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
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 120
- 试验地点
- 1
- 主要终点
- Heat-Related Illness Symptom Score (HRISS)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Supportive Care
- 盲法
- Single (Outcomes Assessor)
盲法说明
Statistical analyst: Masked where feasible
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
研究组 & 干预措施
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.
干预措施: AI-Assisted Personalized Heat-Risk Alert System (Behavioral)
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.
干预措施: Generic PMD Heat-Health Advisory (Behavioral)
结局指标
主要结局
Heat-Related Illness Symptom Score (HRISS)
时间窗: 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.
次要结局
- 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)
研究者
Prof. Dr. Shamaila Mohsin, PhD
Head of Department (HoD), Public Health Department
National University of Medical Sciences, Pakistan