Abstract
Objective: The objective of this meta-analysis was to establish the prevalence of poor sleep quality among individuals aged over 60 years old, utilizing the Pittsburgh Sleep Quality Index (PSQI).
Method : To identify appropriate records, a thorough search of PubMed, Scopus, and Web of Knowledge databases was conducted until May 22, 2024. The quality of the included studies was assessed using the Hoy tool. To calculate the pooled prevalence with a 95% confidence interval, the random effects model was utilized. Additionally, subgroup analyses were carried out to investigate the potential sources of heterogeneity.
Results: This study examined 52 cross-sectional studies encompassing 24,217 individuals aged 60 and older. The quality of the studies was assessed, with 4 rated as moderate and 48 as good. China reported the lowest prevalence of poor sleep quality at 14%, while Malaysia recorded the highest at 96%. The overall pooled prevalence was found to be 50% (95% CI: 45-55%), exhibiting significant heterogeneity across the studies (I² = 99.60%, Pheterogeneity < 0.001). Subgroup analysis indicated that geographic location significantly influenced this heterogeneity (p < 0.001), with Africa showing a lower prevalence of poor sleep quality at 27% (95% CI: 23%-32%) compared to other regions. However, no significant differences were observed in the impact of other factors, such as gender, level of development, sample size, survey time, PSQI cut-off, and response rate on the overall heterogeneity of the analyzed data.
Conclusion: This study reveals that approximately half of older adults experience poor sleep quality. However, this prevalence varies across different locations, underscoring the necessity for targeted interventions that consider regional factors influencing sleep health.
Key Words: Aged, Cross-Sectional Studies, Epidemiology, Prevalence, Sleep Disorders
The physiology of sleep undergoes changes as individuals age, and there is an increased prevalence of sleep disorders among the elderly population (1). Sleep quality is determined by an individual’s personal experience and satisfaction with their sleep (2). Among the elderly, poor sleep quality is the third most prevalent issue after headaches and digestive disorders (3). Poor sleep quality in the elderly can lead to various detrimental effects on their physical and mental health, such as an increased risk of falls, impaired cognitive function and memory, higher likelihood of experiencing depression and anxiety, weakened immune system, elevated risk of chronic diseases like diabetes and cardiovascular disease, and ultimately a reduced quality of life and well-being (4-7). These negative effects can lead to a considerable financial burden on society. According to a study performed in 2018, sleep disorders in the United States were projected to incur a total of $94.9 billion in direct medical expenses (8). In Australia, a separate study revealed that the financial impact of sleep disorders was estimated at $10.0 billion, equivalent to 0.73% of the country's gross domestic product. Additionally, the nonfinancial cost was estimated to be $25.4 billion, accounting for 3.2% of the total burden of disease in Australia for that year (9).
In 2015, approximately 900 million individuals were aged 60 years or older, with projections indicating that this number will increase to nearly 2 billion by the year 2050 (10). Considering the significant rise in the aging population globally, it is imperative to investigate the spectrum of age-related issues, including poor sleep quality, to develop effective interventions. A meta-analysis in China found that approximately 36% of older adults suffer from poor sleep quality, with higher rates in rural areas (11). Another meta-analysis of community-dwelling older adults reported significant prevalence rates of sleep disorders, including obstructive sleep apnea (46%) and poor sleep quality (40%) (12). Studies have indicated that a substantial proportion of older adults experience poor sleep quality, with estimates varying from 38% to 77% (13, 14), in contrast to the lower rates of 15.9% to 22.3% found in the broader population (1). These differences can be attributed to variations in the populations studied and the assessment tools utilized to evaluate sleep quality.
Sleep quality can be evaluated through objective and subjective approaches. Objective measures, such as polysomnography and actigraphy, are not easily accessible to most clinicians in their daily practice. These methods are costly, time-consuming, and impractical for large-scale epidemiological and research studies (15). Conversely, subjective methods are more frequently employed, with the Pittsburgh Sleep Quality Index (PSQI) being a widely recognized tool for evaluating sleep quality (16). Its extensive validation across diverse populations establishes its reliability as a trusted assessment tool (17, 18). The PSQI is straightforward to administer, making it suitable for large-scale studies, and provides a comprehensive evaluation of sleep quality by examining various domains, such as sleep duration and disturbances. Additionally, its widespread use in previous research allows for comparative analyses.
Given the absence of global prevalence data on poor sleep quality using a standardized measurement in older adults, this study aims to address this gap. The objectives of this study are: 1) to determine the worldwide prevalence of poor sleep quality among older adults aged 60 years and above, utilizing the PSQI questionnaire, and 2) to investigate the factors contributing to the variability in sleep quality through subgroup analyses.
Materials and Methods
This systematic review and meta-analysis followed the guidelines provided by the Meta-Analyses of Observational Studies in Epidemiology (MOOSE) (19) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (20).
Information Sources
Electronic databases such as PubMed, Scopus, and Web of Knowledge were searched to find published literature up until May 22, 2024. The reference lists of eligible studies were also checked for any potential additional citations.
Search Strategy
To develop search strategies for systematic reviews, the PECOS statement (Population, Exposure, Comparison, Outcomes, and Study design) was utilized (21). The PECOS in this meta-analysis was considered: (P) old people, (E) not applicable, (C) not applicable, (O) prevalence of poor sleep quality, and (S) cross sectional studies.
The search keywords included (Aged OR Elderly OR Old OR Aging OR "Older adult") AND ("Sleep Wake Disorder" OR "Sleep Wake Disorders" OR "Sleep Disorder" OR "Sleep Disorders" OR "Sleep disturbance" OR "Sleep disturbances" OR "Sleep quality" OR "Sleep disruption" OR "Sleep disruptions" OR "Sleep problem" OR "Sleep problems" OR "Pittsburgh Sleep Quality Index" OR "PSQI") AND ("Cross sectional" OR "Cross section" OR Prevalence OR Prevalent OR Survey OR Frequency OR Epidemiology).
Eligibility Criteria
The inclusion criteria for the studies were (1) original articles and (2) cross-sectional studies that assessed sleep quality among old people using the PSQI questionnaire. The exclusion criteria were as follows: (1) lack of access to the full text of the articles, (2) non-English full text of the articles, (3) Age group below 60 years (4) non-reported age, (5) non-reported PSQI cut off (6) multiple articles with the same survey, and (7) insufficient information to calculate the prevalence of poor sleep quality.
Study Selection
In this meta-analysis, a single author conducted a search of databases and removed any duplicate studies. The articles for this meta-analysis were selected by two reviewers (MM and FKh), who independently selected articles that met the eligibility criteria. Any disagreements between the two reviewers were resolved by a third researcher.
