Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.

The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.

Musicotherapy mobile applications: what level of evidence and potential role in psychiatric care? A systematic review.

Context: In our times of smartphone ubiquity, mobile applications are an inescapable daily life tool, including in health care. Music therapy has already proven its worth, notably in mental health. Hence, we were interested in the mobile app format for this type of therapy, its level of evidence, how to use it in daily psychiatric care and the leads for future research and innovation. Method: This study carries out a systematic review of scientific literature of this topic on two search engines, PubMed and PubPsych, using these key-words: [(web-application) OR (web-app) OR (smartphone) OR (apps) OR (app)) AND ((music) OR (music therapy) OR (melody)]. Outcome: Out of a total of 282 studies found by keyword, 31 are included in this review. Several outcomes emerge. These studies relate to existing applications like Music Care, Calm or Unwind, on application prototypes or a potential use of music streaming applications on health care. They involve many different populations and clinical situations, including in hospital environments, for patients with chronic illnesses, different age ranges or for the general population. These musical interventions show a significant effect mainly for anxious symptoms, but also for depression, sleep disorders, pain and other psychiatric or psycho-somatic syndromes. These applications have no significant adverse effects. Conclusion: This review shows that music therapy apps have several potentials for improving mental health care. It could assist psychiatric usual care and could potentially lower medication intake. Nevertheless, the studies on the topic are limited and recent but they open prospects for future research.

Digital phenotype of mood disorders: A conceptual and critical review.

Background: Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care. Objective: The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders. Methods: We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. Results: Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). Conclusion: The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.