Identifying Mechanisms That Explain the Relationship between Digital Technology Use and Psychosocial Well-Being

Background: Over the last decade, there has been a substantial amount of research examining the effects of digital technology use (DTU) on well-being. Despite a plethora of research in this area, findings have been inconsistent, making it difficult to conclude whether, how, and for whom DTU may impact well-being. Thus, the current critical review examined and integrated evidence from meta-analytical, longitudinal, and experimental studies to answer the following research questions: (1) What is the temporal relationship between DTU and well-being?; and (2) What mechanisms may explain this relationship?

Methods: Experimental, longitudinal, and meta-analytic studies examining the association between DTU (i.e. social media, smartphone use, or screen time) and at least one element of well-being (i.e. psychopathology, social well-being, psychological well-being) were included. Articles were categorized by: study design, aspect of DTU measured, and aspect of well-being measured. Evidence was then critically evaluated and synthesized to answer the research questions identified above.

Results: A total of 63 articles were included in this review, including 10 meta-analyses, 37 longitudinal studies, and 16 experimental studies. Overall, evidence from meta-analyses suggest that DTU and well-being are negatively correlated, but with weak effect sizes (rs < 0.15). However, evidence from longitudinal and experimental studies was mixed and contradictory, precluding any general conclusions as to the temporal or causal relationship between DTU and well-being. For instance, of 31 effects from 12 longitudinal studies of DTU and depression, 3 found that DTU predicted depression, 4 found that depression predicted DTU, 1 found a bi-directional relationship, and the rest found non-significant relationships. Evidence from studies that examined mediators/moderators of the DTU--well-being effect suggest that several mechanisms may (partially) explain this relationship. These mechanisms include behavioral mechanisms--which includes sleep disruption and sedentary behavior--and psychosocial mechanisms, which includes passive/active use, exposure, cybervictimization, social connectedness, and social comparison. Notably, evidence suggests that an individual’s pre-existing level of well-being, as well as important sociodemographic variables (e.g. gender), likely influence the degree to which they engage in these different mechanisms.

Conclusions and Implications: Overall, evidence suggests that the relationship between DTU and well-being is a complex process that implicates multiple mechanisms. General DTU does not appear to be inherently harmful or beneficial, but rather depends on how it is being used, what it is being used for, with whom it is being used, and what one is exposed to online. Given the ubiquity of DTU, especially among youth, these findings have important implications for social work researchers and practitioners. Because patterns of DTU and their effects on well-being are likely population-specific, those working with marginalized populations should go beyond simply assessing frequency of DTU and aim to understand what aspects of DTU may be linked to harms or benefits. This more nuanced approach will further elucidate important mechanisms at play, which can then inform potential psychosocial interventions to maximize benefit and reduce harm.