Associations Between Screen Exposure,Home Media Environments,and Indicators of School Readiness in Toddlers and Preschoolers from Kakamega County,Kenya

Abstract

Objective. Early childhood screen-time impacts school readiness, particularly in low- and middle-income countries. This study examined associations between screen-time and readiness for formal schooling among toddlers and preschoolers in Kakamega County, Kenya. Methods. A cross-sectional study of 144 children aged 2 to 5 years was conducted using the Denver II developmental screening and ECDI2030 to assess school readiness. Parent-reported questionnaires captured demographic data and screen media exposure. Results. Participants’ median age was 48 months (IQR = 20.75), with an average screen-time of 2.0 hours/day (IQR = 1.425), exceeding recommended guidelines of 1 hour. Children exceeding recommended screen-time had 52% lower odds of readiness, while parental supervision increased readiness odds by 68%. While parental age was associated with readiness, child sex, household income, and educational content showed no significant associations. Conclusion. Excessive screen-time may result in lower readiness, emphasizing risks in low-resource settings. Parental involvement and culturally tailored interventions are vital for early childhood development.

Keywords

school readiness screen media screen time early childhood development parental involvement culturally-sensitive interventions Kenya Africa

Introduction

As the world progresses into a digital era, the influence of screen exposure on the developmental stages of children becomes a significant area of study. In Kakamega County, Kenya, the infiltration of digital media into the fabric of daily life has made it imperative to examine its impacts on toddlers and preschool children’s readiness for school. The concept of school readiness has attracted substantial attention, especially considering its pivotal role in predicting later academic success.¹ School readiness is a multifaceted construct that includes traditional academic competencies, such as language, literacy, and mathematics, as well as non-cognitive skills like self-regulation, social skills, and motor control, all of which are essential for children to successfully adapt to school demands.^2,3

Good school readiness is increasingly recognized as a precursor to meeting academic curricula and encompasses a child’s ability to learn without emotional complexity, thus facilitating effective participation in academic life.^4,5 Children’s early academic success is critically linked to their later educational achievements, and as such, the determinants of school readiness are wide-ranging. Structural, demographic, household, and child factors all contribute to this readiness. Recent attentions are now turning to the role of screen-based media exposure within the home environment as a significant and modifiable influence.^6,7 The last 2 decades have seen a surge in young children’s use of screen devices for both educational and entertainment purposes, leading to an unprecedented level of screen time. The rapid increase in screen device usage, over the years, among young children, has resulted in a profound change in their daily lives and interactions with media.^8
-10 In Kenya, technological proliferation, particularly in terms of mobile and internet use among the youth, has been significant, with a rise in both opportunities for learning and potential developmental risks.^11,12

Recently, international guidelines aimed at optimizing health behaviors in early childhood, such as those by the American Academy of Pediatrics (AAP)¹³ and the World Health Organization (WHO)¹⁴ have been developed. These guidelines have recommend limited digital media exposure for young children, even though adherence remains a challenge, with screen time often exceeding these limits.^12,14
-16 This challenge is particularly pronounced in low- and middle-income countries like Kenya, where rapid technological advancements have dramatically increased mobile phone proliferation and internet access, especially among the youth. In the unique African setting, this surge in technology use has transformed the ways children engage with the world, offering new opportunities for connectivity and learning. However, it also introduces potential risks to their development,¹¹ as this surge in technology use unfolds within a Kenyan socio-cultural contexts that may not align with the assumptions of international guidelines.

The nuances of the screen time debate are complex, with some potentially beneficial, but mostly detrimental outcomes associated with media use in early childhood.^17
-19 These outcomes are influenced by various factors, including the child’s age, content, context, and the nature of child-caregiver interactions during media exposure.^20,21 However, the specific associated risks of screen time for children’s school readiness within the unique socio-cultural and economic contexts of developing African settings—and by extension, similar settings in other low- and middle-income countries—remain largely unexplored.

