Poor Sleep Quality in Aging: The Association with Mental Health
1
Department of Psychology, Sapienza University of Rome, Via dei Marsi 78, 00185 Rome, Italy
2
Department of Dynamic and Clinical Psychology and Health Studies, Sapienza University of Rome, 00185 Rome, Italy
3
Body and Action Laboratory, IRCCS Santa Lucia Foundation, Via Ardeatina 306, 00179 Rome, Italy
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(3), 1661; https://doi.org/10.3390/ijerph20031661
Submission received: 23 December 2022
/
Revised: 13 January 2023
/
Accepted: 14 January 2023
/
Published: 17 January 2023
(This article belongs to the Special Issue Sleep: A Global Public Health Issue)
Abstract
:Sleep disturbances are common in the elderly. A primary sleep disorder can result from the physiological decline of aging; however, secondary sleep problems result from various causes involving physical and mental health. Since little is known about the relationships between sleep quality and mental health in aging, the present study aims to understand how different aspects generally associated with sleep (e.g., psychological and physiological factors, and sleep medication) may predict poor sleep quality in different stages of the lifespan. Therefore, we conducted several analyses (ANOVAs, Pearson correlations, and linear regressions) to test the hypotheses of the study. Accordingly, from a pool of 180 participants (elderly, middle-aged, and young adults), 143 individuals with poor sleep quality were selected. Different predictive patterns in the three groups emerged. Specifically, the use of sleep medication associated with worse sleep conditions is predicted by poor sleep quality in the elderly and by depression in young adults. In contrast, worsening sleep quality is predicted by depression in middle-aged adults. Previous studies focused on the transitions from good to poor sleep quality, while this is the first study to have examined the features of poor sleep quality in aging, highlighting different sleep patterns across the lifespan. This evidence should be considered from a preventive perspective.
1. Introduction
The progressive worldwide increase of the elderly population represents a significant public health problem owing to the pathological implications frequently associated with aging [1,2,3,4].
Mental health clearly has a prominent position in the constellations of symptoms characterizing aging, owing to its role in cognitive impairment, physical, and functional decline, and in increased healthcare demand and costs [5,6].
From a mental health perspective, progressive aging is associated with worsening mood, increased anxiety, and emotional dysregulation [7,8]. Another recurring problem highlighted in studies dealing with aging is the quality of sleep. Owing to the association of sleep quality with mental and physical health [9,10,11], daily activities and quality of life [9], sleep remains an aspect that has been largely investigated as a prodrome of aging decline (for a review, see Casagrande et al., [12]).
Usually, aging is characterized by difficulty beginning and maintaining sleep (e.g., insomnia [13]) and high daytime sleepiness [12]. Accordingly, the most frequent sleep disorders reported in the lifespan are insomnia, circadian rhythm disorders, breathing-related sleep disorders (SBD), restless legs syndrome (RLS), and REM sleep behavior disorders (RBD) [13,14,15,16,17,18]. Owing to the relationships mentioned above, studies have usually focused on analyzing the interactions between mental health and sleep from a preventive perspective [19,20,21], while others have considered overt pathologies in various sleep disorders [22,23,24].
Nevertheless, sleep quality changes due to physical and psychological disorders during the lifespan are less clear. Understanding the features of this relationship in aging may help counteract poor sleep quality, which is a risk factor for age-related physiological and cognitive decline [25,26]. However, highlighting that poor sleep quality is determined by different factors in young and middle-aged people can allow the planning of specific counteracting and prevention programs for people of different ages.
Accordingly, the present study aimed to examine the association between poor sleep quality and mental health during a person’s lifespan. It is well-known that elderly and young adults generally have unhealthy sleep habits and lower sleep quality than middle-aged adults. However, changes in the characteristics of sleep quality across the lifespan (from young adulthood to late adulthood) have not been further analyzed, and no information about how sleep quality is affected by mental and physical health (e.g., blood pressure) is available. Accordingly, we aimed to understand how different aspects that generally affected sleep (i.e., mental health, use of medication, etc.) may predict poor sleep quality during the lifespan, among elderly, middle-aged, and young adults.
We hypothesized that poorer sleep quality would be differently associated with mental health (e.g., anxiety and depression) in older adults than in younger ones [11,27,28,29]. Furthermore, we posited that elderly people, middle-aged people, and young adults would report different patterns of sleep quality.
2. Materials and Methods
2.1. Participants
A total of 180 participants (age range: 19–88 years) were recruited through public notices and voluntarily took part in the study. The total sample was divided into three groups according to age, comprising 60 elderly individuals (age range: over 65; mean age: 70.3 ± 5.39; 56.7% females), 70 middle-aged adults (age range: 36–64; mean age: 53.4 ± 6.87; 50% females), and 50 young adults (age range: 18–35; mean age: 24.0 ± 4.51; 58% females).
Regarding the presence of poor sleep quality, 143 of the participants were selected on the basis of the presence of poor sleep quality (score greater than 5 on the Pittsburgh Sleep Quality Index (PSQI)), and were included in the final sample. Specifically, 48 were elderly (mean age: 70.3 ± 5.62; 62.25% females); 52 were middle-aged (mean age: 54.3 ± 6.30; 51.93% females); and 43 were young adults (mean age: 26.0 ± 4.62; 53.49% females). A history of psychiatric or psychological diagnosis, and chronic medical conditions (e.g., dementia, stroke, cardiovascular diseases, and cancer) were adopted as exclusion criteria.
2.2. Measures
2.2.1. Socio-Demographic and Anamnestic Information
Demographic data (age, years of education, gender, and marital status), lifestyles (coffee, smoking, and alcohol consumption), medical history, and psychiatric information were collected for each patient by face-to-face interview.
2.2.2. Blood Pressure
Systolic blood pressure (SBP), diastolic blood pressure (DBP), and heart rate were recorded using an automatic electronic sphygmomanometer validated for self-measurement (“personal check” PIC), according to European guidelines criteria.
The device records brachial blood pressure using the oscillometric method with a pressure range of 30–260 mmHg and a pulse rate range of 40–199 beats/min.
