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. 2022 Sep 2;94(1):303–314. doi: 10.1111/cdev.13851

Are mobile phone ownership and age of acquisition associated with child adjustment? A 5‐year prospective study among low‐income Latinx children

Xiaoran Sun 1,✉, K Farish Haydel 2, Donna Matheson 2, Manisha Desai 2, Thomas N Robinson 2
PMCID: PMC9780147  NIHMSID: NIHMS1832287  PMID: 36056573

Abstract

This prospective, longitudinal study examined associations between whether and when children first acquire a mobile phone and their adjustment measures, among low‐income Latinx children. Children (N = 263; 55% female; baseline M age = 9.5) and their parents were assessed annually for 5 years from 2012. Children first acquired a mobile phone at a mean (SD) age of 11.62 (1.41) years. Pre‐registered multilevel models tested associations linking phone ownership, phone acquisition age, and the interaction between ownership and acquisition age to levels and changing trends of depressive symptoms, school grades, and reported and objectively assessed sleep. Results showed no statistically significant associations, controlling the False Discovery Rate. Findings suggest an absence of meaningful links from mobile phone ownership and acquisition age to child adjustment.

When should children get their first mobile phone? Is owning a mobile phone harmful or beneficial to children? These questions are on the minds of many of today's parents, educators, and clinical practitioners. Arguments and evidence are growing about benefits and harms associated with mobile phones, the Internet, and social media. Potential benefits include facilitated communication with peers and families and assistance with learning and social development (Ehrenreich et al., 2020; O'Keeffe et al., 2011). Meanwhile, research has identified potential harms for mental health including more depressive symptoms, suicide‐related outcomes, lower self‐esteem (Boers et al., 2019; Twenge & Campbell, 2018; Woods & Scott, 2016), shorter sleep duration and lower sleep quality (Twenge et al., 2017; Woods & Scott, 2016), and lower academic performance (Dempsey et al., 2019). However, null results and negligible effect sizes are common in research examining technology use and child outcomes (George et al., 2020; Orben & Przybylski, 2019a, 2019b).

In line with the growing evidence, theory is being developed to explain how new technologies are influencing child development in many ways. In particular, Navarro and Tudge (2022) adapted the bioecological theory (Bronfenbrenner & Morris, 2006) to incorporate (digital) technology across contexts, persons, processes, and time and named it the “neo‐ecological theory.” As an expansion in this theory, Navarro and Tudge (2022) highlighted the role of digital technology—especially the virtual microsystems that technology creates—in the proximal processes that are driving child development, which include both positive and inverse processes that benefit and harm child adjustment (i.e., children's functioning in their developmental environments that encompasses multiple aspects such as achievement, internal state, and body symptoms; Carey, 2009), respectively. For positive proximal processes, virtual microsystems provide children with access to social support and opportunities for developing agency, independence, social skills, self‐esteem, and coping strategies—developmental assets that are beneficial to mental health and academic performance. However, this theory also highlights the inverse proximal processes in the virtual microsystems, including cyber victimization and social comparisons that can lead to many adjustment problems. Although the neo‐ecological theory does not specify the role of each digital device, mobile phones can be critical for children's access to the Internet and virtual microsystems on a regular basis (Brown et al., 2016).

In the face of development in theory and evidence about technology and child adjustment, research has mainly focused on implications of frequency of use and time spent on technology. Less evidence is available about potential impacts of children having their own mobile phone and their age when they first acquire it. Anecdotal perspectives are highlighted in parenting media about whether and when children should get mobile phones (e.g., Anderson, 2018; Chen, 2016; Curtin, 2017). Public campaigns advocate for parents to wait to give children their first smartphone until a certain age or grade (e.g., Wait Until 8th, n.d.). Further, recent research has suggested youth's different levels of developmental sensitivity to social media at different ages (Orben et al., 2022), grounded in the theoretical tenet that developmental processes, including biological, psychological, and social changes, can alter individuals' sensitivity to the effect of social media use and the direction (positive vs. negative) of the effect (Valkenburg & Peter, 2013). This rationale is also aligned with the argument that because age is associated with the extent of maturity of emotional and cognitive regulatory systems, access to smartphones and the Internet can have different impacts on youth's behaviors, such as cyberbullying, at different ages (Cebollero‐Salinas et al., 2022). Together, these anecdotal and theoretical perspectives highlight the need to study whether and how the age of mobile phone acquisition is associated with child adjustment.

