An Exploration of Electronic Media Use Profiles for Preschoolers of Low-Income Families

Abstract

Objective:

To characterize the electronic media (e-media) use of preschoolers from low-income families comprehensively, in terms of platform interaction potential and content.

Methods:

Parents of 380 preschoolers (mean age, 52.5 + 3.7 months) from diverse, low-income backgrounds reported on their child’s age of exposure to various e-media types, frequency of use, amount of background television, and listed all child shows/cartoons and adult shows/general audience shows the child watches, as well as all electronic games/apps the child plays. We calculated descriptive statistics and conducted latent profile analyses to characterize e-media use.

Results:

Most children in the sample began watching TV before age 1 year and nearly half watch child shows/cartoons several times a day or more. Most children began playing games/apps before age 3 years and more than one-quarter play games several times a day or more. More than 20% of children are exposed to >3 hours of background TV on a typical weekday and 30% are exposed to this amount on a typical weekend day. A Modest E-Media Use profile characterized most children in the sample (70%). Fewer children were characterized by a High Educational Games profile (14%) or a High Adult TV/Elevated Entertainment Games Use and Background TV profile (16%).

Conclusions:

Collectively, the three profiles reflect heterogeneous use patterns with regard to platform interaction potential and educational quality during the course of a typical week. Additional research is warranted to assess linkages between e-media use profiles and indicators of school readiness in cognitive, academic, and social and behavioral domains in diverse, low-income samples.

By some estimates, most children of low-income backgrounds in the United States access electronic media (e-media), including mobile devices, from their first year of life. ¹ Prior research reveals that e-media may serve as a potential benefit or detriment to key school readiness competencies. ^2–8 To illustrate, some retrospective longitudinal studies indicate children who view lower levels of TV in early childhood exhibit stronger school readiness outcomes in the areas of literacy and cognition² and that children with lower levels of e-media use overall (including TV and other devices) exhibit stronger selfregulation skills than their peers with greater such e-media use.³ Beyond sheer amount of e-media use, child-oriented, educational content has been linked positively to aspects of school readiness^4,5, whereas adult-oriented content ^3,6,7 has been linked to poorer outcomes in retrospective longitudinal investigations. Additionally, findings from a controlled experimental design revealed preschoolers were significantly impaired in executive functioning after viewing 9 minutes of fast-paced entertainment content relative to viewing educational content.⁸

Children of low-income backgrounds represent a critical population for such research because they experience significant hindrances with regard to school readiness⁹ and they are more likely than their counterparts from higher-income backgrounds to develop a neurodevelopmental disorder. ¹⁰ Moreover, evidence suggests associations between one type of e-media - television viewing - and school readiness vary with family socioeconomic status and are most pronounced for children living in homes at or near the federal poverty line. ¹¹ However, research characterizing e-media use comprehensively for this population is scarce¹², beyond research indicating that despite income level, low-income households have near universal access to televisions and mobile devices such as tablets and smartphones. ¹ As such, there have been calls for research to more thoroughly examine e-media use for children from lowincome families with particular attention toward differentiating between e-media attributes such as the interaction potential platforms afford (e.g., passive versus active engagement) and content (e.g., educational versus entertainment). ^{2, 3, 7, 11, 12}

Theoretically, e-media use may displace more educational or cognitively stimulating activities, such as reading books, playing with toys, and engaging in conversations with family members. ¹³ However, low-income households may have fewer such resources and opportunities for learning and enrichment than higher-income households, which could translate to fewer educational activities being displaced in lower-income households. ¹⁴ Furthermore, children in low-income households may be exposed to more risk than their counterparts in higher-income families, such as playing in neighborhoods with safety concerns or spending time without adult supervision, ^15–17 in which case emedia may represent a desirable, and potentially beneficial, alternative. It is also possible that some forms of e-media, including those with interaction potential, may support school readiness. ¹³ However, the proliferation of interactive games and apps on mobile devices over the last decade poses challenges to conducting empirical investigations that keep apace in this nascent yet rapidly growing field. In one recent exception, a synthesis of 35 studies using randomized and non-randomized controlled designs revealed positive effects of interactive app use on early school readiness competencies, particularly in the area of mathematics for typically-developing children¹⁸. Considering the potential for TV and interactive games/apps to support aspects of school readiness, it is critical that researchers, clinicians, and early childhood educators, better understand how children from low-income families engage with e-media and whether the e-media they use features educational content that may confer some benefit to their school readiness. The present study thus characterizes e-media use of preschoolers from lowincome households, comprehensively, in terms of interaction potential (e.g., passive versus active engagement potential) and content (educational; entertainment).

