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Association between sleep patterns and depression in older adults: a cross-sectional study using data from the National Health and Nutrition Examination Survey 2007–2014

BMC Geriatrics volume 25, Article number: 56 (2025) Cite this article

Abstract

Objective

To assess the association between sleep patterns and sleep factors (sleep duration, trouble sleeping, sleep disorder) and the risk of depression in older adults.

Methods

A total of 5636 participants (2754 men and 2882 women) aged 60 years and older from the 2007–2014 waves of the National Health and Nutrition Examination Survey (NHANES) were included. Sleep duration, sleep problems, and sleep disorders were assessed in the home by trained interviewers using the Computer-Assisted Personal Interviewing (CAPI) system. The combined sleep behaviours were referred to as ‘sleep patterns (healthy, intermediate and poor)’, with a ‘healthy sleep pattern’ defined as sleeping 7–9 h per night with no self-reported trouble sleeping or sleep disorders. Intermediate and poor sleep patterns indicated 1 and 2–3 sleep problems, respectively. Baseline characteristics of participants analysed using one-way logistic regression. Logistic multiple linear regression was used to assess the association of sleep factors and sleep patterns with the risk of depressive symptoms.Conduct subgroup analyses to ensure robustness of findings.

Results

The overall prevalence of depression was 7.7% among the 5636 participants analysed, with the rate of depression in older women being 1.6 times higher than in older men. The prevalence of depression was higher in older adults with intermediate sleep pattern than in older adults with healthy sleep pattern (OR: 2.28, 95% CI: 1.71–3.03, p < 0.001). The prevalence of depression was higher in older adults with poor sleep pattern than in older adults with healthy sleep pattern (OR: 5.60, 95% CI: 4.25–7.39, p < 0.001). The findings were robust after controlling for sleep items in the PHQ-9.

Conclusion

This nationally representative survey showed a relationship between sleep patterns and depression in older adults. However, the study population was limited to Americans, and we recommend continued investigation of the causal relationship and mechanisms between the two in the future, and further expansion of data sources in order to assess the applicability of the findings.

Peer Review reports

Introduction

The United Nations (UN) Population Division predicts that the geriatric population (aged 60 and over) will increase from 800 million, or 11% of the general population, to 2 billion, or 22% of the population, by 2050 [1]. Depression is the most common mental illness in the world. It has become a major public health problem [2]. It is well known that depression can lead to reduced quality of life, poor adherence to treatment, increased length of hospital stay and use of health services, and in severe cases, suicide [3]. Depression is more common than other emotional changes in older people. It is associated with a high mortality rate and pathological comorbidity [4]. The global prevalence of major depression among older people was 13.3%, 11.9% among older women and 9.7% among older men [5]. One in two older people receiving care at home had probable depression; 13.4% of the sample had major depression; the study sample included 811 community-dwelling adults aged 60 [6]. In a cross-sectional study, 299 older people living in Hanoi, Vietnam, were approached for data collection. Self-reported depression among the elderly was 66.9% (32.8% mild, 30.4% moderate and 3.7% severe) [7]. The rates of depression were 26.67% and 38.37% for men and women, respectively. The data came from the China Health and Retirement Longitudinal Study (CHARLS) follow-up survey (2011 and 2013–2015), which included 3337 residents aged 45 years or older [8]. Mental health status significantly increased the level of medical expenditure among the rural elderly in China, contributing to 47.26% of total personal expected medical expenditure [9].

The prevalence of sleep disorders is higher in older adults. Loud snoring, which is more common in older people, can be a symptom of obstructive sleep apnoea, which puts a person at risk of cardiovascular disease, headaches, memory loss and depression. Restless legs syndrome and periodic limb movement disorder, which disrupt sleep, are more common in older people. Prevalence increases with age: 3.2% in children under 10, 5.2% in young adults and 22.3% in people aged 75 and over [10]. The pooled prevalence of sleep problems in older Chinese adults was 35.9%, with a total of 47 studies included [11]. More than a third of participants aged 65 and over reported suboptimal sleep duration. During an average follow-up of 3.52 years, 1,522 (16.1%) participants from a nationally representative sample of 9,430 adults aged 50 and over developed at least one symptom of insomnia [12]. Short sleep duration had a greater negative impact on morbidity, with a reduction in health-related quality of life (HRQOL), while long sleep duration had a greater negative impact on mortality [13].

