PMD Heat-Health Advisory
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.
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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.
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Heat-Related Illness Symptom Score (HRISS)
Periodo de tiempo: Baseline and six weeks after randomization
|
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.
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Baseline and six weeks after randomization
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
|
Heat-Health Protective Behavior Checklist (HPBC)
Periodo de tiempo: Baseline and six weeks after randomization
|
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.
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Baseline and six weeks after randomization
|
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Heat-Health Knowledge, Attitudes and Practices (KAP) Score
Periodo de tiempo: Baseline and six weeks after randomization
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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.
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Baseline and six weeks after randomization
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Heat-related hospital admissions
Periodo de tiempo: From randomization through six weeks
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Number of unplanned hospital admissions attributable to heat-related illness during the six-week intervention period.
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From randomization through six weeks
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Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Investigadores
- Investigador principal: Shamaila Mohsin, PhD Public Health, Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Actual)
1 de septiembre de 2026
Finalización primaria (Estimado)
13 de octubre de 2026
Finalización del estudio (Estimado)
30 de octubre de 2026
Fechas de registro del estudio
Enviado por primera vez
12 de septiembre de 2026
Primero enviado que cumplió con los criterios de control de calidad
12 de septiembre de 2026
Publicado por primera vez (Actual)
17 de septiembre de 2026
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
17 de septiembre de 2026
Última actualización enviada que cumplió con los criterios de control de calidad
12 de septiembre de 2026
Última verificación
1 de septiembre de 2026
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- 757-AAA-ERC-AFPGMI
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
NO
Descripción del plan IPD
Individual participant-level data will not be made publicly available because the study involves sensitive clinical and health-related information from the participant population.
Data will be retained and managed in accordance with the approved study protocol, institutional requirements, and applicable ethical and privacy requirements.
De-identified aggregate findings will be reported in publications and other appropriate dissemination outputs.
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
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