INTRODUCTION
Smartphones are a normative part of adolescent daily life. Most youth enter smartphone ownership between 10–12 years old in the US,1 yet there is variability across individuals. There are benefits and risks associated with early smartphone ownership and use. Although smartphones offer social opportunities, they can also detract from the quality of in-person interactions.2 Further, smartphones can expose youth to inappropriate content and cyberbullying, interfere with sleep, and may negatively impact mental health and impulsivity.3 Given recent legislation regarding a minimum age for social media use, it is of interest to understand the sociodemographic factors related to the age of smartphone ownership. This study leverages a large national dataset to examine sociodemographic predictors of first smartphone ownership age in adolescent youth.
METHODS
The ongoing ABCD Study collected demographic and screen use data (among other domains) from 11,878 children and primary caregivers at baseline (Mage=9.9 years old) beginning in 2016 with annual follow-up assessments.4 The analytic sample included all participants with complete data (N=9,475). Parents reported sociodemographic information at baseline. Parents were asked each year at what age their child received their first smartphone.4 Mplus version 8.3 was used for analyses.5 Descriptive statistics compared sociodemographic characteristics across smartphone-ownership timing. We then estimated a cross-sectional multivariable logistic regression model to examine associations between key sociodemographic predictors (sex, race/ethnicity, household income, household structure, and parental education)6 and first smartphone ownership age. Race/ethnicity variables were included because the purpose of this study was to identify disparities by sociodemographic factors, and prior studies show variability in technology use patterns in youth based on this social construct.7 Self-reported categories were based on survey responses. We assessed multicollinearity via variance inflation factors (VIF>5). Early smartphone ownership <10 years old) was modeled as a binary outcome with odds ratios > 1 indicating higher odds of early ownership. This cutoff was based on prior studies showing youth receive smartphones near age 10, on average.1 Analyses applied ABCD propensity sampling weights and used cluster-robust standard errors with family ID as the clustering unit to account for twins/siblings. Statistical significance was defined as p-value < .05 and testing was two-sided.
RESULTS
Descriptive statistics (Table 1) showed that the mean age of smartphone ownership was 10.9 years old and only 4% of youth were without a smartphone by age 14. In multivariable logistic regression (Table 2), adjusted odds of early ownership (<10 years) were highest for Black (OR=3.01 95% CI: 2.50–3.64), Multi-racial/ethnic (OR=1.79, 95% CI: 1.45–2.20), and Hispanic (OR=1.41, 95% CI: 1.18–1.70) relative to White youth. Youth living with a single parent (OR=2.09, 95% CI: 1.77–2.46) or cohabiting partners (OR=2.11, 95% CI: 1.64–2.71) were more likely to experience early smartphone ownership, compared to living with married parents. Lower parental education (<Bachelor’s degree) was also associated with higher odds (OR=1.72, 95%CI: 1.46–2.02) of early smartphone ownership. Finally, female youth were more likely to demonstrate early smartphone ownership than males (OR=1.41, 95% CI: 1.24–1.60). The maximum VIF was 1.3, indicating minimal multicollinearity among predictors and that variance inflation was well below conventional concern thresholds.
Table 1.
Descriptive statistics showing the proportion of each sociodemographic category across smartphone age of ownership categories (N=9,475)
| 8 & under | 9–10 | 11–12 | 13–14 | No phone | ||
|---|---|---|---|---|---|---|
| Mage(SD) | N (%) | N (%) | N (%) | N (%) | N (%) | |
| Total | 10.9 (1.5) | 677 (7.1%) | 2,641 (27.9%) | 5,037 (53.1%) | 744 (7.9%) | 376 (4.0%) |
| Youth sex | ||||||
| Male | 11.1 (1.6) | 312 (6.2%) | 1,416 (28.2%) | 2,612 (51.9%) | 454 (9.0%) | 235 (4.7%) |
| Female | 10.8 (1.5) | 355 (7.5%) | 1,504 (31.9%) | 2,419 (51.4%) | 290 (6.2%) | 141 (3.0%) |
| Race | ||||||
| White | 11.3 (1.5) | 148 (2.9%) | 1,237 (24.3%) | 2,910 (57.2%) | 518 (10.2%) | 273 (5.4%) |
| Asian | 11.3 (1.5) | 7 (3.6%) | 44 (22.9%) | 115 (59.9%) | 14 (7.3%) | 12 (6.3%) |
| Black | 10.1 (1.5) | 278 (19.2%) | 628 (43.3%) | 487 (33.6%) | 33 (2.3%) | 23 (1.6%) |
| Hispanic | 10.8 (1.4) | 152 (7.7%) | 672 (33.8%) | 1,005 (50.6%) | 113 (5.7%) | 44 (2.2%) |
| Multiracial/other | 10.8 (1.5) | 82 (8.0%) | 340 (33.2%) | 514 (50.1%) | 65 (6.3%) | 24 (2.3%) |
| Household income | ||||||
| ≥ $75,000/year | 11.3 (1.5) | 182 (3.6%) | 1,219 (23.9%) | 2,980 (58.5%) | 474 (9.3%) | 237 (4.7%) |
| < $75,000/year | 10.6 (1.7) | 397 (10.3%) | 1,438 (37.4%) | 1,665 (43.3%) | 227 (5.9%) | 122 (3.2%) |
| Parental marital status | ||||||
| Married | 11.2 (1.5) | 239 (3.6%) | 1,657 (25.3%) | 3,722 (56.8%) | 615 (9.4%) | 319 (4.9%) |
| Living with partner | 10.4 (1.5) | 66 (12.2%) | 225 (41.4%) | 218 (40.1%) | 18 (3.3%) | 16 (2.9%) |
| Single | 10.4 (1.5) | 341 (13.7%) | 998 (40.0%) | 1,016 (40.7%) | 103 (4.1%) | 40 (1.6%) |
| Parental education | ||||||
| Bachelor’s degree or more | 11.3 (1.5) | 188 (3.6%) | 1,256 (24.1%) | 3,000 (57.6%) | 497 (9.5%) | 271 (5.2%) |
| Less than bachelor’s degree | 10.6 (1.5) | 477 (10.6%) | 1,663 (36.8%) | 2,023 (44.8%) | 247 (5.5%) | 104 (2.3%) |
Note. Mage = Mean age of smartphone ownership, SD = Standard deviation, N = count, % = proportion of sociodemographic category.
