A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is expe...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-02, Vol.23 (3), p.1585
Main Authors: Ross, Mindy K, Tulabandhula, Theja, Bennett, Casey C, Baek, EuGene, Kim, Dohyeon, Hussain, Faraz, Demos, Alexander P, Ning, Emma, Langenecker, Scott A, Ajilore, Olusola, Leow, Alex D
Format: Article
Language:English
Subjects:
accelerometer
Accelerometers
Accelerometry
Affect
Bipolar disorder
Care and treatment
Change detection
Clustering
Depression - diagnosis
Depression, Mental
digital phenotyping
Emotional disorders
Emotions
Humans
Keyboards
Machine Learning
Mental depression
Methods
mHealth
Model accuracy
Mood disorders
Sensors
Smart phones
Smartphone
Smartphones
Wearable computers
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Summary:The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23031585