Transport · reviewed 2026-05-24

What are the odds of crashing while cycling distracted by a phone?

Evidence quality 4.13/5

Eight-dimension review score against the quality rubric . Each dimension scored 1–5.

D1 Source grounding: 3/5
D2 Source authority: 5/5
D3 Arithmetic: 5/5
D4 Uncertainty: 4/5
D5 Scope: 4/5
D6 Prose: 4/5
D7 Perception honesty: 3/5
D8 Caveat completeness: 5/5

Average 4.13/5

Direct evidence

No reliable estimate

Not quantified

No reliable estimate

We looked for authoritative data on this question and could not find a number we are willing to publish. Possible reasons: sources disagree substantially, the underlying dataset is known to be biased, or the question depends on context we can’t normalize to a common baseline.

This entry uses `no_reliable_estimate: true` because the US literature does not contain the data required to compute a defensible lifetime probability of injury from cycling while distracted by a phone. The structural problem has three layers. First, the numerator: NHTSA's FARS records distracted *drivers* who strike cyclists but does not code the cyclist's own phone use, and the CDC's NEISS-AIP emergency department surveillance explicitly states that injury circumstances are not captured consistently. Second, the denominator: the US does not measure cyclist phone-use exposure-hours, and the closest available figure (Wolfe et al. 2016, 31% of Boston cyclists distracted, of which 13.5% visually/tactilely) is a single-city intersection snapshot, not a national exposure metric. Third, transfer bias: the best peer-reviewed crash-risk evidence is Dutch (Goldenbeld et al. 2012 odds ratio ~1.6–1.8 for every-trip vs never device use in young cyclists; effect absent in older cyclists), but Dutch cyclists ride on protected infrastructure at population trip-shares twenty to thirty times higher than US cyclists, and the Stavrinos et al. 2017 systematic review found only one cycling study globally that met inclusion criteria for mobile-technology crash-risk analysis — and excluded it from the meta- analysis. Even after the Netherlands banned handheld phone use while cycling in July 2019, the academic evaluation concluded "there are no precise figures for how many accidents are caused by phone use while cycling". What a US adult should take from this entry is qualitative: cyclist phone use measurably degrades peripheral vision, speed control, and reaction time, and is associated with elevated self-reported crash odds in young cyclists in the Netherlands; texting is worse than calling; and the elevated risk compounds with the road environment, especially on arterial roads where US cyclist fatalities are concentrated. The quantitative claim — what fraction of US adult cyclists will be injured in a phone-distraction crash over a lifetime — is not estimable from current evidence, and inventing a number to make the entry listable would misrepresent the state of the science.

Know of a rigorous, peer-reviewed source we missed? Please tell us.

An empty bike lane viewed from above, with a faint phone-shaped outline drawn on the asphalt, flat vector illustration in muted tones.

Compare to:

The honest answer to “how likely is a US adult cyclist to be injured in a phone-distraction crash over a lifetime” is that nobody knows, and the gap is structural rather than provisional. NHTSA’s Fatality Analysis Reporting System captures distracted drivers who hit cyclists but not the cyclist’s own phone use at the moment of crash. The CDC’s National Electronic Injury Surveillance System logs an estimated 596,972 emergency department visits for bicycle-related traumatic brain injuries between 2009 and 2018, but the 2021 MMWR report stated outright that “NEISS-AIP narrative descriptions do not provide detailed or consistent information about… injury circumstances”. The strongest peer-reviewed crash-risk estimate is Dutch — Goldenbeld et al. 2012 found self-reported crash odds about 1.6–1.8 times higher in Dutch teen and young-adult cyclists who used portable electronic devices on every trip versus never, with no significant effect detected in older cyclists. Stavrinos et al.’s 2017 systematic review of mobile technology and crash risk identified only one cycling study globally that met inclusion criteria, and excluded it from the meta-analysis. There is no aggregate effect size to draw on.

What the literature does establish qualitatively is consistent. de Waard et al.’s 2010 study in Groningen showed that talking on a phone while cycling narrows peripheral vision, drops speed, and raises perceived workload and risk, while texting produces the largest performance penalty of all the distraction conditions tested — drift in lateral position, longer reaction times, and reduced glance frequency to surrounding traffic. Wolfe et al.’s 2016 Boston observational study found 31.2% of cyclists at four high-traffic intersections were distracted at the moment of observation, split roughly between auditory distraction (17.7%, headphones and earbuds) and visual or tactile distraction (13.5%, phone or other object in hand). The behaviour is widespread, and its effect on the inputs that a cyclist uses to stay upright and avoid collisions is real.