Data Extraction
After selecting eligible articles, two reviewers (EM and NS) independently collected the following information from each study: title, first author name, publication year, survey time, country, sampling method, sample size, response rate, total effective sample size, effective sample size in men and women, the number of total events, the number of total events in men and women, age group (mean ± SD), and PSQI cut-off.
Quality Assessment
The quality of studies was assessed using the Hoy tool (22) in which its 10-item checklist evaluates the quality of studies based on two dimensions: external validity and internal validity. Items 1 to 4 assess the target population, sampling frame, sampling method, and minimal non-response bias to evaluate external validity. Items 5 to 9 assess the data collection method, case definition, study instrument, and mode of data collection to evaluate internal validity. Lastly, item 10 assesses bias related to the analysis. If the answer to an item is “Yes”, the score will be “1 = low risk,” and if it is “No” or “Unclear,” it will be scored “0 = high risk.” Studies were scored as low quality (1–3), moderate quality (4–6), and high quality (7–10).
Outcome Measurement
The primary outcome of this meta-analysis was the prevalence of poor sleep quality, which was measured using the PSQI Questionnaire. This is a self-report questionnaire that evaluates sleep quality over a period of one month. The measure comprises 19 individual items that are grouped into seven components, and the scores from these components are combined to produce a global score ranging from 0 to 21. Higher scores indicate poorer sleep quality (16).Various cutoff points were used to determine poor sleep quality. To determine the prevalence of poor sleep quality in each study, the number of individuals whose questionnaire score exceeded the specified cutoff point was divided by the total number of individuals who completed the questionnaire.
Statistical Analysis
The weighted pooled estimate was calculated using the Freeman-Tukey double arcsine transformation method. A random effects model was employed to determine the pooled prevalence with a 95% confidence interval. A forest plot was used to visually represent the data points and pooled estimate. The degree of heterogeneity between studies was assessed using two statistics: the I2 statistic (23) and the Cochrane Q statistic (24). The I2 statistic categorized heterogeneity as low (25%-49%), moderate (50-74%), or high (≥ 75%). The Cochrane Q statistic tested for statistical significance with a P-value of less than 0.05. Subgroup analyses were performed to examine whether the pooled prevalence estimates of poor sleep quality differed based on various factors such as gender, geographical area, level of development, sample size, survey time, PSQI Cut-Off, and response rate. The aim of these subgroup analyses was to identify potential sources of heterogeneity among the studies included in the meta-analysis. Statistical analyses were conducted using Stata version 11.0 (Stata Corp, College Station, TX)
Results
Study Selection
Out of the 45,446 records that were initially found, 24,217 were removed due to being duplicates. After reviewing the titles and abstracts of 21,229 articles, 147 records were chosen for full-text evaluation. Eventually, 52 records were deemed suitable for meta-analysis, as illustrated in Figure 1.
Figure 1.
Literature Search Flowchart for Identifying Studies on Poor Sleep Quality in Older Adults
Study Characteristics
For this meta-analysis, data from 52 studies involving a total of 55,614 individuals aged 60 years or older were analyzed. These studies were conducted in 22 different countries, including China (19), Thailand (4), Taiwan (4), Japan (3), South Korea (3), Brazil (3), Turkey (3), India (2), Iran (2), Greece (1), Saudi Arabia (1), Nigeria (1), Malaysia (1), Croatia (1), Ecuador (1), Slovenia (1), California (1), and Birmingham, Alabama; Minneapolis, Minnesota; Palo Alto, California (1). The published dates of these records ranged from 2009 to 2023. Out of the 52 studies, only 35 (67.30%) reported response rates, which varied from 28.9% to 99.51%. Most of the studies (38.4%) used a cut-off point of > 5 in the PSQI questionnaire to consider poor sleep quality. Table 1 presents the details of the included records.
Table 1.
Features of Studies Examining Poor Sleep Quality in Older Adults
| First Author, Publication Year | Survey Time | Country |
Sampling
Method |
Age Group
(Mean ± Age) |
Sample Size (Number of Males) |
Response
Rate (%) |
Number of Events in Males | Prevalence of Poor Sleep Quality (%) (95% CI) | PSQI Cut-Off |
|---|---|---|---|---|---|---|---|---|---|
| Chueh et al., 2009 (31) | 2008 | Taiwan | Stratified sampling | ≥ 60 (69.35 ± 5.42) | 1500 (NR) | 84 | NR | 21 (18-23) | ≥ 5 |
| Araujo et al., 2009 (32) | 2007 | Brazil | non-probability sampling | ≥ 60 (70.5 ± 8.1) | 105 (15) | 36.2 | NR | 63 (46-78) | > 5 |
| Wu et al., 2011 (26) | 2001 | Taiwan | Simple random sampling | ≥ 65 (74.75 ± 5.3) | 180 (45) | 55.6 | NR | 49 (39-59) | > 5 |
| Blackwell et al., 2011 (33) | 2005 | United States | NR | ≥ 65 (76 ± 7) | 3135 (NR) | 99 | NR | 44 (42-46) | > 5 |
| Yang et al., 2012(34) | 2008 | Taiwan | NR | ≥ 60 (72.56 ± 4.42) | 225 (91) | 71 | 40 | 42 (34-50) | ≥ 5 |
| Park et al., 2012 (35) | 2010 | South Korea | non-probability sampling | 65-89 (74 ± 5.79) | 162 (41) | NR | NR | 64 (56-72) | > 5 |
| Orhan et al., 2012 (36) | NR | Turkey | NR | ≥ 60 (74 ± 6.7) | 100 (15) | 73 | NR | 60 (48-72) | > 5 |
| Luo et al., 2012 (37) | NR | China | NR | ≥ 60 (72.2 ± 5.9) | 1763 (464) | 61.5 | 166 | 42 (39-45) | > 5 |
