Digital Monitoring in Psychiatry – Bridging Technological Innovation and Clinical Practice in Severe Mental Illness and Underrepresented Psychiatric Populations

Digital monitoring is rapidly reshaping psychiatric research by enabling continuous, real-world assessment of behavior, sleep, activity, social rhythm, and self-reported symptoms through smartphones, wearables, and other connected devices. In contrast to conventional clinic-based evaluations, these approaches offer the possibility of longitudinal, ecologically valid measurement that may improve symptom tracking, relapse detection, and personalized care. Nevertheless, the current evidence base remains methodologically fragmented and has only been partially translated into clinical psychiatry. Many studies remain proof-of-concept, rely on relatively small and selective samples that may increase the risk of overfitting, use short monitoring periods, and adopt heterogeneous sensing, preprocessing, and modeling pipelines. In addition, existing research has often focused on depression and anxiety, while relatively less attention has been paid to severe mental illness, psychosis-spectrum conditions, dementia, and other clinically complex or underrepresented populations. The field has also advanced more quickly in technical innovation than in clinically meaningful interpretation and validation. A focused discussion is therefore needed on how digital monitoring can move from promising signals to robust, interpretable, and clinically useful tools across diverse psychiatric populations.

This Research Topic aims to move the field of digital monitoring in psychiatry beyond early proof-of-concept studies and toward clinically meaningful, methodologically robust, and ethically grounded applications. Digital tools have created new opportunities to capture dynamic changes in mood, cognition, activity, sleep, social behavior, and treatment adherence in real-world settings. At the same time, there is a particular need to extend these advances to underrepresented populations, including individuals with severe mental illness, in whom digital monitoring may offer important opportunities for improving symptom tracking, relapse detection, and personalized care. By bridging together contributions from psychiatry, psychology, neuroscience, data science, and digital health, this Research Topic seeks to highlight recent advances while critically addressing the challenges that remain. Our goal is to foster a collaborative and clinically informed framework in which digital innovation is aligned with psychiatric knowledge, patient needs, and real-world decision-making.

We welcome submissions that address digital monitoring in psychiatry from clinically meaningful, methodologically rigorous, and translational perspectives, with particular interest in research involving severe mental illness and other underrepresented psychiatric populations. Relevant topics include:

• Smartphone- and wearable- based monitoring of psychiatric symptoms, functioning, sleep, activity, and social rhythm

• Passive sensing, ecological momentary assessment, speech or behavioral data, and multimodal digital phenotyping

• Longitudinal studies of symptom change, relapse risk, treatment response, and illness trajectories

• Studies emphasizing clinical interpretability, reproducibility, and external validation across populations

• Research addressing methodological challenges such as small sample sizes, overfitting, short follow-up periods, and heterogeneity in sensing and analytic pipelines

• Research involving severe mental illness, psychosis-spectrum conditions, older adults, dementia, and cross-diagnostic approaches