LAUSR

World Psychiatry 22:1 February 2023 machine learning, but it should be noted that, although this technique may fit the specific population from which the data were obtained, the generalizability of findings may not always be high. In addition, determining how to accommodate differences across patients’ lifestyles is especially important: the identification of digital pheno types common to patients across cultures might be difficult. Nonetheless, the advancement of the above technologies and the accumulation of the relevant knowledge may benefit not only clinical trials but also real-world clinical practice. Gold-standard evaluations may be difficult to perform in time-constrained clinical settings, but “measurement-based psychiatry” could be delivered more easily with those technologies. In fact, commercially available wearable devices can already quantify sleep and activity, and some practitioners may be using such data to treat patients. Specifically, the accumulation of longitudinal data on individual pa tients would be useful for identifying changes over time. A large cohort study that collects digital data would allow to identify which patients with which digital phenotypes respond to which treatments. As a result, the selection of drugs with the greatest likelihood of being effective for individual patients might become possible. Concerns about the use of digital tools in clinical practice should also be considered. The question is what kind of long-term chang es might occur as face-to-face treatment is replaced by the use of information and communication technology and digital tools. One often discussed issue is the digital divide, i.e., the risk that those who are unable to successfully use digital tools will be left out of health care. Since the COVID-19 pandemic, psychiatric care has been delivered almost entirely remotely in some countries, but it is necessary to investigate whether this has the same therapeutic effect as face-to-face care. A large body of evidence already shows that telemedicine is no less effective than face-to-face care, but it remains unresolved whether this is true even for long-term treatment over multiple years. Furthermore, there is a chance that the focus will shift to improving digital device-derived outcomes rather than actual patient recovery, if treatment effects are assessed using digital phenotypes rather than humans. As we accumulate digital phenotypic data in the future, it will be important to study how these data are connected to pathophysiology. For example, studies that explore the relationship between brain functional connectivity and digital phenotypes would be useful. If a treatment has been identified that is effective for a specific pattern of functional connectivity, digital phenotyping may be able to identify the patients who are the best candidates for that treatment. Even if the above-mentioned hurdles are overcome and a regulatory-accepted digital methodology is developed, there is no guarantee that such a methodology would be the best way to quantify mental disorder symptoms over the long term. Sensing technology and analytical methods are constantly evolving, and they can quickly become obsolete. The continued use of once-established standards for many years might nullify the advantages of digital technologies. In conclusion, a great potential seems to have emerged from the use of digital technologies to foster the progress of psychopharmacology. Interdisciplinary research and development with the goal of actually improving the outcomes of people with mental disorders are now needed.