Pan African Clinical Trials Registry
South African Medical Research Council, South African Cochrane Centre
PO Box 19070, Tygerberg, 7505, South Africa
Telephone: +27 21 938 0835 or +27 21 938 0967
Email: pactradmin@mrc.ac.za Website: pactr.samrc.ac.za
Trial no.: PACTR202506914478312 Date of Registration: 04/06/2025
Trial Status: Registered in accordance with WHO and ICMJE standards
TRIAL DESCRIPTION
Public title Physiological and Biomarker Monitoring: Enhancing the Vital Signs and Biomarkers Algorithm of Binah.AI in the Kenyan Population.
Official scientific title Physiological and Biomarker Monitoring: Enhancing the Vital Signs and Biomarkers Algorithm of Binah.AI in the Kenyan Population.
Brief summary describing the background and objectives of the trial This study aims to advance the accuracy and reliability of the Binah.ai algorithm in the Kenyan population. Subjects will be recorded using the Binah.ai Recorder and their vital signs (pulse rate, oxygen saturation and blood pressure) and blood samples will be collected using reference devices and lab tests. A total of 20,000 participants will be recruited globally (Italy, India, Israel, Japan, USA), including 1,000 participants that will be enrolled in Zuri Health Medical Center, Kenya. The process includes training the algorithm with the newly collected data. Once the training is completed, new data collected will be used to validate the performance of the improved algorithm. We expect that this data collection study will improve the overall algorithm performance among the Kenyan population. Vital signs such as temperature, pulse, blood pressure, and respiratory rate are essential for assessing patient health and guiding clinical decisions. In Kenya, they are crucial for triage and early detection, especially in resource-limited settings. However, access to advanced tools like pulse oximeters and lab tests (e.g., HbA1c, lipid profiles) varies widely, particularly in rural areas. This limits consistent monitoring and early intervention. The dual burden of infectious and non-communicable diseases increases the need for effective vital sign assessment. Scoring systems like early warning scores can support timely care, but must be adapted to Kenya’s healthcare infrastructure and workforce challenges for effective implementation.
Type of trial Observational
Acronym (If the trial has an acronym then please provide)
Disease(s) or condition(s) being studied Circulatory System,Nutritional, Metabolic, Endocrine
Sub-Disease(s) or condition(s) being studied
Purpose of the trial Early detection /Screening
Anticipated trial start date 01/08/2025
Actual trial start date
Anticipated date of last follow up 01/11/2025
Actual Last follow-up date
Anticipated target sample size (number of participants) 1000
Actual target sample size (number of participants)
Recruitment status Not yet recruiting
Publication URL
Secondary Ids Issuing authority/Trial register
P856122024 Ethics Research Committe, Kenyatta Hospital-University of Nairobi
STUDY DESIGN
Intervention assignment Allocation to intervention If randomised, describe how the allocation sequence was generated Describe how the allocation sequence/code was concealed from the person allocating the participants to the intervention arms Masking If masking / blinding was used
Single Group Non-randomised Allocation Sequence/Code was not concealed Open-label(Masking Not Used)
INTERVENTIONS
Intervention type Intervention name Dose Duration Intervention description Group size Nature of control
Experimental Group Binah.ai Recorder app Once Within an hour (1 hr) The Binah.ai Recorder is a non-invasive recorder that collects the Red Green Blue (RGB) light reflected from the subject’s face when recording themselves by looking at a mobile device’s front-facing camera (“By Face”) and by wearing the Polar Verity Sense. The measurement methods in use are Remote Photoplethysmography (rPPG) and Photoplethysmography (PPG), respectively. The Recorder application data collection includes videoing the subjects’ faces with a mobile device (iOS and Android) front camera. Vital signs data using the reference devices (i.e., SpO2, BP, PR) will be collected continuously for approximately 1.5 minutes, and simultaneously with the data collected using the Binah.ai Recorder. Several data collection events may be performed per subject using the Binah.ai Recorder and reference devices. 1000
ELIGIBILITY CRITERIA
List inclusion criteria List exclusion criteria Age Category Minimum age Maximum age Gender
1. The subject must have the ability to understand and provide written informed consent. 2. Male or Female subjects ≥18 and ≤65 of age. 3. The subject must be willing and able to comply with study procedures. 4. Subjects with the following co-morbid conditions. Note: Healthy subjects without the following conditions are still eligible i. Subject with Pre-diabetes or diabetes HbA1C 6.4-16% ii. Subject with Hypertension with systolic measurements above 145 mmHg iii. Subject with Total cholesterol with measurements of >240 mg/dl or higher iv. Subjects who are active smokers 1. The subject has compromised circulation, injury, or physical malformation of fingers, hands, ears or forehead/skull or other sensor Region of Interest (ROI), which would limit the ability to test the ROI needed for the study. Note: Certain malformations may still allow subjects to participate if the condition is noted and would not affect the particular areas utilised. 2. The subject has a tattoo in the optical path, which would limit the ability to conduct the tests needed for the study. 3. Subjects with severe contact allergies to standard adhesives, latex or other materials found in pulse oximetry sensors, respiration monitor electrodes or other medical sensors used for measuring vital signs. 4. Subject with a medical condition which, in the opinion of the investigator, does not allow performing the study assessments. 5. Subject unfit to participate in the study at the judgment of the investigator. Adolescent: 13 Year(s)-17 Year(s),Adult: 18 Year(s)-44 Year(s),Aged: 65 Year(s)-79 Year(s),Middle Aged: 45 Year(s)-64 Year(s) 18 Year(s) 65 Year(s) Both
ETHICS APPROVAL
Has the study received appropriate ethics committee approval Date the study will be submitted for approval Date of approval Name of the ethics committee
Yes 19/05/2025 Kenyatta National Hospital University of Nairobi Ethics and Research Committee
Ethics Committee Address
Street address City Postal code Country
Nairobi Nairobi 19676 Kenya
OUTCOMES
Type of outcome Outcome Timepoint(s) at which outcome measured
Primary Outcome Recording of subjects vitals including blood pressure, pulse rate and oxygen saturation using the Binah.ai recorder and reference devices. 30 minutes post recording
Secondary Outcome Blood samples analyses. 1 hour post blood collection.