Other common medical problems of old age such as hypertension, diabetes mellitus, renal failure, respiratory diseases such as asthma, immune disorders, gastroesophageal reflux disease, physical disability, dementia, pain, depression and anxiety are all associated with sleep problems [14]. Risk factors for depression in older people were aspirin use, being aged 80 years or over, sleep problems and persistent sleep problems, hearing problems, poor eyesight and heart disease [15]. Sleep problems, short sleep and a combination of the two increase the risk of depressive symptoms in older people: a 6-year follow-up of the English Longitudinal Study of Ageing [16]. Research suggests that older people in the community who have difficulty sleeping are more likely to suffer from anxiety and depression, and that this lack of sleep may lead to depressive symptoms through its effect on the thickness of the left transverse temporal cortex [17].Respondents with a diagnosis of depression reported significantly higher rates of diagnosed anxiety and sleep problems than those without depression [18].

With socio-economic development and improved medical conditions, life expectancy has increased significantly, and the proportion of older people is rising year by year. Attention to the health of the elderly, especially mental health, has become an important part of social public health.Older people have a higher incidence of sleep disorders due to the deterioration of physiological functions. Sleep disorders not only affect the quality of life of the elderly, but may also lead to a variety of health problems. Older people are at high risk of depression. Depression not only affects the mood and behavior of older persons, but may also lead to a decline in cognitive functioning, seriously affecting the physical and mental health of older persons. Exploration of the relationship between the two: It has been shown that there is a correlation between sleep and depression [19, 20], but the exact mechanism is not clear, especially in the elderly population.In this cross-sectional study, we aimed to examine the association between sleep patterns and depression in older adults using data from the National Health and Nutrition Examination Survey (NHANES).

Methods

Study design

Cross-sectional study. We used the publicly available National Health and Nutrition Examination Survey (NHANES) database in the United States to conduct a retrospective study based on available data, with the aim of analysing historical data to identify patterns, associations or trends, rather than testing hypotheses and interventions. Such studies do not involve new interventions and therefore do not require clinical trial registration. According to the rules of the International Committee of Journal Editors (ICMJE), registration is not required for purely observational trials (i.e. trials in which the investigator does not assign a medical intervention).

Study population

NHANES is a nationally representative survey conducted by the National Center for Health Statistics (NCHS), 24 using stratified, multistage probability cluster sampling to assess the health or nutrition status of the non-institutionalised US population. In this cross-sectional study, we examined publicly available data from participants aged 60 years and older with complete and reliable information (demographics, dietary and health-related behaviours, body measurements, and disease information) collected and extracted from the 2007–2008, 2009–2010, 2011–2012, and 2013–2014 NHANES cycles. The NCHS Research Ethics Review Board approved the NHANES study protocol, and participants provided written informed consent at enrolment. The institutional review board of Jiangnan University Affiliated Hospital determined that the study was exempt because it used publicly available deidentified data, and informed consent was waived. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Data from the NHANES 2007–2014 cycles were analysed, including an initial total of 40,617 participants. Exclusion criteria for this study included: (a) participants younger than 60 years (n = 32758), (b) participants with incomplete information on sleep (n = 36) or depression (n = 1052), (c) participants with missing data on education level, marital status, PIR, body mass index, smoking status, alcohol consumption, diabetes, renal failure, hypertension, high cholesterol (n = 1135). A total of 5636 individuals qualified for analysis, as shown in Fig. 1.