Table 2.
Cross-sectional logistic regression model examining predictors of youth smartphone ownership before age 10 in early adolescent participants enrolled between 2016 and 2018.
| Predictor | OR | 95% CI (OR) |
|---|---|---|
| Youth sex | ||
| Female | 1.38*** | [1.19, 1.60] |
| Male (reference) | ||
| Race | ||
| Asian | 1.03 | [0.53, 2.00] |
| Black | 2.78*** | [2.07, 3.64] |
| Hispanic | 1.36** | [1.10, 1.69] |
| Multiracial/other | 1.69*** | [1.27, 2.25] |
| White, non-Hispanic (reference) | ||
| Household income | ||
| < $75,000 | 1.10 | [0.89, 1.36] |
| ≥ $75,000 (reference) | ||
| Parental marital status | ||
| Living with partner | 1.90*** | [1.41, 2.56] |
| Single parent | 2.01*** | [1.67, 2.44] |
| Married (reference) | ||
| Parental education | ||
| < Bachelor’s degree | 1.70 | [1.40, 2.06] |
| ≥ Bachelor’s degree (reference) |
Note. Smartphone age of ownership outcome was coded as early = 1 (< 10 years old) or later = 0 (10 years old or later). Race/ethnicity dummy variables compare each group (Asian, Black, Hispanic, Other) to non-Hispanic White referent. Household income < $75,000 (1 = yes; 0 = no). Living with partner (1 = yes; 0 = no); Single parent (1 = yes; 0 = no). Parental education ≤ bachelor’s degree (1 = yes; 0 = no). OR = odds ratio. Higher ORs indicate greater odds of early smartphone ownership. 95% CIs are the 2.5th and 97.5th percentiles of the OR distribution.
p < .001,
p < .01.
DISCUSSION
The present study documents sociodemographic correlates of early smartphone ownership using data from a large, demographically diverse cohort of US adolescents. Consistent with prior evidence,1 children from historically marginalized or structurally disadvantaged groups received their first smartphone before age 10. Conversely, adolescents with higher parental education level, married parents, and non-minority race/ethnicity tended to delay ownership. These patterns persisted after mutual adjustment.
Our results extend prior studies linking lower socioeconomic status to technology use.8 Early ownership may reflect greater perceived utility (e.g., safety monitoring in single-parent or lower-resource contexts) and reduced parental gate-keeping capacity. Yet, young smartphone owners may also face higher exposure to online risks such as cyberbullying and sleep disruption.
Study limitations include operationalization of smartphone ownership age (e.g., integer rounded and dichotomized), a simplified list of independent variables, and the cohort’s enrollment period, which may reflect earlier smartphone norms. Future analyses using more current cohorts are needed to test interactions to identify contemporary conditional effects. Despite these limitations, this study provides a baseline for how family and socioeconomic factors relate to early smartphone ownership. These findings underscore the need for policies, guidelines, and parental education emphasizing early training in safe and responsible smartphone use, particularly in historically marginalized populations.
Acknowledgments
Data used in preparation of this article were obtained from the 5.0 data release of the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), previously held in the NIMH Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147. A full list of supporters is available at Federal Partners – ABCD Study (https://abcdstudy.org/about/federal-partners/). We wish to thank all the research staff for designing the study, collecting, and preprocessing data, as well as the children, parents, teachers, and schools who participated in this multisite, longitudinal study.
Funding/Support:
Work on this manuscript was supported in part by the National Institute of Health and the National Institute on Drug Abuse, awarded to Dr. Assaf Oshri (PI-R01 DA055630-01, R01 DA058334-01A1, and Co-I P50DA051361).
Role of Funder/Sponsor:
The funder had no role in the design and conduct of the study.
Conflict of Interest Disclosures:
LH reported receiving research funding from NICHD and the Stephen and Pam Della Pietra Family Foundation during the conduct of the study, personal fees from Children and Screens Institute and the National Sleep Foundation, and is currently serving as an expert witness in litigation about social media. The other authors have no conflicts of interest relevant to this article to disclose.
Abbreviations:
- ABCD
Adolescent Brain Child Development
- CI
Confidence Interval
- OR
Odds Ratio
- VIF
Variance Inflation Factor
Footnotes
Ethics Approval and Consent to Participate
All methods were carried out in accordance with relevant guidelines and regulations. Study procedures for the Adolescent Brain Cognitive Development (ABCD) Study were approved by the Institutional Review Boards at each of the 21 participating sites, including the University of California, San Diego IRB, which serves as the central IRB (reference number: IRB #160091). Parents/guardians provided informed consent, and children provided informed assent prior to participation. Informed consent was obtained from all participants included in this study. No identifiable images or personal details of participants are presented in this manuscript.
Data Availability Statement:
The data that support the findings of this study are available to qualified researchers with a valid data use contract in the NIMH Data Archive (NDA) at https://nda.nih.gov, NDA Collection #2573, DOI: 10.15154/z563-zd24.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available to qualified researchers with a valid data use contract in the NIMH Data Archive (NDA) at https://nda.nih.gov, NDA Collection #2573, DOI: 10.15154/z563-zd24.