RECRUITMENT CENTRES
Name of recruitment centre Street address City Postal code Country
Zuri Health 132 Manyani Court, Nairobi Kenya
FUNDING SOURCES
Name of source Street address City Postal code Country
Binah.ai 265 Franklin St Suite 1702 Boston, MA 02110 Boston MA 02110 United States of America
SPONSORS
Sponsor level Name Street address City Postal code Country Nature of sponsor
Primary Sponsor Binah.ai 265 Franklin St Suite 1702 Boston, MA 02110 Boston MA United States of America Commercial Sector/Industry
COLLABORATORS
Name Street address City Postal code Country
CONTACT PEOPLE
Role Name Email Phone Street address
Principal Investigator Borna Nyaoke borna@zuri.health +254720734479 132 Manyani Court, Manyani East Road, Lavington, Westlands
City Postal code Country Position/Affiliation
Nairobi 103785 Kenya Responsible for the entire research
Role Name Email Phone Street address
Public Enquiries Uzodinma Umeh uzodinma@zuri.health +2348065741635 132 Manyani Court, Manyani East Road, Lavington, Westlands
City Postal code Country Position/Affiliation
Nairobi 103785 Kenya Chief Medical Officer
Role Name Email Phone Street address
Scientific Enquiries Barbara Tarno barbara@zuri.health +254731700049 132 Manyani Court, Manyani East Road, Lavington, Westlands
City Postal code Country Position/Affiliation
Nairobi Kenya Co investigator
REPORTING
Share IPD Description Additional Document Types Sharing Time Frame Key Access Criteria
Yes IPD will be shared through Zuri Health and open data sharing platforms. The data may be obtained by sending a request to Zuri Health, de-identified or aggregated data will be shared. Informed Consent Form,Study Protocol 24 months of last subject visit Data requestors seeking individual participant level data will enter into an appropriate agreement or terms of use
URL Results Available Results Summary Result Posting Date First Journal Publication Date
No
Result Upload 1: Result Upload 2: Result Upload 3: Result Upload 4: Result Upload 5:
Result URL Hyperlinks Link To Protocol
Result URL Hyperlinks
Changes to trial information
Section Name Field Name Date Reason Old Value Updated Value
Trial Information Trial description 30/05/2025 Background added This study aims to advance the accuracy and reliability of the Binah.ai algorithm in the Kenyan population. Subjects will be recorded using the Binah.ai Recorder and their vital signs (pulse rate, oxygen saturation and blood pressure) and blood samples will be collected using reference devices and lab tests. A total of 20,000 participants will be recruited globally (Italy, India, Israel, Japan, USA), including 1,000 participants that will be enrolled in Zuri Health Medical Center, Kenya. The process includes training the algorithm with the newly collected data. Once the training is completed, new data collected will be used to validate the performance of the improved algorithm. We expect that this data collection study will improve the overall algorithm performance among the Kenyan population. This study aims to advance the accuracy and reliability of the Binah.ai algorithm in the Kenyan population. Subjects will be recorded using the Binah.ai Recorder and their vital signs (pulse rate, oxygen saturation and blood pressure) and blood samples will be collected using reference devices and lab tests. A total of 20,000 participants will be recruited globally (Italy, India, Israel, Japan, USA), including 1,000 participants that will be enrolled in Zuri Health Medical Center, Kenya. The process includes training the algorithm with the newly collected data. Once the training is completed, new data collected will be used to validate the performance of the improved algorithm. We expect that this data collection study will improve the overall algorithm performance among the Kenyan population. Vital signs such as temperature, pulse, blood pressure, and respiratory rate are essential for assessing patient health and guiding clinical decisions. In Kenya, they are crucial for triage and early detection, especially in resource-limited settings. However, access to advanced tools like pulse oximeters and lab tests (e.g., HbA1c, lipid profiles) varies widely, particularly in rural areas. This limits consistent monitoring and early intervention. The dual burden of infectious and non-communicable diseases increases the need for effective vital sign assessment. Scoring systems like early warning scores can support timely care but must be adapted to Kenya’s healthcare infrastructure and workforce challenges for effective implementation.