What the literature does not establish, and probably cannot from currently available US data, is a single lifetime probability. Even after the Netherlands banned handheld phone use while cycling in July 2019, the academic evaluation of that ban concluded that there are no precise figures for how many accidents are caused by phone use while cycling. The Dutch evidence is the closest the world has to a quantitative answer, and it lives at the level of self-reported odds ratios in subgroups, not per-cyclist-year incidence — and Dutch cyclists ride on protected infrastructure at population trip-shares twenty to thirty times higher than US cyclists, which makes any direct transfer suspect. The honest reader-facing takeaway is qualitative. Distraction worsens cycling performance, texting is worse than calling, the effect concentrates in younger cyclists, and the consequences compound with the road environment — the same arterial roads where roughly two thirds of US cyclist fatalities already occur. The site treats “we looked and the evidence does not support a number” as a legitimate state rather than a gap to paper over, and this is one of those entries.

Claim ledger

Every number below is what each source reported, with the verbatim quote we relied on and how we arrived at our figure. Click any link to verify directly.

[1] Ergonomics (Taylor & Francis), via PubMed — Mobile phone use while cycling: incidence and effects on behaviour and safety Peer-reviewed

Mobile phone use while cycling: incidence and effects on behaviour and safety

Statistic

In Groningen, NL, 2.2% of observed cyclists were talking on a phone and 0.6% were texting or dialling; only 0.5% of accident-involved cyclists reported phone use at the time of the crash

Excerpt

“"In Groningen 2.2% of cyclists were observed talking on their phone and 0.6% were text messaging or entering a phone number. In study 2, accident-involved cyclists responded to a questionnaire. Only 0.5% stated that they were using their phone at the time of the accident… Telephoning coincided with reduced speed, reduced peripheral vision performance and increased risk and mental effort ratings. Text messaging had the largest negative impact on cycling performance. Higher mental workload and lower speed may account for the relatively low number of people calling involved in accidents." ”

Source data from

2010-01-01

Accessed

2026-05-24 · archived copy

Calculation

de Waard et al. 2010 is the foundational observational study on cyclist mobile phone prevalence in the Netherlands. It is cited here for two reasons: (1) it establishes that the behavioural effect of phone use on cycling performance is real and measurable (peripheral vision narrows, speed drops, perceived risk rises), and (2) the 0.5% self-report among crash-involved cyclists is not a crash-risk rate — it is a snapshot of what crashed cyclists recall doing, on a non-representative sample, in the Netherlands. Even if taken at face value, it cannot be converted to a US per-crash or per-cyclist-year probability because the denominator (total cycling-phone-use exposure-hours) is not measured. No conversion to native or normalized US-adult lifetime probability is performed.
[2] Journal of Safety Research (Elsevier), via PubMed — The use and risk of portable electronic devices while cycling among different age groups Peer-reviewed

The use and risk of portable electronic devices while cycling among different age groups

Statistic

Self-reported crash odds were ~1.6× higher for Dutch teen cyclists and ~1.8× higher for young adult cyclists who used portable electronic devices on every trip vs cyclists who never used them; no significant effect detected for middle-aged or older cyclists

Excerpt

“"The odds of being involved in a bicycle crash are higher for teen cyclists (factor 1.6) and young adult cyclists (factor 1.8) who use electronic devices on every trip compared to cyclists who never use these devices. For middle-aged and older adult cyclists, the use of portable electronic devices was not a significant predictor of bicycle crashes, but frequency of cycling in demanding traffic situations was." ”

Source data from

2012-02-01

Accessed

2026-05-24 · archived copy

Calculation

Goldenbeld et al. 2012 is the strongest peer-reviewed estimate of crash-risk elevation from cyclist device use. The reported odds ratios (1.6 and 1.8) are self-reported, age-stratified, and Dutch — they describe relative risk between "every trip" and "never" device users in the same age cohort, not absolute probability per trip or per year. The effect disappears in older cohorts, suggesting either behavioural compensation or selection. Multiplying any baseline US cyclist crash probability by ~1.7 would be inappropriate because (a) the underlying US baseline for distraction-attributable crashes does not exist, (b) Dutch cyclists ride on protected infrastructure that US cyclists do not, and (c) "every trip" device use is a behavioural extreme, not the modal phone-using cyclist. The OR is cited as qualitative direction evidence (distraction increases crashes for younger cyclists) without a quantitative US transfer.
[3] Journal of Trauma Nursing (Wolters Kluwer), via PubMed Central — Distracted biking: an observational study Peer-reviewed