This study, therefore, aimed to investigate the influence of screen media exposure and the home media environment on the school readiness of children aged 2 to 5 years (toddlers and preschoolers) growing up in Kenya. It also sought to examine the association between adherence to screen exposure guidelines and preschool readiness,^22,23 with the view to contributing to a more comprehensive understanding of these dynamics. Ultimately, the findings aim to inform evidence-based policies and practices in developing rural and urban African contexts while enriching the global discourse on childhood development in the digital age, particularly within the context of low- and middle-income countries, where the needs are more paramount.

Methods

In this study we used the STROBE cross-sectional checklist when writing our report.^24,25 The cross-sectional design employed for this study was conducted on randomly selected children aged 2 to 5 years attending selected Early Childhood Development and Education (ECDE) centers in Kakamega County, in Kenya. ECDE centers in Kenya play a crucial role in the foundation of a child’s education. The idea behind these centers is to provide young children, typically between the ages of 3 to 6 years, with a solid educational and developmental start before they enter primary school.²⁶ In the ECDE program, parents and guardians are encouraged to be actively involved in their children’s early education. The centers often work with families to support the child’s learning and development at home as well. It aims to be inclusive, accommodating children with special needs and ensuring that all children, regardless of their abilities or backgrounds, have access to quality early education.^26,27 In total, Kakamega County has about 30 000 pupils enrolled in 917 ECDE centers.²⁷

For this study, a multi-stage sampling technique was employed to ensure representativeness across Kakamega County and to minimize selection bias. The randomization process began with creating clusters of ECDE centers based on the sub-counties within Kakamega County. A simple random sampling method was then used to select 1 cluster of schools from these sub-counties. Since the selected ECDE centers had varying enrollment sizes, a proportionate sampling approach was used to allocate children into groups according to the enrollment sizes within the selected cluster. In the third stage, one-third of these groups were randomly selected using a random number generator. Finally, a systematic sampling method was applied to select the desired minimum sample size from these groups by choosing every Nth child from a randomly ordered list, ensuring representativeness. For this study, permission to engage with the children and their parents was obtained from both the local county directorate for education and the respective heads of schools of each participating ECDE centers, who served as entry points.

The inclusion criteria for this study were as follows: the child and the family’s willingness to participate, clinically having a normal developmental stage, and living with both parents. Children excluded from this study were based on the following criteria: having any chronic illness, clinically abnormal development, intellectual disability, and limited parental literacy that affected effective communication and potentially impact their capacity to effectively understand and engage in the study processes. Written informed consent was obtained from the parents in line with the ethical approval obtained for this study.

A total of 169 participants were evaluated for inclusion in the study. The retained sample included a total of 144 parents of toddlers and preschool children, with a median child age of 48 months (IQR = 20.75). The sex distribution among the children was nearly balanced, with males slightly more represented (52%). A minimum sample size of 128 participants was predetermined for this study using a power analysis to ensure sufficient power to detect an association between screen time exposure and school readiness among children aged 2 to 5 years attending ECDE centers in Kakamega County. Assuming a moderate effect size (Cohen’s d = 0.5) for the association between screen time and school readiness, a significance level of 0.05, an 80% power (1−β = .8), and a 10% contingency to allow for potential non-responses and incomplete data, a minimum of 128 participants was required to detect 10% difference in mean response in line with the study objectives. Participants were evaluated through a Denver II developmental screening test which was applied by a child development specialist.²⁸ Among those evaluated, 8 children with developmental delay, 10 children with other forms of intellectual and neurological disorders were excluded from the study. Besides, 7 children whose parents were divorced or who were living with grandparents were excluded to avoid the possible confounding effect of this situation on school readiness. The study group consisted of the remaining 144 children and their parents, with the children classified into 2 groups: toddlers (aged 24-36 months) and preschoolers (aged 37-60 months). This classification was based on aligning the included children with the cognitive and social development stages typical of toddlers and preschoolers,²⁹ as well as for the purpose of comparison of screen exposure time distribution between the 2 developmental stages of interest to this study.