A modest number of studies have examined implications of youth's mobile phone ownership. Some studies suggested negative effects of ownership on child adjustment measures. A cross‐sectional study among 12‐ to 17‐year‐old adolescents found smartphone ownership (82.6% of participants) associated with later bedtimes, but not significantly related to sleep disturbances or depressive symptoms (Lemola et al., 2014). Another study with longitudinal, two‐wave data found children's mobile phone ownership at age 9 (39.8%) associated with lower math and reading performances at age 13 (Dempsey et al., 2019). In contrast, other studies suggested little evidence of ownership effects. Using a two‐wave dataset, studies among 9‐ to 15‐year‐old adolescents found that mobile phone ownership (64%) was not related to concurrent academic performance and mental and physical health (George et al., 2020), or mental health outcomes 1–2 years later, including depression, conduct problems, inattention or hyperactivity, and worry (Jensen et al., 2019). Cross‐sectional or two‐wave designs, however, limit the understanding about how mobile phone ownership may impact within‐person changes in adjustment over time. With these designs, it was also hard to estimate children's age at the first acquisition of mobile phones or examine potential impacts of timing.

Accordingly, this study used data from a recent 5‐year prospective cohort of children to observe the age when they first received their own mobile phones (i.e., acquisition age), and to examine whether and how mobile phone ownership and acquisition age were associated with concurrent levels and prospective changes in adjustment measures. We used an ethnic homogeneous design (McHale et al., 2011) to focus on the implications of mobile phone ownership for Latinx children from low‐income neighborhoods. In the United States, Latinx or Hispanics are the largest racial and ethnic minority child population and the fastest‐growing among all racial and ethnic groups under age 18 (Child Trends, 2018; Lopez et al., 2018). Recent statistics have revealed that adult Hispanics have reported higher dependency on smartphones than White and Black adults (Pew Research Center, 2019), and that Latinx children (aged 8–18 years) spend more time on entertainment screen use than White and Black children (Rideout et al., 2022). However, little is known about the potential impacts of digital technology among Latinx children. From a cultural ecological perspective, findings from predominantly White samples may not generalize to ethnic minority children (García Coll et al., 1996) and thus previous findings may not apply. In addition, we utilized data from a weight gain prevention study among low‐income Latinx children with overweight or obesity. We considered the study to have broad implications for Latinx children given that more than half (55.6%, in 2017–2018) of Mexican American children aged 2–19 years are overweight or obese and that the overweight and obesity are more prevalent among children from low‐income households than those from middle‐ or high‐income households (Tsoi et al., 2022).

Although understudied, some research does exist among Latinx and low‐income children. It has been suggested that social media can serve as an important source for health‐related information among Latinx youth (Stevens et al., 2017). Mobile phones allow public health interventions to more easily reach Latinx adolescents (Vyas et al., 2012). In contrast, socioeconomically disadvantaged children may be especially vulnerable to harmful impacts of digital devices (Odgers, 2018), such as interruptions of sleep (George et al., 2020). This can be especially problematic given that children with lower socioeconomic backgrounds are more likely to own mobile phones at an early age (Dempsey et al., 2019). In the face of this mix of findings, there is still insufficient empirical evidence for whether owning mobile phones and earlier acquisition benefits, harms, or has no effects on adjustment outcomes among these children.

We designed the current study to help answer the important practical questions about whether and when children should receive their own mobile phones, and to help address research gaps about mobile phone ownership among ethnic minority children. Using prospective, longitudinal data, first we described the pattern of mobile phone ownership among a sample of low‐income, Latinx children by estimating the acquisition age and prevalence of ownership by age. Second, we examined whether and how ownership status and acquisition age were associated with children's levels and changes in depressive symptoms, school grades, and sleep outcomes. Specifically, given that child adjustment outcomes differ in both levels and longitudinal trajectories, both of which are associated with media use (Houghton et al., 2018), we hypothesized that ownership status and acquisition age would be associated with both the level and the longitudinal changing trend in each adjustment measure. Further, informed by the previous finding that mobile phone acquisition age may moderate effects of digital experiences (Cebollero‐Salinas et al., 2022), we also hypothesized that mobile phone ownership associations with levels and changes in each adjustment measure would differ by different acquisition ages, that is, acquisition age would moderate these associations. The hypotheses did not specify the directionality given both potential positive and negative effects of mobile phones on children in prior research. Hypotheses and methods were pre‐registered on Open Science Framework (https://osf.io/hukfd).

