Back to Journals » Clinical Interventions in Aging » Volume 14

Correlates of Health-Related Quality of Life Among Chinese Older Adults with Mild Cognitive Impairment

Authors Song D, Yu DSF, Li PWC, He G, Sun Q

Received 19 August 2019

Accepted for publication 1 December 2019

Published 16 December 2019 Volume 2019:14 Pages 2205—2212


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Zhi-Ying Wu

Download Article [PDF] 

Dan Song,1 Doris SF Yu,2 Polly WC Li,2 Guijuan He,1 Qiuhua Sun1

1School of Nursing, ZheJiang Chinese Medical University, Hangzhou, People’s Republic of China; 2The Nethersole School of Nursing, The Chinese University of Hong Kong, HKSAR

Correspondence: Qiuhua Sun
School of Nursing, ZheJiang Chinese Medical University, Room 807, 23 Building, Hangzhou, People’s Republic of China
Tel/Fax +86-571- 86613674
Email [email protected]

Purpose: This study aimed to assess the health-related quality of life (HRQoL) and identify the important correlates of HRQoL in older Chinese adults with mild cognitive impairment (MCI).
Patients and methods: A cross-sectional study design was adopted. A total of 204 older adults with MCI were enrolled in this study. HRQoL was evaluated by the Quality of Life–Alzheimer’s disease. Hierarchical regression analysis was conducted to investigate the sociodemographic, disease-related, psychological, and behavioral factors associated with the HRQoL of individuals with MCI.
Results: Hierarchical regression analysis indicated that old age (Beta = −0.131, p =0.024), low income (Beta = 0.128, p = 0.032), depressive symptoms (Beta = −0.564, p < 0.001), and poor sleep quality (Beta = −0.169, p =0.004) were significantly associated with the HRQoL of individuals with MCI.
Conclusion: Caring for older Chinese adults with MCI should focus on sociodemographically disadvantaged groups with advanced age and low income. Rehabilitation programs that effectively alleviate depressive symptoms and improve sleep quality should be applied to older adults with MCI to enhance their HRQoL.

Keywords: mild cognitive impairment, health-related quality of life, correlates



Along with the rapid worldwide population aging, there is a dramatic increase in the incidence and prevalence of cognitive impairment.1 Mild cognitive impairment (MCI) is a transitional stage between normal aging and dementia, which is highly prevalent among older adults with an estimated prevalence ranging from 16% to 22.2% worldwide.24 Individuals with MCI constitute a high-risk group for developing dementia. It is estimated that 10.2% to 33.6% of the MCI patients convert to dementia annually,5 whereas the annual conversion rate to dementia is around 1–3% in overall older adults.6,7 Living with MCI has posed considerate challenges to one’s daily function and psychosocial well-being.8 With the trajectory of MCI, the disease condition becomes increasingly complex and devastating. Therefore, promoting or maintaining their health-related quality of life (HRQoL) is considered as the ultimate treatment goal.9

HRQoL is concerned with health aspects including physical, psychological and social well-being and the effect of a specific illness or treatment on these parameters.10 QoL is an important health outcome for older adults with MCI, as it is multidimensional in nature, thus enabling the healthcare providers to take a comprehensive measurement of the disease and treatment effects. Moreover, HRQoL could be understood from the individual’s perspective, thus providing valuable information that aids healthcare providers in their efforts to develop patient-centered approach to help those with MCI.

Numerous studies have explored the factors associated with HRQoL in the general elderly population; the existing literature suggest that various socio-demographic characteristics (old age, being female, being single, low education, low income, living alone), poor physical health, low psychological status, and sleep disturbance may compromise HQRoL in general older adults.1113 Given the important role of HRQoL, emerging studies began to explore the factors related to HRQoL in MCI. However, few studies have provided a comprehensive analysis of the factors associated with HRQoL in MCI. The focus has been placed on identifying the independent relationship of HRQoL with either socio-demographic or disease-related variables.1416

For socio-demographic factors, Muangpaisan et al14 identified that poor education and low financial status are associated with a low HRQoL in patients with MCI in Thailand. For disease-related factors, Hsiao et al15 examined the relationship between cognitive function and HRQoL in Taiwanese older adults with MCI and identified the positive relationship; Kameyama et al16 explored the relationship between functional abilities and HRQoL in Japanese older adults with MCI and found a positive relationship.

