Smartphone screen time statistics: the full data

How much time do people actually spend on smartphones?

The short answer: it depends on who you ask, how you ask, and where you look. Official UK statistics do not currently publish a single, headline figure for average daily smartphone minutes across the population. Instead, the most reliable data comes from specific contexts: observed behaviour on the road, self-reported surveys, and cohort studies that measure screen time in defined groups.

This page collects the most citable smartphone screen time statistics available from UK government sources and peer-reviewed research, with each figure linked to its source. It covers usage volume where measured, problematic use, demographic breakdowns, and the parenting practices that shape adolescent screen time.

Observed smartphone use while driving

The Department for Transport commissions roadside observation surveys of hand-held mobile phone use by drivers. These are among the few UK statistics that measure phone use directly, rather than relying on self-report.

In Great Britain in autumn 2021, 1.0% of drivers were observed using a hand-held mobile phone while driving¹. Of these, 0.6% were holding the phone to their ear and 0.4% were holding it in their hand¹.

The same survey found a clear vehicle-type split: 1.9% of van drivers were observed using a mobile phone while driving, compared with 0.8% of car drivers¹.

By 2023, the observed rate in England had fallen. The Department for Transport's latest roadside survey found that 0.3% of all vehicle drivers were observed using a mobile phone while driving on weekdays in England in autumn 2023². Scotland recorded a similar proportion based on data collected in early 2024².

The Department for Transport cautions that changes in survey methodology mean the 2023 results are not directly comparable with previous surveys in 2021 and 2017². The 2021 figure of 0.8% for car drivers in England and Wales compared with 0.6% in 2017¹.

Who is most likely to use a phone while driving?

The 2021 roadside survey included demographic breakdowns by sex and estimated age. Males and drivers estimated as aged 17 to 29 were more likely to be observed using a mobile phone while driving than females and drivers estimated as aged 60 or over¹.

Self-reported data tells a broader story. The Crime Survey for England and Wales, cited in the Department for Transport release, asked about all phone use while driving, including hands-free use via Bluetooth, voice control or a dashboard holder. For 2019 to 2020, 49.7% of males reported using a mobile while driving, compared with 43.8% of females¹.

The gap between observed hand-held use and self-reported any phone use is important. It suggests that while relatively few drivers are visibly holding a phone, a much larger share use their phones in some form while driving¹.

Problematic smartphone use: prevalence statistics

"Problematic smartphone use" (PSU) refers to patterns of phone use that resemble behavioural addiction: loss of control, preoccupation, and continued use despite negative consequences. It is measured using validated questionnaires rather than raw screen time.

One large random sample of Chinese undergraduates, conducted between April and May 2015, found that 21.3% of the 1,062 smartphone users surveyed met criteria for problematic smartphone use³.

The same study identified several risk factors for PSU. Majoring in the humanities, high monthly income from family (≥1500 RMB), serious emotional symptoms, high perceived stress, and perfectionism-related factors (high doubts about actions, high parental expectations) were all associated with problematic use³.

The authors note that further longitudinal studies are needed to establish whether PSU is a transient or chronic condition³. The figure of 21.3% is specific to Chinese undergraduates in 2015 and should not be read as a universal prevalence rate.

Screen time and mental health: the genetic question

A key question in the research is whether screen time causes poorer mental health, or whether shared genetic factors explain the link. A 2026 study using data from the Avon Longitudinal Study of Parents and Children, a UK cohort of children born in 1991–1992, examined this directly.

The study included 3,003 participants and looked at screen time at ages 16, 22 and 26, and depressive symptoms at age 26⁴.

Some, but not all, forms of screen time were associated with higher depressive symptom scores. For example, time spent using a phone, tablet or e-book at age 22 was linked with higher scores on the Short Mood and Feelings Questionnaire: β = 0.10 (95% CI 0.07, 0.14) on weekdays and β = 0.08 (95% CI 0.04, 0.11) on weekends⁴. Television time at age 26 showed a similar pattern⁴.

The key finding came when the researchers tested for genetic confounding. The associations were attenuated in a genetic sensitivity analysis, suggesting genetic confounding is present in the relationship between screen time and depressive symptoms⁴. In other words, the relationship is not straightforwardly causal.

Adolescent screen time: what parents do matters

Two studies from the Adolescent Brain Cognitive Development (ABCD) Study, a large US cohort, examined how media parenting practices relate to adolescent screen time and smartphone use.

A cross-sectional analysis of 10,048 adolescents aged 12–13 (48.3% female, 45.6% racial/ethnic minorities) found clear patterns⁵:

• Parent screen use, family mealtime screen use, and bedroom screen use were associated with greater adolescent screen time and more problematic social media, video game and mobile phone use⁵.
• Parental use of screens to control behaviour (as a reward or punishment) was associated with higher screen time and greater problematic video game use⁵.
• Parental monitoring of screens was associated with lower screen time and less problematic social media and mobile phone use⁵.
• Parental limit setting was associated with lower screen time and less problematic social media, video game and mobile phone use⁵.

A follow-up prospective analysis of 7,947 adolescents (mean age 12.9 years) tracked whether these associations held over time⁶. The findings were consistent: use of screens to control behaviour and adolescent bedroom screen use were associated with greater screen and smartphone time, while parental monitoring and limiting of screen time were associated with lower adolescent screen time⁶. Parental monitoring was also associated with lower smartphone time⁶.

The practical implication from both studies is clear: the practices that reduce adolescent screen time are monitoring and limit-setting, while modelling heavy screen use, using screens as rewards, and allowing bedroom screens are linked with more use^5,6.

