CA Wyse CA Celis Morales, Graham Fan Ward AM Curtis Mackay DJ Smith ME Bailey Biello JM Gill JP Pell N Y J D S Adverse metabolic and mental health outcomes associated with shiftwork in a population-based study of 277,168 workers in UK biobank Journal Article In: Annals of Medicine, 2017. Abstract | Links | BibTeX | Tags: 12761, mental health, shiftwork @article{Wyse2017,
title = {Adverse metabolic and mental health outcomes associated with shiftwork in a population-based study of 277,168 workers in UK biobank},
author = {CA Wyse, CA Celis Morales, N Graham,Y Fan, J Ward, AM Curtis, D Mackay, DJ Smith,ME Bailey,S Biello, JM Gill, JP Pell},
url = {https://www.ncbi.nlm.nih.gov/pubmed/28166415},
year = {2017},
date = {2017-02-26},
journal = {Annals of Medicine},
abstract = {BACKGROUND:
Reported associations between shiftwork and health have largely been based on occupation-specific, or single sex studies that might not be generalizable to the entire working population. The objective of this study was to investigate whether shiftwork was independently associated with obesity, diabetes, poor sleep, and well-being in a large, UK general population cohort.
METHODS:
Participants of the UK Biobank study who were employed at the time of assessment were included. Exposure variables were self-reported shiftwork (any shiftwork and night shiftwork); and outcomes were objectively measured obesity, inflammation and physical activity and self-reported lifestyle, sleep and well-being variables, including mental health.
RESULTS:
Shiftwork was reported by 17% of the 277,168 employed participants. Shiftworkers were more likely to be male, socioeconomically deprived and smokers, and to have higher levels of physical activity. Univariately, and following adjustment for lifestyle and work-related confounders, shiftworkers were more likely to be obese, depressed, to report disturbed sleep, and to have neurotic traits.
CONCLUSIONS:
Shiftwork was independently associated with multiple indicators of poor health and wellbeing, despite higher physical activity, and even in shiftworkers that did not work nights. Shiftwork is an emerging social factor that contributes to disease in the urban environment across the working population. Key messages Studies have linked shiftwork to obesity and diabetes in nurses and industry workers, but little is known about the implications of shiftwork for the general workforce In this large cross sectional study of UK workers, shiftwork was associated with obesity, depression and sleep disturbance, despite higher levels of physical activity. Shiftwork was associated with multiple indicators of compromised health and wellbeing and were more likely to report neurotic traits and evening preference.},
keywords = {12761, mental health, shiftwork},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND:
Reported associations between shiftwork and health have largely been based on occupation-specific, or single sex studies that might not be generalizable to the entire working population. The objective of this study was to investigate whether shiftwork was independently associated with obesity, diabetes, poor sleep, and well-being in a large, UK general population cohort.
METHODS:
Participants of the UK Biobank study who were employed at the time of assessment were included. Exposure variables were self-reported shiftwork (any shiftwork and night shiftwork); and outcomes were objectively measured obesity, inflammation and physical activity and self-reported lifestyle, sleep and well-being variables, including mental health.
RESULTS:
Shiftwork was reported by 17% of the 277,168 employed participants. Shiftworkers were more likely to be male, socioeconomically deprived and smokers, and to have higher levels of physical activity. Univariately, and following adjustment for lifestyle and work-related confounders, shiftworkers were more likely to be obese, depressed, to report disturbed sleep, and to have neurotic traits.
CONCLUSIONS:
Shiftwork was independently associated with multiple indicators of poor health and wellbeing, despite higher physical activity, and even in shiftworkers that did not work nights. Shiftwork is an emerging social factor that contributes to disease in the urban environment across the working population. Key messages Studies have linked shiftwork to obesity and diabetes in nurses and industry workers, but little is known about the implications of shiftwork for the general workforce In this large cross sectional study of UK workers, shiftwork was associated with obesity, depression and sleep disturbance, despite higher levels of physical activity. Shiftwork was associated with multiple indicators of compromised health and wellbeing and were more likely to report neurotic traits and evening preference. |
McIntosh A. M. Stewart, John Smith Davis Sudlow Corvin Nicodemus Kingdon Hassan Hotopf Lawrie Russ Geddes Wolpert Wolbert Porteous R A D J K C A K K D L M S M T C J R M E D J Data science for mental health: a UK perspective on a global challenge Journal Article In: Lancet Psychiatry, 2016. Abstract | Links | BibTeX | Tags: data science, mental health @article{McIntoshAM2016,
title = {Data science for mental health: a UK perspective on a global challenge},
author = {McIntosh, A. M.
Stewart, R.
John, A.
Smith, D. J.
Davis, K.
Sudlow, C.
Corvin, A.
Nicodemus, K. K.
Kingdon, D.
Hassan, L.
Hotopf, M.
Lawrie, S. M.
Russ, T. C.
Geddes, J. R.
Wolpert, M.
Wolbert, E.
Porteous, D. J.},
url = {http://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(16)30089-X/fulltext?elsca1=etoc},
year = {2016},
date = {2016-10-04},
journal = {Lancet Psychiatry},
abstract = {Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those with many different variables and data types). Mental health research, diagnosis, and treatment could benefit from data science that uses cohort studies, genomics, and routine health-care and administrative data. The UK is well placed to trial these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generation Scotland, and the Clinical Record Interactive Search (CRIS) programme. Data science has great potential as a low-cost, high-return catalyst for improved mental health recognition, understanding, support, and outcomes. Lessons learnt from such studies could have global implications.},
keywords = {data science, mental health},
pubstate = {published},
tppubtype = {article}
}
Data science uses computer science and statistics to extract new knowledge from high-dimensional datasets (ie, those with many different variables and data types). Mental health research, diagnosis, and treatment could benefit from data science that uses cohort studies, genomics, and routine health-care and administrative data. The UK is well placed to trial these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generation Scotland, and the Clinical Record Interactive Search (CRIS) programme. Data science has great potential as a low-cost, high-return catalyst for improved mental health recognition, understanding, support, and outcomes. Lessons learnt from such studies could have global implications. |
Tyrrell, Jessica; White, Mathew; Barrett, George; Ronan, Natalie; Phoenix, Cassandra; Whinney, David; Osborne, Nicholas Mental Health and Subjective Well-being of Individuals with Meniere's: Cross-sectional Analysis in the UK Biobank Journal Article In: Otology & Neurotology, 2015. Links | BibTeX | Tags: Ménières, mental health @article{Tyrrell2015,
title = {Mental Health and Subjective Well-being of Individuals with Meniere's: Cross-sectional Analysis in the UK Biobank},
author = {Jessica Tyrrell and Mathew White and George Barrett and Natalie Ronan and Cassandra Phoenix and David Whinney and Nicholas Osborne},
url = {http://journals.lww.com/otology-neurotology/Abstract/publishahead/Mental_Health_and_Subjective_Well_being_of.97613.aspx},
year = {2015},
date = {2015-02-28},
journal = {Otology & Neurotology},
keywords = {Ménières, mental health},
pubstate = {published},
tppubtype = {article}
}
|