Participants & procedure

Participants included mothers and fathers from 183 couples with a young child who took part in the Daily Family Life Project,20 a longitudinal study of parenting and family relationships conducted from 2014 to 2016. Participants were recruited through letters and phone calls to families who were part of a family research database in a Northeastern U.S. state, as well as via flyers in the local community. Announcements were also posted to various online resources and listservs in order to expand our reach to individuals in other areas of the U.S. To be eligible to participate, individuals had to be at least 18 years old, a parent of a child age 5 years or younger, speak English, and currently live with their child and spouse/partner. Their spouse/partner also had to be willing to participate. Participants were emailed a survey link through which they completed informed consent and a baseline online survey via Qualtrics. Participants completed follow-up online assessments at ~1, 3, and 6 months.

In the present study, we first excluded 11 families whose child was younger than 1 year at baseline, since the behavior rating items were not standardized for infants. We utilized data from all remaining parents who had data from at least one time point, which resulted in an analytic sample of 337 parents (171 mothers and 166 fathers; 92% of the original sample of 366 parents); 70% of these 337 parents had data across all time points. In the analytic sample of 337 parents, families resided in the following U.S. regions: 54% Northeast, 16% Midwest, 15% South, and 15% West. On average, mothers were 31.7 years old (SD = 4.3; range 22–42), and fathers were 33.3 (SD = 4.9; range 22–52). Most families (61%) had more than one child (M = 1.90, SD = 0.91), and the index child was 3.0 years old on average (SD = 1.2; Range = 1.0–5.5 years; 55% female). Most parents were married (94%), and had at least a Bachelor’s degree (72%). The race/ethnicity breakdown was 91% Caucasian, 3% Latino, 2% Black/African American, 2% Asian American, and 2% Other. Median yearly household income was ~69,500 (M = $74,870, SD = $39,470), with 21% of families reporting some form of state or federal assistance (e.g., medical assistance, food stamps). Utilizing chi-squares and t-tests, we found that parents in our analytic sample were in a longer relationship (t (360) = 1.945, p = 0.052) and had more children (t (360) = 3.79, p < 0.001) than excluded participants; the samples were otherwise similar.

Measures

Technoference in parent–child activities

At each data collection wave, technoference (i.e., technology interference) in mother–child activities and father–child activities was assessed via mother and father self-report. Items were adapted from the Technology Device Interference Scale (TDIS),4 a measure of technoference in couple relationships that is associated with couple relationship health.4 Instead of assessing duration of parent media use, this scale measures the extent to which different forms of technology intrude in or interrupt interpersonal interactions and activities during daily routines; the scale used in the current study was reworded to refer specifically to interactions with one’s child, and has been used in in prior research.17 Items asked, “On a typical day, about how many times do the following devices interrupt a conversation or activity you are engaged in with your child?” The 6 items on the scale included:1 cellphone/smartphone,2 television,3 computer,4 tablet,5 iPod, and6 video game console. Parents responded to each item on a 7-point scale ranging from 0 (none) to 6 (more than 20 times). As this is a count measure and we expected there to be variability (as opposed to consistency) within individuals’ responses, it was not appropriate to calculate Cronbach’s alpha17 (however, the alpha ranged from 0.69 to 0.82 across time points). Parents were queried about different devices separately because of the assumption that different modes of technology use may interfere with parent–child activities to varying degrees, yet the level of technoference from various devices was often correlated (inter-item correlations 0.24 to 0.71, ps < 0.001). Items were therefore averaged, with higher scores representing more frequent technoference in parent–child activities.

Child externalizing and internalizing behavior problems

At each data collection wave, parents completed the internalizing (36 items) and externalizing scales (24 items) of the Child Behavioral Checklist (CBCL).21 These items ask parents to rate their child now or within the past 2 months on a 3-point scale ranging from 0 (not true) to 2 (very true or often true). Internalizing consists of items such as “whining,” “sulks a lot,” and “feelings are easily hurt.” Externalizing consists of items such as “can’t sit still, restless, or hyperactive,” “easily frustrated,” and “temper tantrums or hot temper.” Items were summed to produce separate mother and father ratings of internalizing and externalizing child behavior (Cronbach’s alphas for internalizing ranged from 0.89 to 0.94 across time points; alphas for externalizing ranged from 0.92 to 0.93). We then converted raw sum scores to normed externalizing and internalizing T-scores for analysis. Additionally, we conducted post-hoc analyses with the internalizing subscales (Emotional Reactivity, Anxiety/Depression, Somatic Complaints, and Withdrawal) and externalizing subscales (Attention Problems and Aggression).

Parenting Stress

At each data collection wave, parents completed 27 items from the Parenting Stress Index (PSI).22 We used 27 items from the 36-item PSI Short Form due to lower factor loadings on 9 of the items, as others have done.23,22 Items were averaged to produce an overall stress score (Cronbach’s alphas ranged from 0.91 to 0.94 across time points).

