Passive sensing data collection also has the potential to be a more accurate and less burdensome approach to detection when compared with traditional paper-based screening tools.
Given that maternal mental illness is associated with disrupted sleep, lack of social engagement, lack of stability in daily schedules, and altered interaction patterns with their infants, there are many opportunities to apply passive sensing data collection to improve diagnosis, monitoring, and treatment for mothers with depression. In a high-income country study of women with perinatal depression, the degree of depressed mood was associated with radius travelled as measured with GPS women who traveled larger radii had milder depression than the women with severe depression who had smaller travel radii. Passive sensing can help to better understand maternal mental health by recording physical activity, location, sleep, mother–child interaction, and the auditory environment. The prevalence of postpartum depression in LMIC ranges from 3 to 32%. One mental illness of high prevalence and societal impact is postpartum depression, particularly because young mothers are often not identified or treated in LMIC. Combined with effective interventions, passive sensing data collection has the potential to address major public mental health issues in LMIC. Passive sensing data collection can be especially helpful for health initiatives in low- and middle-income countries (LMIC), which are characterized by limited access to specialty health services and where some populations have low literacy. Moreover, unobtrusively collected audio has the potential to reveal vocal biomarkers for depression and other mental illnesses thanks to advances in deep learning and other artificial intelligence applications.
#Phone does not pick up my radbeacon Bluetooth#
Other studies have similarly explored the potential of using passive sensing in depression, bipolar disorder, and schizophrenia using GPS location, accelerometers to monitor activity, and various other functions captured by Bluetooth devices. Passive sensing has recorded time away from home and activity levels to identify risk of early dementia. Passive sensing has been used to identify mood instability. Passive sensing data was collected with people with mental illness in Australia. There are a number of initiatives to explore potential benefits from using passive sensing data in mental health and behavioral health studies. Passive sensing data provides a window onto experiences, behavior, and environments of individuals, all of which are important to understand mental health and mental illness.īecause the field of mental health lacks objective markers of disease such as viral loads, pathogen detection, and point-of-care testing for disease status, passive sensing provides a unique objective reference for mental health status. Passive sensors also provide information on the number of steps taken in a day, heart rate variability, exposure to light and sound, and proximity to others with mobile devices. For example, accelerometers on smartphones can detect activities such as walking, riding in a vehicle, and standing, and the Global Positioning System (GPS) captures location. Passive sensing on mobile devices refers to the capture of information that does not require users’ active input while they go about their daily lives.
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