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Mobile devices can assist individuals in maintaining good health by tracking their slumber, physical activity, and heart rate, while also uncovering concerns related to mental wellness, recent investigations indicate.

In a paper released in JAMA Network Open, scholars from the University of Michigan, University of Minnesota, and University of Pittsburgh employed smartphone sensors as unobtrusive monitors of daily habits. These digital traces recorded basic activities, such as our movements, sleep patterns, or phone usage, while also offering unexpected observations about how our mental states reflect in daily practices.

Researchers discovered that numerous mental disorders exhibit comparable behavior patterns, such as increased time spent indoors, sleeping in, and infrequent phone charging. Such actions may indicate an individual’s level of what is termed the “p-factor,” which connects a variety of mental health challenges.

Aidan Wright, a psychology professor and the Phil F. Jenkins Research Professor of Depression at the University of Michigan’s Eisenberg Family Depression Center, mentioned that the team identified certain behaviors, such as reduced phone calls or decreased walking, that corresponded to specific challenges like diminished social interaction or feelings of illness.

“These results imply that significant types of mental health disorders are identifiable through smartphone sensors, suggesting that this technology could be utilized for symptom tracking and research on diverse psychiatric issues,” stated Wright, the study’s senior author.

The research drew on data from smartphone sensors utilized by 557 participants over a span of 15 days in 2023, making it one of the most substantial studies of its kind. Despite widespread optimism in employing smartphone sensors and wearables to diagnose and monitor mental health disorders, progress has been limited, Wright noted. “This is partly due to the fact that most digital psychiatry efforts have not applied our understanding of how mental illness is structured in individuals when determining targets for prediction and monitoring,” he elaborated.

Digital psychiatry has relied on classifications from the Diagnostic and Statistical Manual of Mental Disorders, or DSM-5, which are inadequate targets for identification and observation due to their heterogeneity. This signifies they consist of various symptom combinations that may have distinct behavioral signatures, while often overlapping with other classifications, Wright explained.

Compounding the issue is that in clinical contexts, most individuals possess multiple diagnoses, leading to ambiguity about which might account for their conduct, he mentioned.

“In essence, these classifications do a poor job of delineating mental health issues,” he added.

Whitney Ringwald, an assistant professor of psychology at the University of Minnesota and the study’s leading author, expressed that the findings offer a deeper comprehension of how different psychopathologies may hinder the functioning of those affected in daily life.

Mental health conditions often arise slowly and are best addressed early, before they escalate into severe and debilitating forms. Wright emphasized that monitoring is challenging, and “the existing methods are inadequate and not nearly effective enough.”

“The capacity to utilize passive sensing to connect individuals with support before situations worsen could yield massive advantages, incorporating improved outcomes, reduced expenses, and diminishing stigma,” remarked Wright.

Other co-authors included Grant King, a graduate student at the University of Michigan, and Colin Vize, an assistant professor of psychology at the University of Pittsburgh.

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