Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning

• Published in: Computers in biology and medicine 2024-09, Vol.180, p.108959, Article 108959
• Main Authors: Rykov, Yuri G., Ng, Kok Pin, Patterson, Michael D., Gangwar, Bikram A., Kandiah, Nagaendran
• Format: Article
• Language: English
• Subjects: Accuracy; Adults; Anxiety; Biomarkers; Body temperature; Clinical trials; Cognitive ability; Correlation analysis; Deep learning; Dementia; Dementia disorders; Depression; Digital biomarkers; Disorders; Electrocardiography; Emotional disorders; Heart rate; Impairment; Longitudinal studies; Machine learning; Mental depression; Mental disorders; Mild cognitive impairment; Mood; Mood disorders; Neuropsychiatric symptoms; Older adults; Older people; Physiology; Population studies; Psychopathology; Remote monitoring; Remote sensors; Sensors; Signs and symptoms; Skin; Stress; Systematic review; Wearable sensor data; Wearable technology; Wrist
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108959

Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. This trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled “Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment”. •Wearable-based biomarkers predict neuropsychiatric symptoms in older adults.•Trained model accurately predicts severity of depressive symptoms on sleep-data.•Physiological biomarkers with deep-learning features improve prediction accuracy.•Continuous & passive monitoring of neuropsychiatric symptoms with wearable sensor.