| Jirong et al., 2012 (38) | 2005 | China | NR | > 90 (93.52 ± 3.37) | 660 (220) | NR | NR | 42 (38-46) | > 5 |
| MH Lo et al., 2012 (39) | 2010 | China | non-probability sampling | ≥ 60 (76.06 ± 7.6) | 318 (47) | 97 | 3 | 78 (73-82) | ≥ 5 |
| Jie Li et al., 2012 (40) | 2009 | China | Stratified-cluster sampling | 60-98 (68.44 ± 7.01) | 2700 (1169) | 89.5 | 498 | 50 (48-52) | > 7 |
| Daglar et al., 2014 (41) | 2012 | Turkey | NR | 62-88 | 112 (NR) | NR | NR | 60 (50-69) | ≥ 5 |
| Kanga et al., 2015 (42) | 2011 | South Korea | Systematic sampling | ≥ 60 (68.2 ± 6.1) | 459 (143) | 76 | 51 | 45 (40-51) | > 5 |
| Ichimori et al., 2015 (43) | 2009 | Japan | non-probability sampling | 64-90 (82.8 ± 5.3) | 49 (8) | 89 | NR | 34 (20-50) | ≥ 5 |
| Chien et al., 2015 (44) | NR | Taiwan | NR | 65-96 (77.9 ± 7.3) | 236 (76) | 90.3 | NR | 52 (45-59) | > 5 |
| Aunjitsakul et al., 2016 (45) | 2015 | Thailand | Cluster sampling | 65-99 (72.3 ± 5.5) | 604 (176) | NR | 106 | 63 (59-66) | > 5 |
| Fawale et al., 2017 (46) | 2014 | Nigeria | non-probability sampling | ≥ 65 (72.7 ± 7.8) | 528 (NR) | 81 | NR | 27 (23-32) | > 5 |
| Thichumpa et al., 2018 (47) | 2015 | Thailand | Simple random sampling | ≥ 60 (67.8 ± 7.1) | 279 (108) | 95 | NR | 42 (36-48) | > 5 |
| Siripanich et al., 2018 (48) | NR | Thailand | Stratified sampling | 60-97 | 208 (76) | NR | 52 | 66 (59-73) | ≥ 5 |
| Štefan et al., 2018 (49) | 2018 | Croatia | Simple random sampling | ≥ 65 (80 ± 3) | 1187 (393) | 75.3 | NR | 54 (51-58) | ≥ 5 |
| Liu et al., 2018 (50) | 2015 | China | Simple random sampling | 60-97 (72.71 ± 8.29) | 1050 (509) | 98.3 | NR | 31 (28-34) | > 7 |
| Jiyeon Ha et al., 2018 (51) | 2018 | Japan | non-probability sampling | ≥ 65 (72.76 ± 4.41) | 176 (31) | 99 | NR | 42 (35-50) | ≥ 5 |
| Ning Li et al., 2019 (28) | 2011 | China | NR | ≥ 60 | 3325 (1488) | 98.4 | 143 | 14 (13-16) | > 7 |
| Kumar et al., 2019 (52) | 2017 | Malaysia | non-probability sampling | 70-98 (74.5 ± 8.4) | 151 (77) | NR | NR | 96 (92-99) | > 5 |
| Ansari et al., 2019 (53) | 2016 | Iran | non-probability sampling | ≥ 60 (68.5 ± 6.78) | 200 (72) | 65 | 43 | 60 (51-68) | > 5 |
| Honarvar et al., 2019 (54) | 2017 | Iran | Cluster sampling | ≥ 60 (67 ± 6.1) | 408 (186) | 94 | 77 | 55 (50-60) | > 5 |
| BruttO et al., 2020 (55) | 2017 | Ecuador | non-probability sampling | ≥ 60 (70.3 ± 7.8) | 437 (125) | 69.3 | 33 | 30 (25-36) | ≥ 6 |
| Jia et al., 2020 (56) | 2016 | China | Cluster sampling | ≥ 60 (70.41 ± 7.63) | 1763 (818) | 94.27 | NR | 50 (48-52) | > 7 |
| Zitser et al., 2020 (27) | 2011 | USA | NR | 67-84 (74 ± 0.3) | 32 (13) | NR | NR | 22 (9-40) | ≥ 5 |
| Nanthakwang et al., 2020 (57) | 2017 | Thailand | Simple random sampling | ≥ 60 (69.03 ± 7.3) | 1180 (422) | NR | 177 | 44 (41-47) | ≥ 5 |
| Ilkhan et al., 2020 (57) | 2019 | Turkey | NR | ≥ 65 (75.49 ± 8.62) | 1150 (670) | NR | NR | 83 (81-86) | ≥ 5 |
| Zhang et al., 2020 (58) | 2017 | China | Stratified-cluster sampling | ≥ 60 | 588 (253) | 86.2 | 125 | 50 (46-55) | > 7 |
| Sun et al., 2020 (59) | 2014 | China | Stratified sampling | 70-87 (77.6 ± 3.9) | 1726 (816) | NR | NR | 43 (41-46) | > 5 |
| Zhao Hu et al., 2020 (60) | 2018 | China | Simple random sampling | ≥ 60 (79.1 ± 8.7) | 829 (367) | 98 | 251 | 67 (64-71) | > 5 |
| Xidi Zhu et al., 2020 (61) | 2018 | China | Stratified-cluster sampling | ≥ 60 | 829 (376) | 98.5 | 251 | 67 (64-71) | ≥ 5 |
| SILVA et al., 2020 (25) | 2018 | Brazil | NR | 63-73 (69.64 ± 0.66) | 105 (16) | NR | 11 | 53 (43-63) | > 5 |
| Zhang et al., 2020 (62) | 2018 | China | Stratified-cluster sampling | ≥ 65 (72 ± 6.09) | 3911 (1892) | NR | 253 | 21 (20-22) | > 7 |
| Bader A. Alqahtani, 2021 (63) | 2019 | Saudi Arabia | NR | ≥ 60 (69.9 ± 6.2) | 276 (176) | 97 | NR | 66 (60-72) | > 5 |
| Benli Xue et al., 2021 (64) | 2019 | China | Stratified-cluster sampling | ≥ 60 | 3266 (1515) | 99.51 | NR | 49 (47-51) | ≥ 5 |
| Xinyi Liu et al., 2021 (65) | 2019 | China | NR | ≥ 60 (70.2 ± 6.1) | 1367 (290) | 68.4 | NR | 34 (31-37) | ≥ 7 |
| Mota et al., 2021 (66) | 2019 | Brazil | NR | ≥ 60 | 156 (39) | 51.9 | 21 | 67 (55-77) | ≥ 5 |
| Wen-Qi Xu et al., 2021 (67) | 2020 | China | NR | 60-101 (73.72 ± 8.27) | 717 (265) | NR | NR | 53 (49-56) | ≥ 7 |
| Hao et al., 2021 (68) | 2017 | China | non-probability sampling | ≥ 60 (67.69 ± 6.05) | 250 (106) | 98.8 | NR | 40 (34-46) | > 7 |
| Jesudoss et al., 2021 (69) | 2019 | India | non-probability sampling | 60-81 (70.5 ± 7.61) | 100 (50) | NR | NR | 66 (56-75) | > 5 |
| Yokoro et al., 2021 (70) | 2017 | Japan | NR | ≥ 65 (80 ± 5.7) | 170 (77) | 94 | NR | 37 (29-45) | ≥ 6 |
| Cao et al., 2022 (71) | 2022 | China | NR | ≥ 65 (72.74 ± 6.47) | 1200 (629) | 97.8 | NR | 41 (38-44) | > 7 |
| Sik Chu et al., 2022 (72) | 2018 | South Korea | NR | ≥ 65 (73.5 ± 0.11) | 4756 (2029) | NR | NR | 39 (38-41) | > 6 |
| Regati et al., 2022 (73) | 2019 | India | NR | 60-88 (73.4 ± 6.2) | 422 (52) | 28.9 | 25 | 52 (43-62) | ≥ 5 |
| Wang et al., 2022 (74) | 2021 | China | Cluster sampling | ≥ 60 | 613 (165) | NR | 15 | 18 (15-22) | > 7 |
| Liu et al., 2022 (75) | 2021 | China | Simple random sampling | ≥ 60 (77.32 ± 8.87) | 1206 (597) | NR | NR | 61 (58-64) | > 7 |
| Lorber et al., 2022 (76) | 2019 | Slovenia | non-probability sampling | 67-99 (79 ± 9.04) | 2000 (278) | 41.5 | NR | 84 (81-86) | ≥ 5 |
| Papadopoulou et al., 2023 (77) | 2014 | Greek | Simple random sampling | ≥ 65 (74.6 ± 8.3) | 6745 (1750) | 50.4 | NR | 41 (39-43) | ≥ 5 |
*NR = Non-Reported
Quality of Studies
All records were evaluated as moderate (n = 4) and good quality (n = 48), with scores ranging from 6 to 10. The quality scores of the articles were distributed as follows: 6 (n = 4), 7 (n = 9), 8 (n = 13), 9 (n = 11), and 10 (n = 15) (Table 2).