Ethical Considerations

Ethical clearance for this study was obtained from the Institutional Scientific and Ethics Review Committee in May 2022. The study adhered to the provisions of the Helsinki Declaration, with approved informed consent and assent procedures in place. Parental consent was obtained through signed documents provided 1 week prior to data collection, ensuring adequate time for review and decision-making. After securing parental consent, each child was individually asked if they wished to participate. Only those who explicitly expressed willingness and comfort were included in the study. Assent from children was obtained using age-appropriate explanations of the study procedures and participant rights in simple, child-friendly language. To ensure voluntary participation, both parents and children were informed and regularly reminded of their rights to withdraw from the study or decline to answer any questions at any point during data collection.

Measures

Control variables – parent-reported demographic data

Parents reported on each household’s socio-demographic characteristics including age and sex of child, parental age, parental education and employment status, family structure, number of siblings, and socio-economic status (SES). The SES was classified according to the official poverty limits.^30,31

Predictor variable – parent-reported family and child screen media characteristics

Parents self-reported on their own and the daily screen-based activities of their children. To obtain more accurate and consistent information, and to examine TV and mobile devices separately, information about screen time was queried in 2 separate parts TV and mobile devices (smartphone, tablet, laptop). Parents equally reported on the home media environment (12 items), children’s patterns of exposure to screens (19 items) and readiness outcomes using the UNICEF ECDI2030 validated questionnaire.³²

Outcome variable - early childhood development

School readiness skills were assessed using the UNICEF’s Early Childhood Development Index (ECD12030). The test had a total of 20 items and 4 subdomains that is physical (4 items), cognitive (5 items), language (4 items), socio-emotional competencies (5 items), and general knowledge (2 items). It was adopted to determine the school readiness outcomes of the children, using the prescribed guideline available online (https://data.unicef.org/resources/early-childhood-development-index-2030-ecdi2030/).³² For each correct answer, a Yes was scored 1 point, a No was scored 2 points, and Uncertain was scored 8 points.³² The total of all 4 sub-dimensions, matched with age cut-norms of the children gave the appropriate school readiness score. Based on age-norms scores of the participating children, they were then classified as either “school ready” or “non-school ready.”

Data preparation, validation, and analytic procedures

Data collected for this study was validated using MS Excel (v.2016). We reviewed all reported screen time data, including extreme values. Extreme values, such as the 8 and 13 hour daily screen times reported by 2 parents, were verified through repeated inquiries to ensure accuracy. The assumption of normality was not met with and without outliers, as well as in appropriate visualization charts. These values were retained in the dataset to avoid introducing selection bias and to provide a comprehensive representation of the range of reported behaviors.

Statistical analyses were performed using the Statistical Package for the Social Sciences Statistics (SPSS v25) statistical software. Appropriate descriptive statistics were computed and reported in median and interquartile range (IQR) to best describe the distribution of variables. Within groups comparison for categorical variables in each of the factors examined were tested using Chi square test, while the influence of independent and confounding variables were tested using binary logistics regression model to examine the likelihood of influence (OR at 95% confidence interval). All inferential analysis were conducted at α 0.05.

Results

Table 1 provides a comprehensive overview of the study variables, including home media environment, screen exposure, and school readiness. Most parents were in the young adult age group, with a significant proportion (68.8%) being self-employed. A large percentage of the households represented were of lower-income (95%). Additionally, 41% of the parents had achieved post-secondary education, though differences in educational attainment among respondents were not statistically significant (P = .099). The age and sex distributions of the children were approximately symmetrical around the median, indicating no significant skew in age distribution. The average screen time per day among the participating children was 2.0 hours (IQR = 1.425), with a leptokurtic distribution (skewness = 3.855; kurtosis = 24.115), shown in Figure 1.

Table 1.

Distribution of Study Variables Defining Screen Exposure and School Readiness (n-144).

