A correlational study from Nature by Amy Orben and Andrew K. Przybylski that finds negligible effects of screen time on adolescents' psychological well-being - barely more harmful than eating potatoes and vegetables, and certainly not a cause for significant concern. Unfortunately, the article is hidden behind a paywall, but you can find a pre-print at https://www.amyorben.com/pdf/2019_orbenprzybylski_nhb.pdf

The study looks at a vast dataset - well over 350,000 subjects - and seems very well conducted (nb. I am not familiar with the Specification Curve Analysis technique used and know little of its appropriateness, so I could be mistaken) though, as with all academic papers, it's important to take a long critical look at what it does and doesn't show. The authors of the study are careful to note this themselves, in their discussion. What aspects of well-being are missed and are the measures reliable? What kinds of technology use are examined and how reliably does it distinguish them? How does the picture change at the extremes, especially with excessive use? Does this predict how an individual might be affected? It is important to remember that averages and effect sizes do not predict the effects on specific individuals, and that there is some evidence that outliers can and do experience both bad and good effects that are anything but average. It is also important to note that this does relatively little to distinguish different kinds of digital technology use, although it does look from some of the detailed figures as though game playing (considered in only one of the datasets, and not the most reliable) may have a neutral or even positive effect, albeit a very small one.

Here's the abstract:

The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby
leading to potential false positives and conflicting results. Here we address these methodological challenges by applying speci-fication curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and ado-lescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change.