What the research actually says about heavy phone use

By Unplug · Published · Updated · 7 min read

In short

  • People spend a lot of time online, but most of the figures come from surveys, not from measuring what people actually do.
  • Some of the best-known claims are weaker than their reputation. A phone nearby draining your attention has mixed support, and viral figures about a shrinking “attention span” rest on weak or unverifiable sources.
  • Cutting back on social media shows small average benefits in some trials and none in others. The evidence on adults is thinner than on teenagers.

Phones get blamed for a lot. Some of that holds up, some does not, and some is simply not known yet. This is what we could check, and how far you can trust it.

How much time do we spend?

Internet users spend about 6 hours and 38 minutes a day online, on all devices, according to a commercial survey reported by DataReportal (Kepios, 2025). That covers laptops, tablets and work, not only phones. The page also does not explain its method in detail.

So treat it as a rough picture. A survey answer is not the same as logged use. The safe reading is that many people spend a lot of time online, and nobody knows the exact figure.

Attention and interruptions

Gloria Mark, who studies how people work with computers, reports that the average time spent on a single screen fell from about 150 seconds (2.5 minutes) in 2003-04 to about 47 seconds by 2020. These numbers come from workplace logging studies of office workers, and we cite an interview with her, not the primary paper (Mark, 2024). Note what is measured: time on one screen. It is not “attention span”.

Interruptions have a cost, but it is a mixed one. In an experiment, people who were interrupted finished their tasks in less time, with no difference in quality. They also reported more stress, frustration, time pressure and effort (Mark, Gudith & Klocke, 2008).

Notifications alone can get in the way too. In a lab experiment, phone notifications significantly disrupted performance on an attention-demanding task (Stothart, Mitchum & Yehnert, 2015). It was a short task done by students in a lab. We do not know how much of this carries over to an ordinary day.

Does a phone on the desk drain your mind? Contested

A well-known 2017 study claimed that the mere presence of your own phone reduces the mental capacity you have available (Ward et al., 2017). This is often called the “brain drain” effect. Later work is less clear.

A pre-registered replication found that the effect did not replicate: there was no difference by where the phone was (Ruiz Pardo & Minda, 2022). A meta-analysis of 22 studies and 43 effects found an overall effect that was small and negative (Hedges g = -0.14, 95% CI -0.24 to -0.03), with high heterogeneity, which means results differed a lot from study to study (Böttger, Poschik & Zierer, 2023).

The effect on memory was significant (g = -0.23, 95% CI -0.36 to -0.10). On attention it was not (g = -0.07, 95% CI -0.21 to 0.06). The pooled data also mix phone use and phone presence, so they cannot tell us much about a silent phone lying nearby.

A fair summary: if there is an effect, it is small, it may differ by what is measured, and the original claim is contested.

Does cutting back help? What the experiments found

Experiments are the best tool here, because they can separate cause from correlation. The results are mixed.

In a US online randomised trial, the researchers recruited 2,743 users and asked some of them to deactivate Facebook for 4 weeks. Their Facebook use dropped by roughly an hour a day, and their self-reported well-being improved by a small but significant amount. The authors stress that the causal effects are far smaller than correlational estimates suggest (Allcott et al., 2020). It covered only Facebook, with volunteers who were paid, over 4 weeks.

A trial with students tried a cap of 10 minutes a day on Facebook, Instagram and Snapchat for about 2.5 months. It analysed 122 students (60 in the treatment group, 62 in the control group). Among Android users with tracked data, social media use was 8.1 minutes a day in the treatment group against 24.2 in the control group. Total digital activity did not fall, because people shifted to messaging apps. There was no significant effect on life satisfaction (p = 0.855), mental well-being (p = 0.872) or grades (p = 0.239). The authors note that the study had low statistical power to detect small effects (Collis & Eggers, 2022).

Two meta-analyses pool such trials. One covered 10 randomised trials with 1,491 participants. Reducing or stopping social media gave a small reduction in depressive symptoms (g = 0.25, 95% CI 0.10 to 0.41) after adjusting for publication bias. The trials were short and mostly with young adults (May, Malouff & Meynadier, 2025). The other found that digital detox was associated with a small improvement in subjective well-being (SMD = 0.21, 95% CI 0.06 to 0.34), again with high heterogeneity and mostly young participants (Ansari et al., 2024).

Reviews also suggest that reducing use may work better than total abstinence, and that the evidence is still too limited to guide policy (Marciano, Jindal & Viswanath, 2024).

Put together: small average benefits in some trials, none in others, short follow-ups, and little data on older adults.

The bigger picture: how large are the effects?

Most studies on screens and well-being are correlational, so they cannot show what causes what. In three large US and UK datasets covering 355,358 adolescents in total, technology use explained at most 0.4% of the variation in well-being (Orben & Przybylski, 2019). That is a small share, though the study is correlational and looked only at teenagers.