2.2.3. Pittsburgh Sleep Quality Index
The Pittsburgh Sleep Quality Index (PSQI; [30,31]) was used to assess participants’ sleep quality over the previous 30 days through 19 questions that required information about quantitative and qualitative aspects of sleep habits.
Data were provided on seven different components of sleep: sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction. A global score of sleep quality was calculated for each participant. Higher global scores indicated worse sleep quality. In line with the Italian validation, a cut-off of five was considered to define categorically good or poor sleep quality.
The overall PSQI score was calculated by adding together all the subscale scores.
For additional information, see Buysse et al. [30].
2.2.4. Beck Depression Inventory
The Beck Depression Inventory (BDI; [32]) is a self-reported questionnaire including 21 items that investigate depressive symptomatology (e.g., hopelessness and irritability), cognitive (e.g., guilt and feelings of being punished), and physical symptoms (e.g., fatigue and weight loss) related to depression. Responses are given using a 5-point Likert scale. The global score indicates general depressive symptomatology. Higher scores of BDI are associated with worse conditions. Specifically, a score ranging between 0 and 13 is associated with “no depression” (i.e., no significant depressive symptoms), between 14 and 19 indicates “mild depression”, between 20 and 29 indicates “moderate depression”, and between 30 and 63 indicates “severe depression”.
2.2.5. Toronto Alexithymia Scale-20
The Toronto Alexithymia Scale-20 (TAS-20; [33,34]) is a self-reported questionnaire assessing alexithymia via 20 items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The test enables the assessment of three facets of alexithymia: difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and externally oriented thinking (EOT). The scores range from 20 to 100 and provide both categorical and continuous information. Higher scores are associated with higher alexithymia. Scores ranging from 52 to 60 indicate moderate levels of alexithymia, and scores above 60 indicate high levels of alexithymia.
2.2.6. State–Trait Anxiety Inventory
The State–Trait Anxiety Inventory (STAI; [35,36]) is a self-assessment questionnaire used to evaluate state and trait anxiety on a 4-point Likert scale (1 = not at all; 4 = very much). In line with the aim of the study, only trait anxiety (20 items) was considered. A higher STAI score identifies more elevated levels of anxiety. The questionnaire did not report a specific cut-off score for anxiety. However, conventionally, scores higher than 50.49 for men and higher than 53.10 for women indicate elevated trait anxiety.
2.3. Procedure
After signing an informed consent statement, the participants were subjected to blood pressure recordings (three times); then, weight and height were measured. Afterwards, the participants underwent the socio-demographic and anamnestic interview, and completed the questionnaires (Pittsburgh Sleep Quality Index, Toronto Alexithymia Scale-20, State–Trait Anxiety Inventory, and Beck Depression Inventory). The whole procedure lasted about 30 min.
2.4. Data Analysis
Analyses of variance (ANOVAs) were conducted to determine differences between the groups (elderly, middle-aged, and young adults) in the indices of PSQI and physical and mental health variables. To further analyze significant between-group differences, the Tukey post-hoc test was adopted. Correlations were then conducted to examine the association between the sleep indices assessed by PSQI and psychological (BDI, STAI, TAS-20) and physiological characteristics (BMI, SBP, DBP). Finally, in line with the main aim of the study, multiple regression analyses examined whether sleep quality was predicted by physiological and self-reported mental health separately in each group.
Two models were tested for each age group to analyze the possible predictors of changes in sleep quality reported by the global sleep quality index of the PSQI (model 1: physiological predictors, including BMI and blood pressure indices (SBP, DBP); model 2: psychological predictors, including STAI score, BDI score, and global TAS-20 score). Moreover, owing to the significant differences between groups in the use of medication to sleep, further multiple regressions were used to assess the predictor role of both physiological and psychological variables on medication use (model 1: physiological predictors, including BMI and blood pressure indices (SBP; DBP); model 2: psychological predictors, including STAI score, BDI score, global TAS-20 score, and total sleep quality). All statistical analyses were conducted with JASP, and a p < 0.05 was considered significant.
2.5. Ethics Statement
This study was approved by the Institutional Review Board of the Department of Psychology, Sapienza University of Rome (protocol number: 0001063). A waiver of written informed consent was granted; eligible participants signed informed consent statements before completing the questionnaire battery.
3. Results
Descriptive statistics for participants’ characteristics, Pittsburgh Sleep Quality Index subscales, and physiological and psychological aspects are reported in Table 1, Table 2, and Table 3, respectively.
3.1. Correlations
3.2. Regression
In each group of participants, linear regressions examined the relationship of the physiological (model 1) and psychological (model 2) dimensions with the PSQI global score and the use of sleep medicine. See Table 6 and Table 7.
3.2.1. Elderly
In elderly people, neither model predicted an increase in the PSQI global score (i.e., worse sleep quality). However, model 2 was marginally significant (F6,39 = 2.10; p = 0.08).
Regarding the use of sleep medication, model 2 was significant (F7,38 = 2.47; p = 0.03). In particular, high scores in global PSQI were associated with higher use of medication.
3.2.2. Middle-Aged Adults
In middle-aged adults, the global PSQI score was predicted by model 2 (F6,42 = 2.78; p = 0.02). In particular, depressive symptoms significantly predicted higher PSQI scores. However, regarding the use of sleep medication, neither regression model was significant.
3.2.3. Young Adults
In younger adults, neither model 1 nor model 2 predicted the global PSQI score. However, psychological factors predicted the use of sleep medicine (model 2) (F7,33 = 2.66; p = 0.03). In particular, higher depressive symptoms scores predicted higher use of sleep medication.
4. Discussion
The general increase in the incidence and severity of sleep disorders due to psychological, behavioral, and neurological changes associated with aging is well known [12,37,38]. Our results confirm a negative age-associated sleep quality trend [39]. Elderly people reported worse conditions than both young and middle-aged adults.
Interestingly, these findings were highlighted by considering three groups of individuals, all manifesting poor sleep quality. Although the participants showed the same severity of sleep disorders, this condition was predicted by different risk factors, highlighting a trend characterized by a different pattern across the lifespan. No physiological or psychological variable considered in this study was associated with poor sleep quality in younger and elderly people, while depression was able to predict low sleep quality in middle-aged individuals. Some of these results can be explained by the worse sleep condition of elderly people compared with young and middle-aged individuals, which allows elderly people to use more sleep medication.
Specifically, although the regression models did not highlight the role of physiological variables as predictors of sleep quality changes, higher diastolic blood pressure was associated with reduced sleep duration and efficiency in the overall sample. These results could be explained by an alteration of homeostasis via the autonomic nervous system [40], and by features of blood pressure indices (i.e., diastolic and systolic blood pressure; [41,42]), which in previous studies were related to higher night-time physiological activation [43,44]. Blood pressure directly corresponds to cardiac output, and is remarkably easy to alter, for example, by daily activity or psychological condition [1,42,45,46,47,48,49].
Moreover, another explanation regards the association between diastolic blood pressure and sleep quality as an early subclinical index of sleep disorders, such as obstructive sleep apnea [50], outlining it as a long-term predictor of sleep disorders. Anxiety and depressive symptoms are positively correlated with subjective sleep quality, sleep latency, sleep disturbance, daytime sleepiness, and global PSQI score (i.e., higher anxiety and depression are associated with worse sleep conditions). These results confirm an association between mental health and sleep quality [51,52,53].
If the general results broadly confirmed the role of psychological and physiological conditions in sleep disturbance [54], further interesting results emerged when analyses were conducted separately in the different age groups. In fact, the main aim of this work was to define possible predictors of worse sleep quality in different stages of adult life, to define risk factors of poor sleep quality in aging, and to identify specific sleep risk factors for each stage of aging. Since global sleep quality and use of medicine significantly differed between the groups, these dimensions were considered from young to elderly in order to highlight the different patterns.
In young adults showing poor sleep quality, no specific predictors of this condition were highlighted; however, depression predicted the use of sleep medication. This finding agrees with the results of other studies reporting increased use of drugs in young people with poor sleep quality, including pre-clinical conditions of sleep diseases [55,56]. Many young adults use drugs, prescribed or not, to improve sleep quality and sleep time [57]. Moreover, using medication to treat depression and other psychiatric conditions in young adults may result in disrupted sleep, which can explain why this association was reported in individuals with poor sleep quality [58].
However, further studies should investigate this association by using polysomnographic recordings and more extensive psychological assessment. A more comprehensive evaluation could differentiate between different clinical conditions associated with poor sleep quality, such as sleep disorders and psychopathologies.
For middle-aged adults, depression appears to be associated with worse sleep quality. These results are in line with previous research data. Middle-aged and older adults with major depression demonstrated agitation and somatic symptoms more than younger people with major depression (for a meta-analysis, see Hegeman et al., [59]).
The rate of sleep disturbance in depression ranges from 10–40% in middle-aged adults, with a lower rate in late adulthood than in early adulthood [60]. Moreover, it has been reported that mental health conditions influence middle-aged and older adults in their sleep architecture. In particular, depressive symptoms have been shown to be the main risk factors for sleep disturbance and low sleep quality [61]. Our study confirms this association between poor sleep quality and depressive symptomatology, starting in middle-age, although no conclamant depressive disorder was reported.
Considering elderly people, a general alteration in mental health predicts worse sleep quality. Moreover, higher use of sleep medication is associated with poorer sleep quality.
In summary, to answer the questions raised in this research, some psychological variables play a greater role than others in predicting sleep disturbances. From a mental health point of view, anxiety appears to have a moderate role in predicting sleep alteration in a population of elderly people who already exhibit poor sleep quality, as suggested by prior studies that reported an association between anxiety and sleep disturbances [62]. However, the evidence generally suggests that there is a close interplay between mental health (i.e., anxiety/depression [63]), sleep quality and sleep disorders (i.e., insomnia), and health-related quality of life in older adults [64,65]. In this sense, the significant association that emerged in the second regression model, i.e., mental health conditions and the use of sleep medication, can highlight the different patterns of sleep quality in young and elderly people. While in elderly people the use of sleeping drugs is associated significantly with effective poor sleep quality assessed by the global quality sleep index, in young adults depression is the main mental health dimension associated with the use of sleeping drugs [51,62,63]. In conclusion, in young individuals, attempts to alleviate mental health conditions can allow the development of other symptoms, such as sleep disorders. Conversely, the worse sleep pattern characterizing elderly people can justify their use of sleep medication. Further studies should investigate the relationship between sleep disorders and drug use in aging with polysomnographic methods and from a longitudinal perspective.
Limitations
One of the main limitations of this study is its cross-sectional nature, which does not allow for establishing the direction of causality between aging and physiological and psychological conditions, as well as their connections. Another limitation of the study is the self-reported nature of sleep conditions. While this method of defining sleep quality is supported by the literature, our study cannot define a sleep disorder diagnosis in the participants, reducing the generalizability of the evidence, since this can be associated only with altered sleep quality rather than with diagnosed sleep disorders. Accordingly, further studies should overcome these limitations by using polysomnographic sleep recordings.
A further limitation can be identified in the study’s consideration of a restricted frame of mental health. Other dimensions, such as mental distress, anger, personality traits (e.g., impulsivity), and cognitive traits (e.g., executive functioning) associated with altered homeostasis and the physiology of sleep, should be included in future studies [66,67].
5. Conclusions
People diagnosed with mental illness are more likely to have poor sleep quality than the general population. Starting from this assumption, this study expands this notion, underlining that some physical and mental variables can affect sleep quality in people with poor sleep quality. These conditions change between younger, middle-aged, and elderly people. To counteract this pattern, it could be useful to plan specific interventions to improve sleep in young, middle-aged, and elderly people; for example, it might be useful to improve mood in the middle-aged and young groups, and sleep quality in the elderly. In fact, the presence of poor subjective sleep quality could be a prodrome of a diagnosable sleep disorder, and could indicate a worsening of the general psychological condition of the individual. Most previous studies had focused on the first part of the continuum, i.e., the transition from good to poor sleep quality. In contrast, as far as we know, no studies had investigated the phenomena that characterize established poor sleep quality.
