Short introduction: Artificial intelligence (AI) algorithms together with advances in data storage have made it possible to better characterize, predict, prevent, and treat a range of psychiatric illnesses. The rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector allow the development of many tools and AI application in mental health. This is particularly true since COVID. This article presents below, briefly, what are digital health and digital mental health. The content of this article is extracted from a scientific article that I co-authored and cited at the end.
Target audience: Everyone
Keywords: Digital mental health, Digital Health, Artificial intelligence, biases
Digital health can be defined as the concept of healthcare meeting the Internet (Klonoff et al, 2019). It ranges from telehealth and telecare systems (May et al, 2003) to patient portals and personal health records (Devlin et al, 2016, Pagliari et al, 2007), mobile applications (Bailey et al, 2014), and other online platforms and devices.
However, and as opposed to digitized versions of traditional health approaches, digital health interventions (DHIs) (Murray et al, 2016) utilize artificial intelligence (AI) algorithms and other machine learning (ML) systems to monitor and predict symptoms of patients in an adaptive feedback loop (Palanica et al, 2020). Improvements in ML over recent years have demonstrated potential within a variety of diseases and medical fields including neurological and mental health disorders (Dunn et al, 2018) both at an individual-patient level and applied to larger populations for scalable understanding, management, and intervention of mental health conditions in different cohorts and various settings (Palanica et al, 2020). In addition, and because to our knowledge, effective coverage does not exceed 50% in any country and is much lower in low- and middle-income countries, DHIs also address social problems in the healthcare system such as poor access, uncoordinated care, and increasingly heavy costs (Reti et al, 2010). Digital mental health interventions could thus give much needed attention to underresearched and undertreated populations (Aboujaoude et al, 2020).
Digital Mental Health Technology Advances
The keywords “digital mental health” in PubMed’s search engine (accessed April 2020) show that 2019 has the largest number of published articles compared to any prior year. The trend is also rising for the keywords “mental health mobile apps,” providing evidence that interest in both (i) publication of articles about digital health and (ii) technical advances is rising. Advances in digital health technologies in mental health are occurring at a rapid pace in research laboratories both in academic institutions and in the industry (Allen et al, 2019). The rapidly growing number of biological devices and the exponential accumulation of data in the mental health sector aim at facilitating the four purposes of healthcare: diagnosis, monitoring, treatment, and prevention (Klonoff et al, 2019).
The four lines below are the summary of the introduction section, page 2 of the original article
- For Diagnosis: Illness detection and classification (Allen et al, 2019)
- For Monitoring: Analysis of data generated by personal electronic devices to monitor mental health parameters and detect useful biobehavioral markers that could in turn optimize diagnosis, treatment, and prevention and a global clinical improvement (Huckval et al, 2019).
- For Treatment: Assistance and treatment options (Klonoff et al, 2019) e.g. prescription of video game in mental health for kids with ADHD, EndeavorRx (Kollins et al, 2020).
- For Prevention: Catching new episodes of a given disorder at a very early stage (e.g. in the case of suicide preventions (Christensen et al, 2016)) due to new modes of real-time assessment (Abdullah et al, 2016, Saeb et al, 2015).
To cite this article :
Please consider citing the original scientific article:
LJ. Boulos, A. Mendes, A. Delmas, I.Chraibi K. An Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health. Frontiers in Psychiatry, Frontiers, 2021, 12, ⟨10.3389/fpsyt.2021.574440⟩. ⟨hal-03352727⟩
- Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T. Automatic detection of social rhythms in bipolar disorder. J Am Med Informat Assoc. (2016) 23:538–43. doi: 10.1093/jamia/ocv200
- Aboujaoude E, Gega L, Parish MB, Hilty DM. Editorial: digital interventions in mental health: current status and future directions. Front Psychiatry. (2020) 11:111. doi: 10.3389/fpsyt.2020.00111
- Allen B, Seltzer SE, Langlotz CP, Dreyer KP, Summers RM, Petrick N, et al. A road map for translational research on artificial intelligence in medical imaging: from the 2018 national institutes ofHealth/RSNA/ACR/The academy workshop. J Am College Radiol. (2019) 16:1179–89. doi: 10.1016/j.jacr.2019.04.014
- Bailey SC, Belter LT, Pandit AU, Carpenter DM, Carlos E, Wolf MS. The availability, functionality, and quality of mobile applications supporting medication self-management. J Am Med Informat Assoc. (2014) 21:542–6. doi: 10.1136/amiajnl-2013–002232
- Christensen H, Cuijpers P, Reynolds CF. Changing the direction of suicide prevention research: a necessity for true population impact. JAMA Psychiatry. (2016) 73:435–6. doi: 10.1001/jamapsychiatry.2016.0001
- Devlin AM, McGee-Lennon M, O’Donnell CA, Bouamrane, M.-M., Agbakoba R, et al. Delivering digital health and well-being at scale: lessons learned during the implementation of the dallas program in the United Kingdom. J Am Med Informat Assoc. (2016) 23:48–59. doi: 10.1093/jamia/ocv097
- Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Per Med. (2018) 15:429–48. doi: 10.2217/pme-2018–0044 Elstein, A. S. (1976). Clinical judgment: psychological research and medical practice. Science., 194(4266), 696–700.
- Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. NPJ Digital Med. (2019) 2:88. doi: 10.1038/s41746–019–0166–1
- Huckvale K, Wang CJ, Majeed A, Car J. Digital health at fifteen: More human (more needed). BMC Med. (2019) 17:62. doi: 10.1186/s12916–019–1302–0
- Klonoff DC, King F, Kerr D. New opportunities for digital health to thrive. J Diabetes Sci Technol. (2019) 13:159–63. doi: 10.1177/1932296818822215
- Kollins SH, DeLoss DJ, Cañadas E, Lutz J, Findling RL, Keefe RSE, et al. A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. Lancet Digital Health. (2020) 2:e168–78. doi: 10.1016/S2589–7500(20)30017–0
- May C, Mort M, Williams T, Mair F, Gask L. Health technology assessment in its local contexts: studies of telehealthcare. Soc Sci Med. (2003) 57:697–710. doi: 10.1016/S0277–9536(02)00419–7
- Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, et al. Evaluating digital health interventions: key questions and approaches. Am J Prev Med. (2016) 51:843–51. doi: 10.1016/j.amepre.2016.06.008
- Pagliari C, Detmer D, Singleton P. Potential of electronic personal health records. BMJ. (2007) 335:330–3. doi: 10.1136/bmj.39279.482963.AD
- Palanica A, Docktor MJ, Lieberman M, Fossat Y. The need for artificial intelligence in digital therapeutics. Digital Biomarkers. (2020) 4:21–5. doi: 10.1159/000506861
- Reti SR, Feldman HJ, Ross SE, Safran C. Improving personal health records for patient-centered care. J Am Med Informat Assoc. (2010) 17:192–5. doi: 10.1136/jamia.2009.000927
- Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, et al. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. (2015) 17:e175. doi: 10.2196/jmir.4273