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AI-Assisted Personalized Heat-Risk Alerts (HEAT-CARE)

2026년 9월 12일 업데이트: 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.

연구 개요

상태

모병

정황

개입 / 치료

상세 설명

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.

연구 유형

중재적

등록 (추정된)

120

단계

  • 해당 없음

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 장소

  • 파키스탄
    • Punjab Province
      • Gujranwala, Punjab Province, 파키스탄, 52250
        • 모병
        • Gondal Medical Complex

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

아니

설명

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.

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

  • 주 목적: 지지 요법
  • 할당: 무작위
  • 중재 모델: 병렬 할당
  • 마스킹: 하나의

무기와 개입

참가자 그룹 / 팔
개입 / 치료
실험적: 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.
행동: 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.
활성 비교기: 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.
행동: Generic PMD Heat-Health Advisory
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.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
Heat-Related Illness Symptom Score (HRISS)
기간: 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.
Baseline and six weeks after randomization

2차 결과 측정

결과 측정
측정값 설명
기간
Heat-Health Protective Behavior Checklist (HPBC)
기간: 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.
Baseline and six weeks after randomization
Heat-Health Knowledge, Attitudes and Practices (KAP) Score
기간: Baseline and six weeks after randomization
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.
Baseline and six weeks after randomization
Heat-related hospital admissions
기간: From randomization through six weeks
Number of unplanned hospital admissions attributable to heat-related illness during the six-week intervention period.
From randomization through six weeks

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

National University of Medical Sciences, Pakistan

수사관

  • 수석 연구원: Shamaila Mohsin, PhD Public Health, Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (실제)

2026년 9월 1일

기본 완료 (추정된)

2026년 10월 13일

연구 완료 (추정된)

2026년 10월 30일

연구 등록 날짜

최초 제출

2026년 9월 12일

QC 기준을 충족하는 최초 제출

2026년 9월 12일

처음 게시됨 (실제)

2026년 9월 17일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 9월 17일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 9월 12일

마지막으로 확인됨

2026년 9월 1일

추가 정보

이 연구와 관련된 용어

키워드

추가 관련 MeSH 약관

기타 연구 ID 번호

  • 757-AAA-ERC-AFPGMI

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

아니요

IPD 계획 설명

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.

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

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