| Title |
The Epidemiology of Depressive Symptoms and Poor Sleep: Findings from the English Longitudinal Study of Ageing (ELSA)
|
|---|---|
| Published in |
International Journal of Behavioral Medicine, December 2017
|
| DOI | 10.1007/s12529-017-9703-y |
| Pubmed ID | |
| Authors |
Lydia Poole, Marta Jackowska |
| Abstract |
The reasons for the comorbidity between depressed mood and poor sleep are not well understood. Participants were 5172 adults aged 50 years and older from the English Longitudinal Study of Ageing. Sleep was measured via self-report and depressive symptoms using the Centre for Epidemiological Studies Depression scale. Greater depressive symptoms and sleep complaints were associated with female sex, non-cohabitation, relative poverty, smoking, infrequent physical activity, infrequent alcohol consumption, higher body mass index (BMI), diagnosis of hypertension, coronary heart disease, diabetes/high blood glucose, pulmonary disease, arthritis, and higher levels of fibrinogen and C-reactive protein (all p < 0.05). At a 4-year follow-up, depressive symptoms and sleep complaints were both predicted by baseline depressive symptoms and sleep complaints, relative poverty, smoking, physical inactivity, BMI, and arthritis (all p < 0.05). Depressive symptoms and sleep complaints share a range of correlates cross-sectionally and prospectively. These findings highlight the common comorbidity between depressive symptoms and sleep complaints underscoring the need for further research to understand their combined detrimental effect on long-term health and wellbeing. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 109 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 12 | 11% |
| Student > Master | 11 | 10% |
| Student > Postgraduate | 9 | 8% |
| Researcher | 8 | 7% |
| Student > Ph. D. Student | 7 | 6% |
| Other | 18 | 17% |
| Unknown | 44 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 17% |
| Psychology | 13 | 12% |
| Nursing and Health Professions | 9 | 8% |
| Neuroscience | 6 | 6% |
| Computer Science | 2 | 2% |
| Other | 12 | 11% |
| Unknown | 49 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 May 2018.
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#14,960,072
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Outputs from International Journal of Behavioral Medicine
#634
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#252,410
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Outputs of similar age from International Journal of Behavioral Medicine
#12
of 14 outputs
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