We also pre‐registered a plan for exploratory analyses on the condition where effects hypothesized were nonsignificant according to analysis. In that case, first we would explore whether acquisition age had a quadratic effect on adjustment measures, based on prior findings about nonlinear associations between technology use and psychological well‐being (Przybylski & Weinstein, 2017). Second, we would explore whether child sex, sexual maturity, and highest parental education and household income might moderate the associations between mobile phone ownership and acquisition age with adjustment. These potential moderations were informed by previous studies suggesting different association patterns of mobile phone activities and adjustment measures by sex (Seo et al., 2016), maturity as the perceived timing for mobile phone ownership (Moreno et al., 2019), and family socioeconomic resources differentiating implications of technology for child adjustment (George et al., 2020; Odgers, 2018). Third, given differences in the types of digital platforms accessed by smartphones and non‐smartphones (e.g., internet access, social media, video, and TV streaming) we explored the effects of ownership of smartphones and smartphone acquisition age on the adjustment measures.

METHODS

Participants

This study used data from the baseline and 1‐, 2‐, 3‐, and 4‐year follow‐up assessments from Stanford GOALS, a randomized controlled trial of a weight gain prevention for low‐income 7‐ through 11‐year‐old children with overweight or obesity and their families (Robinson et al., 2013, 2021). Participants were recruited through primary care providers and clinics, schools, community centers, churches, and other community locations in low‐income, primarily Latinx neighborhoods, in Northern California, USA. Eligibility criteria at baseline included: (1) Family had at least one child aged 7–11 years with body mass index (BMI) ≥85th percentile for age and sex on the 2000 CDC BMI reference; (2) children had not been diagnosed with a medical condition that affects growth (including eating disorders) or were taking medication that affects growth; (3) children had no condition limiting participation in interventions or assessments; (4) children and their parent or guardian could read, understand or complete informed consent in English or Spanish; (5) families were not planning to move away from Northern California during the course of the study.

Among the 605 families initially screened for the program, 268 children from 241 households and at least one of their parents or guardians (95% female) participated at baseline. Families were randomly assigned to a multi‐component, multi‐level, multi‐setting (MMM) intervention group or a health education comparison group. For the current study, we excluded five children who were non‐Latinx by parent report.

Overall, 263 children (55% female; baseline M age = 9.5 years, SD = 1.5) from 236 households identifying their children as Hispanic or Latino were included in this analysis. Among the 177 households where parents reported total household income range at baseline, 53.7% and 74.0% had income below $25,000 and below $35,000, respectively. Table S1 shows the sample's demographic characteristics. Retention rates were 99.2%, 97.0%, 93.9%, and 62.0% at the four follow‐up assessments.

Procedure

Assessments were performed in a clinic, community, or home setting, by trained and certified, bilingual (English and Spanish) data collectors. The study protocol was approved by the University's Administrative Panel on Medical Research in Human Subjects. Signed, written informed consent and assent were obtained from parents or guardians and children, respectively. Data were collected from August 2012 to October 2017.

Measures

Exposure and outcome variables were all measured at each annual assessment.

Mobile phone ownership status and acquisition age

Parents or guardians reported whether their child had their own mobile phone (i.e., ownership status). Acquisition age was computed as the midpoint between the child's ages at the last report of no mobile phone ownership and the first report where their child had their own mobile phone. For children who already owned mobile phones at baseline (left‐censoring) and who never owned mobile phones throughout all assessments (right‐censoring), acquisition age was imputed, as described below, using a Markov chain Monte Carlo (MCMC) method—a method that has been used to handle both left‐ and right‐censored data in multivariate analysis (Jones, 2021).

While reporting on children's mobile phone ownership, parents also reported whether the mobile phone was a smartphone or non‐smartphone, to be used for exploratory analysis.

Depressive symptoms

We used the 10‐item Child Depression Inventory‐Short Version (Kovacs, 1985). For each item, children selected, on a 3‐point scale, the sentence that described them best for the past 2 weeks, such as 0 = I am sad once in a while, 1 = I am sad many times, and 2 = I am sad all the time. Cronbach's alpha ranged from .68 to .80 across assessments.

Grades

Parents reported on the child's most recent school grades, on a 9‐point scale, from 9 = Mostly A's to 1 = Mostly F's.

Sleep

Parent reports

Bedtime and wake‐up times were reported by parents on school and non‐school nights in a typical week and sleep durations (in hours) were computed. Parents also reported on children's daytime sleepiness in the past week by responding to the 8‐item Daytime Sleepiness subscale of Children's Sleep Habits Questionnaire (Owens et al., 2000), e.g., “child has difficulty getting out of bed in the morning,” on a scale with 1 = rarely (never or once), 2 = sometimes (2–4 times), and 3 = usually (5–7 times), with Cronbach's alpha ranging from .62 to .72 across assessments. A sensitivity analysis was also performed with an index of fewer items with higher alpha values.