Although considerable research efforts have been devoted to understanding the correlates of HRQoL in MCI, less attention has been given to the role of psychological and behavioral factors in influencing HRQoL in MCI. Depressive symptoms are considered as the most predominant psychological symptoms in MCI, with the reported prevalence ranging from 22.3% to 63.3%.17 Depressive symptoms not only affect mood but also worsen daily function, increase morbidity, and complicate the disease management of individuals with MCI.18,19 Older adults with MCI also experience sleep disturbance more often than general older adults, with an estimated prevalence of 38.3–63%.2022 Sleep disturbance causes fatigue, induces emotional distress, and interferes with daily living.23 Therefore, both depressive symptoms and sleep disturbance may play an important role in determining the HRQoL of older adults with MCI. However, the effect of depressive symptoms and sleep disturbance on the HRQoL among older adults with MCI has yet to be investigated.

Despite the important role of HRQoL in MCI, HRQoL is still poorly understood among older adults with MCI in mainland China, which has the largest aging population and faces a great burden of cognitive impairment. HRQoL is also a culture-specific health outcome.24 To tailor interventions that could meet the complex health needs of Chinese older adults with MCI, this study aims to investigate the socio-demographic, disease-related, and physical, psychological, and behavioral-related correlates of HRQoL in MCI.


Ethics Statement

This study was approved by the Survey and Behavioral Research Ethical Committee of the Chinese University of Hong Kong (No. SBREC-20160602). This study was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each participant. All information was kept strictly confidential.

Study Design and Sample

A cross-sectional study design was adopted. Eligible participants were community-dwelling older adults screened with MCI aged 60 or above. MCI was defined by scoring 19–26 on the Montreal Cognitive Assessment (Chinese version, MoCA-C). MoCA is a tool specifically developed for MCI screening;25 it contains subtests for memory, attention, executive function, language, visuospatial ability, and orientation to obtain a compressive view of one’s cognitive function.25 By using a cut-off score of 19 and 26, the MoCA-C gives the optimal sensitivity and specificity in differentiating patients with MCI from those with dementia (sensitivity: 93.2% & specificity: 71.7%) and intact cognitive function (sensitivity: 92.4% & specificity: 88.4%), respectively.26 The influence of education on cognitive function was adjusted by adding one point to those with less than 6 years of education.27

Participants were excluded if they met the following criteria: (1) individuals who scored below 19 on MoCA-C or have a diagnosis of dementia; (2) individuals who have any serious neurological disorders that influence the cognition (e.g., stroke, Parkinson’s disease and head damage); (3) individuals who have impaired hearing or vision that may inhibit them from giving consent and answering the questionnaires.

Regarding sample size estimation, by conservatively estimating a medium effect size (R2=0.13) of the relationship between the independent variable and the group of dependent variables,28 10 independent variables were considered to be included in the multiple regression model. Thus, a minimum of 134 participants were required in the multiple regression model in this study.

Data Collection

Data collection took place in a public community healthcare center in the city of Hangzhou, Southeast China from June 2016 to May 2017. Participants for this study were recruited via the following: 1) study posters attached with the contact information of the researchers; 2) health talks held at the community healthcare center; 3) and word of mouth by the researchers. Individuals who show interest, including those who were self-referred or referred by their family members and general practitioners, were invited for an in-person interview to screen for study eligibility. Finally, a convenience sample of 204 older adults who were detected with MCI and met the inclusion criteria were recruited. Three research nurses consented and collected data from the eligible participants without the presence of the family members of the participants. Data were collected in the form of structured interview, by which the research nurses filled the questionnaires according to the responses of the participants to ensure internal validity.