Table 2.
Quality Assessment of Descriptive Cross-Sectional Studies on Poor Sleep Quality in Older Adults Using the Hoy Tool
| First Author, Publication Year | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Total Score |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chueh et al.,2009 (31) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Araujo et al., 2009 (32) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Chia-Yi Wu et al.,2011 (26) | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Blackwell et al.,2011 (33) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Yang et al., 2012(34) | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Park et al., 2012 (35) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 6 |
| Orhan et al., 2012 (36) | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Luo et al., 2012 (37) | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Jirong et al., 2012 (38) | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| MH Lo et al., 2012 (39) | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Jie Li et al., 2012 (40) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Daglar et al., 2014 (41) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 6 |
| Kanga et al., 2015 (42) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Ichimori et al., 2015 (43) | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Chien et al., 2015 (44) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Aunjitsakul et al., 2016 (45) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Fawale et al., 2017 (46) | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Thichumpa et al., 2018 (47) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Siripanich et al., 2018 (48) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Štefan et al., 2018 (49) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Liu et al., 2018 (50) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Jiyeon Ha et al., 2018 (51) | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Ning Li et al., 2019 (28) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Kumar et al., 2019 (52) | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Ansari et al., 2019 (53) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Honarvar et al., 2019 (54) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| BruttO et al., 2020 (55) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Jia et al., 2020 (56) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Zitser et al., 2020 (27) | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 7 |
| Nanthakwang et al., 2020 (57) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Ilkhan et al., 2020 (57) | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Zhang et al., 2020 (58) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Sun et al., 2020 (59) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Zhao Hu et al., 2020 (60) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Xidi Zhu et al., 2020 (61) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| SILVA et al., 2020 (25) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 6 |
| Zhang et al., 2020 (62) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Alqahtani et al., 2021 (63) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Xue et al., 2021 (64) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Xinyi Liu et al., 2021 (65) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
| Mota et al., 2021 (66) | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Xu et al.,2021 (67) | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Hao et al., 2021 (68) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Jesudoss et al., 2021 (69) | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 6 |
| Yokoro et al., 2021 (70) | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Jiri Cao et al., 2022 (71) | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 8 |
| Chu et al., 2022 (72) | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Regati et al., 2022 (73) | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 7 |
| Wang et al., 2022 (74) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Liu et al., 2022 (75) | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
| Lorber et al., 2022 (76) | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 8 |
| Papadopoulou et al., 2023 (77) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 |
Q1- Was the study's target population a close representation of the national population in relation to relevant variables?
Q2. Was the sampling frame a true and close representation of the target population?
Q3. An appropriate sample size was used? 200: Yes (Low risk), < 200: No (High risk)
Q4. Was the likelihood of non-response bias minimal?
Q5. Did the study evaluate limitations to extrapolation of prevalence to population?
Q6. Were data collected directly from the subjects (as opposed to a proxy)?
Q7. Was an acceptable case definition used in the study?
Q8. Was the study instrument that measured the parameter of interest (e.g. prevalence of anemia) shown to have reliability and validity (if necessary)?
Q9. Was the same mode of data collection used for all subjects?
Q10. Were the numerator(s) and denominator(s) for the parameter of interest appropriate?
* Each Item was scored as follows: yes = 1; no/unclear = 0.
* The quality of the studies was evaluated as follows: low quality, 0–3; moderate quality, 4–6; good quality, 7–10.
Prevalence of Poor Sleep Quality
The prevalence of poor sleep quality among older adults varied greatly across countries, with China reporting the lowest prevalence at 14% and Malaysia the highest at 96%. The overall pooled prevalence was found to be 50% (95% CI:45%–55%). The studies demonstrated significant heterogeneity (I2 = 99.60%, P heterogeneity < 0.001) (Figure 2).
Figure 2.
The Pooled Prevalence of Poor Sleep Quality in Older Adult
Subgroup Analyses
The subgroup analysis results indicated that only the geographical area was identified as a significant factor impacting the heterogeneity of the studies (P < 0.001). Specifically, studies conducted in Africa reported a lower prevalence rate (27%; 95% CI = 23%–32%) compared to those conducted in other regions (48%, 61%, 69%, and 46% for Asia, Europe, Eurasia, and America, respectively). On the other hand, no significant differences were observed in the impact of other factors such as gender, level of development, sample size, survey time, PSQI cut-off, and response rate, on the overall heterogeneity of the analyzed data (Table 3).
Table 3.
Prevalence of Poor Sleep Quality in Older Adults: A Subgroup Analysis across Demographic, Geographical, and Methodological Factors
| Subgroups | Number of Studies | Event | Effective Sample Size |
Pooled Prevalence%
(95% CI) |
Between Studies | Between-Group | ||
|---|---|---|---|---|---|---|---|---|
| I2 | P heterogeneity | Q | P heterogeneity | |||||
| Gender | ||||||||
| Male | 20 | 2,369 | 7,619 | 44 (33-55) | 98.80 | < 0.001 | 1.08 | 0.30 |
| Female | 20 | 4,085 | 9,530 | 52 (42-61) | 98.70 | < 0.001 | ||
| Geographical Area | ||||||||
| Asia | 39 | 15,068 | 37,148 | 48 (43-54) | 99.10 | < 0.001 | 49.96 | < 0.001* |
| Europe | 3 | 2,581 | 5,130 | 61 (34-84) | - | - | ||
| Eurasia | 3 | 1,071 | 1,335 | 69 (48-86) | - | - | ||
| America | 6 | 1,617 | 3,691 | 46 (36-57) | 91.42 | < 0.001 | ||
| Africa | 1 | 117 | 428 | 27 (23-32) | - | - | ||
| Level of Development | ||||||||
| Developing countries | 44 | 4,121 | 8,673 | 50 (45-56) | 99.19 | < 0.001 | 0.39 | 0.53 |
| Developed countries | 8 | 16,333 | 39,059 | 46 (35-58) | 98.90 | < 0.001 | ||
| Sample Size | ||||||||
| < 500 | 26 | 2,426 | 4,720 | 53 (46-60) | 95.85 | < 0.001 | 4.88 | 0.09 |
| 500-1000 | 10 | 3,998 | 7,396 | 53 (41-66) | 99.16 | < 0.001 | ||
| > 1000 | 16 | 14,030 | 35,616 | 42 (34-50) | 99.58 | < 0.001 | ||
| Survey Time | ||||||||
| NR | 4 | 744 | 1,580 | 55 (42-67) | 94.28 | < 0.001 | 5.48 | 0.14 |
| 2001-2010 | 10 | 3,613 | 8,274 | 38 (28-50) | 99.20 | < 0.001 | ||
| 2011-2015 | 10 | 3,774 | 11,231 | 48 (39-58) | 98.35 | < 0.001 | ||
| 2016-2022 | 28 | 12,323 | 26,674 | 53 (46-61) | 99.27 | < 0.001 | ||
| PSQI Cut-Off | ||||||||
| > 5 | 20 | 5,246 | 10,800 | 55 (49-61) | 96.98 | < 0.001 | 49.45 | < 0.001 |
| ≥ 5 | 17 | 7,178 | 14,023 | 54 (44-64) | 99.19 | < 0.001 | ||
| > 6 | 1 | 1,869 | 4,756 | 39 (38-41) | - | - | ||
| ≥ 6 | 2 | 150 | 463 | 32 (28-37) | - | - | ||
| > 7 | 10 | 5,317 | 16,037 | 37 (26-49) | 99.54 | < 0.001 | ||
| ≥ 7 | 2 | 694 | 1,653 | 50 (45-55) | - | - | ||
| Response Rate | ||||||||
| Yes | 35 | 13,074 | 30,339 | 48 (42-54) | 99 | < 0.001 | 0.84 | 0.36 |
| Non-reported | 17 | 7,380 | 17,393 | 53 (45-55) | 99.39 | < 0.001 | ||
P-value < 0.05
Discussion
The meta-analysis findings indicate that approximately 50% (95% CI: 45%–55%) of the older adult population reports poor sleep quality, with prevalence rates ranging significantly from 14% in China to 96% in Malaysia. Subgroup analyses revealed that only geographical region significantly affects the heterogeneity of the pooled prevalence.
In this review, the global prevalence of poor sleep quality, reported at 50.0%, aligns with findings from Brazil (53%) (25) and Taiwan (49%) (26). However, these figures are significantly higher than those observed in population-based studies in the United States (22%) (27) and China (14%) (28). A comprehensive meta-analysis encompassing a sample of 995,544 participants across 36 countries identified obstructive sleep apnea as the most prevalent sleep disorder worldwide, affecting 46.0% of individuals, followed by poor sleep quality (40.0%), other sleep problems (37.0%), insomnia (29.0%), and excessive daytime sleepiness (19.0%) (12). Furthermore, a meta-analysis conducted in China revealed a pooled prevalence of sleep disturbances among older adults at 35.9% (95% CI: 30.6%–41.2%) (29).
The substantial heterogeneity observed among the studies (I² = 99.60%) suggests that multiple factors may influence sleep quality in older adults. In our analysis, geographical location emerged as a significant determinant of prevalence rates, with studies conducted in Africa reporting notably lower rates compared to those in other regions. However, it is essential to acknowledge that our study included only one sample from Africa, which may not adequately represent the entire continent. Furthermore, a review indicates that sleep parameters in low- and middle-income countries exhibit similarities to those in high-income countries (29). The absence of significant differences related to other demographic and methodological factors, such as gender and sample size, underscores the importance of cultural, environmental, and potentially healthcare-related influences on sleep quality. Additionally, a meta-analysis highlighted considerable variability in sleep quality that could not be accounted for by regional factors, rurality, gender, age group, or sleep assessment methods (30). A separate meta-analysis conducted in China also found no significant differences in sleep quality between genders or between rural and urban populations (29).
Limitation
The current study has several strengths that contribute to the reliability and generalizability of its findings. These include a large sample size, a comprehensive search strategy, the use of studies with consistent methods for measuring sleep quality, inclusion of high-quality studies, representation of various regions of the world, and the use of subgroup analysis. However, there are also limitations to the study, such as high heterogeneity among the included studies, inadequate reporting of sampling methods in most studies, which limits subgroup analysis, and the lack of representation from the Africa continent. These limitations should be considered when interpreting the results to gain a more complete understanding of the global prevalence of poor sleep quality among adolescents.
Conclusion
This study reveals that approximately half of older adults experience poor sleep quality, a significant concern given its potential impact on their overall health and well-being. However, this prevalence is not uniform; it varies considerably across different geographic locations, indicating that environmental, cultural, and socioeconomic factors play a crucial role in shaping sleep health outcomes. Such disparities underscore the necessity for targeted interventions that consider these regional factors, ensuring that strategies to improve sleep quality are tailored to the specific needs and circumstances of diverse populations.
Acknowledgment
I would like to express my gratitude to all the responsible authors who provided us with the full texts of their articles.
Conflict of Interest
None.
References
- 1.Li J, Vitiello MV, Gooneratne NS. Sleep in Normal Aging. Sleep Med Clin. 2018;13(1):1–11. doi: 10.1016/j.jsmc.2017.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Nelson KL, Davis JE, Corbett CF. Sleep quality: An evolutionary concept analysis. Nurs Forum. 2022;57(1):144–51. doi: 10.1111/nuf.12659. [DOI] [PubMed] [Google Scholar]
- 3.Cotroneo A, Gareri P, Lacava R, Cabodi S. Use of zolpidem in over 75-year-old patients with sleep disorders and comorbidities. Arch Gerontol Geriatr Suppl. 2004(9):93–6. doi: 10.1016/j.archger.2004.04.015. [DOI] [PubMed] [Google Scholar]
- 4.Stone KL, Xiao Q. Impact of Poor Sleep on Physical and Mental Health in Older Women. Sleep Med Clin. 2018;13(3):457–65. doi: 10.1016/j.jsmc.2018.04.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tel H. Sleep quality and quality of life among the elderly people. Neurol Psychiatry Brain Res. 2013;19(1):48–52. [Google Scholar]
- 6.Corbo I, Forte G, Favieri F, Casagrande M. Poor Sleep Quality in Aging: The Association with Mental Health. Int J Environ Res Public Health. 2023;20(3):1661. doi: 10.3390/ijerph20031661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Stone KL, Xiao Q. Impact of Poor Sleep on Physical and Mental Health in Older Women. Sleep Med Clin. 2018;13(3):457–65. doi: 10.1016/j.jsmc.2018.04.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Huyett P, Bhattacharyya N. Incremental health care utilization and expenditures for sleep disorders in the United States. J Clin Sleep Med. 2021;17(10):1981–6. doi: 10.5664/jcsm.9392. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Streatfeild J, Smith J, Mansfield D, Pezzullo L, Hillman D. The social and economic cost of sleep disorders. Sleep. 2021;44(11) doi: 10.1093/sleep/zsab132. zsab132. [DOI] [PubMed] [Google Scholar]
- 10.Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052–90. doi: 10.1016/S0140-6736(18)31694-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lu L, Wang SB, Rao W, Zhang Q, Ungvari GS, Ng CH, et al. The Prevalence of Sleep Disturbances and Sleep Quality in Older Chinese Adults: A Comprehensive Meta-Analysis. Behav Sleep Med. 2019;17(6):683–97. doi: 10.1080/15402002.2018.1469492. [DOI] [PubMed] [Google Scholar]
- 12.Canever JB, Zurman G, Vogel F, Sutil DV, Diz JBM, Danielewicz AL, et al. Worldwide prevalence of sleep problems in community-dwelling older adults: A systematic review and meta-analysis. Sleep Med. 2024;119:118–34. doi: 10.1016/j.sleep.2024.03.040. [DOI] [PubMed] [Google Scholar]
- 13.Jalali R, Mohammadi M, Vaisi-Raygani A, Salari N. The prevalence of sleep disorders among Iranian older adults: A systematic review and meta-analysis. Curr Psychol. :1–8. [Google Scholar]
- 14.Lu L, Wang SB, Rao W, Zhang Q, Ungvari GS, Ng CH, et al. The Prevalence of Sleep Disturbances and Sleep Quality in Older Chinese Adults: A Comprehensive Meta-Analysis. Behav Sleep Med. 2019;17(6):683–97. doi: 10.1080/15402002.2018.1469492. [DOI] [PubMed] [Google Scholar]
- 15.Fabbri M, Beracci A, Martoni M, Meneo D, Tonetti L, Natale V. Measuring Subjective Sleep Quality: A Review. Int J Environ Res Public Health. 2021;18(3):1082. doi: 10.3390/ijerph18031082. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
- 17.de la Vega R, Tomé-Pires C, Solé E, Racine M, Castarlenas E, Jensen MP, et al. The Pittsburgh Sleep Quality Index: Validity and factor structure in young people. Psychol Assess. 2015;27(4):e22–7. doi: 10.1037/pas0000128. [DOI] [PubMed] [Google Scholar]
- 18.Manzar MD, BaHammam AS, Hameed UA, Spence DW, Pandi-Perumal SR, Moscovitch A, et al. Dimensionality of the Pittsburgh Sleep Quality Index: a systematic review. Health Qual Life Outcomes. 2018;16(1):89. doi: 10.1186/s12955-018-0915-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. Jama. 2000;283(15):2008–12. doi: 10.1001/jama.283.15.2008. [DOI] [PubMed] [Google Scholar]
- 20.Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg. 2010;8(5):336–41. doi: 10.1016/j.ijsu.2010.02.007. [DOI] [PubMed] [Google Scholar]
- 21.Morgan RL, Whaley P, Thayer KA, Schünemann HJ. Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018;121(Pt 1):1027–31. doi: 10.1016/j.envint.2018.07.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C, et al. Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement. Journal of clinical epidemiology. 2012;65(9):934–9. doi: 10.1016/j.jclinepi.2011.11.014. [DOI] [PubMed] [Google Scholar]
- 23.Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327(7414):557–60. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11(2):193–206. doi: 10.1037/1082-989X.11.2.193. [DOI] [PubMed] [Google Scholar]
- 25.da Silva RV, Soares NM, Pereira GM, da Silva Júnior EG, do Carmo Eulálio M. Sleep quality features and their association with mood symptoms and cognitive factors in a non-clinical sample of older Brazilian adults. Psychogeriatrics. 2020;20(5):673–80. doi: 10.1111/psyg.12560. [DOI] [PubMed] [Google Scholar]
- 26.Wu CY, Su TP, Fang CL, Yeh Chang M. Sleep quality among community-dwelling elderly people and its demographic, mental, and physical correlates. J Chin Med Assoc. 2012;75(2):75–80. doi: 10.1016/j.jcma.2011.12.011. [DOI] [PubMed] [Google Scholar]
- 27.Zitser J, Allen IE, Falgàs N, Le MM, Neylan TC, Kramer JH, et al. Pittsburgh Sleep Quality Index (PSQI) responses are modulated by total sleep time and wake after sleep onset in healthy older adults. PLoS One. 2022;17(6):e0270095. doi: 10.1371/journal.pone.0270095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Li N, Xu G, Chen G, Zheng X. Sleep quality among Chinese elderly people: A population-based study. Arch Gerontol Geriatr. 2020;87:103968. doi: 10.1016/j.archger.2019.103968. [DOI] [PubMed] [Google Scholar]
- 29.Lu L, Wang SB, Rao W, Zhang Q, Ungvari GS, Ng CH, et al. The Prevalence of Sleep Disturbances and Sleep Quality in Older Chinese Adults: A Comprehensive Meta-Analysis. Behav Sleep Med. 2019;17(6):683–97. doi: 10.1080/15402002.2018.1469492. [DOI] [PubMed] [Google Scholar]
- 30.Simonelli G, Marshall NS, Grillakis A, Miller CB, Hoyos CM, Glozier N. Sleep health epidemiology in low and middle-income countries: a systematic review and meta-analysis of the prevalence of poor sleep quality and sleep duration. Sleep Health. 2018;4(3):239–50. doi: 10.1016/j.sleh.2018.03.001. [DOI] [PubMed] [Google Scholar]
