Heat-related IllnessHEAT-CARENCT07825831
AI-Assisted Personalized Heat-Risk Alerts
A study testing AI-Assisted Personalized Heat-Risk Alert System and Generic PMD Heat-Health Advisory in people with Heat-related Illness and Chronic Disease, run by National University of Medical Sciences, Pakistan at 1 site in Pakistan.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heat-related Illness, Chronic Disease
- What is being tested?
- AI-Assisted Personalized Heat-Risk Alert System and Generic PMD Heat-Health Advisory
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- 1 sitePakistan
- Study dates
- 1 Sep 2026to 30 Oct 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Pakistan1 site, 1 recruiting
- Recruiting
DHQ Teaching Hospital Gujranwala
Gujranwala, Punjab Province, 52250
About this study
Official title: 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.
Read the 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.
- Conditions
- Heat-related Illness, Chronic Disease
- Being tested
- AI-Assisted Personalized Heat-Risk Alert System, Generic PMD Heat-Health Advisory
- Keywords
- Heat-Health Warning Systems, Heat-Related Illness, Chronic Diseases, Artificial Intelligence
- Medical subject headings
- Chronic Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted Personalized Heat-Risk Alert SystemBehavioural
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.
Generic PMD Heat-Health AdvisoryBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| AI-Assisted Personalized Heat-Risk Alert | Experimental | 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. Receives: Behavioral: AI-Assisted Personalized Heat-Risk Alert System |
| Generic PMD Heat-Health Advisory | Active comparator | 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. Receives: Behavioral: Generic PMD Heat-Health Advisory |
What is being measured
The primary outcome is the main question the study is designed to answer.
Heat-Related Illness Symptom Score (HRISS)
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.
Measured: Baseline and six weeks after randomization
Secondary outcomes (3)
Heat-Health Protective Behavior Checklist (HPBC)
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.
Measured: Baseline and six weeks after randomization
Heat-Health Knowledge, Attitudes and Practices (KAP) Score
Heat-Health Knowledge, Attitudes and Practices Score, assessed using the 20-item Heat-Health Knowledge, Attitudes and Practices Questionnaire. The total score ranges from 13 to 65, with higher scores indicating better heat-health knowledge, attitudes, and practices. The questionnaire includes 7 knowledge items, 6 attitude items, and 7 practice items.
Measured: Baseline and six weeks after randomization
Heat-related hospital admissions
Number of unplanned hospital admissions attributable to heat-related illness during the six-week intervention period.
Measured: From randomization through six weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Sep 2026
- Study start1 Sep 2026
- Primary completion13 Oct 2026 (estimated)
- Study completion30 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heat-related IllnessStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Two-arm, parallel-group superiority randomized controlled trial with 1:1 allocation.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National University of Medical Sciences, Pakistan
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Prof. Dr. Shamaila Mohsin, PhD, National University of Medical Sciences, Pakistan
- Investigators
- Shamaila Mohsin, PhD Public Health, Principal investigator, Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07825831
- Sponsor's protocol ID
- 757-AAA-ERC-AFPGMI
- Acronym
- HEAT-CARE
- Status last verified
- Sep 2026
- First submitted
- 12 Sep 2026
- Last updated
- 23 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 23 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07825831
- Acronym / sponsor ID
- HEAT-CARE / 757-AAA-ERC-AFPGMI
- Conditions
- Heat-related Illness, Chronic Disease
- Keywords
- Heat-Health Warning Systems, Heat-Related Illness, Chronic Diseases, Artificial Intelligence
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- National University of Medical Sciences, PakistanUniversity, hospital or other
- Study start
- 1 Sep 2026 (actual)
- Primary completion
- 13 Oct 2026 (estimated)
- Study completion
- 30 Oct 2026 (estimated)
- First posted
- 17 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
- Model description
- Two-arm, parallel-group superiority randomized controlled trial with 1:1 allocation.
| Arm | Type | Receives |
|---|---|---|
| AI-Assisted Personalized Heat-Risk Alert | Experimental | Behavioral: AI-Assisted Personalized Heat-Risk Alert SystemParticipants 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. |
| Generic PMD Heat-Health Advisory | Active comparator | Behavioral: Generic PMD Heat-Health AdvisoryParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Assisted Personalized Heat-Risk Alert SystemA 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. | Behavioural | AI-Assisted Personalized Heat-Risk Alert |
| Generic PMD Heat-Health AdvisoryGeneric 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. | Behavioural | Generic PMD Heat-Health Advisory |
Primary outcome
Heat-Related Illness Symptom Score (HRISS)
Time frame: 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.
3 secondary outcomes
Heat-Health Protective Behavior Checklist (HPBC)
Time frame: Baseline and six weeks after randomization
Heat-Health Knowledge, Attitudes and Practices (KAP) Score
Time frame: Baseline and six weeks after randomization
Heat-related hospital admissions
Time frame: From randomization through six weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2026.
- Individual participant dataSharing planned: No. 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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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Shamaila Mohsin, PhD Public Health principal investigator
Armed Forces Post Graduate Medical Institute, AFPGMI, NUMS, Rawalpindi
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Assisted Personalized Heat-Risk Alert System and Generic PMD Heat-Health Advisory in people with Heat-related Illness and Chronic Disease, run by National University of Medical Sciences, Pakistan at 1 site in Pakistan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 23 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Pakistan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mubra Noor, MS Public Health (+92- 321-6143378, mubranoor111@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Heat-Related Illness Symptom Score (HRISS) (time frame: Baseline and six weeks after randomization). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Sep 2026.