- ICH GCP
- Registro degli studi clinici negli Stati Uniti
- Sperimentazione clinica NCT07825831
AI-Assisted Personalized Heat-Risk Alerts (HEAT-CARE)
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
Panoramica dello studio
Stato
Condizioni
Descrizione dettagliata
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.
Tipo di studio
Iscrizione (Stimato)
Fase
- Non applicabile
Contatti e Sedi
Contatto studio
- Nome: Mubra Noor, MS Public Health
- Numero di telefono: +92- 321-6143378
- Email: mubranoor111@gmail.com
Luoghi di studio
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Punjab Province
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Gujranwala, Punjab Province, Pakistan, 52250
- Reclutamento
- Gondal Medical Complex
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Criteri di partecipazione
Criteri di ammissibilità
Età idonea allo studio
- Adulto
- Adulto più anziano
Accetta volontari sani
Descrizione
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.
Piano di studio
Come è strutturato lo studio?
Dettagli di progettazione
- Scopo principale: Terapia di supporto
- Assegnazione: Randomizzato
- Modello interventistico: Assegnazione parallela
- Mascheramento: Separare
Armi e interventi
Gruppo di partecipanti / Arm |
Intervento / Trattamento |
|---|---|
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Sperimentale: 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.
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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.
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Comparatore attivo: 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.
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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.
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Cosa sta misurando lo studio?
Misure di risultato primarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Heat-Related Illness Symptom Score (HRISS)
Lasso di tempo: Baseline and six weeks after randomization
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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.
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Baseline and six weeks after randomization
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Misure di risultato secondarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Heat-Health Protective Behavior Checklist (HPBC)
Lasso di tempo: Baseline and six weeks after randomization
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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.
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Baseline and six weeks after randomization
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Heat-Health Knowledge, Attitudes and Practices (KAP) Score
Lasso di tempo: Baseline and six weeks after randomization
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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.
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Baseline and six weeks after randomization
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Heat-related hospital admissions
Lasso di tempo: From randomization through six weeks
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Number of unplanned hospital admissions attributable to heat-related illness during the six-week intervention period.
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From randomization through six weeks
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Collaboratori e investigatori
Investigatori
- Investigatore principale: Shamaila Mohsin, PhD Public Health, Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi
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Primo inviato che soddisfa i criteri di controllo qualità
Primo Inserito (Effettivo)
Aggiornamenti dei record di studio
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Maggiori informazioni
Termini relativi a questo studio
Parole chiave
Termini MeSH pertinenti aggiuntivi
Altri numeri di identificazione dello studio
- 757-AAA-ERC-AFPGMI
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Descrizione del piano IPD
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Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .