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Effects of sleep quality on the risk of various long COVID symptoms among older adults following infection: an observational study

BMC Geriatrics volume 25, Article number: 20 (2025) Cite this article

Abstract

Background

The long-term sequelae of coronavirus disease 2019 (COVID-19) and its recovery have becoming significant public health concerns. Therefore, this study aimed to enhance the limited evidence regarding the relationship between sleep quality on long COVID among the older population aged 60 years or old.

Methods

Our study included 4,781 COVID-19 patients enrolled from April to May 2023, based on the Peking University Health Cohort. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) scale. Long COVID was evaluated by well-trained health professionals through patients’ self-reported symptoms. Binary logistic regression models were employed to calculate odds ratios (OR) and 95% confidence intervals (95% CI).

Results

The prevalence of long COVID among older adults was 57.4% (2,743/4,781). Specifically, the prevalence of general symptoms, cardiovascular symptoms, respiratory symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms was 47.7% (2,282/4,781), 3.4% (163/4,781), 35.2% (1683/4,781), 8.7% (416/4,781) and 5.8% (279/4,781), respectively. For each one-point increase in PSQI scores, the risk of long COVID, general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms increased by 3% (95% CI: 1.01, 1.06), 3% (95% CI: 1.01, 1.06), 7% (95% CI: 1.01, 1.13), 11% (95% CI: 1.07, 1.15), and 20% (95% CI: 1.15, 1.25), respectively. In multivariate models, compared with good sleepers, COVID-19 patients with poor sleep quality exhibited an increased risk of general symptoms (aOR = 1.17; 95% CI: 1.03, 1.33), cardiovascular symptoms (aOR = 1.50; 95% CI: 1.06, 2.14), gastrointestinal symptoms (aOR = 2.03; 95% CI: 1.61, 2.54), and neurological and psychiatric symptoms (aOR = 2.57; 95% CI = 1.96, 3.37).

Conclusions

Our findings indicate that poor sleep quality is related to various manifestations of long COVID in older populations. A comprehensive assessment and multidisciplinary management of sleep health and long COVID may be essential to ensure healthy aging in the future.

Peer Review reports

Introduction

Since the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) pandemic, more than four years have passed. As of June 28, 2023, there have been 767,518,23 confirmed cases and 6,947,192 deaths, resulting in a significant disease burden and economic loss globally [1]. In response to the high number of COVID-19 cases, the WHO proposed a definition of long COVID to guide patient recovery [2]. The prevalence of long COVID is reported to be over 50% among the general population [3]. With the increasing number of individuals aged 60 years and older, health aging has become a critical public health issue. Long COVID presents a substantial challenge to healthy aging. Previous studies have shown that older adults are at a higher risk for long COVID [4,5,6]. However, there is a lack of studies reporting the prevalence of various long COVID symptoms among older adults in China. Therefore, understanding the prevalence of long COVID and its risk factors is crucial for the long-term management of older COVID-19 patients.

Sleep disturbances have increasingly been recognized as significant risk factors for morbidity and mortality in recent years [7]. Our previous meta-analysis indicated that the prevalence of poor sleep quality during COVID-pandemic among older populations was 47.12%, which is higher than the rates observed prior to the pandemic [8]. Canever et al. reported that the prevalence of poor sleep quality among community-dwelling older adults during COVID-19 pandemic was 40.0% [9]. One cohort study found that sleep disturbance following hospital admission for COVID-19 is associated with subsequent dyspnea, anxiety, and muscle weakness [10]. One meta-analysis revealed that pre-existing sleep disturbances increased the risk of COVID-19 susceptibility (OR = 1.12), hospitalization (OR = 1.25), mortality (OR = 1.45), and long COVID (OR = 1.36) [11]. However, the aforementioned studies did not investigate the impact of poor sleep quality on the various symptoms of long COVID among the older population following infection.

In order to assist healthcare professionals in managing multimorbidity and convalescence following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) infection among older adults, we aimed to systematically investigate the various long COVID symptoms including general symptoms, cardiovascular symptoms, respiratory symptoms, gastrointestinal symptoms, as well as neurological and psychiatric symptoms among the older Chinese population. Furthermore, our large sample observational study explored the effect of poor sleep quality on long COVID, providing evidence for the prevention and management of older COVID patients.