Screen exposure variables and levels of school readiness	Frequency distribution (n = 144)
Screen exposure variables and levels of school readiness	N (%tage share)	*P-value
Parent’s age group
Younger adulthood	99 (68.8)	.000
Older adulthood	45 (31.2)
Parent’s occupational class
Self employed	92 (63.9)	.001
Paid employed	52 (36.1)
Participant’s household income class
Low income	99 (68.7)	.000
Middle income	38 (26.4)
High income	7 (4.9)
Parent’s educational attainment level
Basic	47 (32.6)	.099
Secondary	38 (26.4)
Post-secondary	59 (41.0)
Child’s age (Months)
Toddler	29 (20.1)	.000
Preschooler	115 (79.9)
Child sex
Female	69 (47.9)	.617
Male	75 (52.1)
Participant’s relationship type with child
Non-biological	30 (20.8)	.000
Biological	114 (79.2)
Presence of media-screen-exposure enablers in participant’s household
Absent	84 (58.3)	.046
Present	60 (41.7)
Number of rooms in participant’s home
One	4 (2.8)	.000
Two	50 (34.7)
Three or more	90 (62.7)
Number of adults in participant’s household
One	18 (12.4)	.000
Two	63 (43.8)
Three or more	63 (43.8)
Number of children under 5 years in participant’s household
One	87 (60.4)	.000
Two	36 (25.0)
More than two	21 (14.6)
Level of device(s) used enablement for school readiness
Non-school-readiness enabler	43 (29.9)	.000
School-readiness enabler	101 (70.1)
Level household control on child’s use of media-screen-exposure device(s)
Uncontrolled	24 (16.7)	.000
Minimally controlled	22 (15.3)
Controlled	98 (68.1)
Average media-screen-exposure time (Classified based on WHO recommendation)
One hour or less	36 (25.0)	.000
More than one hour	108 (75.0)
Level of school readiness
Not ready	13 (9.0)	.000
Ready	131 (91.0)

N, frequency; %tage, percentage share.

X² test @ α 0.05.

Figure 1.

Histogram showing distribution of average time spent on screen devices by toddlers and preschoolers in the study (n = 144).

Regarding media access, 58% of children had access to various media devices, while 70.1% of parents reported that these devices contained educational content conducive to school readiness. Although 68.1% of media device usage was under adult supervision, 75% of children still exceeded 1 hour of screen time daily.

Comparative Analysis of Screen Time Distribution Among Toddlers and Preschoolers

A significant interaction, illustrated in Figure 2, illustrates the distribution of screen time among toddlers (aged 24-36 months) and preschoolers (aged 37-60 months). We then analyzed the percentage of each group that falls into various categories of average time spent per day on media devices (measured in hours). The distribution shows different amounts of usage, ranging from 0.5 to 13 hours per day, with the degree of divergence of the lines indicating the screen time usage between toddlers and preschoolers. Toddlers showed a downward trend in screen time, with fewer children in this group falling into the higher screen time categories. Conversely, preschoolers generally demonstrated an upward trend in screen time.

Figure 2.

Comparison of screen-time distribution among toddlers and preschoolers (n = 144).

Regarding extreme screen time exposure, like 8 and 13 hours per day, the plot shows lines that are relatively flat across both groups, indicating that a small percentage of both toddlers and preschoolers are exposed to very high amounts of screen time. However, in the final regression analysis extreme values, such as the 8 and the 13 hour daily screen times reported by 2 parents, were noted during the analysis as outliers.

Influence of Media-Screen Exposure on School Readiness

Table 2 reports that time spent on screens was not a statistically significant predictor of school readiness (P = .206). However, children who spent more than the WHO/AAP-recommended 1 hour per day on screens showed a 52% decrease in the odds of being school-ready (OR 0.480, 95% CI 0.071-3.267), though this association was not statistically significant (P = .453). Additionally, while extreme values, analyzed as outliers, were included in the regression model to preserve the integrity of the data, they represented a minority of cases and did not significantly affect the overall regression results.

Table 2.

Analysis of the Influence of Media-Screen Exposure on School Readiness (n = 144).