METHODS

We analyzed parent-reported survey data from the first wave of a larger multiple-cohort longitudinal observational study of preschoolers from low-income families. The present study used a retrospective design to characterize incoming preschoolers’ e-media use, as a complement to the overarching study’s research aims (i.e., to examine linkages between behavioral engagement, executive functioning, and school readiness across preschool and kindergarten). Research activities were approved by the University of Virginia’s Institutional Review Board for Social and Behavioral Sciences (approval #2015–0443-000).

Participants

A total of 380 children (191 females;189 males) attending publicly-funded preschool programs, eligible to matriculate into kindergarten in September, 2017 (i.e., age 4 years by September 30, 2016; M=52.5 months; SD=3.7), participated in the study. In terms of race/ethnicity the largest percentage of children were identified as Black/African-American (52.4%), followed by White (23.7%), Hispanic/Latino of any race (9.5%), two or more races, non-Hispanic (8.9%), other race (3.4), or missing (2.1%). The average annual total family income was $36,177 (SD= $26,576), and the average income-to-needs ratio was 1.46 (SD=1.08), where 1.0 indicates the federal poverty line. Eligibility requirements for preschool attendance included one or more of the following: family income at or below 200% of the federal poverty guidelines (350% for students with special education needs); family homelessness; parents/guardians lack high school diploma. Parents/guardians completed an informed consent agreement during the first month preschool.

Measures

E-Media Use.

Parents reported on their child’s e-media use/exposure as part of a survey administered during the first month of preschool (Sep. – Oct., 2016). Survey items were adapted from the 2013 Zero to Eight Common Sense Media Research Study. ¹⁹ Items addressed the following topics: age of exposure to various e-media types; frequency of e-media use; and amount of time television is on in the background in the home. Parents also listed the names of all specific children’s shows or cartoons (Child TV) and all specific adult shows or general audience shows (Adult TV) their child watches, as well as all specific games/apps (Games) their child plays on a mobile device (like a smartphone, iPad, or tablet), game console, or other handheld device, for later coding by the study team.

Age of Exposure to Various E-media Types.

Parents reported the age their child began watching TV, using a mobile device, using a computer, and using a console player, from among the following responses: Before age 1 year; between age 1–2 years; between age 2–3 years; between age 3–4 years; does not do this.

Frequency of E-media Use.

Parents reported the frequency with which their child viewed Child TV and Adult TV and played Games using the following possible responses: 1 = never; 2 = less than once a week; 3 = once a week; 4 = several times a week; 5 = once a day; 6 = several times a day.

Amount of Background TV Exposure.

Parents reported their child’s background television exposure for an average weekday and weekend day. Possible responses included: 1 = no time at all; 2 = about 30 min.; 3 = about 1 hour; 4 = about 2 hours; 5 = about 3 hours; and 6 = more than 3 hours. Background TV exposure items were weighted by a multiplier of .7143 (i.e., 5 of 7 days) and .2857 (i.e., 2 of 7 days) for weekdays and weekend days, respectively, and then summed to derive each child’s average daily amount of background TV for the latent profile analyses described in the data analysis plan.

Content Coding.

We adapted an existing coding scheme ^{20, 21} to classify the content of each parent-reported Child TV, Adult TV, and Games item into one of three categories: (1) Educational (EDUC); (2) Entertainment (ENT); or (3) Uncodable. E-media were classified as EDUC if the program or game demonstrates a clear intent to educate preschool-age children, with an explicit cognitive or prosocial focus: A cognitive focus involves teaching a lesson with content similar to that found in schools (e.g., math or literacy skills), and a prosocial focus involves teaching a lesson about appropriate behavior or interpersonal interactions (e.g., sharing, friendships). E-media were classified as ENT if there was no clear intent to educate (i.e., no explicit cognitive or prosocial focus). Finally, e-media for which the content could not be determined, due to the response being vague or due to not being able to locate and evaluate the content, were classified as Uncodable (e.g., YouTube; cartoons; Disney Channel; sports; learning games; Xbox). Due to the intended audience, all Adult TV items were coded either as ENT or Uncodable; no adult TV items were coded as EDUC. In some cases, parents listed items under an incorrect category (e.g., Adult TV items under Child TV). For consistency, coders consulted CommonSenseMedia.org for the intended age range and reclassified such items appropriately before assigning a code.