Methods

Study design and participants

Our data was collected from the Peking University Health Cohort in Anning, Yunnan (PKUHC-AN), which is registered on ClinicalTrials.Gov (NCT05825651). To investigate the short-term and long-term health effects of COVID-19 among older adults, the PKUHC-AN was conducted in Anning, Yunnan, China, from April to May 2023. Trained family physicians gathered demographic characteristics, lifestyle habits, and health status using a standardized structured questionnaire [12]. If participants were illiterate, investigators assisted them in completing the questionnaire. The inclusion criteria were as follows: (1) aged ≥ 60 years old; (2) resided in Anning for the past six months and have no plans to move away in the next 1 year; (3) older adults who are willing to participate in the study and provide informed consent. Participants who were unable to answer questions or communicate were excluded from the study. All included participants were informed of the study protocol, and their oral informed consent was obtained at the first investigation.

The study was approved by the institutional review boards at Peking University (IRB00001052-21126). We utilized the Two-Sided Confidence Intervals for the Odds Ratio in PASS software to calculate the required sample size (N = 4,094) based previous study which reported the association between mild anxiety and poor sleep quality in the COVID patients [10]. Initially, 11,527 older adults were enrolled; after excluding 94 older adults aged less than 60 years old, 6,532 older adults who never had COVID-19, and 120 older adults with sleep disorders due to long COVID, a total of 4,781 probable or confirmed COVID-19 patients were included (Fig. 1). Each participant was asked about their COVID-19 status during a single appointment, following the guidelines established by the WHO [13]. Two definitions were employed to identify confirmed COVID- 19 cases [12]. Probable COVID-19 cases were defined as individuals who exhibited clinical symptoms and had a history of contact with a probable or confirmed case or were linked to a COVID-19 cluster. Sleep disorders due to long COVID as one of long COVID symptoms, are defined as any sleep disturbances occurring within three months of the onset of COVID-19 and that last for at least two months and could not be explained by an alternative diagnosis based on previous research and definition from the WHO [2, 14, 15].

Fig. 1
figure 1

Study flowchart. Notes: COVID-19 = coronavirus disease 2019

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Assessment of sleep quality

Poor sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) [16]. The Chinese version of PSQI demonstrated good had good sensitivity and specificity, with values of 98.3% and 98.2%, respectively [17]. This scale reflects sleep over the past month, and includes 7 subscales based on 18 items: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleeping medications, and daytime dysfunction. The PSQI is scored on a scale from 0 to 21, with higher scores indicating poorer sleep quality. A score greater than 7 is indicative of a poor sleep quality [17, 18].

Assessment of long COVID

In accordance with previous research and definition from the WHO [2, 14, 15], we asked participants if they experienced the following symptoms within three months of the onset of COVID-19 and that last for at least two months and could not be explained by an alternative diagnosis. Finally, long-COVID symptoms were classified into five categories: general symptoms (fever, fatigue, inability to perform exercise, myalgia, neuralgia, headache, dizziness, chest distress, abnormal sensations, tinnitus, blurred vision, loss of taste/smell, rash, hair loss), respiratory symptoms (sore throat, dyspnea, cough, nasal obstruction, runny nose, dysphagia), cardiovascular symptoms (chest pain, rapid heartbeat), gastrointestinal symptoms (diarrhea, abdominal pain, constipation, loss of appetite, nausea, vomiting), and neurological and psychiatric symptoms (memory problems, cognitive impairment, difficulty in concentration, anxiety, depression) [14, 19].

Covariates

We included 19 covariates that encompass demographic characteristics (age, gender, ethnicity, educational level, residence, monthly income during the COVID-19 pandemic, marital status, and living status), health status (body mass index [BMI, kg/m2], history of chronic diseases, COVID-19 vaccination, social support, depression, falls in the past year, and cognitive impairment), and lifestyle habits (smoking status, drinking status, exercise, balanced diet).

All covariates were self-reported and assessed by their family doctor. Monthly income during the COVID-19 pandemic was classified into four groups based on interquartile ranges. The BMI was categorized according to the cutoff points established by the Working Group on Obesity in China [20]. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), which has a total score ranging from 0 to 27, with scores greater than 4 indicating the presence of depression [21]. Social support was evaluated using the social support rating scale (SSRS), which comprises three sub-domains: objective support, subjective support, and support utilization [22]. The total SSRS score ranges from 12 to 66, with higher scores indicating better social support; scores below 20 represented low social support [23]. Participants’ cognitive function was assessed using the widely recognized Chinese version of the Mini-Mental State Examination (MMSE), which consists of 11 questions covering orientation, registration, attention, calculation ability, recall, and language ability [24]. Several items of the Chinese version were modified to align with the cultural context of China, while maintaining good validity and reliability [25]. All questions were answered by the respondents without a proxy. Total MMSE scores range from 0 to 30, with cutoff points set as 17 for illiteracy, 20 for primary school education, and 24 for junior high school education and above in this population [25].

Data analysis

The baseline characteristics of the study population were described as the means ± standard deviations (SDs) for the continuous variables and as percentages for the categorical variables. Chi-square test was employed for univariate analysis. We performed Logistic regression to estimate odds ratios (OR) with 95% confidence intervals (95% CI), making various adjustments for potential confounders. Model 1 adjusted for basic demographic characteristics. Based on model 1, we further adjusted for health status in the model 2, then subsequently adjusted for lifestyle habits in model 3. The significance of interactions was tested by including a product term in model 3 in the stratified analysis. The dose–response relationship between PSQI scores and long COVID was examined using restricted cubic spline analysis with three knots. All analyses were conducted using R version 4.2.0. Two-sided P values of less than 0.05 were considered statistically significant.

Results

Participant characteristics

The characteristics of the 4,781 COVID-19 patients revealed a mean (SD) age of approximately 72.2 (± 6.39) years, with 57.1% being women. Among these patients, 1,882 (39.4%) reported poor sleep quality. According to χ2 tests, except BMI (P > 0.05), other characteristics differed across sleep quality groups (all P < 0.05). Poor sleep quality was more prevalent among older individuals, females, and minorities, as well as those who were illiterate, lived in urban areas, had higher monthly income during the COVID-19 pandemic, were widowed, divorced or unmarried, lived alone, had a history of chronic diseases, had no COVID-19 vaccination, had normal social support, were depressive, had fallen in the past year, exhibited cognitive impairment, were former smokers, consumed alcohol less than once a week, maintained an unbalanced diet, and exercised approximately 2 to 5 times per week (Table 1).

Table 1 Characteristics of the COVID-19 patients
Full size table

Effects of poor sleep quality on long COVID

The prevalence of long COVID among older adults was 57.4% (2,743/4,781). The prevalence of general symptoms, cardiovascular symptoms, respiratory symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms was 47.7% (2,282/4,781), 3.4% (163/4,781), 35.2% (1,683/4,781), 8.7% (416/4,781), and 5.8% (279/4,781), respectively. The crude rates of long COVID, general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms were higher in the poor sleep quality group compared to the good sleep quality group (all P < 0.01), with the exception of respiratory symptoms.

The logistic models with penalized splines showed statistically significant linear relationships between PSQI scores and long COVID, general symptoms, cardiovascular symptoms, gastrointestinal symptoms, as well as neurological and psychiatric symptoms (all P < 0.05). Additionally, significant non-linear relationships were observed between PSQI scores with long COVID, general symptoms, gastrointestinal symptoms, as well as neurological and psychiatric symptoms (all P < 0.05) (Fig. 2). For every one-point increase in PSQI scores, the risk of long COVID, general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms increased by 3% (95% CI: 1.01, 1.06), 3% (95% CI: 1.01, 1.06), 7% (95% CI: 1.01, 1.13), 11% (95% CI: 1.07, 1.15), and 20% (95% CI: 1.15, 1.25), respectively.

Fig. 2
figure 2

Multivariable logistic regression model with penalized splines on effects of PSQI scores on different long COVID symptoms among older adults. Notes: A. long COVID; B. general symptoms; C. cardiovascular symptoms; D. respiratory symptoms; E. gastrointestinal symptoms; F. neurological and psychiatric symptoms. Model adjusted for age, gender, ethnicity, educational level, household registration, monthly income during COVID-19 pandemic, marital status, living status, BMI, self-reported previous chronic diseases, COVID-19 vaccination, social support, depression, cognitive impairment, fall in the past year, smoking status, drinking status, exercise, and balanced diet. The red line represented odds ratio; the dotted line represented 95% confidence ratio. aOR, adjusted odds ratio; BMI, body mass index; 95% CI, 95% confidence interval; COVID-19, coronavirus disease 2019; PSQI, Pittsburgh Sleep Quality Index

Full size image

In the multivariable-adjusted analysis, after adjusting for all covariates, compared with older patients with good sleep quality, those with poor sleep quality had an increased risk of general symptoms (aOR = 1.17; 95% CI = 1.03, 1.33), cardiovascular symptoms (aOR = 1.50; 95% CI = 1.06, 2.14), gastrointestinal symptoms (aOR = 2.03; 95% CI = 1.61, 2.54), and neurological and psychiatric symptoms (aOR = 2.57; 95% CI = 1.96, 3.37) (Table 2). Subgroup analyses are presented in Table S1-S6. For instance, compared to those with other educational levels, illiterate older patients with poor sleep quality had higher risk of long COVID (aOR = 1.68; 95% CI = 1.21, 2.34) and respiratory symptoms (aOR = 1.51; 95% CI = 1.09, 2.21) (both P values for the interactions < 0.05).

Table 2 Effects of poor sleep quality on long COVID among older COVID-19 patients in multivariable models
Full size table

Discussion

Our large sample observational study investigated the prevalence of long COVID and explored the relationship between poor sleep quality on long COVID among Chinese older adults. The prevalence of long COVID was over 50% among older adults, which aligns with our previous meta-analysis [26]. Among the various symptoms, the prevalence of general symptoms was the highest, followed by respiratory symptoms, gastrointestinal symptoms, neurological and psychiatric symptoms, and cardiovascular symptoms. Importantly, we found that poor sleep quality was associated with an increased risk of general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms.

Compared to older patients with good sleep quality, the risk of experiencing general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms increased by 17%, 50%, 103% and 257%, respectively. Some studies have reported that poorer sleep quality was associated with the risk of long-term COVID-19 symptom at one/three months [27, 28]. To date, there are limited studies focusing on the effects of sleep health and long COVID among older patients. Jackson et al. found that sleep disturbances were associated with dyspnoea, anxiety, and muscle weakness in COVID-19 patients after hospital admission, which somewhat supports the findings of our study [10]. Sleep problems commonly elevate levels of inflammatory cytokines, which can exacerbate brain injury and finally reslut in neurological or cardiovascular symptoms [29]. Furthermore, during an infectious disease outbreak, fear of infection or movement restrictions often lead to sleep deterioration, compounding the fatigue and physical exhaustion [30, 31]. One study reported that diurnal variation in the intestinal microbiota may be involved in the correlation between gastrointestinal symptoms and sleep disturbances [32]. It is important to note that we did not assess the pre-COVID sleep pattern; therefore our study cannot rule out the influence of pre-existing sleep disorders on long COVID [11]. Nevertheless, our findings still provide valuable evidence for the prevention and management of long COVID through the improvement of sleep quality among older COVID patients.

In our study, the prevalence of poor sleep quality among older COVID-19 patients was 39.4%. Ayşe et al. reported that 70.37% of older COVID-19 patients in Turkey experienced poor sleep quality [33]. Additionally, females, minorities group, those who were illiterate, resided in urban areas, were widowed, divorced or unmarried, as well as lived alone, had a higher prevalence of poor sleep quality. Almondes et al. reported that older women were more likely to experience worsening sleep problems than men [34]. Our study provides a framework for understanding the basic population characteristics of older COVID-19 patients with poor sleep quality. Furthermore, depressive patients had poor sleep quality easily, which was supported by numerous studies [35, 36]. The social isolation and home confinement during COVID-19 pandemic resulted in abruption in lifestyle‑related behavior including sleep [37]. Providing social support may be effective in improving sleep quality [38].

Limitations

The large sample size and comprehensive items ensure a certain degree of statistical power and accuracy. However, several limitations should be emphasized. First, our study was cross-sectional, which means that causal relationship could not be established. In addition, although we have ruled out sleep disorders due to long COVID to minimize the inversion of causality, it is important to note that sleep disorders due to long COVID are self-reported. Consequently, some participants who may not have been aware of sleep disorders due to long COVID were included in our study. More tools to distinguish sleep disorders due to long COVID from other sleep disorders should be developed. Future cohort studies or random controlled trials should be carried out in the future to explore the causal associations between sleep quality on the risk of various long COVID symptoms among older adults following infection [39]. Additionally, due to the difficulty in determining which infection caused long COVID, we cannot specifically assess the duration of long COVID or its effects on our results. Secondly, while PSQI provides a better reflection of the subjective sleep in older adults and facilitates sleep monitoring in a large population, objective sleep measurements remain necessary. Thirdly, we included influencing factors from multiple aspects, but potential recall bias and residual confounding bias still exist, which limits the inference of causality.

