Mental Health Risk

the mental health risk metric offers a comprehensive, holistic approach to evaluating an individual’s potential risk of developing a mental health condition over time such conditions are characterised by significant disturbances in cognition, emotional regulation, or behaviour this metric is a clinically validated benchmark that aligns with standard assessments like the phq 9, gad 7, and csai 2 questionnaires, focusing on physiological markers of stress and anxiety how is it calculated? intelliprove’s processing engine uses machine learning informed by hrv metrics, facial micro expressions, and context based reasoning to assess mental health risk by comparing these factors over time, the metric provides a risk level of low, medium, or high relative to the user’s baseline when is the first reading available? to receive an initial reading, users must complete at least two face scans per week for three consecutive weeks the first score will be available after the second face scan in the third week example timeline with face scans week 1 monday & wednesday no mhr available yet week 2 tuesday & friday no mhr available yet week 3 monday & tuesday first mhr score displayed on wednesday accuracy participants were asked to fill in a combination of questionnaires to assess the mental health status of the individual in terms of depression (phq 9) and anxiety (gad 7) based on the outcome of these questionnaires, every participant was classified according to a certain mental health risk profile (low, medium or high) low gad 7<5 and phq 9<5 medium 5≤gad 7≤9 and/or 5≤phq 9≤9 high gad 7>9 and/or phq 9>9 it can be concluded that in 100% of the cases participants with a high mental health risk will also be labeled as ‘high risk’ through the intelliprove processing engine (true positive cases) although the discriminative power of intelliprove between medium and low risk profiles is substantially lower, it can be concluded that 89 1% of the combined medium/low cases will also be labeled as medium/low through intelliprove how can it be used? the mental health risk metric is particularly valuable in preventive care by identifying negative trends early while one bad day doesn’t imply a high mental health risk , a continuous series of negative days or a prolonged decline may signal potential issues, such as burnout by providing a risk score, intelliprove raises awareness about potential concerns, helping users recognize early signs and encouraging timely intervention in cases of high risk, users could be advised to consult with a mental health professional for further support this objective biomarker complements subjective self reporting, offering a fuller picture of the user’s mental health status providing users with a risk score will raise awareness about potential concerns, such as moving towards a long term absence when a high risk is detected, users could be recommended to schedule a call with a therapist or coach for further support and guidance this risk assessment serves as an ideal, more objective metric, complementing the subjective user reports interpreting results mental health risk is returned as a value between 1 and 3 and can be requested as a widgets docid\ oorapckooi6 89xrafduv or via the rest api docid\ biupvixvhltbukklr2ati definitions name unit range programmatic name health profile mental health risk na / score between 0 and 3 0 3 mental health risk mental health values value meaning zone example user text 1 minimal mental health risk green you have a minimal risk of mental health conditions great! 2 average mental health risk green yellow you have a low risk of mental health conditions keep prioritizing your well being and take mental breaks when needed 3 increased mental health risk red you have a higher than normal risk of mental health conditions consider reaching out to a physician or mental health provider if your mental strain persists scientific papers kemp ah, quintana ds, gray ma, felmingham kl, brown k, gatt jm 2010 impact of depression and antidepressant treatment on heart rate variability a review and meta analysis biol psychiatry 67(11) 1067–1074 wang x, wang y, zhou m, li b, liu x, zhu t identifying psychological symptoms based on facial movements front psychiatry 2020 dec 15;11 607890 sharma, d , singh, j , sehra, s s , & sehra, s k (2024) demystifying mental health by decoding facial action unit sequences big data and cognitive computing, 8(7), 78 licht cm, de geus ej, zitman fg, hoogendijk wj, van dyck r, penninx bw 2008 association between major depressive disorder and heart rate variability in the netherlands study of depression and anxiety (nesda) arch gen psychiatry 65(12) 1358–1367 thayer jf, åhs f, fredrikson m, sollers jj 3rd, wager td 2012 a meta analysis of heart rate variability and neuroimaging studies implications for heart rate variability as a marker of stress and health neurosci biobehav rev 36(2) 747–756