Before assigning a code, coders consulted a list of exemplar TV programs published in prior studies ^{20, 21} to determine whether the item had been classified as EDUC or ENT previously. If so, coders assigned the established code to the item. For items not previously classified, coders obtained information from CommonSenseMedia.org, and in conjunction with the coding scheme definitions, classified the item into one of the three aforementioned categories. Coders conferenced with the first author weekly to justify the code for each item not previously classified, and reached a consensus on any item for which the appropriate code was uncertain. The exemplar list was expanded iteratively during the weekly conferences. After all items were coded, 100 percent of the items were verified against the full exemplar list by a second coder and discrepancies were documented. The first author reviewed and reconciled all discrepancies. Percent agreement between the first and second coder was .988 (2129 of 2155 items) for Child TV, .994 (505 of 508 items) for Adult TV, and .932 (954 of 1024 items) for Games.

Frequency of Educational and Entertainment E-media Use.

To better understand how frequently each child accesses educational and entertainment content across Child TV, Adult TV, and Games, we created a score for each e-media type by multiplying the value for frequency of e-media use (never = 1; less than once a week =2; once a week=3; several times a week=4; once a day=5; several times a day=6) by the proportion of e-media content coded as EDUC, ENT, or Uncodable. Possible scores ranged from 1 to 6. For example, if a parent reported a child watched Child TV several times a day (6 points on the Likert scale), with 80% of the Child TV items coded as EDUC and 20% of the Child TV items coded as ENT, the child would receive a score of 4.8 for EDUC Child TV (i.e., 6 x .80), 1.2 for ENT Child TV (i.e., 6 x .20), and 0 for Uncodable Child TV.

Covariates.

Covariates for all analyses included child age, sex, income-to-needs ratio, and scores on the 13-item parent-report Family Involvement Questionnaire, which measures parent’s engagement in educational activities at home and school through items such as “I bring home learning materials for my child (videos, etc.).” ²²

Data Analysis Plan

Data analysis was conducted in two phases. First, we conducted descriptive statistics to characterize children’s e-media use. Next, we conducted latent profile analysis to determine the extent to which patterns reflecting combinations of e-media use characterize individual children. This analytic approach allowed us to derive categorical latent variables, which represent profiles of children with shared e-media use patterns. Five variables capturing frequency and content of e-media use (i.e., EDUC Child TV; ENT Child TV; Adult TV; EDUC Games; ENT Games) and background TV exposure were included. All variables were standardized to be on the same scale to facilitate interpretation.

Models with profiles added iteratively were estimated in MPlus version 7, ²³ using full information maximum likelihood (FIML) estimation to handle missing data. Models with two, three, four, and five profiles were compared to determine the number of e-media use patterns that best fit the data. To that end, we considered multiple indices of overall and comparative model fit, classification certainty or entropy, and parsimony and interpretability. ²⁴ In particular, we examined the Akaike Information Criterion (AIC), and the Sample-size Adjusted BIC (ABIC). Lower values in these indices indicate better fit. We then reviewed the Vuong-Lo-Mendell-Rubin likelihood ratio test and the Adjusted Lo-Mendell-Rubin likelihood ratio test (Adjusted LRT). These statistical tests compare the current model to a model with one fewer profile, and a significant p-value suggests retaining the current model. Next, we considered the entropy value for which higher values (> 0.80) signify a more accurate solution. Finally, we examined the proportion of children classified into each profile to describe their distribution across profiles. Profiles were interpreted and labeled by examining the means and standard deviations of children’s frequency and content of e-media use and background TV exposure.

RESULTS

Age of Exposure to E-media

Results presented in Supplemental Table 1 indicate children in this diverse, low-income sample, largely begin watching TV before age 1 year (60.8% of children) whereas 36.6% begin watching TV after age 1 year. Although fewer children begin using mobile devices than watching TV before age 1 year, more children begin using a mobile device by age 3 years (73.2%) than after age 3 years (24.7%). By age 4 years, nearly all children reportedly watch TV (97.3%) and use mobile devices (95.0%) as compared to fewer than two thirds of children who use console players or computers.

Frequency of E-media Use

Findings presented in Table 1 indicate nearly all children in the sample (95.7%) watch Child TV several times a week or more, and nearly half (48.3%) watch Child TV several times a day -- more than the number who watch Child TV at lower frequencies. Additionally, nearly three-fourths of children (72.5%) play Games several times a week or more frequently. By comparison, children reportedly watch Adult TV less frequently, with 41.4% never having watched Adult TV and about one quarter (25.5%) watching Adult TV once per week or more.

