Dynamics of adolescents' smartphone use and well-being are positive but ephemeral

Abstract

Well-being and smartphone use are thought to influence each other. However, previous studies mainly focused on one direction (looking at the effects of smartphone use on well-being) and considered between-person effects, with self-reported measures of smartphone use. By using 2548 assessments of well-being and trace data of smartphone use collected for 45 consecutive days in 82 adolescent participants (M_age = 13.47, SD_age = 1.62, 54% females), the present study disentangled the reciprocal and individual dynamics of well-being and smartphone use. Hierarchical Bayesian Continuous Time Dynamic Models were used to estimate how a change in frequency and duration of smartphone use predicted a later change in well-being, and vice versa. Results revealed that (i) when participants used the smartphone frequently and for a longer period, they also reported higher levels of well-being; (ii) well-being positively predicted subsequent duration of smartphone use; (iii) usage patterns and system dynamics showed heterogeneity, with many subjects showing reciprocal effects close to zero; finally, (iv) changes in well-being tend to persist longer than changes in the frequency and duration of smartphone use.

Figure 1

Graphical representation of a hierarchical Bayesian continuous time structural equation model with two manifest indicators (Y1 and Y2) measuring within-effects of two latent processes (ETA1 and ETA2). Legend: Y1 = Well-being; Y2 = Frequency/Duration ofsmartphone use; Manifest Mean = Continuous manifest intercept (Between-person component); Manifestvar = variance and covariance of manifest indicators (i.e., measurement error); eta1 = latent process of well-being; eta2 = latent process of frequency/duration of use; a1 = drift_eta1_eta1 (auto-effect of well-being); a2 = drift_eta2_eta2 (auto-effect of frequency/duration of use) c1 = drift_eta2_eta1 (cross-effect of well-being on frequency/duration of use); c2 = drift_eta1_eta2 (cross-effect of frequency/duration of use on well-being); Diff = covariance of the latent process. In the model, process intercepts are set to 1.00. Regressions and variances in the latent portion are all (after the first time point) conditional on the time interval.

Figure 2

Representation of discrete time parameters of auto- and cross-effects of the Hierarchical Bayesian Continuous Time Dynamic Model of well-being and frequency of smartphone use. Legend: eta1.eta1 = auto-regressions of well-being; eta2.eta2 = auto-regressions of frequency of smartphone use; eta2.eta1 = cross-regressions of well-being on the frequency of smartphone use; eta1.eta2 = cross-regressions of the frequency of smartphone use on well-being.

Figure 3

Observed data points of well-being (Y1) and frequency of use (Y2) for four specific subjects over 44 days. Auto-effects of Y1 are more stable over time, whereas Y2 showed more variation.

Figure 4

Representation of discrete time parameters of auto- and cross-effects of the Hierarchical Bayesian Continuous Time Dynamic Model of well-being and duration of smartphone use. Legend: eta1.eta1 = auto-regressions of well-being; eta2.eta2 = auto-regressions of duration ofsmartphone use; eta2.eta1 = cross-regressions of well-being on the duration of smartphone use; eta1.eta2 = cross-regressions of the duration of smartphone use on well-being.

Figure 5

Observed data points of well-being (Y1) and frequency of use (Y2) for four specific subjects over 44 days. Auto-effects of Y1 are more stable over time, whereas Y2 showed more variation.

Figure 6

Distribution of individual continuous auto- and cross-effect parameters of the Hierarchical Bayesian Continuous Time Dynamic Model of well-being and frequency of smartphone use.

Figure 7

Distribution of individual continuous auto-and cross-effect parameters of the Hierarchical Bayesian Continuous Time Dynamic Model of well-being and duration of smartphone use.