AI-Assisted Personalized Heat-Risk Alerts (HEAT-CARE)
12. September 2026 aktualisiert von: 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.
Studienübersicht
Status
Rekrutierung
Bedingungen
Intervention / Behandlung
Detaillierte Beschreibung
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.
Studientyp
Interventionell
Einschreibung (Geschätzt)
120
Phase
- Unzutreffend
Kontakte und Standorte
Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.
Studienkontakt
- Name: Mubra Noor, MS Public Health
- Telefonnummer: +92- 321-6143378
- E-Mail: mubranoor111@gmail.com
Studienorte
-
-
Punjab Province
-
Gujranwala, Punjab Province, Pakistan, 52250
- Rekrutierung
- Gondal Medical Complex
-
-
Teilnahmekriterien
Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.
Zulassungskriterien
Studienberechtigtes Alter
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Nein
Beschreibung
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.
Studienplan
Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.
Wie ist die Studie aufgebaut?
Designdetails
- Hauptzweck: Unterstützende Pflege
- Zuteilung: Zufällig
- Interventionsmodell: Parallele Zuordnung
- Maskierung: Single
Waffen und Interventionen
Teilnehmergruppe / Arm |
Intervention / Behandlung |
|---|---|
|
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.
|
|
Aktiver Komparator: 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.
|
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.
|
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Heat-Related Illness Symptom Score (HRISS)
Zeitfenster: 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
|
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
Heat-Health Protective Behavior Checklist (HPBC)
Zeitfenster: 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
Zeitfenster: 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
Zeitfenster: 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
|
Mitarbeiter und Ermittler
Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.
Ermittler
- Hauptermittler: Shamaila Mohsin, PhD Public Health, Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi
Studienaufzeichnungsdaten
Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.
Haupttermine studieren
Studienbeginn (Tatsächlich)
1. September 2026
Primärer Abschluss (Geschätzt)
13. Oktober 2026
Studienabschluss (Geschätzt)
30. Oktober 2026
Studienanmeldedaten
Zuerst eingereicht
12. September 2026
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
12. September 2026
Zuerst gepostet (Tatsächlich)
17. September 2026
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
17. September 2026
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
12. September 2026
Zuletzt verifiziert
1. September 2026
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- 757-AAA-ERC-AFPGMI
Plan für individuelle Teilnehmerdaten (IPD)
Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?
NEIN
Beschreibung des IPD-Plans
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.
Arzneimittel- und Geräteinformationen, Studienunterlagen
Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt
Nein
Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt
Nein
Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .