TY - GEN
T1 - Detecting Mental Disorders with Wearables
T2 - 8th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2023
AU - Dai, Ruixuan
AU - Kannampallil, Thomas
AU - Kim, Seunghwan
AU - Thornton, Vera
AU - Bierut, Laura
AU - Lu, Chenyang
N1 - Funding Information:
This publication was supported by the Fullgraf Foundation. The analysis and result are based on "All of Us" research program, which is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the "All of Us" Research Program would not be possible without the partnership of its participants.
Funding Information:
Our study cohort is a part of the "All of Us" research program (Registered Tier Data, tier 51) funded by the National Institutes of Health (NIH) in the United States. The research program aims to enroll a diverse population to accelerate biomedical research and precision medicine [33]. The program incorporates wearable technologies to gather digital phenotypes. Participants with any Fitbit devices (Fitbit, Inc. San Francisco2) can share their wearable data via the Bring-Your-Own-Device (BYOD) project [1], which links participants’ Fitbit accounts to the "All of Us" program. More than 11,600 participants have contributed to the Fitbit dataset, which contains the Fitbit daily summaries, intraday heart rate time series and intraday step time series. The "All of Us" program also collects professional clinical surveys, electronic health records (EHRs), biosamples, demographics, and other patient characteristics (e.g., family health history). The EHRs include diagnoses of mental health disorders by healthcare providers, which contain the standard diagnosis codes (e.g., ICD-10 [36], SNOMED[45]) and the timestamps of the diagnoses.
Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/5/9
Y1 - 2023/5/9
N2 - Depression and anxiety are among the most prevalent mental disorders, and they are usually interconnected. Although these mental disorders have drawn increasing attention due to their tremendous negative impacts on working ability and job performance, over 50% of patients are not recognized or adequately treated. Recent literature has shown the potential of using wearables for expediting the detection of mental health disorders, as physical activities are reported to be related to some mental health disorders. However, most prior studies on mental health with wearables were limited to small cohorts. The feasibility of detecting mental disorders in the community with a large and diverse population remains an open question. In this paper, we study the problem of detecting depression and anxiety disorders with commercial wearable activity trackers based on a public dataset including 8,996 participants and 1,247 diagnosed with mental disorders. The large cohort is highly diverse, spanning a wide spectrum of age, race, ethnicity, and education levels. While prior studies were usually limited to shallow machine learning models and feature engineering to accommodate the small sample sizes, we develop an end-to-end deep model combining a transformer encoder and convolutional neural network to directly learn from daily wearable features and detect mental disorders. WearNet achieves an area Under the Receiver Operating Characteristic curve (AUROC) of 0.717 (S.D. 0.009) and an AUPRC of 0.487 (S.D. 0.008) in detecting mental disorders while outperforming traditional and state-of-the-art machine learning models. This work demonstrates the feasibility and promise of using wearables to detect mental disorders in a large and diverse community.
AB - Depression and anxiety are among the most prevalent mental disorders, and they are usually interconnected. Although these mental disorders have drawn increasing attention due to their tremendous negative impacts on working ability and job performance, over 50% of patients are not recognized or adequately treated. Recent literature has shown the potential of using wearables for expediting the detection of mental health disorders, as physical activities are reported to be related to some mental health disorders. However, most prior studies on mental health with wearables were limited to small cohorts. The feasibility of detecting mental disorders in the community with a large and diverse population remains an open question. In this paper, we study the problem of detecting depression and anxiety disorders with commercial wearable activity trackers based on a public dataset including 8,996 participants and 1,247 diagnosed with mental disorders. The large cohort is highly diverse, spanning a wide spectrum of age, race, ethnicity, and education levels. While prior studies were usually limited to shallow machine learning models and feature engineering to accommodate the small sample sizes, we develop an end-to-end deep model combining a transformer encoder and convolutional neural network to directly learn from daily wearable features and detect mental disorders. WearNet achieves an area Under the Receiver Operating Characteristic curve (AUROC) of 0.717 (S.D. 0.009) and an AUPRC of 0.487 (S.D. 0.008) in detecting mental disorders while outperforming traditional and state-of-the-art machine learning models. This work demonstrates the feasibility and promise of using wearables to detect mental disorders in a large and diverse community.
KW - Deep Learning
KW - Large Cohort
KW - Mental Health
KW - Time Series
KW - Wearables
UR - http://www.scopus.com/inward/record.url?scp=85159686666&partnerID=8YFLogxK
U2 - 10.1145/3576842.3582389
DO - 10.1145/3576842.3582389
M3 - Conference contribution
AN - SCOPUS:85159686666
T3 - ACM International Conference Proceeding Series
SP - 39
EP - 51
BT - Proceedings - 8th ACM/IEEE Conference on Internet of Things Design and Implementation, IoTDI 2023
PB - Association for Computing Machinery
Y2 - 9 May 2023 through 12 May 2023
ER -