Fig. 1
figure 1

Flowchart of participant selection

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Assessment of sleep factors and definition of a sleep pattern

Sleep assessment includes sleep factors (sleep duration, trouble sleeping, sleep disorder) and sleep patterns [20]. Sleep duration is measured by answering the question,“How much sleep do you get (hours)”. We classify sleep duration into short sleep (< 7 h), normal sleep (7–9 h) and long sleep (> 9 h). Trouble sleeping is obtained by answering the question “Ever told doctor had trouble sleeping?“. Sleep disorder was obtained by answering the question “Ever told by doctor have sleep disorder?“. The sleep pattern is based on the combined score of 3 sleep factors, which are classified as healthy sleep, intermediate and poor sleep. Sleep duration 7–9 h (score = 1), sleep duration < 7 or > 9 h (score = 0). Answer “Ever told doctor had trouble sleeping?“. Answer Yes (score = 0) and No (score = 1). Answer “Ever told by doctor have sleep disorder?“. Answer Yes (score = 0) and No (score = 1). Finally, an overall score ranging from 0 to 3 was obtained and overall sleep pattern was defined as poor sleep pattern (0 ≤ overall sleep score ≤ 1), intermediate sleep pattern (overall sleep score = 2) and healthy sleep pattern (overall sleep score = 3).

Assessment of depressive symptoms

Depressive symptoms in this study were assessed using the Patient Health Questionnaire-9 (PHQ-9). Trained interviewers administered these questions in the Mobile Examination Centre (MEC) using the Computer Assisted Personal Interview (CAPI) system, which has built-in consistency checks to minimise data entry errors. In addition, the CAPI system includes online help screens to clarify any terms used in the questionnaire.To ensure the quality of the data, approximately 5% of the interviews were recorded and reviewed for quality control purposes. The PHQ-9 consisted of nine questions related to depressive mood, and the total score was calculated by summing the scores for each item. The response categories for the nine-item instrument, “not at all”, “several days”, “more than half the days” and “almost every day”, were given a score ranging from 0 to 3. The PHQ-9 scale had a range of 0 to 27. A score of 10 or more was considered indicative of a depressive state [21,22,23].

Other covariate

A variety of potential covariates were assessed according to the literature, including age, body mass index (BMI), sex, race/ethnicity, educational level, marital status, poverty income ratio (PIR), smoking status, alcohol consumption, diabetes, renal failure, hypertension and high cholesterol. BMI was calculated as weight (kg) divided by height squared (kg/m2), sex (male, female), race (Mexican American, other Hispanic, non-Hispanic black, other race including multiracial, non-Hispanic white). Educational level (less than 9th grade, 9-11th grade, 12th grade without diploma, high school graduate/GED or equivalent, some college or AA degree, college graduate or higher), marital status (married/cohabiting, widowed, divorced, separated, never married), poverty income ratio (PIR), with values categorised as either < 1 or ≥ 1 [24]. Smoking status was obtained by answering the question “Have you ever smoked at least 100 cigarettes in your lifetime? Smoking status was categorised as yes (≥ 100 cigarettes in lifetime), no (<100 cigarettes in lifetime) [25]. Alcohol consumption was assessed by answering the question “Have you had at least 12 drinks/1 yr? Alcohol use was categorised as yes (≥ 12 drinks/1 year), no (<12 drinks/1 year) [25]. Diabetes was obtained by answering “Has your doctor told you you have diabetes?” and diabetes was categorised as yes, no and borderline [26]. Kidney failure, high blood pressure and high cholesterol are based on whether doctors reported excessive levels of yes or no.

Statistical analysis

The Kolmogorov-Smirnov test was used to determine whether variables were normally distributed. Normally distributed variables were presented as mean (standard deviation), while skewed variables were presented as median (interquartile range, 25–75%). Categorical variables were presented as proportions (%). Continuous data were compared using t-tests, and categorical data were compared using the χ2 test.

We conducted a stratified analysis combining depression scores and sleep patterns and factors. One-way logistic regression analysis was used to analyze participants’ sleep patterns and baseline characteristics of depression. The relationship between sleep patterns and the likelihood of developing depressive symptoms was examined using multivariable logistic regression models. Model 1 was unadjusted, model 2 adjusted for age, sex, BMI, race, education level, marital status, PIR, model 3 adjusted for age, sex, BMI, race, education level, marital status, PIR, smoking status, alcohol consumption, and model 4 adjusted for all variables including age, sex, BMI, race, education level, marital status, PIR, smoking status, alcohol consumption, diabetes, renal failure, hypertension, high cholesterol. To exclude that the sleep items in the PHQ-9 confounded the results, the sleep items in the PHQ-9 were analysed as covariates in model 5. To further explore the relationship between sleep and depressive symptoms, we performed a subgroup analysis. A statistically significant difference was defined as a bilateral p-value of 0.05. The likelihood ratio test was used to examine interactions between subgroups.

