A unique holistic approach to assess the risk of an individual to develop 

 over time. A mental health condition is characterized by a relevant disturbance in an individual’s cognition, emotional regulation, or behaviour.

The mental health risk is a clinically-validated biomarker benchmarked to questionnaires to assess the mental health status of the individual in terms of physiological stress and anxiety (PHQ-9, GAD-7, CSAI-2).

The IntelliProve processing engine uses a combination of machine learning informed by selected HRV metrics, facial micro expression patterns and context-based reasoning over time to determine the Mental Health Risk. This metric lets the user know if their risk of mental health conditions is low, medium, or high compared to their baseline.

The Mental Health Risk (MHR) is based on an individual's past measurements. It is required that the user performs 

2 measurements per week for at least 3 weeks 

After the second measurement in the third week, the user will receive a score.

It is therefore also essential that you uniquely specify the user taking a health scan, so that we can keep track of their performed measurements. The 

When working preventively, it is crucial to identify potential negative trends. While experiencing one bad day may not indicate a high risk of developing a mental health condition, an ongoing series of negative experiences or a long-term downward trend can be critical. A common example of this is individuals who are on the path to burnout. They may start accumulating stress, leading to a decline in sleep quality, and often remain in denial about their feelings until a breaking point occurs. With the Mental Health Risk initiative, our aim is to raise awareness among users about the possibility of a breakdown and encourage early intervention.

 will raise awareness about potential concerns, such as moving towards a 

. When a high risk is detected, users can be recommended to schedule a call with a therapist for further support and guidance. 

This risk assessement is the ideal (more objective) biomarker in addition to subjective user reporting.

Mental Health Risk is returned as a part of the output response JSON, with the key ‘mental_health_risk’.

Learn about our unique holistic approach to assessing an individual's risk of developing a mental health condition over time. Our machine learning algorithm incorporates HRV metrics, facial micro expression patterns, and context-based reasoning to calcula