Table 1.

Number and percentage of children watching Child TV, Adult TV, and playing Games, by frequency

	Watch Child TV		Watch Adult TV		Play Games
	N	%	N	%	N	%
Has never done this
	0	0	154	41.4	18	4.8
Less than once a week			123	33.1	42	11.2
	8	21
Once a week
	8	2.1	31	8.3	43	11.5
Several times a week
	92	24.5	37	10.0	112	30.0
Once a day
	86	22.9	15	4.0	58	15.5
Several times a day
	181	48.3	12	3.2	101	27.0
Missing	5	1.3	8	2.1	6	1.6
Total	380	100	380	100	380	100

Note. Due to rounding, percentages for Watch Child TV, Adult TV, and Play Games may not add to 100. Survey item wording was “How often does your child do the following: Watch children’s cartoons or shows/ Watch adult shows or general audience shows/ Play games on a mobile device (like a smartphone, iPad, or tablet) or on a game console or other handheld device?“

Amount of Background TV Exposure

As indicated in Table 2, more than one-fifth of children (22.4%) in the study sample receive more than three hours of background TV exposure on a typical weekday, and nearly one-third (30.0%) receive that amount of exposure on a typical weekend day. On weekend days, the percentage of children with more than three hours of background TV exposure (30.0%) is greater than the percentage of children with Background TV exposure at all other lower levels.

Table 2.

Number and percentage of children exposed to background TV during a typical weekday and weekend day, by amount

	Weekday		Weekend Day
	N	%	N	%
No time at all	73	19.2	70	18.4
About 30 min.	61	16.1	43	11.3
About 1 hour	73	19.2	50	13.2
About 2 hours	55	14.5	51	13.4
About 3 hours	25	6.6	41	10.8
More than 3 hours	85	22.4	114	30.0
Missing	8	2.1	11	2.9
Total	380	100	380	100

Note. Due to rounding, percentages for weekdays and weekend days may not add to 100.

E-media Content

As indicated in Table 3, children whose parents listed at least one Child TV item (92.6% of the sample) watched more educational Child TV, on average (58%), than entertainment Child TV (25%). Fewer parents (43.2%) listed at least one Adult TV item as compared to those who listed at least one Child TV item (92.6%). Children whose parents listed at least one Games item (78.9% of the sample) played more entertainment Games on average (54%) than educational Games (26%). Twenty percent of Games were Uncodable (such as when parents indicated “learning games” or “educational games”).

Table 3.

Mean, standard deviation, and range for proportions of e-media content children view/use

	Obs.	Mean	SD	Range
Child TV
EDUC	352	0.58	0.33	0–1
ENT	352	0.25	0.27	0–1
Uncodable	352	0.17	0.30	0–1
Missing	28
Adult TV
EDUC	n/a	n/a	n/a	n/a
ENT	164	0.76	0.38	0–1
Uncodable	164	0.24	0.38	0–1
Missing	216
Games
EDUC	300	0.26	0.34	0–1
ENT	300	0.54	0.39	0–1
Uncodable	300	0.20	0.32	0–1
Missing	80

Note. EDUC indicates educational content; ENT, entertainment content.

Additionally, as reported in Table 4, children who played greater proportions of educational Games watched less entertainment Child TV and Adult TV; children who played greater proportions of entertainment Games also watched more entertainment Child TV and Adult TV; and children receiving more exposure to background TV also watched more Adult TV and played more entertainment Games.

Table 4.

Bivariate correlations among e-media use variables

	EDUC Child TV	ENT Child TV	Adult TV	EDUC Games	ENT Games	Background TV
EDUC Child TV	1
ENT Child TV	−0.44***	1
Adult TV	0.10	0.03	1
EDUC Games	−0.01	−0.13*	−0.15**	1
ENT Games	0.10	0.17**	0.21***	−0.50***	1
Background TV	0.09	−0.01	0.15**	−0.01	0.13*	1

Note. EDUC indicates educational content; ENT, entertainment content.