- 31.Chueh KH, Yang MS, Chen CS, Chiou SM. Poor sleep quality and alcohol use problems among elderly Taiwanese aboriginal women. Int Psychogeriatr. 2009;21(3):593–9. doi: 10.1017/S1041610209008850. [DOI] [PubMed] [Google Scholar]
- 32.Araújo CL, Ceolim MF. [Sleep quality of elders living in long-term care institutions] Rev Esc Enferm USP. 2010;44(3):619–26. doi: 10.1590/s0080-62342010000300010. [DOI] [PubMed] [Google Scholar]
- 33.Blackwell T, Yaffe K, Ancoli-Israel S, Redline S, Ensrud KE, Stefanick ML, et al. Association of sleep characteristics and cognition in older community-dwelling men: the MrOS sleep study. Sleep. 2011;34(10):1347–56. doi: 10.5665/SLEEP.1276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Yang CY, Chiou AF. Predictors of sleep quality in community-dwelling older adults in Northern Taiwan. J Nurs Res. 2012;20(4):249–60. doi: 10.1097/jnr.0b013e3182736461. [DOI] [PubMed] [Google Scholar]
- 35.Park JH, Yoo MS, Bae SH. Prevalence and predictors of poor sleep quality in Korean older adults. Int J Nurs Pract. 2013;19(2):116–23. doi: 10.1111/ijn.12047. [DOI] [PubMed] [Google Scholar]
- 36.Orhan FO, Tuncel D, Taş F, Demirci N, Ozer A, Karaaslan MF. Relationship between sleep quality and depression among elderly nursing home residents in Turkey. Sleep Breath. 2012;16(4):1059–67. doi: 10.1007/s11325-011-0601-2. [DOI] [PubMed] [Google Scholar]
- 37.Luo J, Zhu G, Zhao Q, Guo Q, Meng H, Hong Z, et al. Prevalence and risk factors of poor sleep quality among Chinese elderly in an urban community: results from the Shanghai aging study. PLoS One. 2013;8(11):e81261. doi: 10.1371/journal.pone.0081261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Jirong Y, Changquan H, Hongmei W, Bi-Rong D. Association of sleep quality and dementia among long-lived Chinese older adults. Age (Dordr) 2013;35(4):1423–32. doi: 10.1007/s11357-012-9432-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Lo CM, Lee PH. Prevalence and impacts of poor sleep on quality of life and associated factors of good sleepers in a sample of older Chinese adults. Health Qual Life Outcomes. 2012;10:72. doi: 10.1186/1477-7525-10-72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Li J, Yao YS, Dong Q, Dong YH, Liu JJ, Yang LS, et al. Characterization and factors associated with sleep quality among rural elderly in China. Arch Gerontol Geriatr. 2013;56(1):237–43. doi: 10.1016/j.archger.2012.08.002. [DOI] [PubMed] [Google Scholar]
- 41.Daglar G, Pinar SE, Sabanciogullari S, Kav S. Sleep quality in the elderly either living at home or in a nursing home. Australian Journal of Advanced Nursing, The. 2014;31(4):6–13. [Google Scholar]
- 42.Kang SH, Yoon IY, Lee SD, Kim T, Lee CS, Han JW, et al. Subjective memory complaints in an elderly population with poor sleep quality. Aging Ment Health. 2017;21(5):532–6. doi: 10.1080/13607863.2015.1124839. [DOI] [PubMed] [Google Scholar]
- 43.Ichimori A, Tsukasaki K, Koyama E. Illuminance, Subjective Sleep Quality, and Psychosomatic Health in Elderly Individuals Requiring Care: A Survey of Japan's Hokuriku Region in Winter. J Community Health Nurs. 2015;32(2):104–14. doi: 10.1080/07370016.2015.1026158. [DOI] [PubMed] [Google Scholar]
- 44.Chien MY, Chen HC. Poor sleep quality is independently associated with physical disability in older adults. J Clin Sleep Med. 2015;11(3):225–32. doi: 10.5664/jcsm.4532. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Aunjitsakul W, Pitanupong J, Werachattawan N. Sleep quality among elderly people in songkhla province, thailand: A two-stage cluster sampling study. Journal of the Medical Association of Thailand. 2018;101(1) [Google Scholar]
- 46.Fawale MB, Ismaila IA, Mustapha AF, Komolafe MA, Ibigbami O. Correlates of sleep quality and sleep duration in a sample of urban-dwelling elderly Nigerian women. Sleep Health. 2017;3(4):257–62. doi: 10.1016/j.sleh.2017.05.008. [DOI] [PubMed] [Google Scholar]
- 47.Thichumpa W, Howteerakul N, Suwannapong N, Tantrakul V. Sleep quality and associated factors among the elderly living in rural Chiang Rai, northern Thailand. Epidemiol Health. 2018;40:e2018018. doi: 10.4178/epih.e2018018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Siripanich C. Health status and behavior influences to sleep quality among community-dwelling elderly in Chanthaburi Province. Thailand: 2017. [Google Scholar]
- 49.Štefan L, Vrgoč G, Rupčić T, Sporiš G, Sekulić D. Sleep Duration and Sleep Quality Are Associated with Physical Activity in Elderly People Living in Nursing Homes. Int J Environ Res Public Health. 2018;15(11):2512. doi: 10.3390/ijerph15112512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Liu Y, Li T, Zhang R, Guo L, Liu K. Poor sleep quality and late-life depression among the elderly in urban communities in liaoning, China: A moderated mediation analysis. Arch Gerontol Geriatr. 2018;79:158–63. doi: 10.1016/j.archger.2018.09.002. [DOI] [PubMed] [Google Scholar]
- 51.Ha J, Kim J. Factors influencing perceived health status among elderly workers: occupational stress, frailty, sleep quality, and motives for food choices. Clin Interv Aging. 2019;14:1493–501. doi: 10.2147/CIA.S210205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Kumar S, Wong PS, Hasan SS, Kairuz T. The relationship between sleep quality, inappropriate medication use and frailty among older adults in aged care homes in Malaysia. PLoS One. 2019;14(10):e0224122. doi: 10.1371/journal.pone.0224122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Ansari M, Doulatshahi B, Sahaf R. Comparing negative emotional components in older people with normal and poor quality of sleep. Iranian Journal Of Ageing. 2019;14(2):200–11. [Google Scholar]
- 54.Honarvar B, Lankarani KB, Azadegan M, Khaksar E, Jafari F, Fard TR. The prevalence and predictors of sleep disturbance in the elderly: A population-based study in Shiraz, Iran. Shiraz E-Medical Journal. 2019;20(11) [Google Scholar]