Accelerometry measurement

At each data collection, children were instructed to wear accelerometers (Actigraph GT3X+, Actigraph, LLC.) at their right hip for seven 24‐hour days, which assessed accelerations against gravity in three axes at 40‐Hertz. Based on accelerometry data, we first used an adapted version of the refined sleep algorithm (RSA; Barreira et al., 2015) to compute sleep onset time and sleep duration on each day. Then we computed the school night and non‐school night average sleep onset and duration by taking the average of these two indices across those measured on Sunday through Thursday nights, and across those on Friday and Saturday nights, respectively. We also computed the standard deviation of onset and duration across school nights to index irregularity in school night sleep onset and duration. For each child at each assessment, we only computed irregularity if they had at least three school nights of valid data.

Covariates

Covariates for analytic models measured at baseline included child age (in years), sex (0 = female; 1 = male), birth order (coded as the order among all their siblings in the household), birth country of the child and the parent (0 = U.S.; 1 = non‐U.S.), parent or guardian marital status (0 = married or living as married; 1 = other) and highest education level (the highest education level completed between parents or guardians: 5 = 6th grade or less; 7 = 7th‐8th grade; 10 = 9th‐12th grade; 12 = high school graduate; 13 = completed some college credit but no degree; 14 = technical degree or associate's degree;16 = bachelor's degree; and 18 = master's, professional, or doctoral degree), numbers of children and adults living in the household, and intervention status (0 = control group; 1 = intervention group).

Covariates measured at each assessment included parent employment status (0 = not employed; 1 = employed full‐ or part‐time), total household income (15 = $14,999 or less; 20 = $15,000–$24,999; 30 = $25,000–$34,999; 42 = $35,000–$49,999; 62 = $50,000–$74,999; 112 = $75,000–$149,000; 175 = $150,000–$199,999; 200 = $200,000 or more), how often English was spoken at home (1 = never; 2 = sometimes; 3 = about ½ the time; 4 = most of the time; and 5 = always), and child's stage of sexual maturity (where girls reported their breast and pubic hair Tanner stages, from 1 to 5, and boys reported their pubic hair and testes, scrotum, and penis Tanner stages, from 1 to 5, based on sex‐specific drawings and descriptions of the Tanner stages; Morris & Udry, 1980). Moreover, in analytic models with accelerometry sleep measures, we adjusted for the number of available measurements at school and non‐school nights at each assessment for the school and non‐school night sleep outcomes, respectively.

Analysis

We described mobile phone ownership prevalence by age using a cumulative frequency curve based on the sample's available observations, with child age (continuous) along the X axis and proportion of children owning mobile phones as values on the Y axis. At each age value, children were counted as available observations if they fulfilled either of two criteria: (1) mobile phone ownership was observed at that age; or (2) ownership could be inferred from existing observations (inference rules detailed in Table S2).

We then used multilevel modeling for confirmatory analyses testing the hypothesized associations of mobile phone ownership status and acquisition age with each adjustment measure. Two‐level models (Levels 1 and 2 for within‐person and between‐person associations) were estimated, with sandwich estimator applied additionally to account for the non‐independence of observations from children from the same households (Liang & Zeger, 1986). For fixed effects, we estimated three models for each outcome variable: Model 1 tested main effects of mobile phone ownership and acquisition age; in Model 2, we entered the ownership × time (since baseline; in years) interaction to test how ownership is associated with the longitudinal changing trend of the outcome; in Model 3, we tested the ownership × acquisition age and ownership × acquisition age × time interactions to test whether the ownership effects on levels and changes in the outcome differed by acquisition age. A nonsignificant ownership × acquisition age × time interaction was omitted from the final Model 3 to show results for the lower‐order, ownership × acquisition age interaction (Aiken & West, 1991). Models included random intercepts and slopes of time as random effects. In total, for each outcome variable we tested five associations, and we controlled the false discovery rate (FDR) using the Benjamini–Hochberg approach (Benjamini & Hochberg, 1995): p values, ranked from smallest to largest, were compared to .01, .02, .03, .04, and .05, respectively.

Acquisition age and non‐binary covariates were sample‐mean centered. We handled the censored data for acquisition age and missing data with multivariate imputation using chained equations (MICE), an algorithm based on the MCMC method (van Buuren & Groothuis‐Oudshoorn, 2011), by creating five imputation datasets with 100 iterations and pooling results with Rubin's rules (Barnard & Rubin, 1999; Rubin, 1987). We performed power analysis for the two‐level models with Monte Carlo simulation (Arend & Schäfer, 2019). Based on the sample size of 263 with five assessments, power estimation revealed the detectable effect sizes with 80% power to be 0.10 for Level 1 direct effect (i.e., ownership effect), 0.19 to 0.29 for Level 2 direct effect (i.e., acquisition age effect), and 0.33 for cross‐level interactions (i.e., ownership × acquisition age). Thus, nonsignificant results in the models would imply either small associations that are not detectable at this level of power or an absence of associations.

Using the same set of imputed data, we conducted two sets of exploratory analysis. First, on the condition of nonsignificant results from the main analysis on mobile phone ownership and acquisition age, we would test (1) the quadratic term of acquisition age, and (2) the interactions between each potential moderator (i.e., child sex, sexual maturity, and highest parental education and household income) and mobile phone ownership and acquisition age. Second, to explore the effects of smartphone ownership (instead of smartphones and non‐smartphone mobile phones combined), we repeated the study model testing procedure by replacing mobile phone ownership and acquisition age with smartphone ownership and smartphone acquisition age.

All analyses were performed in R, version 3.6.1.

RESULTS

The mobile phone ownership rates were 14.8%, 27.7%, 50.0%, 68.3%, and 91.9% across the five annual assessments. As Figure 1 shows, prevalence of mobile phone ownership increased by age, with the steepest increase in acquisition from age 11 to 13, and with no apparent differences by sex. The estimated mean (SD) of first acquisition age of mobile phones (both smartphones and non‐smartphones) was 11.62 (1.41) years, with the minimum, 1st quartile, median, 3rd quartile, and maximum being 7.70, 10.70, 11.60, 12.55, and 15.25 years, respectively. Among the children who had their own mobile phones, the proportions of children who had smartphones were 43.6%, 63.9%, 84.9%, 98.2%, and 99.0% across the five assessments. The prevalence of smartphone ownership by age is shown in Figure S1. The pattern was similar to that of mobile phone ownership despite slightly lower prevalence between ages 9 to 13. The estimated mean (SD) of first acquisition age of smartphones was 11.82 (1.39) years, with the minimum, 1st quartile, median, 3rd quartile, and maximum being 7.70, 11.00, 11.75, 12.85, and 15.25 years, respectively.

FIGURE 1.

FIGURE 1

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Prevalence of mobile phone ownership by age.

Table 1 shows means and standard deviations of the adjustment outcomes. To correct for skewness, we applied log‐transformation to depressive symptoms, square‐root‐transformation to accelerometer‐measured school night sleep onset irregularity and duration irregularity, and square‐transformation to grades. Figures 2 and 3 visualize distributions of each outcome grouped by ownership status and acquisition age (before or at and after 11.62 years) before adjustment; the statistical significance of effects of ownership status and acquisition age were tested in the multilevel models as described below.

TABLE 1.

Means (M) and standard deviations (SD) for the adjustment measures at each assessment

Baseline, M (SD) 1‐year follow‐up, M (SD) 2‐year follow‐up, M (SD) 3‐year follow‐up, M (SD) 4‐year follow‐up, M (SD)
Non‐sleep outcomes
Depressive symptoms 3.02 (2.91) 2.24 (2.41) 1.99 (2.58) 1.67 (2.33) 1.88 (2.50)
Academic performance 7.47 (1.24) 7.33 (1.37) 7.22 (1.49) 7.31 (1.45) 7.33 (1.39)
Sleep—parent reports
SN bedtime a 1.11 (0.64) 1.18 (0.68) 1.31 (0.68) 1.48 (0.75) 1.54 (0.73)
SN sleep duration 9.73 (0.74) 9.71 (0.72) 9.55 (0.75) 9.29 (0.84) 9.22 (0.86)
NSN bedtime a 2.19 (0.94) 2.29 (0.90) 2.37 (0.94) 2.61 (1.15) 2.59 (1.06)
NSN sleep duration 9.98 (1.31) 10.06 (1.19) 10.13 (1.39) 10.01 (1.40) 10.30 (1.27)
Daytime sleepiness 12.95 (3.05) 12.02 (3.20) 12.18 (3.33) 12.16 (3.40) 11.72 (3.08)
Sleep—accelerometry measurements
SN sleep onset a 2.46 (0.93) 2.58 (0.92) 2.77 (1.13) 2.85 (1.11) 3.01 (1.40)
SN sleep onset irregularity 0.83 (0.54) 0.85 (0.65) 0.93 (0.63) 0.95 (0.62) 1.14 (0.75)
SN sleep duration 8.68 (1.06) 8.67 (1.06) 8.56 (1.25) 8.45 (1.33) 8.52 (1.58)
SN sleep duration irregularity 1.32 (0.91) 1.27 (0.93) 1.31 (0.86) 1.38 (0.89) 1.51 (1.03)
NSN sleep onset a 3.20 (1.21) 3.23 (1.25) 3.56 (1.30) 3.57 (1.56) 3.63 (1.60)
NSN sleep duration 8.53 (1.27) 8.64 (1.61) 8.57 (1.73) 8.56 (1.56) 8.85 (1.83)
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Abbreviations: NSN, non‐school night; SN, school night.

a

Indicated by hours later than 8 pm.