^***p<.001,

^**p<.01,

^*p<.05

E-media Use Patterns

We compared models with two, three, four, and five profiles to determine the number of e-media use patterns that best fit the data (see Table 5). Based on the two likelihood ratio tests (i.e., LRT and Adjusted LRT) the candidate solutions included three profiles (LRT = −2817.44; p<.01, Adj. LRT = 150.38, p<.01) and four profiles (LRT = −2740.44, p<.01; Adj. LRT = 106.37, p<.01). Both solutions revealed emedia use patterns that were easily interpreted. A four-profile solution showed slightly better model fit based on reductions of AIC and ABIC, as well as an increase in the entropy value. However, in the fourprofile solution one profile had a small percentage of children (7%; N = 29) and the pattern was very similar to another profile characterized by high educational Games use. After examining both options, the three-profile solution was selected as the most parsimonious solution. Figure 1 illustrates that a Modest E-Media Use profile characterized most children in the sample (70%). Fewer children were characterized by a High Educational Games Use profile (14%) and a High Adult TV/Elevated Entertainment Games Use and Background TV profile (16%). As presented in Table 6, in comparison to children in the High Adult TV/Elevated Entertainment Games Use and Background TV profile, children in the High Educational Games Use profile watch significantly less Adult TV, receive significantly less Background TV exposure, and play significantly more educational Games and fewer entertainment Games. In comparison to the High Adult TV/Elevated Entertainment Games Use and Background TV profile, children in the Modest E-Media Use profile watch significantly less educational Child TV and Adult TV, receive significantly less Background TV exposure, and play significantly fewer entertainment Games. Finally, as compared to the High Educational Games Use profile, children in the Modest E-Media Use profile play significantly fewer educational Games and significantly more entertainment Games.

Table 5.

Fit indices for a 2-, 3-, 4-, and 5- profile solution

	2 profiles	3 profiles	4 profiles	5 profiles
AIC	5672.88	5533.88	5437.95	5238.94
ABIC	5687.46	5552.83	5463.27	5269.63
LRT	−2910.08, p=.11	−2817.44, p<.01	−2740.44, p<.01	−2619.18, p=.28
Adj. LRT	180.92, p=.11	150.38, p<.01	106.37, p<.01	77.55, p=.29
Entropy	0.86	0.78	0.79	0.92

Note. AIC indicates Akaike Information Criterion; ABIC, Sample-size Adjusted Bayesian Information Criterion; LRT, Vuong-Lo-Mendell-Rubin likelihood ratio test; Adj. LRT, Adjusted Lo-Mendell-Rubin likelihood ratio test.

Figure 1.

Graphical representation of the final three-profile solution using parent-reported e-media use. Note. EDUC indicates educational content; ENT, entertainment content. All e-media use categories were z-scored to be on the same scale so that a higher score indicates more e-media use/exposure.

Table 6.

Mean, standard deviations, and ranges of e-media use/exposure for the three profiles and full sample

	Modest E-Media Use profile (N = 267; 70%)			High EDUC Games Use profile (N = 52; 14%)			High Adult TV/Elevated ENT Games Use and Background TV profile (N = 61; 16%)			Full sample (N = 380)
	M	SD	Range	M	SD	Range	M	SD	Range	M	SD	Range
EDUC Child TV	2.88b	1.82	0–6	3.14	1.75	0–6	3.42	1.8	0–6	3.01	1.82	0–6
ENT Child TV	1.33	1.43	0–6	0.96	1.20	0–4	1.47	1.5	0–6	1.30	1.42	0–6
Adult TV	0.40b	0.74	0–2	0.48b	0.93	0–4	3.58	1.0	2–6	0.92	1.42	0–6
EDUC Games	0.54a	0.81	0–2.5	4.03b	1.16	2.5–6	0.47	0.8	0–3	1.13	1.59	0–6
ENT Games	2.52ab	1.85	0–6	0.55b	0.89	0–3	3.58	1.9	0–6	2.36	1.95	0–6
Back ground TV	3.37b	1.73	1–6	3.34b	1.76	1–6	4.32	1.7	1–6	3.51	1.76	1–6

Note. EDUC indicates educational content; ENT, entertainment content.

^aLetters indicate significant differences.

^bLetters indicate significant differences.

Values reflect the frequency and content of e-media use, as described in the Methods.

DISCUSSION

Consistent with prior research, children in this diverse, low-income sample began using e-media, particularly TV and mobile devices, from the first year of life. ¹ Most children in the present sample also watch TV and play games/apps on mobile devices several times a week or more, and nearly one-third receive three or more hours of background TV exposure on a typical weekend day (about one-fifth receive this amount of exposure on a typical weekday). Also consistent with prior research, children in this low-income sample begin watching TV earlier than using other forms of e-media, and watch TV more frequently than using e-media on other platforms. ¹⁹ However, it is also clear that considering TV viewing alone would underestimate the amount of time children spend engaging with e-media, and the extent to which children use e-media platforms that afford the potential for active (versus passive) engagement. For example, on average, 27 percent of children in the study sample reportedly play Games several times a day (which may be in addition to television viewing, a form of e-media 48 percent of the sample use several times a day, on average). Likewise, considering TV viewing alone would not adequately capture the e-media content to which preschoolers are exposed. For example, although 58 percent of the Child TV was classified as educational, only 26 percent of Games were classified as educational (with 54 percent of Games classified as entertainment). Taken together, these results suggest the importance of considering children’s entire e-media diet to inform clinician-parent and educator-parent discussions, and additional research on e-media use and exposure.