Outcome variable	Independent variables	Beta coef.	Standard error	Wald	*P-value	Odds ratio	95% CI
Outcome variable	Independent variables	Beta coef.	Standard error	Wald	*P-value	Odds ratio	Lower	Upper
School readiness	Intercept	8.036	6.063	1.757	.998	3091.063	-	-
	Parent’s age group	1.865	0.934	3.982	.046	6.453	1.034	40.288
	Parent’s occupational class	1.488	0.918	2.626	.105	4.427	0.732	26.763
	Participant’s household income class	−0.959	0.759	1.595	.207	0.383	0.087	1.697
	Parent’s educational attainment level	0.118	0.500	0.056	.813	1.125	0.423	2.997
	Child’s age (Months)	−0.065	0.031	4.334	.037	0.937	0.881	0.996
	Child’s sex	−0.588	0.777	0.573	.449	0.555	0.121	2.548
	Participant’s relationship type with child	−0.208	1.016	0.042	.838	0.812	0.111	5.952
	Presence of media-screen-exposure enablers in participant’s household	0.138	0.822	0.028	.866	1.148	0.229	5.757
	Number of rooms in participant’s home	−0.733	0.705	1.081	.298	0.480	0.121	1.913
	Number of adults in participant’s household	−0.537	0.561	0.917	.338	0.585	0.195	1.754
	Number of children under 5 years in participant’s household	−1.065	0.756	1.984	.159	0.345	0.078	1.518
	Level of device(s) used enablement for school readiness	−0.733	1.018	0.519	.471	0.480	0.065	3.529
	Level household control on child’s use of media-screen-exposure device(s)	0.516	0.607	0.723	.395	1.676	0.510	5.508
	Average time spent/day on media-screen-exposure device(s; hrs)	0.352	0.278	1.598	.206	1.422	0.824	2.453
	Average time spent/day being within WHO media-screen-exposure threshold (Max 1 hour/day)	−0.734	0.978	0.563	.453	0.480	0.071	3.267

Binary Logistics Regression Model @ α 0.05; Reference Category – Last.

Furthermore, other factors, such as parental age, household characteristics, and screen time controls were also examined. Children of older parents, for instance, demonstrated significantly higher odds for school readiness (OR 6.453, 95% CI 1.034-40.288, P = .046). Conversely, other household factors, such as parental employment status, educational attainment, absence of screen-viewing enablers, and limited device use controls, did not show statistically significant associations with school readiness (P = .105, .813, .866, .395, respectively) but were associated with increased odds of readiness (OR 4.427, 1.125, 1.148, and 1.676, respectively), thus warranting the need for caution in interpreting these results.

Even though child’s sex was not a significant predictor of school readiness (P = .449), females had about 45% decreased likelihood of being school-ready (OR 0.555, 95% CI 0.121-2.548) compared to males. On the other hand, child’s age, measured in months, was a significant predictor, showing a 6% decrease in the odds for school readiness with every month increase in age (OR 0.937, 95% CI 0.881-0.996, P = .037). Additionally, household factors such as lower income, non-biological caregivers, fewer household rooms, fewer adults in the household, fewer under-5 children, and less frequent use of educational devices, were associated with a reduced likelihood of school readiness (Table 2). Nevertheless, these factors did not have a statistically significant influence (P = .207, .838, .298, .338, .159, .471, respectively). Children exposed to screen devices with higher levels of household control had approximately 68% increased odds of being school-ready (OR 1.676, 95% CI 0.510-5.508).

Discussion

This study investigated the relationship between screen exposure time and home media environment and school readiness among toddlers and preschool children in Kenya. Although Kakamega is not an urban center itself, much like the rest of Africa, it represents a mix of urban and rural areas. The urban centers within it contribute significantly to regional economic and social development.

Although screen time alone was not a significant predictor of school readiness scores, the findings tell another story when we consider international school readiness guidelines.^9,13,14,33 More specifically, children who spent more time on screens beyond the recommended guidelines for toddlers and preschoolers had less chances of being school-ready. This trend aligns with prior research suggesting that excessive screen time can negatively impact young child development.^34
-36 For example, Irzalinda and Latifah (2023) observed similar developmental risks in children with increased screen exposure, as documented in their systematic review.³⁷ Our findings also suggest that even though many home media environment factors did not predict school readiness scores among toddlers and preschoolers, we also found that children from homes with restricted access to media devices and content were generally more school-ready than their peers with unrestricted access. This supports findings by Hinkley et al (2015), who emphasized the role of the home environment in shaping children’s media use and its developmental outcomes.³⁸ Our observations further underscore the importance of parental regulation, indicating that active parental involvement and management of screen time can mitigate potential negative effects of media exposure.³⁹