Health-Related Quality of Life

The HRQoL was measured by the Quality of Life –Alzheimer’s disease (Chinese version, QOL-AD-C). QOL-AD is a 13-item questionnaire that was specifically designed to assess HRQoL among those with cognitive impairment.9 It uses simple and straightforward language and includes assessments of behavioral competence, the objective environment, psychological well-being, and perceived life quality, which are important for persons affected by cognitive impairment.9 Responses are structured in a four-choice format that is consistent across all questions, and all items are rated based on the respondent’s current HRQoL. Overall scores were computed by summing the 13 items, for a total possible score ranging from 13 to 52, with higher scores indicating better HRQoL. Its Cronbach’s alpha in this study was 0.800.

Potential Correlates of HRQoL

The correlates to be investigated included socio-demographic factors (age, gender, education, marital status, income, living conditions), disease-related factors (cognitive function and functional abilities), depressive symptoms and sleep quality. A socio-demographic sheet was designed to obtain the socio-demographic data. Cognitive function was indicated with the MoCA score. Functional status was assessed by Functional Activities Questionnaire (FAQ-C). FAQ evaluates complex functional and social behaviors of older adults that are probably impaired during early cognitive decline stage,29 which are not covered by the Lawton Instrumental Activities of Daily Living scale (IADL). Therefore, FAQ shows better sensitivity than IADL in detecting functional impairment in those with early cognitive impairment (sensitivity: 0.85 vs. 0.57).29 Depressive symptoms were assessed by the 30-item Geriatric Depression Scale (Chinese version, GDS-C). The scale results of using dichotomous questions presented a total score ranging from 0 to 30, with high scores representing highly depressive symptoms.30 A cut-off score ≥10 gives a sensitivity of 0.94 and a specificity of 0.80 for screening clinical level of depression among elderly people.30 Its Cronbach’s alpha in this study was 0.784. Sleep quality was measured by the Pittsburgh Sleep Quality Index (Chinese version, PSQI-C). It provides a global sleep quality score (maximum =21) based on 7 components (maximum sub-scale score=3) including sleep quality, latency, duration, efficiency, disturbance, use of sleep medication, and daytime dysfunction due to poor sleep quality.31 A global score ≥ 6 yielding a diagnostic sensitivity of 90% and specificity of 67% in differentiating good and poor sleepers in Chinese older adults.32 Its Cronbach’s alpha in this study was 0.736.

Statistical Analysis

Statistical analysis was performed with SPSS version 22. Appropriate descriptive statistics were used to summarise the characteristics of the participants. Bivariate correlations between the potential correlates and HRQoL were calculated using Pearson’s correlation and Spearman’s Rho for the continuous and ordinal variables, respectively. For the discrete variables (gender, residence, and marital status), independent t-tests were used to examine for any significant difference in the HRQoL between participants with different characteristics. Hierarchical regression analysis was further conducted to identify the independent correlates of HRQoL in MCI. The first level included socio-demographic characteristics (age, gender, education, marital status, income, and residence). At the second level, the disease-related factors (cognitive function and functional status) were entered. Next, depressive symptoms (GDS mean) were entered. The last level included sleep quality (PSQI mean). Regression diagnostics were performed to determine whether relevant statistical assumptions were met. The significance level α was set at 0.05, and all comparisons were two-tailed.


Characteristics of the Participants

A total of 204 older adults screened with MCI were recruited into this study. The characteristics of the participants are presented in Table 1. The mean age of the participants was 75.97 years, and about 78.4% were female. Above half of the participants (56.9%) have no less than 6 years’ education. About 70% of the participants had monthly income below the average level in the local city (i.e., 4000 CNY according to the public data). Less than 30% of the participants were unmarried, and 21.5% of the participants lived alone. One-third of the participants indicated the presence of functional impairment. The mean MoCA score was 22.47 (SD=1.94). The mean GDS score was 5.69 (SD=3.87); using a cut-off score of 10, 25.1% of the participants were classified as having possible clinical depression. The mean PSQI score was 8.92 (SD=4.13); using a cut-off score of 6, 74.5% of the participants were classified as having sleep disturbance.