An important consequence of poor mental health and sleep quality is an increased demand for health services, with associated socioeconomic costs. For these reasons, it is relevant to consider this issue. Further studies should confirm and extend the results of this study, also using polysomnographic recordings to assess the quality of sleep in order to plan diversified interventions to enhance sleep quality according to the individual’s age. Improving sleep quality would entail a better general state of physical, psychological, and cognitive health, with evident beneficial effects on aging.
Author Contributions
Conceptualization, I.C., G.F. and F.F.; methodology, G.F. and F.F.; formal analysis, I.C., G.F. and F.F.; data curation, I.C. and M.C.; writing—original draft preparation, I.C., G.F. and F.F; writing—review and editing, I.C., G.F., F.F. and M.C.; supervision, M.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Department of Psychology—University of Rome Sapienza (protocol number: 0001063).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Data are available. Please contact corresponding authors.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Forte, G.; Casagrande, M. Effects of Blood Pressure on Cognitive Performance in Aging: A Systematic Review. Brain Sci. 2020, 10, 919. [Google Scholar] [CrossRef] [PubMed]
- Walke, R.S. Effectiveness of Laughter Therapy on Level of Stress and Assertiveness among Elderly at Old Age Homes in Selected Areas. Int. J. Nurs. Care 2021, 9, 1–4. [Google Scholar] [CrossRef]
- Guarino, A.; Favieri, F.; Boncompagni, I.; Agostini, F.; Cantone, M.; Casagrande, M. Executive Functions in Alzheimer Disease: A Systematic Review. Front. Aging Neurosci. 2019, 10, 437. [Google Scholar] [CrossRef] [PubMed]
- Guarino, A.; Forte, G.; Giovannoli, J.; Casagrande, M. Executive functions in the elderly with mild cognitive impairment: A systematic review on motor and cognitive inhibition, conflict control and cognitive flexibility. Aging Ment. Health 2020, 24, 1028–1045. [Google Scholar] [CrossRef] [PubMed]
- Kasajima, M.; Eggleston, K.; Kusaka, S.; Matsui, H.; Tanaka, T.; Son, B.-K.; Iijima, K.; Goda, K.; Kitsuregawa, M.; Bhattacharya, J.; et al. Projecting prevalence of frailty and dementia and the economic cost of care in Japan from 2016 to 2043: A microsimulation modelling study. Lancet Public Health 2022, 7, e458–e468. [Google Scholar] [CrossRef]
- Mohapatra, G.; Arora, R.; Giri, A.K. Establishing the relationship between population aging and health care expenditure in India. J. Econ. Adm. Sci. 2022. ahead-of-print. [Google Scholar] [CrossRef]
- Baxter, K.; Glendinning, C. The role of emotions in the process of making choices about welfare services: The experiences of disabled people in England. Soc. Policy Soc. 2013, 12, 439–450. [Google Scholar] [CrossRef] [Green Version]
- Mattila, A.K.; Salminen, J.K.; Nummi, T.; Joukamaa, M. Age is strongly associated with alexithymia in the general population. J. Psychosom. Res. 2006, 61, 629–635. [Google Scholar] [CrossRef]
- Tel, H. Sleep quality and quality of life among the elderly people. Neurol. Psychiatry Brain Res. 2013, 19, 48–52. [Google Scholar] [CrossRef]
- Wu, M.-C.; Yang, Y.-C.; Wu, J.-S.; Wang, R.-H.; Lu, F.-H.; Chang, C.-J. Short sleep duration associated with a higher prevalence of metabolic syndrome in an apparently healthy population. Prev. Med. (Baltim.) 2012, 55, 305–309. [Google Scholar] [CrossRef]
- Gulia, K.K.; Kumar, V.M. Sleep disorders in the elderly: A growing challenge. Psychogeriatrics 2018, 18, 155–165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Casagrande, M.; Forte, G.; Francesca, F.; Corbo, I. Sleep Quality and Aging: A Systematic Review on Healthy Older People, Mild Cognitive Impairment and Alzheimer’ s Disease. Int. J. Environ. Res. Public Health 2022, 19, 8457. [Google Scholar] [CrossRef] [PubMed]
- Roepke, S.K.; Ancoli-Israel, S. Sleep Disorders in the Elderly. Indian J. Med. Res. 2010, 131, 302–310. [Google Scholar]
- Auger, R.R.; Burgess, H.J.; Emens, J.S.; Deriy, L.V.; Thomas, S.M.; Sharkey, K.M. Clinical Practice Guideline for the Treatment of Intrinsic Circadian Rhythm Sleep-Wake Disorders: Advanced Sleep-Wake Phase Disorder (ASWPD), Delayed Sleep-Wake Phase Disorder (DSWPD), Non-24-Hour Sleep-Wake Rhythm Disorder (N24SWD), and Irregular Sleep-W. J. Clin. Sleep Med. 2015, 11, 1199–1236. [Google Scholar] [CrossRef] [PubMed]
- Falck, R.S.; Davis, J.C.; Best, J.R.; Li, L.; Chan, P.C.Y.; Wyrough, A.; Landry, G.J.; Liu-Ambrose, T. Buying time: A proof-of-concept randomized controlled trial to improve sleep quality and cognitive function among older adults with mild cognitive impairment. Trials 2018, 19, 445. [Google Scholar] [CrossRef]
- Guarnieri, B.; Adorni, F.; Musicco, M.; Appollonio, I.; Bonanni, E.; Caffarra, P.; Caltagirone, C.; Cerroni, G.; Concari, L.; Cosentino, F.; et al. Prevalence of Sleep Disturbances in Mild Cognitive Impairment and Dementing Disorders: A Multicenter Italian Clinical Cross-Sectional Study on 431 Patients. Dement. Geriatr. Cogn. Disord. 2012, 33, 50–58. [Google Scholar] [CrossRef] [Green Version]
- Riemann, D.; Baglioni, C.; Bassetti, C.; Bjorvatn, B.; Groselj, L.D.; Ellis, J.G.; Espie, C.A.; Garcia-Borreguero, D.; Gjerstad, M.; Gonçalves, M.; et al. European guideline for the diagnosis and treatment of insomnia. J. Sleep Res. 2017, 26, 675–700. [Google Scholar] [CrossRef]
- Tatineny, P.; Shafi, F.; Gohar, A.; Bhat, A. Sleep in the elderly. Mo. Med. 2020, 117, 490. [Google Scholar]
- Zhang, J.; Xu, Z.; Zhao, K.; Chen, T.; Ye, X.; Shen, Z.; Wu, Z.; Zhang, J.; Shen, X.; Li, S. Sleep Habits, Sleep Problems, Sleep Hygiene, and Their Associations With Mental Health Problems Among Adolescents. J. Am. Psychiatr. Nurses Assoc. 2018, 24, 223–234. [Google Scholar] [CrossRef]
- Reid, K.J.; Chang, A.-M.; Dubocovich, M.L.; Turek, F.W.; Takahashi, J.; Zee, P.C. Familial advanced sleep phase syndrome. Arch. Neurol. 2001, 58, 1089–1094. [Google Scholar] [CrossRef]
- Scott, A.J.; Webb, T.L.; Martyn-St James, M.; Rowse, G.; Weich, S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Med. Rev. 2021, 60, 101556. [Google Scholar] [CrossRef] [PubMed]
- Gadie, A.; Shafto, M.; Leng, Y.; Kievit, R.A. How are age-related differences in sleep quality associated with health outcomes? An epidemiological investigation in a UK cohort of 2406 adults. BMJ Open 2017, 7, e014920. [Google Scholar] [CrossRef]
- Ancoli-Israel, S.; Ayalon, L.; Salzman, C. Sleep in the Elderly: Normal Variations and Common Sleep Disorders. Harv. Rev. Psychiatry 2008, 16, 279–286. [Google Scholar] [CrossRef] [PubMed]
- Haseli-Mashhadi, N.; Dadd, T.; Pan, A.; Yu, Z.; Lin, X.; Franco, O.H. Sleep quality in middle-aged and elderly Chinese: Distribution, associated factors and associations with cardio-metabolic risk factors. BMC Public Health 2009, 9, 130. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Leng, M.; Yin, H.; Zhang, P.; Jia, Y.; Hu, M.; Li, G.; Wang, C.; Chen, L. Sleep quality and health-related quality of life in older people with subjective cognitive decline, mild cognitive impairment, and Alzheimer disease. J. Nerv. Ment. Dis. 2020, 208, 387–396. [Google Scholar] [CrossRef] [PubMed]
- Zhou, G.; Liu, S.; Yu, X.; Zhao, X.; Ma, L.; Shan, P. High prevalence of sleep disorders and behavioral and psychological symptoms of dementia in late-onset Alzheimer disease: A study in Eastern China. Medicine 2019, 98, e18405. [Google Scholar] [CrossRef]
- Miller, M.A.; Cappuccio, F.P. Inflammation, Sleep, Obesity and Cardiovascular Disease. Curr. Vasc. Pharmacol. 2007, 5, 93–102. [Google Scholar] [CrossRef]
- Krističević, T.; Štefan, L.; Sporiš, G. The associations between sleep duration and sleep quality with body-mass index in a large sample of young adults. Int. J. Environ. Res. Public Health 2018, 15, 758. [Google Scholar] [CrossRef] [Green Version]
- Reynolds, C.F.; Kupfer, D.J. Depression and aging: A look to the future. Psychiatr. Serv. 1999, 50, 1167–1172. [Google Scholar] [CrossRef]
- Buysse, D.J.; Reynolds, C.F.; Monk, T.H.; Berman, S.R.; Kupfer, D.J. The Pittsburgh Sleep Quality Index: A New Instrument for Psychiatric Practice and Research. Psychiatry Res. 1989, 28, 193–213. [Google Scholar] [CrossRef]
- Curcio, G.G.; Tempesta, D.; Scarlata, S.; Marzano, C.; Moroni, F.; Rossini, P.M.; Ferrara, M.; De Gennaro, L. Validity of the Italian version of the Pittsburgh Sleep Quality Index (PSQI). Neurol. Sci. 2013, 34, 511–519. [Google Scholar] [CrossRef] [PubMed]
- Beck, A.T.; Ward, C.H.; Mendelson, M.; Mock, J.; Erbaugh, J. An Inventory for Measuring Depression. Arch. Gen. Psychiatry 1961, 4, 561–571. [Google Scholar] [CrossRef] [PubMed]
- Bagby, R.M.; Parker, J.D.; Taylor, G.J. The twenty-item toronto alexithymia scale-i. item selection and cross-validation of the factor structure. J. Psychosom. Res. 1994, 38, 23–32. [Google Scholar] [CrossRef] [PubMed]
- Bressi, C.; Taylor, G.; Parker, J.; Bressi, S.; Invernizzi, G. Cross Validation of The Facture Structure of the 20-Item Toronto Alexithymia Scale: An Italian Multicenter Study. J. Psychosom. Res. 1996, 41, 551–559. [Google Scholar] [CrossRef] [PubMed]
- Spielberger, C.D. State-Trait Anxiety Inventory for Adults. APA PsycTests 1983. [Google Scholar] [CrossRef]
- Pedrabissi, L.; Santinello, M. Verifica della validità dello STAI forma Y di Spielberger. [Verification of the validity of the STAI, Form Y, by Spielberger.]. Giunti Organ. Spec. 1989, 191–192, 11–14. [Google Scholar]
- Espiritu, J.R.D. Aging-Related Sleep Changes. Clin. Geriatr. Med. 2008, 24, 1–14. [Google Scholar] [CrossRef]
- Gleason, K.; McCall, W.V. Current Concepts in the Diagnosis and Treatment of Sleep Disorders in the Elderly. Curr. Psychiatry Rep. 2015, 17, 45. [Google Scholar] [CrossRef]
- Ohayon, M.M.; Carskadon, M.A.; Guilleminault, C.; Vitiello, M.V. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: Developing normative sleep values across the human lifespan. Sleep 2004, 27, 1255–1273. [Google Scholar] [CrossRef]
- Lee YJ, G.; Jeong, D.U. Obstructive sleep apnea syndrome is associated with higher diastolic blood pressure in men but not in women. Am. J. Hypertens. 2014, 27, 325–330. [Google Scholar] [CrossRef] [Green Version]
- Forte, G.; Troisi, G.; Pazzaglia, M.; De Pascalis, V.; Casagrande, M. Heart Rate Variability and Pain: A Systematic Review. Brain Sci. 2022, 12, 153. [Google Scholar] [CrossRef] [PubMed]
- Forte, G.; Favieri, F.; Pazzaglia, M.; Casagrande, M. Mental and Body Health: The Association between Psychological Factors, Overweight, and Blood Pressure in Young Adults. J. Clin. Med. 2022, 11, 1999. [Google Scholar] [CrossRef] [PubMed]
- Eisensehr, I.; Ehrenberg, B.L.; Noachtar, S.; Korbett, K.; Byrne, A.; McAuley, A.; Palabrica, T. Platelet activation, epinephrine, and blood pressure in obstructive sleep apnea syndrome. Neurology 1998, 51, 188–195. [Google Scholar] [CrossRef] [PubMed]
