Predicting depressive symptoms using smartphone data

Ware, Shweta; Yue, Chaoqun; Morillo, Reynaldo; Lü, Jin; Chen, Shang; Bi, Jinbo; Kamath, Jayesh; Russell, Alexander; Bamis, Athanasios; Wang, Bin

doi:10.1016/j.smhl.2019.100093

Cited by 67 publications

(64 citation statements)

References 20 publications

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“…It is interesting to consider that the same underlying signal may be present in both GPS data and ambient audio data. A study by Ware et al [ 58 ] also used the circadian movement feature in the prediction of depression. Although this feature was one of many used in a classifier that achieved an F1 score as high as 0.86, it is unclear to what degree this feature alone was associated with the severity of depressive symptoms.…”

Section: Discussionmentioning

confidence: 99%

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

Matteo¹,

Fotinos²,

Lokuge³

et al. 2020

JMIR Form Res

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Background Objective and continuous severity measures of anxiety and depression are highly valuable and would have many applications in psychiatry and psychology. A collective source of data for objective measures are the sensors in a person’s smartphone, and a particularly rich source is the microphone that can be used to sample the audio environment. This may give broad insight into activity, sleep, and social interaction, which may be associated with quality of life and severity of anxiety and depression. Objective This study aimed to explore the properties of passively recorded environmental audio from a subject’s smartphone to find potential correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. Methods An Android app was designed, together with a centralized server system, to collect periodic measurements of the volume of sounds in the environment and to detect the presence or absence of English-speaking voices. Subjects were recruited into a 2-week observational study during which the app was run on their personal smartphone to collect audio data. Subjects also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the environmental audio of 84 participants with sufficient data, and correlations were measured between the 4 audio features and the 4 self-report measures. Results The regularity in daily patterns of activity and inactivity inferred from the environmental audio volume was correlated with the severity of depression (r=−0.37; P<.001). A measure of sleep disturbance inferred from the environmental audio volume was also correlated with the severity of depression (r=0.23; P=.03). A proxy measure of social interaction based on the detection of speaking voices in the environmental audio was correlated with depression (r=−0.37; P<.001) and functional impairment (r=−0.29; P=.01). None of the 4 environmental audio-based features tested showed significant correlations with the measures of generalized anxiety or social anxiety. Conclusions In this study group, the environmental audio was shown to contain signals that were associated with the severity of depression and functional impairment. Associations with the severity of social anxiety disorder and generalized anxiety disorder were much weaker in comparison and not statistically significant at the 5% significance level. This work also confirmed previous work showing that the presence of voices is associated with depression. Furthermore, this study suggests that sparsely sampled audio volume could provide potentially relevant insight into subjects’ mental health.

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Section: Discussionmentioning

confidence: 99%

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

Matteo¹,

Fotinos²,

Lokuge³

et al. 2020

JMIR Form Res

View full text Add to dashboard Cite

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“…Based on the results of the factor analyses, we chose to reduce the number of independent variables in the subsequent analyses by using the within–person daily, and two–week aggregate factor scores representing the movement–based behavioural dimensions of distance, entropy, and irregularity. Factor analyses and the extraction of factor scores were performed using the psych package (Revelle, 2020) for R (R Core Team, 2019). Descriptive statistics for the factor scores are included in Table 2 (see Table S1 for descriptive statistics of the untransformed mobility metrics).…”

