Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep
Christensen MA, Bettencourt L, Kaye L, Moturu ST, Nguyen KT, Olgin JE, Pletcher MJ, Marcus GM
PloS one · 152 citas
Etiquetas de revisión
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Cómo se estudió
- Diseño
- Estudio de cohortes (indexado por PubMed)
- Estudiado en
- Personas
- Resultado principal
- Marcadores de salud y funcionamiento
Quién lo pagó
- Financiación
- Financiado por la industria
- Organización sin fines de lucro
- Sarnoff Cardiovascular Research Foundation
- Gobierno
- National Institutes of Health
- Universidad u hospital
- University of California, San Francisco
- Gobierno
- National Institute on Alcohol Abuse and Alcoholism
- Gobierno
- National Heart, Lung, and Blood Institute
- Gobierno
- National Institute of Neurological Disorders and Stroke
- Gobierno
- National Institute of Biomedical Imaging and Bioengineering
- Gobierno
- Office of Behavioral and Social Sciences Research
- Gobierno
- National Institute on Minority Health and Health Disparities
- Gobierno
- NIMHD NIH HHS
- Gobierno
- NIBIB NIH HHS
- Autores
- Al menos un autor declara un vínculo económico con la industria
- Subvenciones
- National Institute of Biomedical Imaging and Bioengineering (1U2CEB021881); National Institute of Biomedical Imaging and Bioengineering (U2C EB021881); National Institute on Minority Health and Health Disparities (R25MD006832); National Institutes of Health (R25MD006832)
Según 11 financiadores declarados y la declaración de financiación del texto completo.
Publicación
- Publicado
- 2016-11-09 · PLoS One · vol. 11 · número 11 · pág. e0165331
- Editorial
- Public Library of Science
- Citado
- 335 citas · más que el 100% de estudios similares · 25.1× el promedio de su campo
- Impacto
- Entre el 10 % más citado de su campo
- Referencias
- 22 trabajos
- Acceso
- Acceso abierto (revista) · CC-BY
- Áreas de investigación
- Mobile Health and mHealth Applications · Technology Use by Older Adults · Child Development and Digital Technology
- Palabras clave
- smartphone screen time, sleep quality, sleep duration, sleep efficiency, sleep latency, bedtime screen use, age differences, racial and ethnic differences, socioeconomic status
- MeSH
- humans, multivariate analysis, linear models, prospective studies, cross-sectional studies, sleep, geography, time factors, internet, adult, middle aged, united states, female, male, self report, smartphone, surveys and questionnaires
8 autores
- Matthew A. Christensen🇺🇸University of California, San Francisco
- Laura Bettencourt🇺🇸University of California, San Francisco
- Leanne Kaye
- Sai T. Moturu
- Kaylin T. Nguyen🇺🇸University of California, San Francisco
- Jeffrey E. Olgin🇺🇸University of California, San Francisco
- Mark J. Pletcher🇺🇸University of California, San Francisco
- Gregory M. Marcus🇺🇸 · autor de correspondenciaUniversity of California, San Francisco
Resumen
El texto original del estudio, en el idioma en que se publicó.
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.
Aims
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.
Resumen vía Europe PMC. Los derechos de autor siguen siendo de los autores o la editorial (CC BY).
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