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

2026年9月12日 更新者：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.

調査の概要

状態

募集

条件

介入・治療

詳細な説明

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.

研究の種類

介入

入学 (推定)

120

段階

  • 適用できない

連絡先と場所

このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。

研究連絡先

研究場所

  • パキスタン
    • Punjab Province
      • Gujranwala、Punjab Province、パキスタン、52250
        • 募集
        • Gondal Medical Complex

参加基準

研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。

適格基準

就学可能な年齢

  • 大人
  • 高齢者

健康ボランティアの受け入れ

いいえ

説明

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.

研究計画

このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。

研究はどのように設計されていますか？

デザインの詳細

  • 主な目的：支持療法
  • 割り当て：ランダム化
  • 介入モデル：並列代入
  • マスキング：独身

武器と介入

参加者グループ / アーム
介入・治療
実験的：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.
行動：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.
アクティブコンパレータ：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.
行動：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.

この研究は何を測定していますか？

主要な結果の測定

結果測定
メジャーの説明
時間枠
Heat-Related Illness Symptom Score (HRISS)
時間枠：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

二次結果の測定

結果測定
メジャーの説明
時間枠
Heat-Health Protective Behavior Checklist (HPBC)
時間枠：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
時間枠：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
時間枠：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

協力者と研究者

ここでは、この調査に関係する人々や組織を見つけることができます。

スポンサー

National University of Medical Sciences, Pakistan

捜査官

  • 主任研究者：Shamaila Mohsin, PhD Public Health、Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi

研究記録日

これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。

主要日程の研究

研究開始 (実際)

2026年9月1日

一次修了 (推定)

2026年10月13日

研究の完了 (推定)

2026年10月30日

試験登録日

最初に提出

2026年9月12日

QC基準を満たした最初の提出物

2026年9月12日

最初の投稿 (実際)

2026年9月17日

学習記録の更新

投稿された最後の更新 (実際)

2026年9月17日

QC基準を満たした最後の更新が送信されました

2026年9月12日

最終確認日

2026年9月1日

詳しくは

本研究に関する用語

キーワード

追加の関連 MeSH 用語

その他の研究ID番号

  • 757-AAA-ERC-AFPGMI

個々の参加者データ (IPD) の計画

個々の参加者データ (IPD) を共有する予定はありますか?

いいえ

IPD プランの説明

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.

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米国FDA規制医薬品の研究

いいえ

米国FDA規制機器製品の研究

いいえ

この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。

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