- Lanfranchi, P.A.; Pennestri, M.-H.; Fradette, L.; Dumont, M.; Morin, C.M.; Montplaisir, J. Nighttime blood pressure in normotensive subjects with chronic insomnia: Implications for cardiovascular risk. Sleep 2009, 32, 760–766. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Casagrande, M.; Favieri, F.; Guarino, A.; Di Pace, E.; Langher, V.; Germanò, G.; Forte, G. The night effect of anger: Relationship with nocturnal blood pressure dipping. Int. J. Environ. Res. Public Health 2020, 17, 2705. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Casagrande, M.; Favieri, F.; Langher, V.; Guarino, A.; Di Pace, E.; Germanò, G.; Forte, G. The night side of blood pressure: Nocturnal blood pressure dipping and emotional (dys)regulation. Int. J. Environ. Res. Public Health 2020, 17, 8892. [Google Scholar] [CrossRef]
- Forte, G.; De Pascalis, V.; Favieri, F.; Casagrande, M. Effects of blood pressure on cognitive performance: A systematic review. J. Clin. Med. 2019, 9, 34. [Google Scholar] [CrossRef] [Green Version]
- Casagrande, M.; Boncompagni, I.; Mingarelli, A.; Favieri, F.; Forte, G.; Germanò, R.; Germanò, G.; Guarino, A. Coping styles in individuals with hypertension of varying severity. Stress Health 2019, 35, 560–568. [Google Scholar] [CrossRef]
- Casagrande, M.; Mingarelli, A.; Guarino, A.; Favieri, F.; Boncompagni, I.; Germanò, R.; Germanò, G.; Forte, G. Alexithymia: A facet of uncontrolled hypertension. Int. J. Psychophysiol. 2019, 146, 180–189. [Google Scholar] [CrossRef] [Green Version]
- Sharabi, Y.; Dagan, Y.; Grossman, E. Sleep apnea as a risk factor for hypertension. Curr. Opin. Nephrol. Hypertens. 2004, 13, 359–364. [Google Scholar] [CrossRef]
- Alvaro, P.K.; Roberts, R.M.; Harris, J.K. A systematic review assessing bidirectionality between sleep disturbances, anxiety, and depression. Sleep 2013, 36, 1059–1068. [Google Scholar] [CrossRef] [PubMed]
- Nyer, M.; Farabaugh, A.; Fehling, K.; Soskin, D.; Holt, D.; Papakostas, G.I.; Pedrelli, P.; Fava, M.; Pisoni, A.; Vitolo, O.; et al. Relationship between sleep disturbance and depression, anxiety, and functioning in college students. Depress. Anxiety 2013, 30, 873–880. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Neckelmann, D.; Mykletun, A.; Dahl, A.A. Chronic insomnia as a risk factor for developing anxiety and depression. Sleep 2007, 30, 873–880. [Google Scholar] [CrossRef] [PubMed]
- Freeman, D.; Sheaves, B.; Waite, F.; Harvey, A.G.; Harrison, P.J. Sleep disturbance and psychiatric disorders. Lancet Psychiatry 2020, 7, 628–637. [Google Scholar] [CrossRef]
- Buysse, D.J.; Angst, J.; Gamma, A.; Ajdacic, V.; Eich, D.; Rössler, W. Prevalence, course, and comorbidity of insomnia and depression in young adults. Sleep 2008, 31, 473–480. [Google Scholar] [CrossRef]
- Rigg, K.K.; Ford, J.A. The misuse of benzodiazepines among adolescents: Psychosocial risk factors in a national sample. Drug Alcohol Depend. 2014, 137, 137–142. [Google Scholar] [CrossRef]
- Boafo, A.; Greenham, S.; Sullivan, M.; Bazaid, K.; Suntharalingam, S.; Silbernagel, L.; Magner, K.; Robillard, R. Medications for sleep disturbance in children and adolescents with depression: A survey of Canadian child and adolescent psychiatrists. Child Adolesc. Psychiatry Ment. Health 2020, 14, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Owens, J.; Au, R.; Carskadon, M.; Millman, R.; Wolfson, A.; Braverman, P.K.; Adelman, W.P.; Breuner, C.C.; Levine, D.A.; Marcell, A.V.; et al. Insufficient sleep in adolescents and young adults: An update on causes and consequences. Pediatrics 2014, 134, e921–e932. [Google Scholar] [CrossRef] [Green Version]
- Hegeman AJ, M.; Kok, R.M.; Van Der Mast, R.C.; Giltay, E.J. Phenomenology of depression in older compared with younger adults: Meta-analysis. Br. J. Psychiatry 2012, 200, 275–281. [Google Scholar] [CrossRef]
- Wilson, S.; Nutt, D. Sleep Disorders; Oxford University Press: Oxford, UK, 2008. [Google Scholar]
- Hsu, M.F.; Lee, K.Y.; Lin, T.C.; Liu, W.T.; Ho, S.C. Subjective sleep quality and association with depression syndrome, chronic diseases and health-related physical fitness in the middle-aged and elderly. BMC Public Health 2021, 21, 164. [Google Scholar] [CrossRef]
- Mallon, L.; Broman, J.E.; Hetta, J. Sleeping difficulties in relation to depression and anxiety in elderly adults. Nord. J. Psychiatry 2000, 54, 355–360. [Google Scholar]
- Jansson-Fröjmark, M.; Lindblom, K. A bidirectional relationship between anxiety and depression, and insomnia? A prospective study in the general population. J. Psychosom. Res. 2008, 64, 443–449. [Google Scholar] [CrossRef]
- Kennair, L.E.O.; Hagen, R.; Hjemdal, O.; Havnen, A.; Ryum, T.; Solem, S. Depression, Anxiety, Insomnia, and Quality of Life in a Representative Community Sample of Older Adults Living at Home. Front. Psychol. 2022, 13, 811082. [Google Scholar] [CrossRef] [PubMed]
- Yu, B.; Steptoe, A.; Niu, K.; Ku, P.W.; Chen, L.J. Prospective associations of social isolation and loneliness with poor sleep quality in older adults. Qual. Life Res. 2018, 27, 683–691. [Google Scholar] [CrossRef] [PubMed]
- Harrington, J.J.; Avidan, A.Y. Treatment of sleep disorders in elderly patients. Curr. Treat. Options Neurol. 2005, 7, 339–352. [Google Scholar] [CrossRef] [PubMed]
- Asplund, R. Sleep disorders in the elderly. Drugs Aging 1999, 14, 91–103. [Google Scholar] [CrossRef]
Table 1.
