Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples

Müller S, Chen X-W, Peters H, Chaintreau A, Matz S (2021)
Scientific Reports 11(1): 14007.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Müller, SandrineUniBi ; Chen, Xiao-WuUniBi; Peters, Heinrich; Chaintreau, Augustin; Matz, Sandra
Abstract / Bemerkung
Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research on mobile sensing technologies and machine learning have suggested that a person’s depression can be passively measured by observing patterns in people’s mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.
Erscheinungsjahr
2021
Zeitschriftentitel
Scientific Reports
Band
11
Ausgabe
1
Art.-Nr.
14007
eISSN
2045-2322
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Universität Bielefeld im Rahmen des DEAL-Vertrags gefördert.
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https://pub.uni-bielefeld.de/record/2956139

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Müller S, Chen X-W, Peters H, Chaintreau A, Matz S. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports. 2021;11(1): 14007.
Müller, S., Chen, X. - W., Peters, H., Chaintreau, A., & Matz, S. (2021). Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports, 11(1), 14007. https://doi.org/10.1038/s41598-021-93087-x
Müller, S., Chen, X. - W., Peters, H., Chaintreau, A., and Matz, S. (2021). Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports 11:14007.
Müller, S., et al., 2021. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports, 11(1): 14007.
S. Müller, et al., “Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples”, Scientific Reports, vol. 11, 2021, : 14007.
Müller, S., Chen, X.-W., Peters, H., Chaintreau, A., Matz, S.: Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports. 11, : 14007 (2021).
Müller, Sandrine, Chen, Xiao-Wu, Peters, Heinrich, Chaintreau, Augustin, and Matz, Sandra. “Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples”. Scientific Reports 11.1 (2021): 14007.
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