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.

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.

 

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. https://doi.org/10.1016/j.jad.2021.08.052.
  • 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. https://doi.org/10.2196/24352.
  • 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. https://doi.org/10.1097/hrp.0000000000000268.
  • 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. https://doi.org/10.1016/j.ijmedinf.2020.104131.
  • 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. https://doi.org/10.2196/mhealth.9691.

2. Digital phenotyping to distinguish mood disorders

  • M. Faurholt-Jepsen, D. A. Rohani, J. Busk, M. L. Tønning, M. Vinberg, J. E. Bardram, and L. V. Kessing (2022), “Discriminating between patients with unipolar disorder, bipolar disorder, and healthy control individuals based on voice features collected from naturalistic smartphone calls”, Acta Psychiatrica Scandinavica, 145(3), 255-267. https://doi.org/10.1111/acps.13391.
  • M. Faurholt-Jepsen, D. A. Rohani, J. Busk, M. Vinberg, J. E. Bardram, and L. V. Kessing (2021), “Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states”, International Journal of Bipolar Disorders, 9(1), 38. https://doi.org/10.1186/s40345-021-00243-3.
  • G. Gillet, N. M. McGowan, N. Palmius, A. C. Bilderbeck, G. M. Goodwin, and K. E. A. Saunders (2021), “Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations”, Frontiers in psychiatry, 12, 610457. https://doi.org/10.3389/fpsyt.2021.610457.

3. Digital markers in predicting affective states

  • M. Faurholt-Jepsen, D. A. Rohani, J. Busk, M. Vinberg, J. E. Bardram, and L. V. Kessing (2021), “Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states”, International Journal of Bipolar Disorders, 9(1), 38. https://doi.org/10.1186/s40345-021-00243-3.
  • H. Daus, and M. Backenstrass (2021), “Feasibility and Acceptability of a Mobile-Based Emotion Recognition Approach for Bipolar Disorder”, International Journal of Interactive Multimedia and Artificial Intelligence, 7(2), 7-14. http://doi.org/10.9781/ijimai.2021.08.015.
  • G. Gillet, N. M. McGowan, N. Palmius, A. C. Bilderbeck, G. M. Goodwin, and K. E. A. Saunders (2021), “Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations”, Frontiers in psychiatry, 12, 610457. https://doi.org/10.3389/fpsyt.2021.610457.
  • I. M. Raugh, S. H. James, C. M. Gonzalez, H. C. Chapman, A. S. Cohen, B. Kirkpatrick, G. P. Strauss (2020), “Geolocation as a Digital Phenotyping Measure of Negative Symptoms and Functional Outcome”, Schizophrenia Bulletin, 46(6), 1596–1607. https://doi.org/10.1093/schbul/sbaa121.

4. Digital markers in predicting illness severity

  • H. -Y. Su, C. -H. Wu, C. -R. Liou, E. C. -L. Lin, and P. See Chen (2021), “Assessment of Bipolar Disorder Using Heterogeneous Data of Smartphone-Based Digital Phenotyping”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4260-4264. https://doi.org/10.1109/ICASSP39728.2021.9415008.
  • J. Zulueta, A. Piscitello, M. Rasic, R. Easter, P. Babu, S. A. Langenecker, M. McInnis, O. Ajilore, P. C. Nelson, K. Ryan, A. Leow (2018), “Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study”, Journal of Medical Internet Research, 20(7), e241. https://doi.org/10.2196/jmir.9775.