JMIR Publications

ABSTRACT

Background:

Specialized studies have shown that smartphone-based social interaction data are predictors of depressive and anxiety symptoms. Moreover, at times during the COVID-19 pandemic, social interaction took place primarily in virtual space. To appropriately test these objective data for their added value for epidemiological research during the pandemic, it is necessary to include established predictors.

Objective:

Therefore, via a comprehensive model, we investigated the extent to which smartphone-based social interaction data contribute to the prediction of depressive and anxiety symptoms, also taking into account established and pandemic-specific predictors.

Methods:

We developed the CORONA HEALTH APP and obtained participation by 490 Android users who agreed to allow us to collect smartphone-based social interaction data between July 2020 and February 2021. In a cross-sectional design, we automatically collected data concerning average app usage in terms of the categories (video-)telephony, messenger use, social media use and SMS use as well as pandemic-specific predictors and sociodemographic covariates. We statistically predicted depressive and anxiety symptoms using elastic net regressions. To exclude overfitting, we used tenfold cross-validation.

Results:

Of the smartphone-based social interaction data included, only messenger use proved to be a significant negative predictor of depressive and anxiety symptoms. Video calls were negative predictors only for depressive symptoms and text messages were negative predictors only for anxiety symptoms.

Conclusions:

The results show the relevance of smartphone-based social interaction data in predicting depressive and anxiety symptoms. However, even taken together in the context of a comprehensive model with well-established predictors, they only add a small amount of value.