This research highlights the socio-cultural specifics of African settings under development, like Kakamega County. It reinforces the need for similar investigations in developing countries, as advocated by Kabali et al⁴⁰ This emphasis on the local context is crucial, as cultural nuances often shape media practices and child development in ways that global guidelines may not fully capture. The variability in access to educationally enabled devices and parental capacity to mediate screen use suggests that children from different socioeconomic backgrounds may experience different impacts from media exposure.^41,42 This disparity has important implications for efforts to close school readiness gaps and promote equity in educational outcomes, particularly in low- and middle-income countries.

More so, our observations revealed that older preschoolers generally spent more time on screens than toddlers and can indicate a pattern of permissiveness in parents. While lower screen time among toddlers may facilitate more interactive play, which is essential for developmental milestones, the increased screen exposure observed in preschoolers could potentially hinder social, emotional, and cognitive development, raising concerns about overexposure.^43
-46 This pattern likely explains the lower chances of kindergarten preparedness with older preschoolers indicating a lower likelihood of being school-ready, compared with toddler lifestyles. Modifiable factors that affect early brain and child development can negatively impact a child’s learning trajectory, influencing social, emotional, cognitive, and physical development^1
-5,43
-46 and, in the case of this study, affect their potential for school readiness.

Children in homes with restricted access to media devices and those exposed to educationally enabled content had better readiness outcomes, highlighting the importance of a controlled and supportive home media environment. Parental mediation emerged as a crucial factor in mitigating potential negative effects of media exposure, underscoring the importance of active parental engagement, as recommended by international guidelines.^13,14

The implications of this research extend beyond Kenya to other low- and middle-income countries. They underscore the importance of educating parents, especially younger ones, about the effects of early media exposure. This study suggests that interventions which aim to improve school readiness would benefit from using a holistic approach, addressing the child’s broader social and developmental environment rather than focusing solely on media exposure. Specifically, our findings demonstrate that excessive screen time is associated with decreased chances of school readiness, while parental supervision of media use and restricting media to educational content-enhanced devices improve these odds. These observations reinforce the American Academy of Pediatrics’ guidelines, which advocates for limited screen time and active parental involvement in curating media content,^13,33 which is crucial for nurturing healthy child development.^33,47,48

However, our findings should be considered within the socio-economic context, particularly in light of the rural and urban challenges faced in African settings. Effective interventions must account for varying patterns of media access and usage influenced by these socio-economic factors. Connell et al⁴⁹ previously emphasized the importance of contextual sensitivity for effective interventions. Moreover, the significant association between parental age and school readiness, alongside the lack of significant associations with child sex and family size, presents a complex picture that warrants further investigation.

To this end, this study has limitations that should be considered when interpreting the findings. First, excessive screen time, defined by international guideline non-adherence predicted less school readiness. This finding should be interpreted with reasonable caution due to both the cross-sectional design and the presence of several extreme values in the dataset. Extreme values, such as the 8 and the 13 hour daily screen times reported by some parents, were included in the final analyses to reflect the full range of behaviors. However, this did not seem to have influenced observed trends. Nevertheless, the inclusion of outliers, such as 8 and 13 hour daily screen times, reflects atypical circumstances and underscores the need for further research to explore the contextual factors driving such behaviors. We did not observe inflated standard errors or other indicators of inferential invalidation and restricted ourselves to non-causal interpretations of concurrently collected data. Embedded within the first is a second limitation. This study relied on parent-reported measures of screen time and home media environment, which are subject to under- or over reporting of their child’s media usage and screen exposure time. In guideline driven parental and lifestyle choices, social desirability can be a double-edged sword. Parental perceptions about the impact of technology on children vary greatly based on their socio-economic status and other psycho-social factors.⁵⁰ Some may see it as a valuable educational tool, while others are deeply concerned about its potential harm. These beliefs can significantly influence how parents report their child’s screen time. Moreover, some parents may believe that significant technology use indicates a forward-thinking approach to parenting, aligning with modern ideas of educational technology and digital literacy whereas others may feel societal pressure to limit screen time due to concerns about potential negative risks for cognitive, social, and motor skill preparedness for important school transitions,^51,52 such as attention problems, sleep disturbances, and obesity. Parents may under-report their child’s actual screen time to portray themselves as responsible and attentive caregivers who effectively monitor their child’s technology use because they fear criticism from other parents or perceived disapproval from healthcare professionals or educators. Finally, although we examined a range of socio-demographic variables, we did not account for broader systems that may impact media access and usage patterns in households. This omission could limit the generalizability of our findings to other socio-economic settings in Africa, particularly in the contexts of low- and middle-income settings, where access to resources varies.

