Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep.
BACKGROUND
Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality.


OBJECTIVE
The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep.


METHODS
We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index-PSQI) were obtained by survey. Linear regression was used to obtain effect estimates.


RESULTS
Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency.


CONCLUSIONS
These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.
Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.
Matthew A
MA
Christensen
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
Laura
L
Bettencourt
Ginger.io Incorporated, San Francisco, California, United States of America.
Leanne
L
Kaye
Ginger.io Incorporated, San Francisco, California, United States of America.
Sai T
ST
Moturu
Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.
Kaylin T
KT
Nguyen
Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.
Jeffrey E
JE
Olgin
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
Mark J
MJ
Pletcher
Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.
Gregory M
GM
Marcus
D000328:Adult; D003430:Cross-Sectional Studies; D005260:Female; D005843:Geography; D006801:Humans; D020407:Internet; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011446:Prospective Studies; D057566:Self Report; D012890:Sleep / Q000502:physiology*; D000068997:Smartphone / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires; D013997:Time Factors; D014481:United States