Chronic Disease, Heat-related Illness
Conditions
Keywords
Heat-Health Warning Systems, Heat-Related Illness, Chronic Diseases, Artificial Intelligence
Brief summary
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.
Detailed description
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.
Interventions
BEHAVIORALAI-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.
BEHAVIORALGeneric 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.
Sponsors
National University of Medical Sciences, Pakistan
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
SINGLE (Outcomes Assessor)
Masking description
Statistical analyst: Masked where feasible
Intervention model description
Two-arm, parallel-group superiority randomized controlled trial with 1:1 allocation.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Heat-Related Illness Symptom Score (HRISS) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Heat-Health Protective Behavior Checklist (HPBC) | 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. |
| Heat-Health Knowledge, Attitudes and Practices (KAP) Score | 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. |
| Heat-related hospital admissions | From randomization through six weeks | Number of unplanned hospital admissions attributable to heat-related illness during the six-week intervention period. |
Countries
Pakistan
Contacts
CONTACTMubra Noor, MS Public Health
PRINCIPAL_INVESTIGATORShamaila Mohsin, PhD Public Health
Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi
Outcome results
None listed