Section: Methodsmentioning

confidence: 99%

“…Although all of these indicators tap into the broader construct of subjective well-being, it is possible that travelling large distances is associated with higher levels of energy, but also with higher levels of stress. By measuring a wide set of indicators simultaneously, we are able to test for differential effects and investigate whether various indicators (Canzian & Musolesi, 2015) Location variance À (Saeb et al, 2016;Saeb, Zhang, Kwasny, et al, 2015;Yue et al, 2018) Standard deviation of longitude and latitude + (Ghandeharioun et al, 2017) Radius NA (Pratap et al, 2019) Speed À (Yue et al, 2018) Irregularity Circadian movement À (Saeb et al, 2016;Saeb, Zhang, Kwasny, et al, 2015 Farhan et al, 2016;Wang et al, 2014;Yue et al, 2018) Entropy Home stay + (Farhan et al, 2016;Howe et al, 2017;Saeb, Zhang, Kwasny, et al, 2015;Yue et al, 2018) Home stay À (Chow et al, 2017) Entropy À (Farhan et al, 2016;Saeb et al, 2016;Saeb, Zhang, Kwasny, et al, 2015;Yue et al, 2018) Multiple features in prediction Multiple NA (Mehrotra & Musolesi, 2018;Saeb et al, 2017;Wahle, Kowatsch, Fleisch, Rufer, & Weidt, 2016;Ware et al, 2020;Xu et al, 2019;Zakaria et al, 2019) Loneliness Distance Distance travelled À (Wang et al, 2014)…”

mentioning

confidence: 99%

Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking

et al. 2020

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People interact with their physical environments every day by visiting different places and moving between them. Such mobility behaviours likely influence and are influenced by people's subjective well‐being. However, past research examining the links between mobility behaviours and well‐being has been inconclusive. Here, we provide a comprehensive investigation of these relationships by examining individual differences in two types of mobility behaviours (movement patterns and places visited) and their relationship to six indicators of subjective well‐being (depression, loneliness, anxiety, stress, affect, and energy) at two different temporal levels of analysis (two‐week tendencies and daily level). Using data from a large smartphone‐based longitudinal study (N = 1765), we show that (i) movement patterns assessed via GPS data (distance travelled, entropy, and irregularity) and (ii) places visited assessed via experience sampling reports (home, work, and social places) are associated with subjective well‐being at the between and within person levels. Our findings suggest that distance travelled is related to anxiety, affect, and stress, irregularity is related to depression and loneliness, and spending time in social places is negatively associated with loneliness. We discuss the implications of our work and highlight directions for future research on the generalizability to other populations as well as the characteristics of places. © 2020 European Association of Personality Psychology

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“…In addition to data imputation, we will further develop robust feature extraction techniques that provide features to the machine learning models. In earlier work [34][35][36][37][38], we have explored using various features from location and activity data e.g., entropy of locations, the amount of time spent at home, circadian movement, similarity in location across days. While these features are correlated with depression, the correlation tends to be relatively low.…”

Section: Of 17mentioning

confidence: 99%

“…Specifically, in the LifeRhythm Project, a 4-year project funded by the National Science Foundation, our group conducted a two-phase study in college age participants with depression (in comparison with a control group). Our results demonstrated that sensory data collected from mobile and wearable devices-without any user interaction-can provide critical information that correlates with depression symptoms, and can be used to automatically detect depression [33][34][35][36][37][38]. Specifically, in Phase I of the project, we developed a smartphone application, called LifeRhythm app, to passively collect sensory data (location, activity, social interaction) for both Andriod and iPhones, the two predominant smartphone platforms.…”

Section: Introductionmentioning

confidence: 99%

Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics

Kamath

Russell

et al. 2020

J Psychiatry Brain Sci

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We report on the newly started project "SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics". The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This project will advance personalized depression treatment by developing a system, DepWatch, that leverages mobile health technologies and machine learning tools. The objective of DepWatch is to assist clinicians with their decision making process in the management of depression. The project comprises two studies. Phase I collects sensory data and other data, e.g., clinical data, ecological momentary assessments (EMA), tolerability and safety data from 250 adult participants with unstable depression symptomatology initiating depression treatment. The data thus collected will be used to develop and validate assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three clinicians will use DepWatch to support their clinical decision making process. A total of 128 participants under treatment by the three participating clinicians will be recruited for the study. A number of new machine learning techniques will be developed.

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Predicting depressive symptoms using smartphone data

Cited by 67 publications

References 20 publications

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study

Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking

Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics

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