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Association of sleep patterns assessed by a smartphone application with work
productivity loss among Japanese employees
#MMPMID41381856
Seol J
; Iwagami M
; Yanagisawa M
NPJ Digit Med
2025[Dec]; 8
(1
): 751
PMID41381856
show ga
Sleep disturbances are an underrecognized factor associated with reduced
workplace productivity ("presenteeism"). Previous studies have largely relied on
self-reported sleep data, limiting their scalability and accuracy. We
investigated associations between smartphone-based sleep metrics and presenteeism
using real-world data from 79,048 working adults in Japan (mean age: 42.1 years
[range: 18-66 years]; 47.8% female). Over 2.1 million nights of sleep data were
collected over 28 days. Sleep parameters included total sleep time (TST), sleep
latency, percent wake after sleep onset (%WASO), chronotype (mid-sleep on free
days corrected for sleep debt), and social jetlag. Generalized additive models
showed U-shaped associations between TST and presenteeism. Longer sleep latency,
higher %WASO, delayed chronotype, and greater social jetlag were each linked to
higher presenteeism scores. Unsupervised clustering using UMAP and the Leiden
algorithm identified five sleep phenotypes: "Healthy Sleepers," "Long Sleepers,"
"Fragmented Sleepers," "Poor Sleepers," and "Social Jetlaggers." The latter two
clusters showed the worst scores for insomnia, daytime sleepiness, and
presenteeism. These findings highlight that not only sleep duration but also
quality, timing, and regularity may be associated with workplace functioning.
Smartphone-based tracking may offer a scalable means of identifying at-risk
individuals and informing future personalized strategies.
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