Conclusions

This study provides valuable insights into the influence of screen exposure and the home media environment on school readiness among toddlers and preschoolers in a mixed rural and urban setting in a middle-income African setting. Excessive screen exposure beyond recommended limits was associated with risks of being unprepared for kindergarten. Children in homes with restricted access to media devices and those exposed to educationally enabled content had better chances of being prepared for formal schooling, which begins with kindergarten onward. This underscores the importance of a controlled and supportive home media environment. Parental regulation practices emerged as a crucial factor in mitigating potential risks of excessive media exposure, underscoring the importance of active parental engagement, as recommended by international guidelines.

Furthermore, the observed disparities in screen time between toddlers and preschoolers suggest that lower screen exposure among younger children may support critical developmental milestones, whereas higher exposure among preschoolers predicts risks for hindered social, emotional, and cognitive indicators of growth. This research reinforces the need to educate parents, especially in low- and middle-income African regions, about the benefits of limiting screen time and promoting balanced activities. Overall, it contributes to a better understanding of early childhood in typically developing settlements and provides insights for similar low- and middle-income African contexts, emphasizing the role of balanced media exposure and a supportive home environment in being prepared for formal schooling.

Footnotes

Acknowledgements

The authors thank and acknowledge the efforts of training research assistants who assisted in the collection of data for this study. Data from this work were presented in part at a symposium in the 27th Biennial Meeting of the International Society for the Study of Behavioral Development (ISSBD 2024), in Lisbon, Portugal. Comments received from audience, panelists, and reviewers of the part submission to the symposium significantly improved the final review of this complete version for publication. The authors acknowledge the inputs, comments, and suggestions proffered by all.

Availability of data and material

The datasets used and analyzed during this study can be made available from the corresponding author upon reasonable request.

Contributors

All co-authors contributed to the study conceptualization, study design, data analyses, and interpretation and presentation of results, drafting and critical revising of the article for important intellectual content. All authors have read and approved the final submitted version of the article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethics approval

The Institutional Scientific and Ethics Review Committee at Masinde Muliro University of Science and Technology approved our study and the corresponding interviews and consent/assent documents for this study (approval no.: MMUST/IERC/057/2022) on May 26th, 2022). Participants/legal guardians in this study gave written and signed consent/assent for their participation before starting the interviews.

Consent to participate

All participants for this study signed written consent/assent to participate in the study and the interviews conducted.

Consent for publication

No identifying information of participants, as detailed in ICMJE Recommendations, has been included in this paper.

Statements and declarations

The corresponding author hereby declares, on behalf of all co-authors, that this manuscript is not under simultaneous consideration for publication in another journal nor has it been published elsewhere. The corresponding author further declares, on behalf of the co-authors, that this manuscript has been read and approved by all the authors, and that the requirements for authorship have been met by all the authors. Each author of this manuscript believes it represents honest work.

Declaration of generative AI and AI-assisted technologies use

During the preparation of this work the authors used chatGPT-4™ in order to grammar check the manuscript, edit sentence structure, and improve readability of the manuscript. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

ORCID iDs

Emmanuel E. Okenwa-Vincent

Rose Opiyo

Benoit Gauthier

Yaser Ramírez-Benítez

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