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

Matteo, Daniel Di; Fotinos, Kathryn; Lokuge, Sachinthya; Yu, Julia; Sternat, Tia; Katzman, Martin A.; Rose, Jonathan

doi:10.2196/18751

Cited by 32 publications

(42 citation statements)

References 53 publications

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“…We note that any speech in the environment, including the playback of recorded speech, will be detected as speech; therefore, this feature is not a perfect proxy for social interaction. The results of the study by Di Matteo et al [30] were reported under the same conditions.…”

Section: Speech Presencesupporting

confidence: 66%

“…The speech presence feature is defined as the number of audio recordings labeled as containing speech divided by the total number of audio recordings. This feature was first proposed by Wang et al [15] and was confirmed to be a good correlate of anxiety and depression in our previous study [30]. We note that any speech in the environment, including the playback of recorded speech, will be detected as speech; therefore, this feature is not a perfect proxy for social interaction.…”

Section: Speech Presencementioning

confidence: 57%

“…Note that the first participant's data, presented in the top half of the figure, is slightly more regular and has a higher daily similarity value than the participant whose data are presented in the bottom half of the figure. This feature was first presented and analyzed in a previous study by Di Matteo et al [30]. Note that the daily similarity feature, as computed in this work, only captures similarity on a short time scale; as a result, it is not clear if long-term stability in social rhythms would be captured.…”

Section: Daily Similaritymentioning

confidence: 99%

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Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Matteo¹,

Fotinos²,

Lokuge³

et al. 2021

J Med Internet Res

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Background The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals’ behaviors to infer their mental states and therefore screen for anxiety disorders and depression. Objective The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression. Methods An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated. Results Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression. Conclusions We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.

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Section: Speech Presencesupporting

confidence: 66%

Section: Speech Presencementioning

confidence: 57%

Section: Daily Similaritymentioning

confidence: 99%

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Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Matteo¹,

Fotinos²,

Lokuge³

et al. 2021

J Med Internet Res

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“…This study used data collected in a previous study [ 4 ]. Participants were recruited from a web-based recruitment platform (Prolific [ 16 ]).…”

Section: Methodsmentioning

confidence: 99%

“…Our prior efforts explored audio (nonlinguistic) features and correlates with mental health scales [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

et al. 2021

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Background The ability to objectively measure the severity of depression and anxiety disorders in a passive manner could have a profound impact on the way in which these disorders are diagnosed, assessed, and treated. Existing studies have demonstrated links between both depression and anxiety and the linguistic properties of words that people use to communicate. Smartphones offer the ability to passively and continuously detect spoken words to monitor and analyze the linguistic properties of speech produced by the speaker and other sources of ambient speech in their environment. The linguistic properties of automatically detected and recognized speech may be used to build objective severity measures of depression and anxiety. Objective The aim of this study was to determine if the linguistic properties of words passively detected from environmental audio recorded using a participant’s smartphone can be used to find correlates of symptom severity of social anxiety disorder, generalized anxiety disorder, depression, and general impairment. Methods An Android app was designed to collect periodic audiorecordings of participants’ environments and to detect English words using automatic speech recognition. Participants were recruited into a 2-week observational study. The app was installed on the participants’ personal smartphones to record and analyze audio. The participants also completed self-report severity measures of social anxiety disorder, generalized anxiety disorder, depression, and functional impairment. Words detected from audiorecordings were categorized, and correlations were measured between words counts in each category and the 4 self-report measures to determine if any categories could serve as correlates of social anxiety disorder, generalized anxiety disorder, depression, or general impairment. Results The participants were 112 adults who resided in Canada from a nonclinical population; 86 participants yielded sufficient data for analysis. Correlations between word counts in 67 word categories and each of the 4 self-report measures revealed a strong relationship between the usage rates of death-related words and depressive symptoms (r=0.41, P<.001). There were also interesting correlations between rates of word usage in the categories of reward-related words with depression (r=–0.22, P=.04) and generalized anxiety (r=–0.29, P=.007), and vision-related words with social anxiety (r=0.31, P=.003). Conclusions In this study, words automatically recognized from environmental audio were shown to contain a number of potential associations with severity of depression and anxiety. This work suggests that sparsely sampled audio could provide relevant insight into individuals’ mental health.

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Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Yadav

Sharma

Patil

2022

Concurrency and Computation

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Depression is the primary cause of illness and injury in the country, with over 280 million people suffering from it as per the 2021 survey. Depression is one of the most common psychiatric illnesses in the world, affecting millions of people. Early detection of major depression symptoms and treatment with timely intervention can help to prevent the emergence of major depression. This has generated the need for some novel techniques to be utilized for depression detection to help doctors in diagnosing and treating depression effectively. Depression can be investigated through online posts, audio files, facial expressions, as well as through video files. In this regard, the research presents a detailed survey of the existing machine learning methods in depression detection, along with the different datasets available. The research explores automated depression detection strategies and the various ways to detect depression from text, audio, and video, and even examines the various systems and procedures for detecting depression using various criteria. More than 140 related research articles were considered for this review, with 80 of them being processed for comparative analysis based on various key performance indicators. The classifications of various methods used to detect stress, anxiety, and depression are highlighted followed by open research challenges and issues while analyzing depression techniques.

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

Cited by 32 publications

References 53 publications

Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study

Smartphone-Detected Ambient Speech and Self-Reported Measures of Anxiety and Depression: Exploratory Observational Study

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

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