The evaluation and intervention of sleep quality are crucial for older adults, particularly following SARS-CoV-2 infection. Research has indicated that even mild infections can lead to brain shrinkage and gray matter loss [40]. SARS-CoV-2 infection has both short-term and long-term effects on health. Sleep health poses a challenge in the context of SARS-CoV-2 infection, especially considering its detrimental impact on long COVID. Therefore, in light of COVID-19 pandemic, it is essential to develop comprehensive assessment and management strategies for sleep quality among older adults to enhance clinical practice related to long COVID.

Conclusion

In conclusion, our findings indicate that the poor sleep quality was related to long COVID including general symptoms, cardiovascular symptoms, gastrointestinal symptoms, and neurological and psychiatric symptoms among older populations. Given that older adults often experience a high burden of multimorbidity, it is essential to focus on comprehensive assessments and multidisciplinary management of sleep health and long COVID. Further research, such as randomized controlled trials studying the impact of intervening on sleep health in older adults and their impacts on long COVID symptoms, as well as identifying whether poor sleep quality increased the risk for developing long COVID is needed.

Data availability

Data are obtained according to corresponding author permission.

Abbreviations

BMI:

Body mass index

COVID-19:

Coronavirus disease 2019

MMSE:

Mini-Mental State Examination

ORs:

Odds ratios

PHQ-9:

Patient Health Questionaire-9 items

PSQI:

Pittsburgh Sleep Quality Index

SARS-CoV-2:

Severe Acute Respiratory Syndrome Coronavirus 2

SDs:

Standard deviations

SSRS:

Social Support Rating Scale

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Acknowledgements

We are also grateful to all family physicians and health workers on collection of data.

Funding

This work was supported by National Natural Science Foundation of China (72122001), Beijing Natural Science Foundation of China (L222027), Yunnan Province—Chunchenjihua (C202012016), and Yunnan Province—Xingdian talent support program (XDYC-MY-2022–0071). The sponsor had no role in the design, methods, data collections, analysis or preparation of paper.

Author information

Author notes

  1. Min Du and Ping Yang are co first authors.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China

    Min Du, Min Liu & Jue Liu

  2. Anning First People’s Hospital, Kunming University of Science and Technology, Yunan, China

    Ping Yang, Manchang Li, Shiping Wang & Chao Song

  3. Jinfang Community Health Center, Anning Medical Community, Yunan, China

    Xuejun Yu

  4. Taiping Community Health Center, Anning Medical Community, Yunan, China

    Taifu Li

  5. Lubiao Community Health Center, Anning Medical Community, Yunan, China

    Chenchen Huang

  6. Institute for Global Health and Development, Peking University, Beijing, China

    Jue Liu

  7. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China

    Jue Liu

  8. Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA

    Jue Liu

Authors
  1. Min Du
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  2. Ping Yang
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  3. Manchang Li
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  4. Xuejun Yu
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  5. Shiping Wang
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  6. Taifu Li
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  7. Chenchen Huang
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  8. Min Liu
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  9. Chao Song
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  10. Jue Liu
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Contributions

CS and JL conceptualised and designed the study, MD, PY, ML, XY, SW, TL, CH, ML, and CS did data acquisition, MD did data curation, formal analysis, and visualization, MD did writing—original draft, PY, ML, XY, SW, ML and JL did writing- reviewing and editing. MD and PY were co-first authors. JL and CS were co-correspondence authors.

Corresponding authors

Correspondence to Chao Song or Jue Liu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the institutional review boards at Peking University (IRB00001052-21126). All participants had oral informed consent at the time of participation. The research has been performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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Supplementary Information

12877_2025_5675_MOESM1_ESM.docx

Supplementary Material 1: Table S1. Subgroup analysis on effects of sleep quality on long COVID among older adults.Table S2. Subgroup analysis on effects of sleep quality on general symptoms among older adults.Table S3. Subgroup analysis on effects of sleep quality on cardiovascular symptoms among older adults. Table S4. Subgroup analysis on effects of sleep quality on respiratory symptoms among older adults. Table S5. Subgroup analysis on effects of sleep quality on gastrointestinal symptoms among older adults. Table S6. Subgroup analysis on effects of sleep quality on gastrointestinal symptoms among older adults.

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Du, M., Yang, P., Li, M. et al. Effects of sleep quality on the risk of various long COVID symptoms among older adults following infection: an observational study. BMC Geriatr 25, 20 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05675-5

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05675-5

Keywords

BMC Geriatrics

ISSN: 1471-2318

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