Table 1 Characteristics of the Participants (N=204)

Factors Associated with HRQoL in the Bivariate Analysis

Table 2 shows the results of bivariate correlation between HRQoL and socio-demographic factors, disease-related factors, depressive symptoms, and sleep quality of patients with MCI. No high covariability (i.e., r≥0.80) existed between potential correlates. A lower level of HRQoL was related to worse cognitive function (r= 0.139, p<0.05), lower functional abilities (r= −0.417, p<0.01), more depressive symptoms (r= −0.651, p<0.01) and poorer sleep quality (r= −0.345, p<0.01). For the discrete variables (gender, residence, and marital status), no significant difference in the HRQoL between participants with different characteristics was detected.

Table 2 Bivariate Correlations Between Potential Correlates and HRQoL in MCI (N=204)

Factors Associated with HRQoL in the Hierarchical Regression Analysis

As shown in Table 3, socio-demographic factors accounted 4% of the variance of HRQoL (p = 0.233). Adding cognitive function and functional abilities in the second block accounted for additional 17.6% of the variance of HRQoL (p<0.001). Depressive symptoms further significantly accounted for 25.3% of the variance of HRQoL (p<0.001). Adding sleep quality to the final model accounted for additional 3.8% of the variance of HRQoL (p = 0.004). In the final modal, older age (Beta = −0.131, p= 0.024), lower income (Beta = 0.128, p = 0.032), more depressive symptoms (Beta = −0.564, p < 0.001) and poorer sleep quality (Beta = −0.169, p = 0.004) significantly contributed to a lower level of HRQoL. The total model explained 49.1% of the variance in HRQoL in MCI. The comparatively high regression coefficient of depressive symptoms indicated its more significant role in affecting HRQoL in MCI.

Table 3 Results of the Hierarchy Regression Analysis (N=204)


This study aimed to assess the level of HRQoL of Chinese older adults with MCI and systematically examine the factors related to the HRQoL in this population. The level of HRQoL in Chinese older adults with MCI as measured by the QoL-AD was comparable to that in Portuguese older adults with MCI (31.69 vs. 32.1). Old age, low income, depressive symptoms, and poor sleep quality were identified as significant correlates of HRQoL in Chinese older adults with MCI.

The positive association between income and HRQoL in this study was consistent with the study findings of Muangpaisan et al,14 which may be explained by the financial stress and the impact of low income on access to care.33 Age was identified as a significant correlate in our work but not in the study of Muangpaisan et al,14 possibly because of the difference in sample size and participants’ characteristics between these studies. Our sample size was larger than that in the previous study (n=204 vs. 85), and the participants in our study were older than their participants (mean age=75.97 vs. 66.7), thereby enabling us to detect the impact of older age on HRQoL. Indeed, old age is associated with worse disease conditions and more restricted social engagement, leading to reduced level of HRQoL.34 The socio-demographic correlates identified here indicate that healthcare providers should make an effort to approach these socio-demographically disadvantaged groups to improve their HRQoL.

Bivariate analysis revealed that cognitive function and functional abilities were significantly associated with HRQoL. However, this association was not found in multiple hierarchy analysis when depressive symptoms were added to the regression model. This finding was consistent with the reviewed literature. Kameyama et al16 performed bivariate analysis and identified the positive relationship between functional abilities and HRQoL in MCI. However, Teng et al35 failed to identify the relationship between functional abilities and HRQoL in MCI when depressive symptoms were included in multiple regression analysis. These findings likely suggested that depressive symptoms considerably affected the cognitive and functional performance of old adults with MCI, which was supported by other studies.36,37