Characteristics of participants.
| Elderly | Middle Aged Adults | Young Adults | |
|---|---|---|---|
| Age—mean (SD) | 70.3 (5.62) | 54.3 (6.30) | 26.0 (4.62) |
| Education—mean (SD) | 14.6 (4.15) | 16.0 (3.97) | 17.1 (2.85) |
| Female—n° (%) |
30 (62.25) |
27 (51.93) |
23 (53.49) |
| Marital Status | |||
| Single/Divorced—n° (%) | 12 (25) | 15 (28.85) | 42 (97.67) |
| Married/Cohabiting—n° (%) | 29 (60.42) | 36 (69.23) | 1 (2.33) |
| Widowed—n° (%) | 7 (14.58) | 1 (1.92) | 0 (0) |
| Lifestyles Habits | |||
| Coffee—n° (%) | 42 (87.5) | 46 (88.46) | 36 (83.72) |
| Smoker—n° (%) | 10 (20.83) | 11 (21.15) | 18 (41.86) |
| Alcohol user—n° (%) | 30 (62.5) | 36 (69.23) | 33 (76.74) |
SD: Standard Deviation.
Table 2.
Between-group comparisons of sleep characteristics (Pittsburgh Sleep Quality Index).
| Elderly | Middle Aged Adults | Young Adults | F | p | η2p | |
|---|---|---|---|---|---|---|
| Subjective Sleep Quality | 1.21 (0.58) | 1.33 (0.62) | 1.07 (0.70) | 1.94 | 0.15 | 0.03 |
| Sleep Latency | 1.04 (0.90) | 1.10 (1.03) | 1.07 (1.01) | 0.04 | 0.96 | 0.01 |
| Sleep Duration | 1.27 (0.74) | 1.06 (0.73) | 0.98 (0.77) | 1.94 | 0.15 | 0.03 |
| Habitual Sleep Efficiency | 2.92 (0.35) | 2.87 (0.40) | 2.86 (0.47) | 0.28 | 0.76 | 0.01 |
| Sleep Disturbance | 1.29 (0.50) | 1.33 (0.59) | 1.16 (0.53) | 1.16 | 0.32 | 0.02 |
| Use of Sleep Medication | 0.92 (1.27) | 0.21 (0.64) | 0.05 (0.31) | 14.00 | <0.01 ab | 0.20 |
| Daytime Dysfunction | 0.65 (0.60) | 0.75 (0.68) | 0.79 (0.71) | 0.59 | 0.56 | 0.01 |
| Global PSQI Score | 9.29 (2.46) | 8.64 (2.33) | 7.98 (2.13) | 3.66 | 0.03 a | 0.05 |
F and p refer to ANOVA comparisons. Significance: p < 0.05. PSQI: Pittsburgh Sleep Quality Index; a: significant difference between elderly and young adults; b: significant difference between elderly and middle-aged adults.
Table 3.
Between-group comparisons of physiological and psychological variables.
| Elderly | Middle Aged Adults | Young Adults | F | p | η2p | |
|---|---|---|---|---|---|---|
| Body Mass Index | 25.90 (3.47) | 25.50 (4.68) | 23.40 (4.40) | 4.64 | 0.01 ac | 0.06 |
| Systolic Blood Pressure | 131.40 (20.25) | 121.70 (16.26) | 112.80 (14.85) | 12.90 | <0.01 abc | 0.16 |
| Diastolic Blood Pressure | 76.80 (9.83) | 77.20 (9.91) | 71.30 (9.35) | 5.12 | 0.01 ac | 0.07 |
| BDI | 8.30 (8.28) | 5.42 (4.15) | 7.11 (6.18) | 2.55 | 0.08 | 0.04 |
| STAI | 39.13 (10.16) | 36.13 (8.30) | 41.40 (10.80) | 3.39 | 0.04 c | 0.05 |
| TAS-20 | 45.83 (14.20) | 38.99 (10.98) | 41.12 (13.00) | 3.71 | 0.03 b | 0.05 |
F and p refer to ANOVA comparison. Significance: p < 0.05. a: significant difference between elderly and young adults; b: significant difference between elderly and middle-aged adults; c: significant differences between middle-aged adults and young adults; BDI: Beck Depression Inventory; STAI: State–Trait Anxiety Inventory; TAS-20: Toronto Alexithymia Scale-20.
Table 4.
Correlations between Pittsburgh Sleep Quality Index and physiological variables.
| Sleep Quality | Sleep Latency | Sleep Duration | Habitual Sleep Efficiency | Sleep Disturbance | Use of Sleep Medicine | Daytime Dysfunction | Global PSQI Score | ||
|---|---|---|---|---|---|---|---|---|---|
| Body Mass Index | r p | 0.06 0.50 | 0.06 0.48 | 0.03 0.76 | 0.00 1.00 | 0.10 0.24 | 0.04 0.60 | −0.01 0.87 | 0.01 0.32 |
| Systolic Blood Pressure | r p | −0.03 0.75 | −0.05 0.60 | 0.06 0.51 | 0.09 0.31 | 0.05 0.57 | 0.09 0.32 | −0.08 0.37 | 0.03 0.72 |
| Diastolic Blood Pressure | r p | −0.03 0.72 | −0.03 0.75 | 0.14 0.10 | 0.17 0.05 * | −0.07 0.40 | −0.09 0.27 | −0.12 0.18 | −0.33 0.70 |
*: p < 0.05.
Table 5.
Correlations between Pittsburgh Sleep Quality Index and psychological variables.
| Sleep Quality | Sleep Latency | Sleep Duration | Habitual Sleep Efficiency | Sleep Disturbance | Use of Sleep Medicine | Daytime Dysfunction | Global PSQI Score | ||
|---|---|---|---|---|---|---|---|---|---|
| BDI | r p | 0.33 <0.01 * | 0.28 <0.01 * | 0.15 0.08 | 0.03 0.72 | 0.26 0.01 * | 0.11 0.20 | 0.16 0.05 | 0.07 0.40 |
| STAI | r p | 0.21 0.01 * | 0.28 <0.01 * | 0.08 0.33 | −0.03 0.75 | 0.14 0.09 | 0.09 0.30 | 0.15 0.08 | 0.30 <0.01 * |
| TAS-20 | r p | 0.15 0.09 | −0.07 0.41 | 0.06 0.49 | −0.04 0.62 | 0.08 0.37 | −0.09 0.28 | 0.07 0.40 | 0.03 0.77 |
*: p < 0.05; BDI: Beck Depression Inventory; STAI: State–Trait Anxiety Inventory; TAS-20: Toronto Alexithymia Scale-20. Note: higher PSQI scores indicate worse sleep characteristics
Table 6.
Linear regression on Pittsburgh Sleep Quality Index.
| Global PSQI Score | Elderly | Middle Aged | Young Adults | |||
|---|---|---|---|---|---|---|
| B | SE (B) | B | SE (B) | B | SE (B) | |
| Model 1 | ||||||
| BMI | 0.01 | 0.11 | −0.11 | 0.08 | 0.17 | 0.08 |
| Systolic Blood Pressure | 0.01 | 0.02 | −0.05 | 0.04 | −0.02 | 0.03 |
| Diastolic Blood Pressure | −0.01 | 0.05 | 0.05 | 0.07 | −0.03 | 0.04 |
| R | 0.07 | 0.27 | 0.37 | |||
| R2 | 0.01 | 0.07 | 0.14 | |||
| p | 0.97 | 0.33 | 0.14 | |||
| Model 2 | ||||||
| BMI | −0.06 | 0.10 | −0.05 | 0.07 | 0.15 | 0.08 |
| Systolic Blood Pressure | 0.03 | 0.02 | −0.04 | 0.04 | 0.01 | 0.03 |
| Diastolic Blood Pressure | −0.04 | 0.04 | 0.04 | 0.06 | −0.05 | 0.05 |
| BDI | 0.08 | 0.06 | 0.25 * | 0.09 | 0.15 | 0.09 |
| STAI | 0.08 | 0.04 | 0.01 | 0.05 | 0.01 | 0.04 |
| TAS-20 | −0.04 | 0.03 | −0.02 | 0.03 | −0.04 | 0.03 |
| R | 0.50 | 0.53 | 0.50 | |||
| R2 | 0.24 | 0.28 | 0.25 | |||
| p | 0.08 | 0.02 * | 0.11 | |||
BDI: Beck Depression Inventory; STAI: State–Trait Anxiety Inventory; TAS-20: Toronto Alexithymia Scale-20; *: p < 0.05; SE: Standard Error.
Table 7.
Linear regression on use of sleep medication.
| Use Of Sleep Medication | Elderly | Middle Aged | Young Adults | |||
|---|---|---|---|---|---|---|
| B | SE (B) | B | SE (B) | B | SE (B) | |
| Model 1 | ||||||
| BMI | −0.03 | 0.06 | −0.02 | 0.02 | 0.01 | 0.01 |
| Systolic Blood Pressure | 0.01 | 0.01 | −0.02 | 0.01 | −0.00 | 0.01 |
| Diastolic Blood Pressure | −0.04 | 0.03 | −0.01 | 0.02 | −0.00 | 0.01 |
| R | 0.25 | 0.20 | 0.11 | |||
| R2 | 0.06 | 0.034 | 0.01 | |||
| p | 0.44 | 0.62 | 0.93 | |||
| Model 2 | ||||||
| BMI | −0.04 | 0.05 | −0.02 | 0.02 | −0.01 | 0.01 |
| Systolic Blood Pressure | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
| Diastolic Blood Pressure | −0.03 | 0.02 | −0.01 | 0.02 | −0.01 | 0.01 |
| BDI | 0.01 | 0.03 | −0.03 | 0.03 | 0.04 * | 0.01 |
| STAI | 0.01 | 0.02 | −0.01 | 0.01 | −0.01 | 0.01 |
| TAS-20 | −0.02 | 0.01 | −0.01 | 0.01 | −0.01 | 0.01 |
| Global PSQI Score | 0.20 * | 0.08 | 0.11 * | 0.05 | 0.01 | 0.02 |
| R | 0.56 | 0.41 | 0.60 | |||
| R2 | 0.31 | 0.20 | 0.36 | |||
| p | 0.03 * | 0.34 | 0.03 * | |||
BDI: Beck Depression Inventory; STAI: State–Trait Anxiety Inventory; TAS-20: Toronto Alexithymia Scale-20; *: p < 0.05; SE: Standard Error.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
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, 1661. https://doi.org/10.3390/ijerph20031661
AMA Style
Corbo I, Forte G, Favieri F, Casagrande M. Poor Sleep Quality in Aging: The Association with Mental Health. International Journal of Environmental Research and Public Health. 2023; 20(3):1661. https://doi.org/10.3390/ijerph20031661
Chicago/Turabian StyleCorbo, Ilaria, Giuseppe Forte, Francesca Favieri, and Maria Casagrande. 2023. "Poor Sleep Quality in Aging: The Association with Mental Health" International Journal of Environmental Research and Public Health 20, no. 3: 1661. https://doi.org/10.3390/ijerph20031661
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