Potential confounding variables

At baseline, parents reported their age, educational attainment, marital status, race/ethnicity, family composition, household income, and child’s age, gender, and health. They also completed measures of coparenting quality, depressive symptoms, and reported on their child’s daily duration of screen media use.

As this sample consists of two-parent families, we controlled for coparenting quality—or how well parents work together in childrearing23—which has been associated with child behavior problems24 and technoference.10 Both parents completed the Coparenting Relationship Scale,23 a 35-item scale (e.g., “When I’m at my wits end as a parent, partner gives me extra support I need” and “My partner undermines my parenting”) rated on a 7-point scale (0 = not true of us to 6 = very true of us). After reverse coding negatively worded items, items were averaged to produce an overall score with higher scores indicating higher coparenting quality (Cronbach’s alpha = 0.94).

Depressive symptoms were measured utilizing the validated Center for Epidemiologic Studies Depression Scale (CES-D).25 Participants rated how often they experienced 20 symptoms (e.g., “I felt depressed” and “I felt sad”) in the past week on a 4-point scale ranging from 0 (rarely or none of the time, less than 1 day) to 3 (most or all of the time, 5 to 7 days). Items were averaged to produce an overall depression score (Cronbach’s alpha = 0.89). We controlled for depressive symptoms as depressed mood has been associated with quality of parent–child interactions26 and greater relationship technoference.4

At baseline, parents rated how much time, on a typical day, their child spent using screen media devices across 8 items (e.g., computer, TV, smartphone, tablet, video games) on an 11-point scale ranging from 0 (None) to 10 (7 or more hours). Items were summed to produce an overall child screen use score (Cronbach’s alpha = 0.78). We controlled for child screen media use because it is strongly associated with both parent media use9 and child social-emotional outcomes.27

Data Analysis

We utilized structural equation modeling (SEM) to test two separate models, one for child externalizing and one for child internalizing, of: (H1) more frequent technoference in parent–child interactions predicting higher ratings of child behavior problems; (H2) higher ratings of child behavior problems predicting higher parenting stress; and (H3) higher parenting stress predicting more frequent technoference in parent–child activities. The models were tested utilizing AMOS.28 Standardized estimates are shown for the models in Figs. 1 and 2. Potential confounders including parent characteristics, child age, child screen use, parent depressive symptoms, and coparenting quality were entered into the models, but were removed from the final models as results did not change significantly. Structural equation modeling was utilized as this allowed us to examine the complex cross-lagged paths between our various predictors and outcomes across multiple waves of data collection simultaneously, while also controlling for prior levels of the variables and better accounting for potential error in the modeling.28 SEM also allows for assessments of model goodness of fit29 and handling of missing data are using full information maximum likelihood estimation.

Fig. 1 Structural equation model of longitudinal associations between parent-reported technology interference in the parent–child relationship and child externalizing behavior, with parenting stress as the mediator between externalizing behavior and later parent–child technology interference. Standardized path estimates are displayed. Mothers’ and fathers’ estimates are displayed as mother/father when found to be significantly different between mothers and fathers; all other model paths were constrained to be equal between mothers and fathers. Parent characteristics, child age, child screen use, parent depressive symptoms, and coparenting quality were controlled but then removed as results did not change. ***p < 0.001, **p < 0.01, *p < 0.05 Full size image

Fig. 2 Structural equation model of longitudinal associations between parent-reported technology interference in the parent–child relationship and child internalizing behavior, with parenting stress as the mediator between internalizing behavior and later parent–child technology interference. Standardized path estimates are displayed. Mothers’ and fathers’ estimates are displayed as mother/father when found to be significantly different between mothers and fathers; all other model paths were constrained to be equal between mothers and fathers. Parent characteristics, child age, child screen use, parent depressive symptoms, and coparenting quality were controlled but then removed as results did not change. ***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.10 Full size image

Based on prior evidence showing different cross-sectional associations between maternal technoference and paternal technoference with child behavior,17 we also examined whether model paths and estimates were significantly different for mothers and fathers. Through a multi-group SEM analysis, we first tested whether the statistical fit of the model significantly worsened when we constrained the model paths to be equal across mothers and fathers. If it is found that the fit worsens significantly, this suggests that at least some of the paths in the model must be different for mothers and fathers. We then compared model paths between mothers and fathers to find where they differed from one another and no longer constrained those specific paths to be equal across mothers and fathers.

Finally, in post-hoc analyses, we examined subscales of the CBCL Internalizing Scale (Emotional Reactivity, Anxiety/Depression, Somatic Complaints, and Withdrawal) and Externalizing Scale (Attention Problems and Aggression) to determine whether specific aspects of child behavior were more strongly associated with parenting stress and parent technoference in our models. As above, these models were tested for differential associations between mothers and fathers.