FIGURE 2.

FIGURE 2

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Levels of adjustment measures by mobile phone ownership status.

FIGURE 3.

FIGURE 3

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Levels of adjustment measures by acquisition age (i.e., before or at/after 11.62 years).

Table 2 shows results for the multilevel models testing the hypothesized associations. Associations with p < .05 were the mobile phone ownership × time interaction associations with depressive symptoms, parent‐reported school night sleep duration, and accelerometry‐measured non‐school night sleep duration. Results from follow‐up simple slope tests showed that compared to the without‐ownership status, with phone ownership, depressive symptoms decreased less per year, γ = −.05, SE = .04, p = .143 versus γ = −.14, SE = .03, p < .001, parent‐reported school night sleep duration decreased more per year, γ = −.17, SE = .04, p < .001 versus γ = −.09, SE = .03, p = .002, and accelerometer‐measured non‐school night sleep duration increased more per year, γ = .30, SE = .10, p = .009 versus γ = .12, SE = .08, p = .140.

TABLE 2.

Estimated parameters for multilevel models testing statistical effects involving mobile phone ownership and acquisition age on adjustment measures

Outcome Ownership Acquisition age Ownership × time Ownership × acquisition age Ownership × acquisition age × time
γ (SE) p γ (SE) p γ (SE) p γ (SE) p γ (SE) p
Non‐sleep outcome
Depressive symptoms −.02 (.06) .733 −.03 (.02) .282 .09 (.04) .018 .05 (.03) .141 −.00 (.03) .962
Grades 2.79 (1.99) .173 .10 (.93) .919 .05 (.80) .952 −.55 (.75) .460 −.04 (.80) .962
Sleep—parent reports
SN bedtime .03 (.08) .758 .02 (.03) .392 .03 (.03) .300 .05 (.03) .088 .00 (.02) .988
SN sleep duration −.01 (.07) .851 −.02 (.03) .439 −.08 (.03) .014 −.02 (.03) .496 −.01 (.03) .833
NSN bedtime .14 (.09) .141 .03 (.04) .458 .05(.04) .182 .03 (.04) .403 .01 (.03) .667
NSN sleep duration .01 (.13) .966 .00 (.04) .916 .01 (.06) .866 .06 (.06) .330 −.02 (.05) .593
Daytime sleepiness −.01 (.25) .985 .06 (.11) .563 .12 (.12) .317 .20 (.14) .167 −.00 (.11) .997
Sleep—accelerometry measurements
SN sleep onset −.07 (.13) .597 −.02 (.04) .690 .01 (.05) .928 .04 (.05) .413 .02 (.04) .548
SN sleep onset irregularity −.01 (.04) .775 −.00 (.01) .925 .03 (.02) .093 .01 (.02) .608 −.01 (.01) .474
SN sleep duration .26 (.19) .204 .04 (.06) .515 −.06 (.07) .371 .01 (.06) .858 .04 (.06) .451
SN sleep duration irregularity .03 (.04) .341 .00 (.01) .680 .02 (.02) .262 .01 (.02) .424 −.00 (.01) .985
NSN sleep onset .08 (.14) .585 .03 (.05) .600 −.03 (.07) .667 .00 (.06) .952 .03 (.05) .567
NSN sleep duration −.25 (.20) .228 −.06 (.06) .286 .18 (.08) .035 .03 (.09) .773 −.01 (.07) .849
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Note: Unstandardized coefficients (γ), standard errors (SE), and p are reported. SN, school night; NSN, non‐school night. Boldface type indicates p < .05. No coefficients met false‐discovery‐rate‐corrected significance levels. All models adjusted for child baseline age, sex, birth order, birth country of the child and the parent, parent/guardian marital status and highest education level, numbers of children and adults living in the household, parent employment status, total household income, how often English was spoken at home, intervention status, child sexual maturity, and time since baseline. In models with accelerometry sleep outcomes, we also adjusted for the number of available measurements at school and non‐school nights in each assessment for the school and non‐school night sleep outcomes, respectively.

However, none of the tested associations were statistically significant after controlling for FDR. Thus, results do not reject the null hypotheses. Together, results suggest that mobile phone ownership and acquisition age did not demonstrate statistically significant associations with levels and changes in these adjustment outcomes.

