Fig. 5
Associations of TikTok, WhatsApp, and Instagram usage with hedonic and eudaimonic well-being outcomes on the same day, the following day (1-day lag), and two days later (2-day lag), results from mixed linear models. Figure shows the unstandardized coefficients and confidence interval between well-being outcomes (Y) and App-specific features (X) of the same day, the following day (1-day lag), and two days later (2-day lag) from mixed linear models, adjusting for well-being outcome of the previous day, age, gender, and school in each model, with time (each day) as level one, and adolescents as level two. Well-being outcomes are collected from self-reported questionnaires, and App-specific features are derived using text extraction pipeline from the cellphone screenshots of the setting pages uploaded by users, both within the same EMA study. Figure shows the results for TikTok, WhatsApp, and Instagram. The results for Game Apps, YouTube, and Snapchat are presented in S-Fig. 5. For each pair of well-being outcomes – App-use feature predictors, we fit a random intercept model and random slope model. We apply the likelihood ratio test to select the best model following the parsimonious rule. Bonferroni correction is applied to the confidence interval to account for multiple testing. For each well-being outcome and App-use feature, we adjust the level of significance by 0.05/# of tests (# = 6) and the confidence interval based on the adjusted level of significance. The confidence interval is adjusted by \(\:Estimate\pm\:qnorm\left(1-\frac{0.05}{2*\left(\#-1\right)}\right)*Standard\_error\), where # = 6. The interactive Plotly version of this plot is available in OSF project in path “/code_and_data_for_replication /within_level”.