Concerning background TV exposure, children in this sample routinely receive 3 or more hours of background TV exposure on weekdays and weekend days. These findings are consistent with prior research indicating background TV exposure is inversely related to family income-to-needs ratio. ²⁵ Coupled with evidence connecting background TV exposure with decrements in parent-child interaction quality and children’s cognitive development, ²⁶ these findings reaffirm the importance of parent education targeting the reduction of background TV, and provide impetus to address research gaps concerning young children’s exposure to other forms of background media (e.g., adult cell phone use) ^{25, 27} as they relate to parent-child interactions and children’s school readiness.

The latent profile analysis revealed children in the present sample exhibit three distinct e-media use and exposure patterns: A Modest E-Media Use profile characterized most children in the sample (70%). Fewer children were characterized by a High Educational Games profile (14%) or a High Adult TV/Elevated Entertainment Games Use and Background TV profile (16%). Collectively, the three profiles illustrate the complexity of preschoolers’ e-media use and exposure, in that no single profile reflects a relatively desirable pattern (e.g., lower proportions of Adult TV, Background TV, entertainment Child TV and entertainment Games relative to educational Child TV and educational Games) or a relatively undesirable pattern. Instead, the profiles reflect heterogeneous use patterns with regard to platform interaction potential and educational quality during a typical week. A recent synthesis of 76 studies examining associations between television viewing and child cognition and behavior suggests the associations are complex and may depend on individual child characteristics, family and social context, and television program features, including content, pacing, and foreground or background exposure. ²⁸ E-media use profiles children in the present sample exhibit account not only for television viewing, but also game/app use and background television exposure, and thus amplify the need to approach preschoolers’ e-media use comprehensively, with attention to both the potential beneficial and detrimental effects of e-media use as an alternative to restricting (or promoting) e-media use across the board. Indeed, a recent study of screen-related parenting practices in low-income Mexican American families recommended that clinicians consider maternal beliefs (such as positive functional use of screen time to keep a child calm or quiet), provide alternative methods for obtaining the perceived functional benefits, and query and validate maternal self-efficacy concerning American Academy of Pediatricsrecommended screen-related parenting practices for preschool-age children. ²⁹ Results of the present study also provide impetus to examine how e-media use patterns relate to indicators of school readiness, given the potential for e-media use profiles to illuminate pathways to school readiness with greater specificity than e-media use frequency or content, considered individually.

To our knowledge, the present study is the first to characterize comprehensive e-media use and exposure patterns in a diverse, low-income sample of preschoolers. Yet, there are a number of limitations to consider. First, e-media use data were gathered from parent-report surveys – a method subject to recall reliability and social desirability concerns. Implementing an e-media use diary ²⁹ likely would have reduced the amount of missing e-media content data and might have produced more reliable use/exposure data, overall. Additionally, the survey did not query whether children viewed Child TV and Adult TV shows on a non-portable device or a mobile device. Given the increasing availability of non-interactive e-media content on mobile devices (e.g., streaming television shows) future research could examine e-media platform as an additional characteristic. Finally, because this was a retrospective study, it is possible we missed or overlooked information that would provide a better understanding of e-media use for preschoolers from low-income families, such as the extent to which parents limit or promote specific e-media use and reasons for doing so.

CONCLUSION

Preschoolers in this sample exhibited three distinct e-media use profiles reflecting heterogeneous use patterns with regard to platform interaction potential and educational quality across a typical week. Additional research is warranted to investigate linkages between e-media use profiles and indicators of school readiness in cognitive, academic, and social and behavioral domains in diverse, low-income samples. Clinicians, educators, and families should examine the entirety of preschooler’s e-media use and consider how incremental adjustments to e-media use may best support children’s school readiness.

What’s New.

Preschoolers from low-income families in this sample exhibit three distinct electronic media use profiles underscoring the need to consider the entirety of children’s electronic media diet to inform clinician-parent and educator-parent discussions and additional research linking electronic media use to school readiness.