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.
Social behaviors play a key role in the spread of COVID-19. Consequently, regional variation in personality traits that capture individual differences in these social behaviors may offer new insight into regional variation in COVID-19 cases. Here we combine self-reported personality data (N ≈ 3.5 million people), official COVID-19 prevalence rates, and behavioral observations (N ≈ 29 million people) to show that regional personality differences in the US and Germany predict the regional onset and growth of COVID-19 cases above and beyond a highly conservative set of socio-demographic, socio-economic, and pandemic-related control variables. Openness and Extraversion were consistently related to earlier COVID-19 onsets and steeper growth rates. The opposite pattern was found for Neuroticism. We also shed light on regional responses to the outbreak, showing that regional personality (i) is associated with objective indicators of regional social distancing, and (ii) predicts self-reported social distancing beyond the effects of individual-level personality.
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