The level of depressive symptoms was identified as the strongest correlate of HRQoL in MCI. Indeed, psychological health constitutes an important domain of HRQoL; moreover, the impact of depressive symptoms extends to different aspects of one’s life experience and significantly compromises the HRQoL of older adults with MCI. Depressive symptoms are not only emotionally distressing but also are companied by low energy, loss of interest and poor concentration, which has a greater impact on one’s ability to carry out daily activities and engage in social activities than that reported with other chronic physical illnesses.38 Furthermore, depressive symptoms have been identified as one of the most prominent risk factors for cognitive impairment in MCI.39 Together, these negative impacts are detrimental to the well-being of older adults with MCI. Despite the prominent effect of depressive symptoms on HRQoL, they have been neglected in the care for MCI population. According to a systematic review on psychosocial interventions for patients with cognitive impairment, no intervention studies have addressed the psychological well-being of individuals in the MCI population.40 Effective assessment and management strategies for depression in this increasing cohort of the population are necessary to promote their HRQoL. In terms of detecting depressive symptoms, older Chinese with depressive symptoms more likely to present somatic symptoms rather than verbalize their negative emotions, possibly hindering them from making a complaint.41 Health education in this regard should also be provided to patients with MCI and their caregivers to facilitate the timely detection and prompt management of depressive symptoms. In terms of managing depressive symptoms, evidence-based cognitive-behavioral interventions42 and exercise therapy43 could be incorporated into care programs for MCI to reduce depressive symptoms. Meaningful and enjoyable social activities can be incorporated to encourage patients with MCI to actively participate in various psychological rehabilitation programs.44

Sleep quality was first identified as an important correlate of HRQoL in MCI. Previous studies conducted in general and chronic disease populations supported the link between poor sleep and impaired HRQoL.4547 Poor sleep causes a lack of energy at daytime48 and negatively influences daily function.49 In addition, poor sleep is associated with increased negative mood.50 These effects considerably diminish the level of HRQoL in this vulnerable clinical cohort. In the present study, the prevalence of sleep disturbance in Chinese older adults with MCI reached 74.5%, which confirmed the high prevalence of sleep disturbance in patients with MCI reported in previous studies.20,21 Sleep disorders should be properly managed for the care of older adults with MCI to improve or maintain their HRQoL. Non-pharmacological treatments have been recommended as the most appropriate initial strategy. Cognitive behavioral treatment, which includes sleep hygiene education, stimulus control, sleep restriction, relaxation training, and cognitive therapy, is highly effective in managing sleep disorders in the general population51 and older adults with dementia.52 Continuing education programs should be provided to healthcare providers to promote professional development in this regard and enhance the care for individuals with MCI.


This study had several limitations. First, our data were obtained from a small convenience sample in one community center, which limits the generalizability of the study findings. Second, this study is cross-sectional in nature, so only association and not causal relationship could be established. Third, although we aimed to comprehensively investigate the potential correlates of HRQoL in MCI, the overall model explained 49.6% of the variance in HRQoL, indicating that other variables that were not assessed in this study might also contribute to HRQoL in MCI. Considering the subjective nature of HRQoL, further studies are recommended to additionally control intra-psychic variables and health beliefs and comprehensively understand the correlates of HRQoL in MCI.


This study revealed that Chinese older adults with MCI had impaired HRQoL. Old age, low income, depressive symptoms, and poor sleep were identified as potential correlates of HRQoL in MCI. The study findings helped target patient groups that were at a high risk of decreased HRQoL. Depressive symptoms and poor sleep among this vulnerable population should be effectively managed to promote or maintain their well-being.


Our thanks go to all participating patients for their good collaboration. We also would like to acknowledge the statistical help from Dr. Kai Chow Choi.


The authors report no conflicts of interest in this work.