As the sample size was based entirely on the available data, no a priori calculation of statistical power was performed. R software (version 4.2.1; R Foundation for Statistical Computing; http://www.Rproject.org), the R survey package (version 4.1-1), and Free Statistics software (version 1.7.1; Beijing Free Clinical Medical Technology Co., Ltd) were used for analyses. In all analyses, a two-sided p-value < 0.05 was used to indicate statistical significance.

Results

Baseline characteristics of participants

Table 1 shows the characteristics of the study population stratified by sleep patterns in NHANES 2007–2014. We found differences between age, BMI, race, education, smoking status, PIR, diabetes mellitus, renal failure, hypertension, high cholesterol, poor sleep and intermediate sleep and healthy sleep (P < 0.001). In addition, participants with healthy sleep patterns were more likely to be female and have a higher BMI. Older adults with poor sleep patterns were more likely to have a history of hypertension and depression than those with healthy sleep patterns.

Table 1 Characteristics of participants by sleep patterns (n = 5636)
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Table 2 shows the characteristics of participants based on their depression scores in a study that included 5636 individuals, of whom 436 scored 10 or more on the depression scale. The mean age of participants with depression was 67.9 years, which was significantly lower than the mean age of those without depression (70.0 years, p < 0.001). In addition, the proportion of men in the non-depression group was significantly higher than that in the depression group (49.8% vs. 38.3%, p < 0.001), while the proportion of women in the non-depression group was significantly lower than that in the depression group (50.2% vs. 61.7%, p < 0.001). BMI, race, education, marital status, PIR, smoking status, diabetes, renal failure, hypertension and sleep variables also differed significantly between the two groups (p < 0.001).

Table 2 Characteristics of participants by depression (n = 5636)
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Relationship between sleep and depressive symptoms in older adults

Table 3 shows the results of the analysis examining the association between sleep and depression symptoms in older adults. Full adjustment for covariates was made in model 4 and it was found that the prevalence of depression in older adults with intermediate sleep patterns was 2.28 times higher than with healthy sleep patterns (OR:2.28, 95% CI: 1.71–3.03, P < 0.001). The prevalence of depression in older adults with poor sleep patterns was 5.6 times higher than with healthy sleep patterns (OR:5.60, 95% CI: 4.25–7.39, P < 0.001). The prevalence of depression in older adults with < 7 h of sleep is 2.04 times higher than with 7–9 h of sleep (OR:2.04, 95% CI: 1.65–2.52,P < 0.001). In contrast, the prevalence of depression in older adults with a sleep duration of > 9 h was not statistically significant compared to those with a sleep duration of 7–9 h (OR:1.47, 95% CI: 0.87–2.48,P = 0.152). Older adults without trouble sleeping 70% lower prevalence of depression (OR:0.3, 95% CI: 0.24–0.37,P < 0.001). 65% lower prevalence of depression in older adults without sleep disorder (OR:0.35, 95% CI: 0.27–0.45,P < 0.001). The PHQ-9 included an assessment of sleep schedules and the DPQ030 was included as a covariate control in model 5 to avoid confounding. It was found that the prevalence of depression in older adults with intermediate sleep patterns was 1.39 times higher than with healthy sleep patterns (OR: 1.39, 95% CI: 1.01–1.92,P=0.043), and the prevalence of depression in older adults with poor sleep patterns was 2.04 times higher than with healthy sleep patterns (OR: 2.04, 95% CI: 1.48–2.8, P < 0.001). There was no statistically significant difference in the prevalence of depression among older adults who slept < 7 h or > 9 h compared with those who slept 7–9 h (OR: 1.1, 95% CI: 0.89–1.36, P = 0.392). Older adults without trouble sleeping 38% lower prevalence of depression (OR: 0.62, 95% CI: 0.49–0.79, P < 0.001). 48% lower prevalence of depression in older adults without sleep disorder (OR: 0.52, 95% CI: 0.38–0.71, P < 0.001).

