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Wireless Mobile Communication and Healthcare. 10th EAI International Conference, MobiHealth 2021, Virtual Event, November 13–14, 2021, Proceedings

Research Article

Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression

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  • @INPROCEEDINGS{10.1007/978-3-031-06368-8_9,
        author={Kennedy Opoku Asare and Aku Visuri and Julio Vega and Denzil Ferreira},
        title={Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression},
        proceedings={Wireless Mobile Communication and Healthcare. 10th EAI International Conference, MobiHealth 2021, Virtual Event, November 13--14, 2021, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2022},
        month={6},
        keywords={Mobile sensing Mental health Depression Anomaly detection},
        doi={10.1007/978-3-031-06368-8_9}
    }
    
  • Kennedy Opoku Asare
    Aku Visuri
    Julio Vega
    Denzil Ferreira
    Year: 2022
    Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-031-06368-8_9
Kennedy Opoku Asare1,*, Aku Visuri1, Julio Vega2, Denzil Ferreira1
  • 1: Center for Ubiquitous Computing
  • 2: Department of Medicine, University of Pittsburgh
*Contact email: kennedy.opokuasare@oulu.fi

Abstract

Research on mobile sensing for mental health monitoring has traditionally explored the correlation between smartphone and wearable data with self-reported mental health symptom severity assessments. The effectiveness of predictive techniques to monitor depression is limited, given the idiosyncratic nature of depression symptoms and the limited availability of objectively labelled depression sensor-driven behaviour. In this paper, we investigate the possibility of using unsupervised anomaly detection methods to monitor the fluctuations of mental health and its severity. Informed by literature, we created a mobile application that collects acknowledged data streams that can be indicative of depression. We recruited 11 participants for a 1-month field study. More specifically, we monitored participants’ mobility, overall smartphone interactions, and surrounding ambient noise. The participants provided three self-reports: Big five personality traits, sleep and depression. Our results suggest that digital markers, combined with anomaly detection methods are useful to flag changes in human behaviour over time; thus, enabling mobile just-in-time interventions for in-the-wild assistance.

Keywords
Mobile sensing Mental health Depression Anomaly detection
Published
2022-06-07
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-06368-8_9
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