1. Bettio LEB, Rajendran L, Gil-Mohapel J, et al. The effects of aging in the hippocampus and cognitive decline. Neurosci Biobehav Rev. 2017;79:66–86. doi:10.1016/j.neubiorev.2017.04.030

2. Petersen RC, Roberts RO, Knopman DS, et al. Prevalence of mild cognitive impairment is higher in men: the Mayo Clinic Study of Aging. Neurology. 2010;75(10):889–897. doi:10.1212/WNL.0b013e3181f11d85

3. Ward A, Arrighi HM, Michels S, et al. Mild cognitive impairment: disparity of incidence and prevalence estimates. Alzheimers Dement. 2012;8(1):14–21. doi:10.1016/j.jalz.2011.01.002

4. Lara E, Koyanagi A, Olaya B, et al. Mild cognitive impairment in a Spanish representative sample: prevalence and associated factors. Int J Geriatr Psychiatry. 2016;31(8):858–867. doi:10.1002/gps.4398

5. Ward A, Tardiff S, Dye C, et al. Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: a systematic review of the literature. Dement Geriatr Cogn Dis Extra. 2013;3(1):320–332. doi:10.1159/000354370

6. Satizabal CL, Beiser AS, Chouraki V, et al. Incidence of dementia over three decades in the framingham heart study. N Engl J Med. 2016;374(6):523–532. doi:10.1056/NEJMoa1504327

7. Doblhammer G, Fink A, Zylla S, et al. Compression or expansion of dementia in Germany? An observational study of short-term trends in incidence and death rates of dementia between 2006/07 and 2009/10 based on German health insurance data. Alzheimer’s Res Ther. 2015;7(1):66. doi:10.1186/s13195-015-0146-x

8. Dean K, Wilcock G. Living with mild cognitive impairment: the patient’s and carer’s experience. Int Psychogeriatr. 2012;24(6):871–881. doi:10.1017/S104161021100264X

9. Logsdon RG, Gibbons LE, McCurry SM, et al. Assessing quality of life in older adults with cognitive impairment. Psychosom Med. 2002;64(3):510–519. doi:10.1097/00006842-200205000-00016

10. Jacobs JE. Quality of life: what does it mean for general practice? Br J Gen Pract. 2009;59(568):807–808. doi:10.3399/bjgp09X472854

11. Tel H. Sleep quality and quality of life among the elderly people. Neurol Psychiat Brain Res. 2013;19(1):48–52. doi:10.1016/j.npbr.2012.10.002

12. Gouveia ÉRQ, Gouveia BR, Ihle A. Correlates of health-related quality of life in young-old and old–old community-dwelling older adults. Qual Life Res. 2017;26(6):1561–1569. doi:10.1007/s11136-017-1502-z

13. Wada T, Ishine M, Sakagami T. Depression in Japanese community-dwelling elderly - Prevalence and association with ADL and QOL. Arch Gerontol Geriatr. 2004;39(1):15–23. doi:10.1016/j.archger.2003.12.003

14. Muangpaisan W, Assantachai P, Intalapaporn S, et al. Quality of life of the community-based patients with mild cognitive impairment. Geriatr Gerontol Int. 2008;8(2):80–85. doi:10.1111/j.1447-0594.2008.00452.x

15. Hsiao H-T, Li S-Y, Yang Y-P, et al. Cognitive function and quality of life in community-dwelling seniors with mild cognitive impairment in Taiwan. Community Ment Health J. 2016;52(4):493–498. doi:10.1007/s10597-016-9993-6

16. Kameyama K, Tsutou A, Fujino H, et al. The relationship between health-related quality of life and higher-level functional capacity in elderly women with mild cognitive impairment. J Phys Ther Sci. 2016;28(4):1312–1317. doi:10.1589/jpts.28.1312

17. Ismail Z, Elbayoumi H, Fischer CE, et al. Prevalence of depression in patients with mild cognitive impairment. JAMA Psychiatry. 2017;74(1):58. doi:10.1001/jamapsychiatry.2016.3162

18. Lam LCW, Tam CWC, Chiu HFK, et al. Depression and apathy affect functioning in community active subjects with questionable dementia and mild Alzheimer’s disease. Int J Geriatr Psychiatry. 2007;22(5):431–437. doi:10.1002/gps.1694

19. Herrmann N, Lanctôt KL, Sambrook R, et al. The contribution of neuropsychiatric symptoms to the cost of dementia care. Int J Geriatr Psychiatry. 2006;21(10):972–976. doi:10.1002/(ISSN)1099-1166

20. McKinnon A, Terpening Z, Hickie IB, et al. Prevalence and predictors of poor sleep quality in mild cognitive impairment. J Geriatr Psychiatry Neurol. 2014;27(3):204–211. doi:10.1177/0891988714527516

21. Rozzini L, Vicini Chilovi B, Conti M. Neuropsychiatric symptoms in amnestic and nonamnestic mild cognitive impairment. Dement Geriatr Cogn Disord. 2008;25(1):32–36. doi:10.1159/000111133

22. Lopez OL, Becker JT, Sweet RA. Non-cognitive symptoms in mild cognitive impairment subjects. Neurocase. 2005;11(1):65–71. doi:10.1080/13554790490896893

23. Chen J-C, Espeland MA, Brunner RL, et al. Sleep duration, cognitive decline, and dementia risk in older women. Alzheimers Dement. 2016;12(1):21–33. doi:10.1016/j.jalz.2015.03.004

24. Patrick DL, Deyo RA. Generic and disease-specific measures in assessing health status and quality of life. Med Care. 1989;27(3 Suppl):S217–S232. doi:10.1097/00005650-198903001-00018

25. Nasreddine ZS, Phillips NA, Bedirian V, et al. The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–699. doi:10.1111/j.1532-5415.2005.53221.x

26. Wen HB, Zhang ZX, Niu FS, et al. The application of montreal cognitive assessment in urban Chinese residents of Beijing. Chin J Int Med. 2008;47(1):36–39.

27. Jin H, Ding B, Yang X, et al. The utility of Beijing version montreal cognitive assessment in ischemic cerebrovascular disease patients of Changsha area and the development of Changsha version montreal cognitive assessment. Chin J Nerv Mentl Dis. 2011;37(6):349–353.

28. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge; 1988.

29. Pfeffer RI, Kurosaki TT, Harrah CH, et al. Measurement of functional activities in older adults in the community. J Gerontol. 1982;37(3):323–329. doi:10.1093/geronj/37.3.323

30. Smarr KL, Keefer AL. Measures of depression and depressive symptoms: beck depression inventory-II (BDI-II), center for epidemiologic studies depression scale (CES-D), geriatric depression scale (GDS), hospital anxiety and depression scale (HADS), and patient health questionnaire. Arthritis Care Res (Hoboken). 2011;63(S11):S454–S466. doi:10.1002/acr.v63.11s

31. Buysse DJ, Reynolds CF, Monk TH, et al. 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

32. Tsai P-S, Wang S-Y, Wang M-Y, et al. Psychometric evaluation of the Chinese Version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects. Qual Life Res. 2005;14(8):1943–1952. doi:10.1007/s11136-005-4346-x

33. Arpey NC, Gaglioti AH, Rosenbaum ME. How socioeconomic status affects patient perceptions of health care: a qualitative study. J Prim Care Community Health. 2017;8(3):169–175. doi:10.1177/2150131917697439

34. Efklides A, Kalaitzidou M, Chankin G. Subjective quality of life in old age in Greece: the effect of demographic factors, emotional state and adaptation to aging. Eur Psychol. 2003;8(3):178–191. doi:10.1027//1016-9040.8.3.178

35. Teng E, Tassniyom K, Lu PH. Reduced quality-of-life ratings in mild cognitive impairment: analyses of subject and informant responses. Am J Geriatr Psychiatry. 2012;20(12):1016–1025. doi:10.1097/JGP.0b013e31826ce640

