The Boredom Dividend: Why Protecting Idle Time Is a Competitive Advantage in 2026

TL;DR. In the 2025 American Time Use Survey, only 20.2% of people aged 15 and over logged any “relaxing and thinking” on an average day: about 21 minutes a day across the population, against 2.61 hours of TV. In the best-known lab test, 12 of 18 men and 6 of 24 women shocked themselves rather than sit alone with their thoughts for 15 minutes. Two popular claims built on this research have not held up: “boredom makes you creative” failed a partial replication with 197 people, and “mind wandering powers creative incubation” failed a conceptual replication with 443. What survives is plainer. Setting a problem aside helps (117 studies, weighted d = 0.29), wandering thought leans towards planning, and people underestimate how engaging just waiting is (six experiments, N = 259). The CEO move: treat a small daily block of no-input time as a protected asset for incubation and planning, not as a creativity machine. The student move: run the two-week self-experiment below and judge the result from your own scorecard, not from the headlines.

This is the “why” layer of the attention cluster. The notification audit shows what to switch off, the dopamine myth explains why phones are hard to put down, and attention as a skill covers what training can fix. The question here: once you have freed a few minutes, what should be in them? The contrarian answer is “nothing”, for specific reasons, with an honest list of the reasons that turned out to be wrong.

This article is general information about research findings. It is not medical or mental-health advice.

Why defend boredom when the goal is better attention?

Most attention advice is subtractive: fewer pings, tabs and switches. It assumes the freed minutes go to focused work. In practice they go to the next input: a podcast fills the walk, a feed fills the queue.

The case for idle time is that some useful mental work only happens without external input: reworking a problem you set aside, rehearsing plans, and noticing that what you are doing has stopped mattering. Boredom researchers treat that last signal as information. In the Meaning and Attentional Components (MAC) model, Westgate and Wilson (2018) describe boredom as “an affective indicator of unsuccessful attentional engagement in valued goal-congruent activity”. It arises from a mismatch between demands and mental resources, a mismatch between the activity and your goals, or both. Eastwood and colleagues (2012) reviewed earlier theories and defined boredom as “the aversive experience of wanting, but being unable, to engage in satisfying activity.”

Read that way, boredom is a warning light. Smother every flicker with a phone and you lose both the warning and the space it would have opened.

What happens when people are left alone with their thoughts?

The study everyone cites is Wilson and colleagues’ 2014 Science paper “Just think”. Across 11 studies, college students spent 6 to 15 minutes alone in a bare room without phones or writing materials. In the six lab studies that used the standard procedure (n = 146), 57.5% rated concentration as difficult, at or above the scale midpoint, 89.0% reported that their mind wandered, and 49.3% reported enjoyment at or below the midpoint. When students did the exercise at home, 32% admitted they had “cheated” by using music, a phone or getting up.

Then came the shocks. In study 10, participants rated an electric shock, then spent 15 minutes thinking with a button that could deliver it again. Counting only people who had said they would pay to avoid another shock, 67% of men (12 of 18) and 25% of women (6 of 24) shocked themselves at least once. One outlier pressed it 190 times and was excluded. Combined, that is 18 of 42 people, or 42.9%.

Three things complicate the famous headline:

• The motive is disputed. Fox and colleagues (2014) examined the participants’ open-ended answers. Of the 18 people who shocked themselves, 14 gave some form of curiosity about the shock as their reason (77.8%), and 4 mentioned boredom (22.2%).
• The monotony effect has been seen again. In Havermans and colleagues (2015), 30 participants self-administered shocks more readily during an hour of one repeated clip than during an hour of documentary. In Nederkoorn and colleagues (2016), 69 participants watched a monotonous, sad or neutral film; the boredom group gave themselves more and stronger shocks, while sadness had no effect. Both point to a drive to break monotony, not a love of pain.
• “Doing beats thinking” generalises. Buttrick and colleagues (2019) replicated the thinking-versus-doing comparison with 2,557 participants at 12 sites in 11 countries. Doing was more enjoyable than thinking for pleasure in every country. Country differences were explained by individual traits, one of which, reported phone use, was negatively associated with enjoying thought.

The newest data adds the most useful twist. Hatano and colleagues (2022) asked university students (six experiments, total N = 259) to predict how much they would enjoy sitting and waiting in a quiet room. Predicted enjoyment and engagement were consistently lower than what participants actually experienced. The underestimate led people to choose internet news checking over waiting, although their actual experiences of the two did not differ statistically. That is the behavioural core of the problem: we avoid idle time partly because we misforecast how it will feel.

How much idle time is left in a normal day?

How full-time workers spend leisure is broken down in active vs. passive recovery. The table below adds the measure that matters here: the share of the whole population that records any idle thinking at all.

ATUS 2025 activity (primary activity)	Average hours per day, population 15+	Percent engaging on an average day	Hours per day among those who engaged
Leisure and sports (all)	5.16	95.1	5.43
Watching TV	2.61	74.3	3.51
Playing games	0.40	15.3	2.60
Computer use for leisure, excluding games	0.22	14.7	1.51
Relaxing and thinking	0.35	20.2	1.72

Source: US Bureau of Labor Statistics, American Time Use Survey, Table A-1, 2025 annual averages. Figures as printed. ATUS records only the main activity, so thinking during a commute or a shower is not counted here.

What the CEOtudent recomputation shows (arithmetic in the published calc script):

• Relaxing and thinking averages 21 minutes a day, which is 6.8% of all leisure time. TV alone takes 50.6%.
• TV time is 7.5 times relaxing-and-thinking time. Add games and leisure computer use and screen leisure is 3.23 hours, or 9.2 times idle thinking.
• 79.8% of people recorded no relaxing and thinking on an average day. Only 25.7% recorded no TV.
• The minority who do take thinking time take a real block of it: 1.72 hours, about 103 minutes, on days they do.

The phone fills the gaps the time-use survey cannot see. In Pew Research Center’s experience sampling study, fielded over one week in November 2014, smartphone owners answered up to 14 short surveys about the previous hour; the analysis covers 1,035 who completed at least 10. Some 77% used their phone at least once to avoid being bored, doing so in an average of 3.5 surveys (25.0% of the maximum). Among 18-29 year olds, 93% did so, in an average of 5.4 surveys (38.6%). The data are old and serve only as a baseline for the habit.

Logged data show the same pattern. Andrews and colleagues (2015) tracked 23 participants’ actual phone use for two weeks. They used their phones a mean of 84.68 times a day for 5.05 hours, and 55% of uses lasted less than 30 seconds. That is the signature of gap-filling: many short checks rather than a few long sessions.

Does the check relieve boredom? Dora and colleagues (2021) logged phone use for 83 PhD candidates who rated fatigue and boredom hourly at work. When boredom rose by half a standard deviation, participants were 1.43 times more likely to pick up their phone in the next 20 minutes. They did not use it for longer, and they reported more fatigue and boredom after using it, not less. At the trait level, a 2026 meta-analysis of 25 studies (n = 15,152) found a moderate correlation between boredom proneness and problematic technology use (r = .38). That is a correlation, not proof of cause.

Does boredom make you more creative?

The evidence for this popular claim is thin.

Mann and Cadman (2014) ran two studies. In Study 1, 80 adults from one church either copied telephone numbers from a directory or did not, then listed uses for polystyrene cups. The bored group produced more answers, but their answers were not rated more creative. Study 2 (90 participants) added a boring reading task and found more creativity after boring tasks on some measures. The authors flagged the contradiction between studies and called for “further replication using much larger numbers of participants.”

That replication arrived as a partial one. Nettinga, Gutglick and Danckert (2023), in the Journal of Boredom Studies, induced boredom or interest in 197 undergraduates using video clips, a design change from the phone directory. They found no positive relation between state or trait boredom and creativity. Higher state boredom before the task was associated with poorer performance on the uses task (r = -0.20). A second experiment with 148 analysed participants and a different, behavioural creativity task again found no relation. Their own summary is blunt: “neither state nor trait boredom promote increased creativity.”

The fair reading: the original evidence was small and internally inconsistent, and the larger follow-up with a control mood failed. Boredom is a signal, not a muse.

Does letting the mind wander help you solve problems?

Here the evidence splits into a robust part and a fragile part.

The robust part: incubation. Sio and Ormerod’s 2009 meta-analysis in Psychological Bulletin pooled 117 studies with 3,606 participants. That is a median of 25 people per study, so the field is built from small experiments. Of the 117 studies, 85 reported positive effects (72.6%). The weighted mean effect was d = 0.29 (95% confidence interval 0.21 to 0.39). The moderators are the practical gold. Divergent thinking tasks benefited more than insight tasks. Longer preparation periods gave larger incubation effects. Filling the break with high-demand tasks shrank the effect. For linguistic insight problems, low-demand tasks beat rest.

The fragile part: mind wandering as the mechanism. Baird and colleagues (2012) gave 145 students creativity problems, then a 12-minute break in one of four conditions. Only the undemanding-task group, which reported more mind wandering than the demanding-task group, improved on problems they had already seen. Then Murray, Liang, Brosowsky and Seli (2021) attempted a conceptual replication across two studies with 443 participants, about 3.1 times the original sample. They “found no evidence” that mind wandering during an incubation interval facilitates divergent creativity. Separately, Smeekens and Kane (2016) measured in-the-moment mind wandering during incubation in three experiments (120, 115 and 107 participants included for analyses, 342 in total). It did not correlate with divergent creativity, which they called “especially problematic” for the claim.

The practical translation: stepping away from a well-prepared problem to do something undemanding is supported; the story that a wandering mind does the creative work during the break is not. Protect the break, and do not grade it by how dreamy it felt.

What is idle time actually good for?

If idle time is not a creativity pill, its case rests on three findings that hold up better.

1. Planning. In Baird, Smallwood and Schooler’s 2011 study (abstract only read), the content of mind wandering was “predominantly future-focused”, and prospective mind wandering “frequently involves autobiographical planning”. In plain terms, a free mind tends to rehearse what comes next.
2. Incubation. As above, the break itself helps when it follows real preparation and is not filled with demanding input.
3. Signal reading. Under the MAC model, boredom flags a task that is too easy, too hard, or meaningless to you. That is useful information for anyone who runs their own calendar.

The cost side is real and should not be hidden. Killingsworth and Gilbert (2010) sampled 2,250 adults through an iPhone app: minds wandered in 46.9% of samples, and in at least 30% during every activity except making love. Pleasant wandering (42.5% of wandering samples) made people no happier than their current activity, and neutral (31%) or unpleasant (26.5%) wandering made them unhappier. Mind wandering explained 10.8% of within-person variance in happiness, against 4.6% for the activity itself, about 2.3 times as much. The framework below therefore separates short, chosen idle time from involuntary drift during a task you care about.

The evidence-strength table

CEOtudent analysis of Wilson 2014, Buttrick 2019, Hatano 2022, Sio and Ormerod 2009, Killingsworth and Gilbert 2010, Mann and Cadman 2014, Nettinga 2023, Baird 2012, Murray 2021 and the other studies listed in the Sources. Ratings are editorial judgements based on sample size, design and replication record as described in the sources.

Claim	Key evidence and sample	Replication record	CEOtudent rating
People enjoy doing more than just thinking	Wilson 2014 (11 studies; n = 146 in lab studies 1-6); Buttrick 2019 (2,557, 11 countries)	Replicated across cultures	Strong
People underestimate how engaging waiting is	Hatano 2022 (six experiments, N = 259)	Consistent within one team; no independent replication found	Moderate
Many prefer a shock to sitting with thoughts	Wilson 2014 study 10 (42 analysed)	Monotony effect seen again (30 and 69 participants); motive disputed (14 of 18 cited curiosity)	Mixed
Setting a problem aside helps solve it	Sio and Ormerod 2009 (117 studies, 3,606 participants, d = 0.29)	Meta-analysis of small studies; strong moderators	Moderate
Wandering thought supports future planning	Baird, Smallwood and Schooler 2011	Single study, read as abstract only; sample size not checked	Weak
Mind wandering is frequent and linked to lower mood	Killingsworth and Gilbert 2010 (2,250 adults, 46.9% of samples)	Very large sample; correlational with time-lag analysis	Moderate
Boredom pushes people to the phone, and the phone does not relieve it	Dora 2021 (83, logged use); 2026 meta-analysis (25 studies, 15,152)	One within-person logged sample; trait link correlational	Moderate
Mind wandering during a break drives creative gains	Baird 2012 (145)	Murray 2021 (443) found no evidence; Smeekens and Kane 2016 (342) no correlation	Failed to replicate
Being bored makes you more creative	Mann and Cadman 2014 (80 + 90)	Nettinga 2023 partial replication (197) and a second test (148) found no positive link	Failed to replicate
A phone’s mere presence drains capacity	Ward and colleagues 2017	Pre-registered direct replication (Ruiz Pardo and Minda 2022) did not replicate	Failed to replicate

For the phone-presence evidence in more depth, see screen time and cognitive performance.

The Idle-Time Budget

CEOtudent editorial framework. It turns the robust findings into a daily allocation. The minute values are CEOtudent design parameters, not research findings, to be tested with the protocol below.

The definition that does the work: idle time means no new external input: no feed, podcast, lyrics, news, messages or “productive” reading. Low-demand movement and chores are allowed, because the incubation evidence favours low-demand activity over demanding fill. TV or scrolling can be valid recovery, but it is input, not idle time.

Block	Example	Daily budget (editorial)	Where to place it	What does not count	Evidence anchor
Transition gaps	Queue, lift, waiting for a meeting, the end of a commute	3 gaps of about 5 minutes, phone pocketed	Wherever they occur; the rule is “do not fill by default”	A “quick check” of messages	55% of logged phone uses last under 30 seconds (Andrews 2015); boredom precedes pickups (Dora 2021)
Incubation block	Walk, dishes, tidying, a shower	15 minutes	Immediately after your hardest preparation session on a stuck problem	Switching to another demanding task	Longer preparation and low-demand fill strengthen incubation (Sio and Ormerod 2009)
Planning drift	Sit or walk with a notebook, no screen	10 minutes	Late afternoon or before the next day’s plan	Writing a to-do list from your inbox	Wandering thought is future-focused (Baird 2011)

Total: about 40 minutes a day, roughly 1.9 times the US average of 21 minutes of relaxing and thinking. That is about 4.2% of a 16-hour waking day (a 16-hour day is an illustrative assumption). It is deliberately small, because the Wilson data suggest long unstructured stretches feel aversive to many people.

Three placement rules:

1. Prepare first, then step away. Incubation is a return on preparation, not a substitute for it.
2. Do not script the drift to make it pleasant. In Wilson’s studies, planning in advance what to think about did not raise enjoyment (4.97 versus 4.94 on the scale). Let it be plain, write down what surfaces, and stop.
3. Treat boredom during real work as a diagnosis, not a cue to scroll. Ask which MAC mismatch it is: too easy, too hard, or meaningless. Then change the task, not the input. The attention residue analysis shows why each unplanned switch carries a cost.

For the physical side, see micro-environments; for longer stretches of attention, deep reading.

A two-week self-experiment with a scorecard

The student move is to test the budget on yourself, because population data cannot predict your own response. The design borrows the Hatano logic: predict first, then measure.

Week 1, baseline. Change nothing except one daily 10-minute sit with no input. Before it, rate predicted enjoyment from 0 to 10. After it, rate actual enjoyment, and write down any plan or idea that surfaced. Each evening, record the phone pickup count from your phone’s built-in screen-time report.

Week 2, the budget. Run all three blocks of the Idle-Time Budget daily. Keep the same predicted and actual ratings for the planning drift, and log the same evening measures.

Scorecard metric	How to measure	Week 1 average	Week 2 average	Counts as “improved” if
Daily phone pickups	Phone’s screen-time report			Lower in week 2
Forecast gap	Actual minus predicted enjoyment for the sit or drift			Gap stays positive (idle time beats your forecast)
Plans or ideas captured	Count of notebook items from idle blocks			Higher in week 2
Stuck problems moved	Yes or no after each incubation block	Not run		At least half “yes”
End-of-day mental fatigue	0 to 10, same time each evening			Lower in week 2
Gaps left unfilled	Tally of transition gaps with no phone			At least 3 a day

Decision rule (editorial): keep the budget if at least three of the six metrics improve, and stop if fatigue rises in week 2.

Read your own data critically. This is a sample of one with no blinding, you know what you hope to find, and week 2 may differ for unrelated reasons such as workload or sleep. A cleaner version alternates budget days and normal days in a fixed order.

What this does not mean

• It does not mean boredom is good for you. Chronic boredom and boredom proneness are linked to problematic technology use in the 2026 meta-analysis. The argument here is for short, chosen idle time, not for enduring a meaningless job.
• It does not mean daydreaming at work is free. In the Killingsworth and Gilbert data, mind wandering went with lower mood.
• It does not mean phones are toxic. The headline “brain drain” effect did not replicate. The issue is narrower: the reflex check removes the gap that incubation and planning need.
• It does not mean any number here is a prescription. The 40-minute budget is an editorial starting point, not a dose established by research.

The CEO move and the student move

CEO: when most people outsource every gap to a feed, the scarce asset is a mind that has had time to rework a problem and rehearse a plan. Calendar the incubation block and planning drift like any investment, and defend them.

Student: two of the most quoted claims here failed when larger teams retested them, and the most famous lab result has a disputed motive. Keep what survived, run your own test, and update when better data arrive.

Frequently asked questions

Is it true that people would rather shock themselves than think?
In one study, 12 of 18 men and 6 of 24 women did so at least once in a 15-minute thinking period. Most gave curiosity as a reason, so the result shows a pull towards stimulation, not that thinking is painful.

Does being bored make you more creative?
The best-known study (Mann and Cadman 2014, 80 and 90 participants) suggested it might. A partial replication with 197 participants and a second test with 148 found no positive link, and higher boredom predicted slightly worse performance.

Does a break help with a hard problem?
Yes, on average. A meta-analysis of 117 studies found a positive incubation effect (d = 0.29), larger after longer preparation and smaller when the break was filled with a demanding task.

How much idle time do people actually have?
In the 2025 American Time Use Survey, people aged 15 and over averaged 0.35 hours a day of relaxing and thinking, and 79.8% recorded none on an average day. TV took 2.61 hours.

Is listening to a podcast on a walk idle time?
Not under this framework. It may be good recovery, but it is input, and incubation and planning depend on not processing new content.

How long should an idle block be?
The research sets no dose. Baird’s break lasted 12 minutes and Wilson’s thinking periods 6 to 15 minutes; the editorial budget uses 5, 10 and 15-minute blocks so the habit starts small.