Table 3 Multivariate regression analysis of the associate between sleep and depression
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Fig. 2
figure 2

Subgroup analyses for sleep patterns and depression

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Subgroup analyses

In the present study, we also performed subgroup analyses according to year-cohort, age, gender and BMI to assess the robustness of the association between sleep patterns and the prevalence of depression in old age. The results are shown in Fig. 2, where we found that the association between geriatric sleep patterns and depression remained robust to subgroup analyses based on year-cohort, age, gender and BMI. We found that in the 2011–2012 cohort, women aged 70 years or older with a BMI < 25 kg/m2 and poor sleep pattern had a higher risk of depression compared with healthy sleep pattern. We also tested their interaction with year-cohort, age, gender and BMI. No associations were found for interaction p-values that reached statistical significance.

Discussion

To our knowledge, this is the first study of the relationship between sleep patterns and depression in older age in a large nationally representative study. We found that the prevalence of depression was 1.6 times higher in older women than in older men. Sleep duration, trouble sleeping and sleep disorders were strongly associated with depression. Subsequently, we learned that older patients who slept less than 7 h, older adults with trouble sleeping or sleep disorder, and older adults with poor sleep patterns were at greater risk for depression.

Among the 436 older depressed patients, older women (61.7%) were about 1.6 times more likely than older men (38.3%). One study reported [27] that older women reported significantly higher levels of depression than men, which is consistent with our findings. There are many reasons why older women are more likely to be depressed than older men. After the menopause, women’s oestrogen levels decrease, which can be associated with increased mood swings and depressive symptoms [28]. Studies have shown that in [29], Women may be more sensitive to certain genes that affect the risk of depression. Second, older women may be more likely to experience certain life events, such as living alone and having children away from home, which can increase the risk of depression. Women are more likely to choose social withdrawal or self-attribution when they face problems, and these strategies may be associated with the development of depression. Finally, older women may take on more caring responsibilities at home, which can lead to stress or depression.

Among older people without depression, 73.2% did not have sleep problems and 26.8% did. This suggests that older people with sleep problems are more likely to develop depression. There are many reasons why older people with sleep problems and sleep disorders are more likely to develop depression. Long-term sleep problems cause chronic stress, which is a known risk factor for depression. Lack of sleep can make it harder for the body and brain to recover, increasing mental stress. Sleep disorders can lead to reduced daily activities in older adults, as they may avoid social activities due to fatigue [30]. This social isolation increases loneliness, which in turn increases the risk of depression [12]. Sleep problems can affect older adults’ quality of life, including daily activities, work productivity and overall well-being. These factors can increase the risk of depression. Inconcentration and reduced performance due to daytime sleepiness may trigger reactive depression, a response to life stressors [31]. At the same time, older people may have multiple chronic conditions, the management of which can affect sleep and mood, increasing the risk of depression. It is therefore very important to identify and treat sleep problems in older people with sleep disorders to reduce the risk of depression. At the same time, older people with depression also need to be assessed for the presence of sleep disorders and consider treatment options.

Short sleep duration (<7 h) increases the risk of depression in older adults, while long sleep duration (>9 h) is not statistically significant, which may be related to their physiology. The National Sleep Foundation recommends [32] For older adults (65 + years), the recommended minimum amount of sleep per night is 7–8 h. Lack of sleep can lead to moody mood, fatigue, irritability, forgetfulness, cognitive deficits [33], as well as obesity [34], diabetes [35] and hypertension [36], which may increase the risk of depression in the elderly. With the increase of age, the elderly body function decline, organs and tissues gradually decline, the metabolic capacity of the body, the body control body sleep hormones also began to slowly decline, seriously affect the elderly sleep, lead to difficult to sleep at night, insomnia increased significantly, thus shortening the sleep time at night [37]. Although studies have shown that sleeping too much or too little can increase the risk of depression, it is natural for older people to lose sleep due to reduced physical function. As the number of older people who sleep too long is not large, it is not statistically significant here.

