STDD: Short-Term Depression Detection with Passive Sensing (original) (raw)
Related papers
Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach
Sensors
Depression is a significant mental health issue that profoundly impacts people’s lives. Diagnosing depression often involves interviews with mental health professionals and surveys, which can become cumbersome when administered continuously. Digital phenotyping offers an innovative approach for detecting and monitoring depression without requiring active user involvement. This study contributes to the detection of depression severity and depressive symptoms using mobile devices. Our proposed approach aims to distinguish between different patterns of depression and improve prediction accuracy. We conducted an experiment involving 381 participants over a period of at least three months, during which we collected comprehensive passive sensor data and Patient Health Questionnaire (PHQ-9) self-reports. To enhance the accuracy of predicting depression severity levels (classified as none/mild, moderate, or severe), we introduce a novel approach called symptom profiling. The symptom profile...
Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors
Frontiers in Psychiatry, 2020
Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors.Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included 8 weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17).Results: Participants wore the right and left wrist sensors 92 and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed–one combining features from smartphone and wearable sensors, one including only features fr...
Objective assessment of depressive symptoms with machine learning and wearable sensors data
2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)
Depression is the major cause of years lived in disability worldwide ; however, its diagnosis and tracking methods still rely mainly on assessing self-reported depressive symptoms, methods that originated more than fifty years ago. These methods, which usually involve filling out surveys or engaging in face-to-face interviews, provide limited accuracy and reliability and are costly to track and scale. In this paper, we develop and test the efficacy of machine learning techniques applied to objective data captured passively and continuously from E4 wearable wristbands and from sensors in an Android phone for predicting the Hamilton Depression Rating Scale (HDRS). Input data include electrodermal activity (EDA), sleep behavior, motion, phone-based communication, location changes, and phone usage patterns. We introduce our feature generation and transformation process, imputing missing clinical scores from self-reported measures, and predicting depression severity from continuous sensor measurements. While HDRS ranges between 0 and 52, we were able to impute it with 2.8 RMSE and predict it with 4.5 RMSE which are low relative errors. Analyzing the features and their relation to depressive symptoms, we found that poor mental health was accompanied by more irregular sleep, less motion, fewer incoming messages, less variability in location patterns, and higher asymmetry of EDA between the right and the left wrists.
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
JMIR mHealth and uHealth, 2016
Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support. The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms. A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and provid...
arXiv (Cornell University), 2022
Background: Major Depressive Disorder (MDD) affects millions of people worldwide , leading to lower quality of life and high medical costs. Despite the existence of psychoand pharmaco-therapy, more than 50% of people with MDD do not receive timely treatment due in part to inaccurate subjective recall and variability in the symptom course. A more objective and frequent monitoring of mental health status may not only improve on subjective recall but help guide treatment selection. Attempts have been made to explore the relationship between measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely evaluate well-being and continuously monitor changes in symptomatology with varying degrees of success. A number of challenges exist for the analysis of this data, however. These include: (i) maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; (ii) distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening participants at high risks; (iii) understanding the heterogeneity with which depression manifests itself in behavioural patterns quantified by the passive features. Objective: We aim to address these three challenges to inform future work in stratified analyses. Methods: Using smartphone and wearable data collected from 479 participants with MDD from the EU IMI RADAR-CNS programme, we extracted 21 features reflecting mobility, sleep, and phone use. We investigated the impact of the number of days of available data on feature quality using intraclass correlation coefficients and Bland-Altman analysis. We then investigated the nature of the correlation between the 8-item Patient Health Questionnaire depression scale (PHQ-8; measured every 14 days) and the features using the participant-mean correlation coefficient, repeated measures correlation coefficient, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioural difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. Results: We demonstrated that 8 (range: 2-12) days were needed for reliable calculation of most features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, while features such as awake duration after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioural difference between periods of depression and no depression. Conclusions: This work contributes to our understanding of how these mobile health derived features are associated with depression symptom severity to inform future work in stratified analyses.
2022
BACKGROUND Depression is a major global cause of morbidity, an economic burden, and the greatest health challenge leading to chronic disability. Mobile monitoring of mental conditions has long been a sought-after metric to overcome the problems associated with the screening, diagnosis, and monitoring of depression and its heterogeneous presentation. The widespread availability of smartphones has made it possible to use their data to generate digital behavioral models that can be used for both clinical and remote screening and monitoring purposes. This study is novel as it adds to the field by conducting a trial using private and nonintrusive sensors that can help detect and monitor depression in a continuous, passive manner. OBJECTIVE This study demonstrates a novel mental behavioral profiling metric (the Mental Health Similarity Score), derived from analyzing passively monitored, private, and nonintrusive smartphone use data, to identify and track depressive behavior and its progre...
Studying Behavioral Changes in Patients with Clinical Depression via Mobile Phone Sensing
There is a growing number of everyday practices that involve the use of the mobile phone like staying in touch with others, looking for help, reading news, coordinating everyday activities via calendars, reminders, or to-do lists. Modern mobile phones include sensors that can be used for inferring such be-havioral information. In this work, we study changes in behavior of seven patients diagnosed with clinical depression, who were being subject to psychological intervention by professional psychologists. We present an analysis derived from mobile phones sensors aimed at studying mobility, interpersonal interactions , and mobile phone usage. Our results show that, as the patients' conditions improved, they moved around the city more, called more frequently, albeit some of them shorter calls, and were using their phones more.
Journal of medical Internet research, 2017
There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable. The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform. A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, c...
2018
Background: Research studies are establishing the use of smartphone-sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns and its analysis helps reveal well-being changes. Depression in diabetes goes highly under-diagnosed and under-reported. The co-morbidity has been associated with increased mortality and worse clinical outcomes; including poor glycemic control and poor self-management. Clinical only intervention has been found to have very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing co-morbid care. Objective: The study aimed to analyse the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes. Methods: A cross-sectional observational study (Project SHADO-Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study smartphone-sensing app passively collected data regarding activity, mobility, sleep and communication from each participant. Self-reported symptoms of depression using validated Patient Health Questionnaire-9 (PHQ-9) was collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states-those with self-reported major depression (PHQ-9 > 9) and those with none (PHQ-9 <= 9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression. Results: A noticeably high prevalence of self-reported depression (30 out of 47 participants, ~65%) was found among the participants. Between depression states, a significant difference was found for average activity rates (day time) among participantday instances with symptoms of major depression (mean=16.06, SD=14.90) and those with none (mean=18.79, SD=16.72); P= .005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean=5.08, SD=3.83) and those with none (mean=8.59, SD=7.05); P < .001. These results suggest that participants with diabetes and symptoms of major depression exhibited lower activity through the day and maintained contact with fewer people. Using all the derived sensing variables, the XGBoost (Extreme Gradient Boosting) machine-learning classifier provided the best performance with an average cross-validation accuracy of 79.07% (95% CI: 74%, 84%) and test accuracy of 81.05% to classify symptoms of depression. Conclusions: Participants with diabetes and self-reported symptoms of major depression were observed to show lower levels of social contact and lower activity levels during the day. While findings must be reproduced in a broader randomized controlled
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently, different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper, we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.