A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

Abstract

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol

“…The ubiquity of mobile digital devices provides greater penetration of the general population and provides for a focused area on which to assess individual as well as combinations of sensor data as predictive factors for health disorders. To date, there have been several published reviews in this field of research Guntuku et al 6; Dogan et al 7; Rohani et al 8; Reinertsen and Clifford9; Cornet and Holden10). However, these reviews have a number of limitations:…”

Continuous Digital Assessment for Weight Loss Surgery Patients

“…We focused on examining the impact of weight loss procedures on 6 daily behavioral features, 3 of which (total sleep time, sleep efficiency, and resting heart rate) were accessible from the public Fitbit application programming interface (https://dev.fitbit.com/build/ reference/web-api/) and 3 of which were manually derived from the minute-level intraday activity data (total step count, fraction of minutes per day with > 0 steps, and 95th percentile heart rate) [15][16][17][18][19][20]. To reduce the impact of day-of-the-week effects, we computed the weekly mean of each feature for each participant for each week of the observation window (online suppl.…”

Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

“…Today, non-invasive body-worn sensors such as accelerometers and gyroscopes can also help to measure a wide range of behavioral factors to inform this process. Indeed, impaired motor function is often found during episodes of SMI [4]. For example, people with bipolar disorder or schizophrenia can be significantly more sedentary than age-and gendermatched healthy controls [5].…”

“…For example, people with bipolar disorder or schizophrenia can be significantly more sedentary than age-and gendermatched healthy controls [5]. Further examples of the use of such passive sensing for behavioural monitoring are presented in [4], [5] which indicate that passively collected behavioral data, using wearables, presents a potentially scalable and at present underutilized opportunity to help with the care of people with SMI.…”