Evidence
Evidence
Scientific evidence and research is essential.
Understanding how technology has been validated and knowing what more should be done will empower our users to choose higher quality products.
Scientific evidence and research is essential.
Understanding how technology has been validated and knowing what more should be done will empower our users to choose higher quality products.
Digital Phenotyping
Digital Phenotyping
At Humin, we believe that digital phenotyping has the potential to bridge the gaps and unmet needs in mental health.
Digital phenotyping allows objective data to be captured automatically and in real-time. Studies have shown that continuous objective data collected from our everyday digital devices can be a valid and feasible tool for characterising mental health conditions.
At Humin, we believe that digital phenotyping has the potential to bridge the gaps and unmet needs in mental health.
Digital phenotyping allows objective data to be captured automatically and in real-time. Studies have shown that continuous objective data collected from our everyday digital devices can be a valid and feasible tool for characterising mental health conditions.
1. Digital phenotyping in psychiatry: Systematic Reviews
L. F. Saccaro, G. Amatori, A. Cappelli, R. Mazziotti, L. Dell’Osso, and G. Rutigliano (2021), “Portable technologies for digital phenotyping of bipolar disorder: A systematic review”, Journal of Affective Disorders, 295, 323-338. Link
O. Flanagan, A. Chan, P. Roop, and F. Sundram (2021), “Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review”, JMIR mHealth and uHealth, 9(9), e24352. Link
J. Benoit, H. Onyeaka, M. Keshavan, and J. Torous (2020), “Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses”, Harvard Review of Psychiatry, 28(5), 296-304. Link
A. Z. Antosik-Wójcińska, M. Dominiak, M. Chojnacka, K. Kaczmarek-Majer, K. R. Opara, W. Radziszewska, A. Olwert, and Ł. Święcicki (2020), “Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling”, International Journal of Medical Informatics, 138, 104131. Link
D. A. Rohani, M. Faurholt-Jepsen, L. V. Kessing, and J. E. Bardram (2018), “Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review”, JMIR Mhealth Uhealth, 6(8), e165. Link
2. Digital phenotyping to distinguish mood disorders
3. Digital markers in predicting affective states
4. Digital markers in predicting illness severity