AI-Assisted Personalized Heat-Risk Alerts

NCT07825831 | Recruiting

• 1 other identifier: 757-AAA-ERC-AFPGMI
• Study Type: interventional
• Target: 120
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Heat-related Illness; Chronic Disease

Keywords

Heat-Health Warning Systems; Heat-Related Illness; Chronic Diseases; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (3)

• Heat-Health Protective Behavior Checklist (HPBC)

  Baseline and six weeks after randomization

• Heat-Health Knowledge, Attitudes and Practices (KAP) Score

  Baseline and six weeks after randomization

• Heat-related hospital admissions

  From randomization through six weeks

Study Arms (2)

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.

Behavioral: AI-Assisted Personalized Heat-Risk Alert System

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.

Behavioral: Generic PMD Heat-Health Advisory

Interventions

AI-Assisted Personalized Heat-Risk Alert System BEHAVIORAL

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.

AI-Assisted Personalized Heat-Risk Alert

Generic PMD Heat-Health Advisory BEHAVIORAL

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.

Generic PMD Heat-Health Advisory

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• National University of Medical Sciences, Pakistan: lead

Study Sites (1)

DHQ Teaching Hospital Gujranwala

Gujranwala, Punjab Province, 52250, Pakistan

RECRUITING

MeSH Terms

Conditions

Chronic Disease

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Shamaila Mohsin, PhD Public Health

  Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Mubra Noor, MS Public Health

CONTACT

+92- 321-6143378; mubranoor111@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Statistical analyst: Masked where feasible
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Department (HoD), Public Health Department

Model Details: Two-arm, parallel-group superiority randomized controlled trial with 1:1 allocation.

Study Record Dates

• First Submitted: September 12, 2026
• First Posted: September 17, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): October 13, 2026
• Study Completion (Estimated): October 30, 2026
• Last Updated: September 23, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

PA (1)