University of Oulu

Opoku Asare, K., Visuri, A., Vega, J., Ferreira, D. (2022). Me in the Wild: An Exploratory Study Using Smartphones to Detect the Onset of Depression. In: Gao, X., Jamalipour, A., Guo, L. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-031-06368-8_9

Me in the wild : an exploratory study using smartphones to detect the onset of depression

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Author: Opoku Asare, Kennedy1; Visuri, Aku1; Vega, Julio2;
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
2Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022092159802
Language: English
Published: Springer Nature, 2022
Publish Date: 2022-09-21
Description:

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.

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Series: Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISSN: 1867-8211
ISSN-E: 1867-822X
ISSN-L: 1867-8211
ISBN: 978-3-031-06368-8
ISBN Print: 978-3-031-06367-1
Issue: 440
Pages: 121 - 145
DOI: 10.1007/978-3-031-06368-8_9
OADOI: https://oadoi.org/10.1007/978-3-031-06368-8_9
Host publication: Wireless mobile communication and healthcare : 10th EAI International Conference, MobiHealth 2021. Virtual event, November 13–14, 2021, proceeedings
Host publication editor: Gao, Xinbo
Jamalipour, Abbas
Guo, Lei
Conference: Wireless Mobile Communication and Healthcare : 10th EAI International Conference, MobiHealth 2021
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: The Me in the Wild study is supported by the Academy of Finland SENSATE (Grant Nos. 316253, 320089), 6Genesis Flagship (Grant No. 318927), and the Infotech Institute University of Oulu Emerging Project. We thank all the participants of the Me in the Wild study.
Academy of Finland Grant Number: 316253
320089
318927
Detailed Information: 316253 (Academy of Finland Funding decision)
320089 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
Copyright information: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2022. This is a post-peer-review, pre-copyedit version of an article published in Wireless mobile communication and healthcare : 10th EAI International Conference, MobiHealth 2021. Virtual event, November 13–14, 2021, proceeedings. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-06368-8_9.