Digital health data from consumer wearable devices and smartphones have the potential to improve our understanding of mental illness. However, in conditions like depression, there is not yet a consistent uniform measurement tool whose result can be reliably used as a gold standard measure of depression severity. This work seeks to specify what symptoms and dimensions of depression can be detected using vitals, activity, and sleep monitored by consumer wearable devices. Machine learning models are fit to digital health data and used to detect responses to individual questions from surveys (self-reports) as well as summary scores from these self-reports. For high performing models, feature importance is investigated. Analysis is conducted on preliminary data from 99 participants of an ongoing study with data from the Apple Watch and iPhone along with validated self-reports relevant to depression severity, anhedonia severity, and sleep quality. Receiver operator characteristic area under the curve (ROC AUC) and average precision are used to assess model performance. The digital health sensor data investigated was found to significantly detect five of 74 measures, including overall depression severity and specific symptoms like poor appetite, aspects of anhedonia, and sleep timings (ROC AUC between 0.63 and 0.72). The features these models use in detection vary per detection task and suggest further areas for investigation to specify the right features to look at per symptom.

Detection of Symptoms of Depression Using Data From the iPhone and Apple Watch

Abstract Given the rate of advancement in predictive psychiatry, there is a threat that it outpaces public and professional willingness for use in clinical care and public health. Prediction tools in psychiatry estimate the risk of future development of mental health conditions. Prediction tools used with young populations have the potential to reduce the worldwide burden of depression. However, little is known globally about adolescents’ and other stakeholders’ attitudes toward use of depression prediction tools. To address this, key informant interviews and focus group discussions were conducted in Brazil, Nepal, Nigeria and the United Kingdom with 23 adolescents, 45 parents, 47 teachers, 48 health-care practitioners and 78 other stakeholders (total sample = 241) to assess attitudes toward using a depression prediction risk calculator based on the Identifying Depression Early in Adolescence Risk Score. Three attributes were identified for an acceptable depression prediction tool: it should be understandable, confidential and actionable. Understandability includes depression literacy and differentiating between having a condition versus risk of a condition. Confidentiality concerns are disclosing risk and impeding educational and occupational opportunities. Prediction results must also be actionable through prevention services for high-risk adolescents. Six recommendations are provided to guide research on attitudes and preparedness for implementing prediction tools. 

No prediction without prevention: A global qualitative study of attitudes toward using a prediction tool for risk of developing depression during adolescence

BACKGROUND
Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making.


OBJECTIVE
This study aimed to develop and validate wearable-based deep learning models to comprehensively predict patient symptoms across various acute psychiatric wards in South Korea.


METHODS
Participants diagnosed with schizophrenia and mood disorders were recruited from 4 wards across 3 hospitals and prospectively observed using wrist-worn wearable devices during their admission period. Trained raters conducted periodic clinical assessments using the Brief Psychiatric Rating Scale, Hamilton Anxiety Rating Scale, Montgomery-Asberg Depression Rating Scale, and Young Mania Rating Scale. Wearable devices collected patients' heart rate, accelerometer, and location data. Deep learning models were developed to predict psychiatric symptoms using 2 distinct approaches: single symptoms individually (Single) and multiple symptoms simultaneously via multitask learning (Multi). These models further addressed 2 problems: within-subject relative changes (Deterioration) and between-subject absolute severity (Score). Four configurations were consequently developed for each scale: Single-Deterioration, Single-Score, Multi-Deterioration, and Multi-Score. Data of participants recruited before May 1, 2024, underwent cross-validation, and the resulting fine-tuned models were then externally validated using data from the remaining participants.


RESULTS
Of the 244 enrolled participants, 191 (78.3%; 3954 person-days) were included in the final analysis after applying the exclusion criteria. The demographic and clinical characteristics of participants, as well as the distribution of sensor data, showed considerable variations across wards and hospitals. Data of 139 participants were used for cross-validation, while data of 52 participants were used for external validation. The Single-Deterioration and Multi-Deterioration models achieved similar overall accuracy values of 0.75 in cross-validation and 0.73 in external validation. The Single-Score and Multi-Score models attained overall R² values of 0.78 and 0.83 in cross-validation and 0.66 and 0.74 in external validation, respectively, with the Multi-Score model demonstrating superior performance.


CONCLUSIONS
Deep learning models based on wearable sensor data effectively classified symptom deterioration and predicted symptom severity in participants in acute psychiatric wards. Despite lower computational costs, Multi models demonstrated equivalent or superior performance than Single models, suggesting that multitask learning is a promising approach for comprehensive symptom prediction. However, significant variations were observed across wards, which presents a key challenge for developing clinical decision support systems in acute psychiatric wards. Future studies may benefit from recurring local validation or federated learning to address generalizability issues.

Comprehensive Symptom Prediction in Acute Psychiatric Inpatients Using Wearable-Based Deep Learning Models: Development and Validation Study (Preprint)

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer mood and diagnose major depressive disorder. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a novel combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2[≥] 80%). We show that the passive behavioral phenotypes enhance the prediction of future mood over and above a baseline model which predicts future mood based on past depression severity alone for 52% of individuals in our cohort. In conclusion, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

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Personalized Mood Prediction from Patterns of Behavior Collected with Smartphones

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Personalized Mood Prediction from Patterns of Behavior Collected with Smartphones