Assessing social anxiety using gps trajectories and point-of-interest data

Huang, Yu; Xiong, Haoyi; Leach, Kevin; Zhang, Yuyan; Chow, Philip I.; Fua, Karl; Teachman, Bethany A.; Barnes, Laura E.

doi:10.1145/2971648.2971761

Cited by 89 publications

(71 citation statements)

References 15 publications

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“…An analysis of the features that were included in the EMIs found that only two studies used automated sensors (Gorini et al, 2010;Repetto et al, 2009). This is despite significant technical work advancing the use of sensors to detect anxious states, which opens the possibility of triggering and tailoring interventions using passive data collection, which reduces the burden on the user (Huang et al, 2016;Miranda, Calderon, & Favela, 2014;Rennert & Karapanos, 2013). Thus, it remains possible that future EMIs that better leverage the unique affordances of technology might be able to create more personal and powerful treatments.…”

Section: Emis For Anxietymentioning

confidence: 99%

Ecological momentary interventions for depression and anxiety

2017

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Ecological momentary interventions (EMIs) are becoming more popular and more powerful resources for the treatment and prevention of depression and anxiety due to advances in technological capacity and analytic sophistication. Previous work has demonstrated that EMIs can be effective at reducing symptoms of depression and anxiety as well as related outcomes of stress and at increasing positive psychological functioning. In this review, we highlight the differences between EMIs and other forms of treatment due to the nature of EMIs to be deeply integrated into the fabric of people's day-to-day lives. EMIs require unique considerations in their design, deployment, and evaluation. Furthermore, given that EMIs have been advanced by changes in technologies and that the use of behavioral intervention technologies for mental health has been increasing, we discuss how technologies and analytics might usher in a new era of EMIs. Future EMIs might reduce user burden and increase intervention personalization and sophistication by leveraging digital sensors and advances in natural language processing and machine learning. Thus, although current EMIs are effective, the EMIs of the future might be more engaging, responsive, and adaptable to different people and different contexts.

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Section: Emis For Anxietymentioning

confidence: 99%

Ecological momentary interventions for depression and anxiety

2017

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“…Advances in mobile phone technology now make it possible to continuously and unobtrusively monitor where someone is without needing to ask. For example, previous research has found that passively sensed location information can predict depressive symptoms with impressive accuracy [12,13], and researchers have begun to explore passively and actively sensed indicators of stress and health behaviors in college students [14], although little work has focused on how to integrate passively sensed data with affective experiences generated from in situ repeated assessments.…”

Section: Introductionmentioning

confidence: 99%

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

et al. 2017

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BackgroundResearch in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms.ObjectiveWe hypothesized that more time spent at home would be associated with more negative and less positive affect.MethodsWe recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants’ personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation.ResultsWe tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day.ConclusionsResults demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.

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“…Time spent at significant location (home, clinic, office, school, etc.) [32], [39], [41], [42], [43], [47], [48], [49], [53], [55], [56], [58], [59], [60], [61], [62], [63], [64], [65], [66] LOC -3 Number of places visited [32], [37], [39], [41], [42], [48], [53], [55], [56], [57], [60], [61], [67] LOC -4 Transition time [32], [37], [41], [42], [43], [50], [61], [64], [66] LOC -5 Routine index [32], [37], [41], [42], [60] LOC-6…”

Section: Loc-2mentioning

confidence: 99%

“…Huang et al [64] LOC Examine the correlation between sensed feature and social anxiety level measured by SIAS score rated from 0 to 4.…”

Section: Betamentioning

confidence: 99%

Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

Fukazawa

Yamamoto

Hamatani

et al. 2020

Journal of Information Processing

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Monitoring mental health has received considerable attention as a countermeasure against the increasing occurrence of mental illness worldwide. However, current monitoring services incur costs because users are required to attach wearable devices or answer questions. To reduce such costs, many studies have used smartphone-based passive sensing technology to capture a user's mental state. This paper reviews those studies from the perspective of machine learning and statistical analysis. Forty-four studies published since 2011 have been reviewed and summarized from three perspectives: designed features, machine learning algorithm, and evaluation method. The features considered include location and mobility, activity, speech, sleep, phone usage, and context features. Tasks are classified as correlation analysis, regression tasks, and classification tasks. The machine learning algorithm used for each task is summarized. Evaluation metrics and cross validation methods are also summarized. For those who are not necessarily machine learning experts, we aim to provide information on typical machine learning framework for smartphonebased mental state estimation. For experts in the field, we hope this review will be a helpful tool to check for potential omissions.

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Assessing social anxiety using gps trajectories and point-of-interest data

Cited by 89 publications

References 15 publications

Ecological momentary interventions for depression and anxiety

Ecological momentary interventions for depression and anxiety

Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students

Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

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