Sources

1. Wilson, T. D., and colleagues (2014). Just think: The challenges of the disengaged mind. Science. DOI 10.1126/science.1250830. Author manuscript, PubMed Central PMC4330241.
2. Fox, K. C. R., Thompson, E., Andrews-Hanna, J. R., and Christoff, K. (2014). Is thinking really aversive? A commentary on Wilson et al.’s “Just think: the challenges of the disengaged mind”. Frontiers in Psychology, 5, 1427.
3. Havermans, R. C., and colleagues (2015). Eating and inflicting pain out of boredom. Appetite. DOI 10.1016/j.appet.2014.11.007 (abstract only).
4. Nederkoorn, C., and colleagues (2016). Self-inflicted pain out of boredom. Psychiatry Research. DOI 10.1016/j.psychres.2016.01.063 (abstract only).
5. Buttrick, N., and colleagues (2019). Cross-cultural consistency and relativity in the enjoyment of thinking versus doing. Journal of Personality and Social Psychology. DOI 10.1037/pspp0000198 (abstract only).
6. Hatano, A., and colleagues (2022). Thinking about thinking: People underestimate how enjoyable and engaging just waiting is. Journal of Experimental Psychology: General. DOI 10.1037/xge0001255 (abstract only).
7. Westgate, E. C., and Wilson, T. D. (2018). Boring thoughts and bored minds: The MAC model of boredom and cognitive engagement. Psychological Review. DOI 10.1037/rev0000097 (abstract only).
8. Eastwood, J. D., and colleagues (2012). The unengaged mind: Defining boredom in terms of attention. Perspectives on Psychological Science. DOI 10.1177/1745691612456044 (abstract only).
9. US Bureau of Labor Statistics. American Time Use Survey, Table A-1: Time spent in detailed primary activities and percent of the civilian population engaging in each activity, averages per day by sex, 2025 annual averages.
10. Pew Research Center (2015). U.S. Smartphone Use in 2015, Chapter Three: A “Week in the Life” Analysis of Smartphone Users. Experience sampling fielded in November 2014.
11. Andrews, S., Ellis, D. A., Shaw, H., and Piwek, L. (2015). Beyond self-report: Tools to compare estimated and real-world smartphone use. PLOS ONE. DOI 10.1371/journal.pone.0139004.
12. Dora, J., van Hooff, M., Geurts, S., Kompier, M., and Bijleveld, E. (2021). Fatigue, boredom and objectively measured smartphone use at work. Royal Society Open Science. DOI 10.1098/rsos.201915.
13. Trait boredom and problematic use of new technologies: A meta-analysis (2026). Psychological Reports. DOI 10.1177/00332941261459995 (abstract only).
14. Mann, S., and Cadman, R. (2014). Does being bored make us more creative? Creativity Research Journal, 26(2), 165-173. Accepted manuscript, University of Central Lancashire repository.
15. Nettinga, J., Gutglick, R., and Danckert, J. (2023). Exploring the relation between state and trait boredom and various measures of creativity. Journal of Boredom Studies, issue 1.
16. Sio, U. N., and Ormerod, T. C. (2009). Does incubation enhance problem solving? A meta-analytic review. Psychological Bulletin. DOI 10.1037/a0014212.
17. Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W. Y., Franklin, M. S., and Schooler, J. W. (2012). Inspired by distraction: Mind wandering facilitates creative incubation. Psychological Science. DOI 10.1177/0956797612446024.
18. Murray, S., Liang, N., Brosowsky, N., and Seli, P. (2021). What are the benefits of mind wandering to creativity? Psychology of Aesthetics, Creativity, and the Arts (abstract only).
19. Smeekens, B. A., and Kane, M. J. (2016). Working memory capacity, mind wandering, and creative cognition: An individual-differences investigation into the benefits of controlled versus spontaneous thought. Psychology of Aesthetics, Creativity, and the Arts. DOI 10.1037/aca0000046.
20. Baird, B., Smallwood, J., and Schooler, J. W. (2011). Back to the future: Autobiographical planning and the functionality of mind-wandering. Consciousness and Cognition. DOI 10.1016/j.concog.2011.08.007 (abstract only).
21. Killingsworth, M. A., and Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science. DOI 10.1126/science.1192439.
22. Ruiz Pardo, A. C., and Minda, J. P. (2022). Reexamining the “brain drain” effect: A replication of Ward et al. (2017). Acta Psychologica. DOI 10.1016/j.actpsy.2022.103717 (abstract only).

Notes on method. The evidence-strength table, the minute and ratio recomputations of the ATUS, Pew, Wilson and Fox figures, and the Idle-Time Budget with its scorecard are CEOtudent analyses or a CEOtudent editorial framework; the budget’s minute values are design choices, not findings. The Ward and colleagues (2017) “brain drain” result is reported only through its failed direct replication and the linked sibling analysis. Figures deliberately not used: the “83% spent no time relaxing or thinking” figure quoted in Wilson and colleagues (2014), because it comes from an older time-use survey edition not opened here (replaced by the 2025 ATUS figure); the 93% figure for 18-29 year olds in the Pew data is used only as a 2014 baseline; and Baird and colleagues’ (2012) percentage gains, which appear only in a chart not read here.

This content was compiled with the support of AI following in-depth research, then written and prepared for publication by the CEOtudent editorial team.

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