Older adults with intermediate sleep pattern had a 2.49 times greater risk of depression than older adults with healthy sleep pattern, while older adults with poor sleep pattern had a 6.76 times greater risk of depression than older adults with healthy sleep pattern. The results remained stable after adjustment for demographics, smoking, alcohol use and comorbidities, suggesting that poor sleep pattern increases the risk of depression in older adults. Insomnia is the most common sleep problem in older people, manifesting as difficulty falling asleep, waking up frequently during the night, or waking up early. Insomnia and depression share common risk factors, and both are associated with serotonin and norepinephrine deficiency, hypothalamic-pituitary-adrenal axis hyperactivity, hyperexcitability, disturbed rapid eye movement (REM) sleep, and reduced slow-wave sleep [38, 39]. In addition, poor sleep quality affects older people’s cognitive functions, including attention, memory and decision-making, which are also typical of depression [40]. Studies suggest that poor sleep quality may be a symptom of depression, and that depression may also cause or worsen sleep problems [41]. This bidirectional relationship means that sleep problems and depressed mood can influence each other, creating a vicious cycle.

Our study used data from a nationally representative sample of the population. The NHANES sampling method ensures that the sample is randomly selected and representative of the entire US population. The current study also has some limitations. First, as a cross-sectional study, the nature of the design does not allow us to determine whether sleep problems caused depression, or depression caused sleep problems, or both may have been influenced by common factors; second, data were collected at a single point in time and could not capture trends in sleep and depression over time. Finally, the reliance on participant self-report may be affected by recall bias, where participants may not be able to accurately recall past sleep or emotional states. Third, the data sources for this study are limited to individuals residing in the US, so the findings may not be fully applicable to other countries, particularly those in Asia, whose lifestyles and cultural backgrounds differ significantly from those in the US.

Conclusions

Overall, this study highlights the association between sleep patterns and the risk of depression in older adults. Further prospective studies are needed to investigate the causal or bidirectional relationship between sleep pattern and risk of depression in older adults. In addition, investigating the genetic associations and underlying mechanisms between sleep and depression is crucial for effective prevention and treatment of depression in older age. We suggest that future studies could expand the data sources to collect data from different countries in order to better assess the applicability of the findings in different cultural and social contexts.

Data availability

The datasets used and/or analyzed during the current study are available from the NHANES database, https://wwwn.cdc.gov/nchs/nhanes/.

Abbreviations

95%CI:

95% Confidence Interval

ANOVA:

Analysis of Variance

N:

Number

NHANES:

National Health and Nutrition Examination Survey

OR:

Odds Ratio

SD:

Standard Deviation

χ2 :

Chi-square

BMI:

body mass index

PIR:

Ratio of family income to poverty

SD:

Standard Deviation

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Authors and Affiliations

  1. Department of Plastic Surgery, Treatment Center of Burn and Trauma, Affiliated Hospital of Jiangnan University, Wuxi, China

    Yi Niu & Shun Yu

  2. Wuxi Medical College of Jiangnan University, Wuxi, China

    Yi Niu & Yuqin Sun

  3. Department of Wound Stoma Care Clinic, Affiliated Hospital of Jiangnan University, Wuxi, China

    Yijie Xie

Authors
  1. Yi Niu
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  2. Yuqin Sun
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  3. Yijie Xie
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  4. Shun Yu
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Contributions

YN and SY designed the study. YN wrote the manuscript. YN, YS and YJ collected, analyzed and interpreted the data. SY critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shun Yu.

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This study did not need to be approved by the Wuxi Medical College of Jiangnan University, Jiangnan University Affiliated Hospital because the data was accessed from NHANES (a publicly available database). All methods were carried out in accordance with relevant guidelines and regulations (declaration of Helsinki). All individuals provided written informed consent before participating in the study.

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Niu, Y., Sun, Y., Xie, Y. et al. Association between sleep patterns and depression in older adults: a cross-sectional study using data from the National Health and Nutrition Examination Survey 2007–2014. BMC Geriatr 25, 56 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-024-05633-7

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-024-05633-7

Keywords

BMC Geriatrics

ISSN: 1471-2318

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