Evidence map›Paper›PMID 41886363›Full record

ArticlePLOS digital health2026

Patterns of smartphone typing performance by time awake: implications for unobtrusive ambulatory mental fatigue assessment.

Yu Fang, Peter Yang, Elena Frank, Cathy Goldstein, Aidan G C Wright, Amy S B Bohnert, Vik Kheterpal, Srijan Sen, Zhenke Wu

Abstract read
In one paragraph

Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yu FangMichigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.ORCID https://orcid.org/0000-0002-2810-806X
Peter YangDepartment of Statistics, University of Michigan, Ann Arbor, Michigan, United States of America.
Elena FrankMichigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Cathy GoldsteinDepartment of Neurology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Aidan G C WrightDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, United States of America.
Amy S B BohnertDepartment of Psychiatry, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Vik KheterpalCareEvolution, Ann Arbor, Michigan, United States of America.
Srijan SenMichigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Zhenke WuDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID https://orcid.org/0000-0001-7582-669X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mental fatigue undermines workplace safety and productivity. Early detection of subtle declines in objective alertness and cognitive performance in workers can enable timely interventions to prevent costly errors and safeguard employee health. However, conventional assessments often require controlled laboratory conditions and prolonged testing, limiting their real-world applicability. Here, we demonstrate an approach that utilizes smartphone keyboard metrics in everyday use to provide a more scalable, continuous, and ambulatory method for evaluating mental fatigue. We examined the adjusted association between novel yet widely available SensorKit typing performance metrics and wearable-derived time since waking from a most recent major sleep episode or napping among 366 first-year training physicians in the United States who generated 45,042 typing sessions over a two-month period. Typing performance, especially typing speed, has a significant non-linear adjusted relationship with time awake. At the population-level, typing speed increases and peaks around 7.5 hours since awake and has a substantial decrease around 15.3 hours of time awake, highly consistent with classical lab-based active task Psychomotor Vigilance Test findings that showed increased response lapses beyond 15.8 hours of wake period. Our findings are relevant for developing a ubiquitous and unobtrusive tool to assess, monitor, and manage mental fatigue on a continuous basis in everyday life, especially for populations in high-risk and high-stake settings.

Identifiers

PMID41886363
PMCPMC13020785

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.