Section Name Field Name Date Reason Old Value Updated Value
Trial Information Trial description 04/06/2025 PACTR Admin This study aims to advance the accuracy and reliability of the Binah.ai algorithm in the Kenyan population. Subjects will be recorded using the Binah.ai Recorder and their vital signs (pulse rate, oxygen saturation and blood pressure) and blood samples will be collected using reference devices and lab tests. A total of 20,000 participants will be recruited globally (Italy, India, Israel, Japan, USA), including 1,000 participants that will be enrolled in Zuri Health Medical Center, Kenya. The process includes training the algorithm with the newly collected data. Once the training is completed, new data collected will be used to validate the performance of the improved algorithm. We expect that this data collection study will improve the overall algorithm performance among the Kenyan population. Vital signs such as temperature, pulse, blood pressure, and respiratory rate are essential for assessing patient health and guiding clinical decisions. In Kenya, they are crucial for triage and early detection, especially in resource-limited settings. However, access to advanced tools like pulse oximeters and lab tests (e.g., HbA1c, lipid profiles) varies widely, particularly in rural areas. This limits consistent monitoring and early intervention. The dual burden of infectious and non-communicable diseases increases the need for effective vital sign assessment. Scoring systems like early warning scores can support timely care but must be adapted to Kenya’s healthcare infrastructure and workforce challenges for effective implementation. This study aims to advance the accuracy and reliability of the Binah.ai algorithm in the Kenyan population. Subjects will be recorded using the Binah.ai Recorder and their vital signs (pulse rate, oxygen saturation and blood pressure) and blood samples will be collected using reference devices and lab tests. A total of 20,000 participants will be recruited globally (Italy, India, Israel, Japan, USA), including 1,000 participants that will be enrolled in Zuri Health Medical Center, Kenya. The process includes training the algorithm with the newly collected data. Once the training is completed, new data collected will be used to validate the performance of the improved algorithm. We expect that this data collection study will improve the overall algorithm performance among the Kenyan population. Vital signs such as temperature, pulse, blood pressure, and respiratory rate are essential for assessing patient health and guiding clinical decisions. In Kenya, they are crucial for triage and early detection, especially in resource-limited settings. However, access to advanced tools like pulse oximeters and lab tests (e.g., HbA1c, lipid profiles) varies widely, particularly in rural areas. This limits consistent monitoring and early intervention. The dual burden of infectious and non-communicable diseases increases the need for effective vital sign assessment. Scoring systems like early warning scores can support timely care, but must be adapted to Kenya’s healthcare infrastructure and workforce challenges for effective implementation.
Section Name Field Name Date Reason Old Value Updated Value
Eligibility Age group 29/05/2025 To include the minimum age, which is 18 Adult: 19 Year-44 Year, Middle Aged: 45 Year(s)-64 Year(s), Aged: 65+ Year(s) Adolescent: 13 Year-18 Year, Adult: 19 Year-44 Year, Middle Aged: 45 Year(s)-64 Year(s), Aged: 65+ Year(s)
Section Name Field Name Date Reason Old Value Updated Value
Outcome OutCome List 30/05/2025 Time point clarified Primary Outcome, Record 1000 subjects’ vitals including: blood pressure, pulse rate and oxygen saturation using the Binah.ai recorder and reference devices. The data will be gathered by trained personnel and used to train and validate the Binah.ai algorithm to enhance the accuracy and reliability among the Kenyan population. , 30 minutes Primary Outcome, Subjects vitals including blood pressure, pulse rate and oxygen saturation using the Binah.ai recorder and reference devices. , 30 minutes post recording
Section Name Field Name Date Reason Old Value Updated Value
Outcome OutCome List 30/05/2025 Time point clarified Primary Outcome, Subjects vitals including blood pressure, pulse rate and oxygen saturation using the Binah.ai recorder and reference devices. , 30 minutes post recording Primary Outcome, Recording of subjects vitals including blood pressure, pulse rate and oxygen saturation using the Binah.ai recorder and reference devices. , 30 minutes post recording
Section Name Field Name Date Reason Old Value Updated Value
Outcome OutCome List 30/05/2025 Outcome and timepoint clarified Secondary Outcome, Collect and analyze blood samples from the 1,000 subjects to gain deeper insight into their health status. The results will be used to train and validate the Binah.ai algorithm to enhance the accuracy and reliability among the Kenyan population. , 1 hour Secondary Outcome, Blood samples analyses., 1 hour post blood collection.
Section Name Field Name Date Reason Old Value Updated Value
Contact People Contacs List 30/05/2025 Email address updated Principal Investigator, Borna, Nyaoke, Dr., bornanyaoke12@gmail.com, borna@zuri.health, +254720734479, 132 Manyani Court, Manyani East Road, Lavington, Westlands, Nairobi, 103785, Kenya, Responsible for the entire research Principal Investigator, Borna, Nyaoke, Dr., borna@zuri.health, , +254720734479, 132 Manyani Court, Manyani East Road, Lavington, Westlands, Nairobi, 103785, Kenya, Responsible for the entire research