36. Wang S, Blazer DG. Depression and cognition in the elderly. Annu Rev Clin Psychol. 2015;11(1):331–360. doi:10.1146/annurev-clinpsy-032814-112828

37. Kim BJ, Liu L, Nakaoka S, et al. Depression among older Japanese Americans: the impact of functional (ADL &IADL) and cognitive status. Soc Work Health Care. 2018;57(2):109–125. doi:10.1080/00981389.2017.1397588

38. Hays RD, Wells KB, Sherbourne CD, et al. Functioning and well-being outcomes of patients with depression compared with chronic general medical illnesses. Arch Gen Psychiatry. 1995;52(1):11. doi:10.1001/archpsyc.1995.03950130011002

39. Richard E, Reitz C, Honig LH, et al. Late-life depression, mild cognitive impairment, and dementia. JAMA Neurol. 2013;70(3):383. doi:10.1001/jamaneurol.2013.603

40. Noone D, Stott J, Aguirre E, et al. Meta-analysis of psychosocial interventions for people with dementia and anxiety or depression. Aging Ment Health. 2018;23(10):1–10.

41. Yu DSF, Lee DTF. Do medically unexplained somatic symptoms predict depression in older Chinese? Int J Geriatr Psychiatry. 2012;27(2):119–126. doi:10.1002/gps.2692

42. Sharon SS, Taki AC. Cognitive behavioral therapies in older adults with depression and cognitive deficits: a systematic review. Int J Geriatr Psychiatry. 2015;30(3):223–233. doi:10.1002/gps.4239

43. Schuch FB, Vancampfort D, Richards J, et al. Exercise as a treatment for depression: a meta-analysis adjusting for publication bias. J Psychiatr Res. 2016;77:42–51. doi:10.1016/j.jpsychires.2016.02.023

44. Croezen S, Avendano M, Burdorf A, et al. Social participation and depression in old age: a fixed-effects analysis in 10 European countries. Am J Epidemiol. 2015;182(2):168–176. doi:10.1093/aje/kwv015

45. Luyster FS, Dunbar-Jacob J. Sleep quality and quality of life in adults with type 2 diabetes. Diabetes Educ. 2011;37(3):347–355. doi:10.1177/0145721711400663

46. Magee CA, Caputi P, Iverson DC. Relationships between self-rated health, quality of life and sleep duration in middle aged and elderly Australians. Sleep Med. 2011;12(4):346–350. doi:10.1016/j.sleep.2010.09.013

47. Sandella DE, O’Brien LM, Shank LK, et al. Sleep and quality of life in children with cerebral palsy. Sleep Med. 2011;12(3):252–256. doi:10.1016/j.sleep.2010.07.019

48. Endeshaw YW. Do sleep complaints predict persistent fatigue in older adults? J Am Geriatr Soc. 2015;63(4):716–721. doi:10.1111/jgs.2015.63.issue-4

49. Kim J, Kim Y, Yang KI, et al. The relationship between sleep disturbance and functional status in mild stroke patients. Ann Rehabil Med. 2015;39(4):545. doi:10.5535/arm.2015.39.4.545

50. Cho HJ, Lavretsky H, Olmstead R, et al. Sleep disturbance and depression recurrence in community-dwelling older adults: a prospective study. Am J Psychiatry. 2008;165(12):1543–1550. doi:10.1176/appi.ajp.2008.07121882

51. Irwin MR, Cole JC, Nicassio PM. Comparative meta-analysis of behavioral interventions for insomnia and their efficacy in middle-aged adults and in older adults 55+ years of age. Health Psychol. 2006;25(1):3–14. doi:10.1037/0278-6133.25.1.3

52. McCurry SM, LaFazia DM, Pike KC, et al. Development and evaluation of a sleep education program for older adults with dementia living in adult family homes. Am J Geriatr Psychiatry. 2012;20(6):494–504. doi:10.1097/JGP.0b013e318248ae79

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF]