Frequently Asked Questions

The IntelliProve API software can be integrated in less than 1 day. We kindly refer to our technical documentation for a step-by-step protocol to integrate our API into your solution.

During the integration process we offer full support to guide you to a successful launch.

Yes, through our API solution it is possible to white label IntelliProve. For solutions that want a branded experience, internally and externally, IntelliProve is the perfect option.

Health checks will be an integral part of your offering, without the need to refer to a third party.

The price is based on the number of health checks per month, so tailored to the volume of your platform. If you are interested in a price proposal completely according to your needs, reach out to us via sales@intelliprove.com or schedule a meeting with our team.

At IntelliProve, we take privacy and security seriously. Our policies have been structured to ensure the highest level of confidentiality and integrity when processing your data. IntelliProve undergoes regular penetration testing and security reviews, designed to be GDPR compliant, and encrypts data at rest and in transit.

Our Data Processing Agreement (which includes our list of sub-processors) is available upon request. Please reach out to privacy@intelliprove.com for more information.

There are various ways to use the IntelliProve's health and well-being biomarkers within your online solution:

• Biomarker values can be displayed directly to the user. For example, every biomarker value is displayed on a tile in a dashboard.
• Biomarkers can be used as input for your own graphs, charts or health scores.
• The result of the biomarkers can be given to your user textually.
• The biomarker values can serve as input to personalise the content you are offering. This content can be vary various: from audiovisual content to health exercises, from food supplements to certain health/well-being sessions.

Biomarkers can easily be linked to your specific offering. In general, the value of a certain biomarker (or combination of biomarkers) defines which part of your offering is recommended to your user. Clear examples:

• Low Acute Mental Stress Score values serve as the starting point to introduce preventive stress-relieving activities.
• High ANS balance values and low Morning Readiness scores can guide the user towards vitamins and food supplements for energy and fatigue.
• Low Resonant Breathing Scores indicates the user could benefit from relaxation exercises focused on respiration.
• ...

For more information, check out the 'How can it be used?' sections of every biomarker on our documentation portal. Additionally, a workshop is offered as part of our product to establish the linkage of the biomarkers to your specific solution.

One of the significant advantages of our API offering is the complete customization of the returned values and follow-up based on the preferences and logic of the platform itself. Our clients employ various methods to interpret the measured values, some concrete examples are:

• Presenting absolute values with color coding to indicate low (e.g. green), medium (e.g. orange), or high levels (e.g. red).
• A dashboard with the biomarkers as tiles. Users can click a biomarker icon, whereafter a banner pops-up with qualitative information accompanied by recommendations to improve or maintain the current wellbeing state.
• Using a single aggregated score or graph that includes numerical or visual indications of benchmark values.

In our documentation portal, you can find definitions, examples of color coding, and qualitative information for each value bracket. In addition, during a short workshop (offered as part of our product), you will be guided in the interpretation of the biomarker according to your specific solution.

An analysis of at least 20 seconds is required to perform a qualitative measurement.

IntelliProve provides a comprehensive .json file containing all available biomarkers. It allows you to manually select the specific biomarkers you want to use and determine their optimal use. This level of customization empowers you to personalize your solution even further.

All biomarkers and parameters, except for the Mental Health Risk, are sent back immediately and represent a unique objective snapshot of that moment of time. By conducting frequent measurements, a trend/progress over time can be obtained.

To calculate Mental Health Risk, the user needs to have performed health scans for the past three weeks. At least two health scans per week are required. This is because our machine learning model has been trained to make a prediction based on a trend over time.

The evaluation of the performance of the algorithms is performed by step-by-step comparative analyses with the current state-of-the-art medical devices (pulse oximeter, sphygmomanometer,…) or questionnaires (e.g. PHQ-9, GAD-7). The accuracy and precision are checked by calculating for example the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Correlation Coefficient (R).

Currently, more than 1 million datapoints (resulting from various collaborations with clinical organisations) are actively used for testing and validating our models.

We kindly refer to our validation and precision report for an in-depth analysis on how our software is tested and validated. In short, the accuracy and precision levels of our product can be found in the table below.

While we often associate biomarkers with physical illness, it is crucial to recognize their relevance in assessing mental well-being as well. We kindly refer to our comprehensive blog post about the use of biomarkers and how they can give professionals unique insights from a patient and provide valuable information for personalized care and treatment plans.

At IntelliProve, ensuring the effectiveness of our product across the population is a top priority. Due to multiple training and testing of our model with test subjects with different skin colors, no reduced performance can currently be observed in people with a dark skin color.

To illustrate, the graph below represents the margin of error of the heart rate (which is a direct derivative of our signal quality) in function of skin color. The smaller this margin of error, the greater the signal quality. For each group this margin of error remains nicely below the threshold of 5bpm (=clinical acceptability criteria). We kindly refer to our validation and precision report for more infomation.