Distracted biking: an observational study

Statistic

Of 1,974 cyclists observed at 4 Boston intersections in summer 2016, 31.2% were distracted; 17.7% by auditory devices (headphones/earbuds) and 13.5% by visual/tactile devices (phone or object in hand)

Excerpt

“"Of the 1,974 bicyclists observed, 615 (31.2%) were distracted. Auditory distractions, predominantly headphones or earbuds, accounted for 17.7%, and visual/tactile distractions, predominantly a phone or other object in the hand, accounted for 13.5%. Reduced attention can place bicyclists and other road users at greater risk of sustaining an injury." ”

Source data from

2016-03-01

Accessed

2026-05-24 · archived copy

Calculation

Wolfe et al. 2016 (observations summer 2015, published 2016) is the best-available US observational prevalence study for cyclist distraction. It establishes that distraction is common (≈1 in 3 observed cyclists at Boston intersections) but does not link observed distraction to crash outcomes — there is no denominator-matched crash count for the same population. The study is cited as the US exposure-prevalence anchor that demonstrates the behaviour is widespread, while explicitly not supplying a crash-risk number. No native or normalized probability is derived.
[4] CDC Morbidity and Mortality Weekly Report (MMWR) — Emergency Department Visits for Bicycle-Related Traumatic Brain Injuries Among Children and Adults — United States, 2009–2018 ↗ 1 other entry Government report

Emergency Department Visits for Bicycle-Related Traumatic Brain Injuries Among Children and Adults — United States, 2009–2018
See all 2 Likelier entries citing this source →

Statistic

An estimated 596,972 ED visits for bicycle-related TBIs occurred in the US during 2009–2018; the surveillance system does not record cyclist phone use, helmet use, or injury circumstances consistently

Excerpt

“"An estimated 596,972 ED visits for bicycle-related TBIs occurred in the United States during this study period (2009–2018)… NEISS-AIP narrative descriptions do not provide detailed or consistent information about helmet use, injury circumstances (e.g., whether the injury occurred on a road or bicycle path), or about a person's level of exposure." ”

Source data from

2021-05-14

Accessed

2026-05-24 · archived copy

Calculation

Sarmiento et al. 2021 (CDC MMWR) is the primary US bicycle-injury surveillance source. It documents that ED visits for bicycle-related TBI are common (≈60,000/year averaged over 2009–2018) but explicitly states that the surveillance instrument does not capture the circumstances that would let an analyst identify which crashes involved cyclist phone use. This source is cited as documentation of the data gap that prevents a US cyclist-phone-distraction injury rate from being estimated — the numerator data does not exist, regardless of what denominator one might assume.
[5] Child Development (Wiley), via PubMed Central — Distracted Walking, Bicycling, and Driving: Systematic Review and Meta-Analysis of Mobile Technology and Youth Crash Risk Peer-reviewed

Distracted Walking, Bicycling, and Driving: Systematic Review and Meta-Analysis of Mobile Technology and Youth Crash Risk

Statistic

Of 41 peer-reviewed studies in a systematic review of mobile technology and youth road crash risk, only one cycling study met inclusion criteria and was excluded from the meta-analysis, so no aggregate effect size for phone-while-cycling crash risk could be computed

Excerpt

“"A single study on bicycling met inclusion criteria, but was omitted from the meta-analysis." ”

Source data from

2017-11-01

Accessed

2026-05-24 · archived copy

Calculation

Stavrinos et al. 2017 is the closest available systematic review on mobile technology and crash risk that includes cycling as a category. The reviewers identified only one eligible cycling study (Kircher et al. 2015, an experimental study of behavioural compensation, not crash incidence), which they then excluded from quantitative synthesis. This is the strongest available evidence that no defensible pooled odds ratio exists for phone-while-cycling crash risk in the published literature. The source is cited as direct documentation of the evidence-base gap that justifies no_reliable_estimate=true.