- 55.Del Brutto OH, Mera RM, Peñaherrera E, Costa AF, Peñaherrera R, Castillo PR. On the Association Between Sleep Quality and Arterial Stiffness: A Population Study in Community-Dwelling Older Adults Living in Rural Ecuador (The Atahualpa Project) J Clin Sleep Med. 2019;15(8):1101–6. doi: 10.5664/jcsm.7798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Jia G, Yuan P. The association between sleep quality and loneliness in rural older individuals: a cross-sectional study in Shandong Province, China. BMC Geriatr. 2020;20(1):180. doi: 10.1186/s12877-020-01554-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Nanthakwang N, Siviroj P, Matanasarawoot A, Sapbamrer R, Lerttrakarnnon P, Awiphan R. Prevalence and associated factors of cognitive impairment and poor sleep quality among community-dwelling older adults in Northern Thailand. Open Public Health J. 2020;13:1. [Google Scholar]
- 58.Zhang J, Zhang Y, Luan Z, Zhang X, Jiang H, Wang A. A study on depression of the elderly with different sleep quality in pension institutions in Northeastern China. BMC Geriatr. 2020;20(1):374. doi: 10.1186/s12877-020-01777-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Sun XH, Ma T, Yao S, Chen ZK, Xu WD, Jiang XY, et al. Associations of sleep quality and sleep duration with frailty and pre-frailty in an elderly population Rugao longevity and ageing study. BMC Geriatr. 2020;20(1):9. doi: 10.1186/s12877-019-1407-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Hu Z, Zhu X, Kaminga AC, Zhu T, Nie Y, Xu H. Association between poor sleep quality and depression symptoms among the elderly in nursing homes in Hunan province, China: a cross-sectional study. BMJ Open. 2020;10(7):e036401. doi: 10.1136/bmjopen-2019-036401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Zhu X, Hu Z, Nie Y, Zhu T, Chiwanda Kaminga A, Yu Y, et al. The prevalence of poor sleep quality and associated risk factors among Chinese elderly adults in nursing homes: A cross-sectional study. PLoS One. 2020;15(5):e0232834. doi: 10.1371/journal.pone.0232834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Zhang YS, Jin Y, Rao WW, Jiang YY, Cui LJ, Li JF, et al. Prevalence and socio-demographic correlates of poor sleep quality among older adults in Hebei province, China. Sci Rep. 2020;10(1):12266. doi: 10.1038/s41598-020-68997-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Alqahtani BA. Association between Physical Frailty and Sleep Quality among Saudi Older Adults: A Community-Based, Cross-Sectional Study. Int J Environ Res Public Health. 2021;18(23):12741. doi: 10.3390/ijerph182312741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Xue B, Xue Y, Dong F, Zheng X, Shi L, Xiao S, et al. The impact of socioeconomic status and sleep quality on the prevalence of multimorbidity in older adults. Front Public Health. 2022;10:959700. doi: 10.3389/fpubh.2022.959700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Liu X, Wang C, Qiao X, Si H, Jin Y. Sleep quality, depression and frailty among Chinese community-dwelling older adults. Geriatr Nurs. 2021;42(3):714–20. doi: 10.1016/j.gerinurse.2021.02.020. [DOI] [PubMed] [Google Scholar]
- 66.Mota SGd, Jesus ITMd, Inouye K, Macedo MNGF, Brito TRPd, Santos-Orlandi AAd. Is poor quality sleep present in older adults with worse social and health status? Texto & Contexto-Enfermagem. 2021;30:e20200614. [Google Scholar]
- 67.Xu WQ, Lin LH, Ding KR, Ke YF, Huang JH, Hou CL, et al. The role of depression and anxiety in the relationship between poor sleep quality and subjective cognitive decline in Chinese elderly: Exploring parallel, serial, and moderated mediation. J Affect Disord. 2021;294:464–71. doi: 10.1016/j.jad.2021.07.063. [DOI] [PubMed] [Google Scholar]
- 68.Hao X, Li M, Li J, Lv M, Qin Y, Li K. Sleep quality in relation to social support and resilience among rural empty-nest older adults in China. Sleep Med. 2021;82:193–9. doi: 10.1016/j.sleep.2021.03.026. [DOI] [PubMed] [Google Scholar]
- 69.Jesudoss D, Lazarus ER, Wahid R. Insomnia and sleep quality among older people residing in old age homes at Andhra Pradesh, India. Int J Afr Nurs Sci. 2023;18:100522. [Google Scholar]
- 70.Yokoro M, Otaki N, Yano M, Imamura T, Tanino N, Maeda K, et al. Lower dietary variety is associated with worse sleep quality in community-dwelling elderly Japanese women. Asia Pac J Clin Nutr. 2021;30(2):275–82. doi: 10.6133/apjcn.202106_30(2).0012. [DOI] [PubMed] [Google Scholar]
- 71.Cao J, Zhou Y, Su MM, Chen WH. Correlation between PTSD and sleep quality in community-dwelling elderly adults in Hunan province of China. Front Psychiatry. 2022;13:978660. doi: 10.3389/fpsyt.2022.978660. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Chu HS, Oh J, Lee K. The Relationship between Living Arrangements and Sleep Quality in Older Adults: Gender Differences. Int J Environ Res Public Health. 2022;19(7):3893. doi: 10.3390/ijerph19073893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Regati M, Vijayakumar P. Study of sleep disorders in the elderly visiting geriatrics department. J Family Med Prim Care. 2022;11(2):614–22. doi: 10.4103/jfmpc.jfmpc_949_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Wang J, Qin W, Pang M, Zhao B, Xu J, Li S, et al. The Effect of Chronic Disease and Mental Health on Sleep Quality among Migrant Elderly Following Children in Weifang City, China. Int J Environ Res Public Health. 2022;19(19):12734. doi: 10.3390/ijerph191912734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Liu S, Hu Z, Guo Y, Zhou F, Li S, Xu H. Association of sleep quality and nap duration with cognitive frailty among older adults living in nursing homes. Front Public Health. 2022;10:963105. doi: 10.3389/fpubh.2022.963105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Lorber M, Kmetec S, Davey A, Mlinar Reljić N, Fekonja Z, Kegl B. Associations between Sleep Quality, Frailty, and Quality of Life among Older Adults in Community and Nursing Home Settings. Int J Environ Res Public Health. 2023;20(6):4937. doi: 10.3390/ijerph20064937. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Papadopoulou SK, Mantzorou M, Voulgaridou G, Pavlidou E, Vadikolias K, Antasouras G, et al. Nutritional Status Is Associated with Health-Related Quality of Life, Physical Activity, and Sleep Quality: A Cross-Sectional Study in an Elderly Greek Population. Nutrients. 2023;15(2):443. doi: 10.3390/nu15020443. [DOI] [PMC free article] [PubMed] [Google Scholar]