Further, we conducted sensitivity analysis to address potential concerns with the medium‐strength alpha values of the 8‐item daytime sleepiness measure. Specifically, we eliminated the last two items that had low correlations (r < .20) with the remaining six items across assessments and performed the analysis again for the 6‐item daytime sleepiness measure (alpha = .72–.80). The results pattern (Table S3) was consistent with the analysis using original measure, ruling out the possibility that lower internal consistency explained the null results.

Given the FDR‐controlled null results, as planned, we performed exploratory analysis to examine the quadratic effect of acquisition age and moderation effects of child sex, sexual maturity, and highest parental education and household income on the associations linking mobile phone ownership and acquisition age to adjustment outcomes. Full results are shown in Tables S4 and S5. Results with p < .05 include the moderations of sex and sexual maturity on the association between ownership and depressive symptoms, and of sexual maturity on the association between ownership and parent‐reported school night sleep duration. Follow‐up analyses revealed that mobile phone ownership was associated with less depressive symptoms for boys (γ = −.13, SE = .07, p = .072) than for girls (γ = .07, SE = .07, p = .359), for children with lower (mean‐1SD) sexual maturity (γ = −.19, SE = .09, p = .031) than for those with higher (mean + 1 SD) maturity (γ = .14, SE = .07, p = .069). Ownership was also associated with 0.12‐hour (SE = .09, p = .199) longer sleep duration among children with lower sexual maturity, and 0.14‐hour (SE = .09, p = .156) shorter sleep duration among children with higher maturity.

Further, we explored the effects of smartphone ownership and smartphone acquisition age on child adjustment measures. As shown in Table S6, the results pattern stayed almost the same with those of mobile phone ownership and acquisition age. However, the smartphone ownership × time interaction association with depressive symptoms was statistically significant even after controlling for FDR, with follow‐up simple slope test showing that compared to non‐smartphone, with smartphone ownership, depressive symptoms decreased less per year, γ = −.04, SE = .04, p = .251 versus γ = −.14, SE = .03, p < .001, which was the same pattern observed with all mobile phone ownership despite being more significant.

DISCUSSION

This observational study provided evidence for the questions about whether and when children should have their own mobile phones. These questions are both practically and theoretically important given the salient role of technology in the social ecology of child development (Navarro & Tudge, 2022). We used prospective, longitudinal data to examine children's ownership of and age of acquiring mobile phones and associations with multiple child adjustment measures. We tested these associations among Latinx children—a fast‐growing yet understudied group. Based on a sample of low‐income Latinx children with overweight or obesity who were assessed annually for five times starting from ages 7 to 11, our results revealed that most of these children first acquired their own mobile phones between around 11 to 13 years old, with a mean of 11.62 and median of 11.60 years. This estimated acquisition age is consistent with data from a cross‐sectional U.S. sample reporting 53% of children had smartphones by age 11 (Common Sense Media, 2019). Past research about children's timing of acquiring mobile phones has relied on retrospective and cross‐sectional data in predominantly White samples. Our longitudinal study design allowed us to uniquely demonstrate the prospective acquisition of mobile phones among low‐income, Latinx children, as they aged.

After controlling for multiple testing, we did not find statistically significant associations linking mobile phone ownership status, acquisition age, nor their interactions with levels or changes in child adjustment measures, including depressive symptoms, grades, and neither reported nor objectively measured sleep timing, duration, and irregularity. This finding was based on rigorously designed, pre‐registered analyses, with multiple imputation for missing data and tests for a sample of adjustment measures previously hypothesized to be associated with mobile phone use in children. Confidence in these results is further strengthened by sensitivity analyses. Based on the power estimation, the null result pattern suggests small or no associations between mobile phone ownership and acquisition age with these adjustment measures among low‐income Latinx children.

These null results add to prior findings in other samples identifying little evidence for the effects of digital technology on child adjustment (George et al., 2020; Jensen et al., 2019; Orben & Przybylski, 2019a, 2019b). Further, the null results regarding mobile phone acquisition age are inconsistent with the developmental sensitivity arguments about technology use, which suggests that the sensitivity mechanism may only apply to certain types of technology, such as social media (Orben et al., 2022), and that more research is needed for investigating sensitivity windows for different types of technology adoption and use. This result pattern also suggests that compared to whether and when to own a mobile phone, it may be more important to consider how children use mobile phones, including their activities, motives, and communication partners (Yang et al., 2021).