An umbrella review gathered 102 meta-analyses on young people and screens. Associations were small to moderate. For example, social media and depression had r = 0.12 (95% CI 0.05 to 0.19). Most of the meta-analyses (95 of 102) had a medium-to-high risk of bias, and the findings are associations, not proof of cause. It covered youth only (Sanders et al., 2024).

Both studies are about young people. For adults, the evidence is thinner.

Sleep

The sleep evidence is more consistent, but it is still observational. A meta-analysis of 20 studies with 125,198 children and adolescents (mean age 14.5) found that bedtime device use was associated with inadequate sleep (OR 2.17, 95% CI 1.42-3.32), poor sleep quality (OR 1.46, 95% CI 1.14-1.88) and excessive daytime sleepiness (OR 2.72, 95% CI 1.32-5.61). An odds ratio above 1 means the outcome was more common among those who used devices at bedtime. The data were self-reported (Carter et al., 2016).

For adults there is less. A cross-sectional survey of 844 Flemish adults found that texting or calling after lights out was associated with worse sleep quality. It was self-reported and cannot show cause (Exelmans & Van den Bulck, 2016).

What this suggests in practice

These are reasonable inferences from the evidence above, not proven prescriptions. This is not medical advice.

A note on who wrote this

We make Unplug, a minimalist Android launcher. Nothing above shows that any app improves well-being, and this article is not evidence for our product.

How we chose these sources

We used the primary papers, or the publisher’s page where we could open it. Where we cite an interview or a report instead, we say so.

We left out viral numbers, such as how many times a day people check their phones and the goldfish attention span, because their sources are weak or cannot be verified.

Date accessed: 5 November 2026.

References

  1. Kepios (2025). Digital 2025: the world is ever more connected. DataReportal, 5 February 2025. https://datareportal.com/reports/digital-2025-sub-section-ever-more-connected
  2. Mark, G. (2024). Interview in UC Newsroom, University of California. https://www.universityofcalifornia.edu/news/how-sharpen-your-attention-and-meet-your-goals-2024
  3. Mark, G., Gudith, D., & Klocke, U. (2008). The Cost of Interrupted Work: More Speed and Stress. CHI 2008. https://doi.org/10.1145/1357054.1357072
  4. Stothart, C., Mitchum, A., & Yehnert, C. (2015). The attentional cost of receiving a cell phone notification. Journal of Experimental Psychology: Human Perception and Performance, 41(4), 893-897. https://doi.org/10.1037/xhp0000100
  5. Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain Drain: The Mere Presence of One's Own Smartphone Reduces Available Cognitive Capacity. Journal of the Association for Consumer Research, 2(2), 140-154. https://doi.org/10.1086/691462
  6. Ruiz Pardo, & Minda (2022). Reexamining the “brain drain” effect: A replication of Ward et al. (2017). Acta Psychologica. https://doi.org/10.1016/j.actpsy.2022.103717
  7. Böttger, T., Poschik, M., & Zierer, K. (2023). Does the Brain Drain Effect Really Exist? A Meta-Analysis. Behavioral Sciences, 13(9), 751. https://doi.org/10.3390/bs13090751
  8. Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020). The Welfare Effects of Social Media. American Economic Review, 110(3), 629-676. https://doi.org/10.1257/aer.20190658
  9. Collis, A., & Eggers, F. (2022). Effects of restricting social media usage on wellbeing and performance: A randomized control trial among students. PLoS ONE. https://doi.org/10.1371/journal.pone.0272416
  10. May, Malouff, & Meynadier (2025). Reducing Social Media Use Decreases Depression Symptoms: A Meta-Analysis of Randomised Controlled Trials. European Journal of Investigation in Health, Psychology and Education. https://doi.org/10.3390/ejihpe15110222
  11. Ansari, Iqbal, Azeem, & Danyal (2024). Cyberpsychology, Behavior, and Social Networking, 27(11), 753-770. https://doi.org/10.1089/cyber.2023.0742
  12. Marciano, Jindal, & Viswanath (2024). Pediatrics. https://doi.org/10.1542/peds.2024-066142
  13. Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3(2), 173-182. https://doi.org/10.1038/s41562-018-0506-1
  14. Sanders et al. (2024). An umbrella review of the benefits and risks associated with youths’ interactions with electronic screens. Nature Human Behaviour, 8(1), 82-99. PMID 37957284. https://pubmed.ncbi.nlm.nih.gov/37957284/
  15. Carter, B., Rees, P., Hale, L., Bhattacharjee, D., & Paradkar, M. S. (2016). Association Between Portable Screen-Based Media Device Access or Use and Sleep Outcomes. JAMA Pediatrics, 170(12), 1202-1208. https://doi.org/10.1001/jamapediatrics.2016.2341
  16. Exelmans, L., & Van den Bulck, J. (2016). Bedtime mobile phone use and sleep in adults. Social Science & Medicine, 148, 93-101. https://doi.org/10.1016/j.socscimed.2015.11.037