Pre-sleep screen time, sleep and obesity

A cross-sectional study of 62 adolescents aged 11–14 years in Fife, Scotland (33 female, 29 male, mean age 12.2 years) used objective sleep measurement (Actigraph GT3X-BT) and body composition measures to examine which components of screen time are shared determinants of poor sleep and higher adiposity⁷.

The study found that excessive screen time in the 30 minutes before sleep and in the first 30 minutes after waking, excessive weekend screen time, and screen time addiction were shared determinants of higher adiposity, a later chronotype (evening preference), and poor sleep outcomes including increased insomnia symptoms and greater sleep onset variability⁷.

The study also reported mediation analyses showing that adolescent wellbeing mediated the association between screen time and sleep problems. For example, wellbeing mediated 36.3% of the association between pre-sleep screen time and insomnia symptoms, and 21.9% of the association between pre-sleep screen time and body fat percentage⁷.

This is a small study, so the figures should be read as indicative rather than definitive. But it is one of the few studies to use objective sleep measurement in this age group.

Device use and sleep in older adolescents

A Spanish study of adolescents aged 17–18 years from a population-based birth cohort in Menorca examined the association between telecommunication device use and sleep. It used the Pittsburgh Sleep Quality Index for subjective sleep (n = 226) and ActiGraph wGT3X-BT for 7 nights for objective sleep (n = 110)⁸.

The study found that higher tablet use was associated with decreased sleep efficiency and increased minutes of wake time after sleep onset: β = -1.15 (95% CI -1.99, -0.31) for sleep efficiency and β = 7.00 (95% CI 2.40, 11.60) for wake after sleep onset, per increase of 10 minutes of daily tablet use⁸.

Habitual and frequent problematic mobile phone use was associated with lower sleep quality, with prevalence ratios of 1.55 (95% CI 1.03, 2.33) and 1.67 (95% CI 1.09, 2.56) respectively⁸.

The authors note that sleep displacement, mental arousal, and exposure to blue light from screens may play a more important role in these associations than exposure to radiofrequency electromagnetic fields⁸.

What the UK government surveys tell us about screen time

The Department for Science, Innovation and Technology (DSIT) launched its Public Engagement Survey in 2025/2026, a continuous push-to-web survey of UK adults aged 16 and over⁹. The survey provides statistically representative national estimates of adult engagement with science and technology, including how people engage with technology in their day-to-day lives⁹.

Fieldwork for the first year was conducted between November 2025 and March 2026⁹. The survey is designed to support understanding of how people engage with science and technology, and will be updated as new waves are published⁹.

Separately, the Community and Engagement Survey (CES) from the Department for Culture, Media and Sport covers people aged 16 and over in England, exploring engagement with community, culture, media and live sport¹⁰. The CES replaced the Participation Survey and Community Life Survey in October 2025, with data covering April 2025 to March 2026¹⁰. The survey includes detailed demographic and geographic breakdowns for media use topics¹⁰.

Neither survey publishes a single "average daily smartphone minutes" figure in the sources provided. The value of these official statistics lies in their representative sampling and demographic detail, which will improve over time as the surveys become established.

How to interpret these statistics

The statistics on this page come from different methods, populations and time periods. That matters for how you read them:

• Observed behaviour (roadside surveys) measures what people actually do, but only in one context: driving^1,2.
• Self-reported screen time (questionnaires) covers broader use but is subject to recall bias^4,8.
• Problematic use scales measure dependency-like patterns, not minutes of use^3,8.
• Cohort studies can track change over time and control for confounders, but their populations are specific (UK, US, Chinese, Spanish)^3,4,5,6,8.

If you are looking for a single number to cite, the most defensible UK figure is the observed hand-held phone use rate while driving: 1.0% of drivers in Great Britain in 2021, falling to 0.3% in England by 2023^1,2. For problematic use, the most cited prevalence figure is 21.3% among Chinese undergraduates in 2015³.

What to do with this information

If you are a parent, the most actionable finding from the research is that monitoring and limit-setting are associated with lower adolescent screen time, while modelling heavy use, mealtime screens, and bedroom screens are associated with more^5,6. A family media use plan that addresses these specific practices is the evidence-aligned approach.

If you are concerned about your own phone use, the sleep research points to one specific, modifiable behaviour: avoid screens in the 30 minutes before sleep⁷. The Scottish study found that pre-sleep screen time was a shared determinant of poor sleep and higher adiposity in adolescents⁷, and the Spanish study found that tablet use before bed was associated with reduced sleep efficiency⁸.

Tools like Unleashed OS can support the kind of habit-tracking and reminders that fit with the limit-setting the research links with lower screen time. It is currently in public beta on iOS via TestFlight.

How to cite this page

If you use these statistics in your own work, cite the underlying source, not this page. The citation format below follows common academic style.

For the driving statistics: Department for Transport (2024). Seatbelt and mobile phone use surveys: 2023. GOV.UK.

For the problematic use prevalence: Long, J., et al. (2016). Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. BMC Psychiatry.

For the parenting practices: Nagata, J.M., et al. (2026). Prospective associations between media parenting practices and early adolescent screen use. Acta Paediatrica.

For the sleep and obesity data: Gale, E.L., Williams, A.J., & Cecil, J.E. (2025). Pre-sleep screen time and screen time addiction as shared determinants of poor sleep and obesity in adolescents. BMC Global and Public Health.

For the genetic confounding study: Xu, J., et al. (2026). Exploring genetic confounding of the associations between screen time and depressive symptoms in adolescence and early adulthood. International Journal of Epidemiology.