Without controlling for FDR with multiple testing, however, statistically significant associations emerged, including the interaction between mobile phone ownership and time, associated with depressive symptoms, parent‐reported school night sleep duration and objectively measured non‐school night sleep duration. Consistent with the neo‐ecological theory that highlights both positive and inverse proximal processes through which technology influences child development (Navarro & Tudge, 2022), these interactions suggest that mobile phone ownership may have both negative and positive implications for changes in these outcomes: Ownership was associated with smaller decreases in depressive symptoms and shorter school night sleep duration over time, but with increases in non‐school night sleep duration over time. In other words, owning a mobile phone may expose children to more negative digital experiences and contribute to slower recoveries from depressive symptoms, and distract them on school nights to make them tend to sleep less over time. Meanwhile, to compensate for shorter sleep duration over time on school nights while owning a mobile phone, children may tend to increase their sleep duration on non‐school nights. Note that one should interpret these interactions with caution because of a more lenient statistical significance threshold. The results revealing the interaction effects emphasize the importance of longitudinal, repeated observations of mobile phone ownership and adjustment measures. These findings also suggest that despite a lack of associations with levels of adjustment, ownership may still impact the changing trends.

Our follow‐up exploratory analyses tested more possibilities of heterogeneity in associations. Results suggested that sex and sexual maturity may be factors to consider in future confirmatory studies of mobile phone ownership and child adjustment. Specifically, mobile phone ownership may have more negative effects on girls than boys (e.g., more depressive symptoms among girls), possibly because girls are more susceptible to negative effects of problem mobile phone use on their depressive symptoms (Seo et al., 2016). Mobile phone ownership also may benefit children earlier in puberty but potentially harm children later in puberty (e.g., less depressive symptoms and longer school night sleep duration with lower maturity versus more depressive symptoms and shorter school night sleep duration among those with higher maturity). This result pattern is consistent with the speculation that pubertal changes, which subsequently lead to differences in other developmental domains such as social interactions, may potentially dispose youth to more negative media effects (Orben et al., 2022).

These possible moderation results suggest that the overall associations may be masking heterogeneity, and highlight the need to examine individual and group differences in whether and how mobile phones may impact child adjustment. Specifically, children may have different levels of susceptibility to media effects (Valkenburg & Peter, 2013), and an important future direction is to adopt a person‐specific approach in research on smartphone effects (Valkenburg et al., 2021). Further, although the results for mobile phones and smartphones mostly converged, we still found indication that the effect of smartphone ownership on the changing trend of depressive symptoms over time may be more salient than that of mobile phone ownership, highlighting the need to examine how the variety of digital platforms accessed by children may differentiate the effects of mobile devices. The exploratory nature of these analyses that were not hypothesized in pre‐registration also means that further confirmatory analyses are needed to hypothesize and test the existence and directionality of these moderation effects.

Despite the strengths, this study has limitations. First, the sample size may have limited statistical power, though the power concern is somewhat alleviated by very high retention rates over the first three follow‐ups. Second, although we used several validated adjustment measures, it is possible that they are not sufficiently sensitive nor comprehensive. Future research may include other measurement approaches and adjustment measures—such as child anxiety. Third, our findings were based on a sample participating in a weight gain prevention intervention study, of which both conditions could have contributed to the null results or changing trends such as the decreases in depressive symptoms observed over time.

Although our findings suggest that whether and when children acquire their own mobile phones seems to have few measurable implications for adjustment, it is possible that for the economically disadvantaged, Latinx children with overweight or obesity in our sample, the positive and negative implications of mobile phone ownership and acquisition age may balance out, leading overall associations to be null. Further, converging with perspectives based on screen time duration studies (Whitlock & Masur, 2019), our results suggest that it may be time to move beyond simply treating mobile phone ownership and acquisition age as unified exposures or behaviors for all children, and to investigate the potential implications of what children are actually seeing and doing on their mobile phones (e.g., Screenomics; Reeves et al., 2020, 2021).

CONFLICT OF INTEREST

We have no known conflict of interest to disclose.

Supporting information

Data S1

Click here for additional data file. (135.8KB, docx)

ACKNOWLEDGMENTS

This research was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number U01HL103629, and the Stanford Data Science Scholarship, Stanford Maternal and Child Health Research Institute and the Department of Pediatrics, Stanford University. The content expressed in this paper is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, the U.S. Department of Health and Human Services, and the U.S. Government. We thank the Stanford GOALS research staff, including health educators, coaches, data collectors, community advisors, community partner organizations; and the children and families who participated in the Stanford GOALS trial. The data and code necessary to reproduce the analyses presented here are publicly accessible, as are the materials necessary to attempt to replicate the findings. Data, code, and materials are available from the first author upon reasonable request. Analyses were pre‐registered and are available at the following URL: https://osf.io/hukfd.

Sun, X. , Haydel, K. F. , Matheson, D. , Desai, M. , & Robinson, T. N. (2023). Are mobile phone ownership and age of acquisition associated with child adjustment? A 5‐year prospective study among low‐income Latinx children. Child Development, 94, 303–314. 10.1111/cdev.13851

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