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SLEEP QUALITY AND DURATION AS DETERMINANTS OF HEALTHY AGING TRAJECTORIES: THE HELIAD STUDY

 

V. Gkotzamanis1, D.B. Panagiotakos1, M. Yannakoulia1, M. Kosmidis2, E. Dardiotis3, G. Hadjigeorgiou4, P. Sakka5, N. Scarmeas6,7

 

1. Department of Nutrition and Dietetics, School of Health Sciences and Education, Harokopio University, Athens, Greece; 2. Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece; 3. School of Medicine, University of Thessaly, Larissa, Greece; 4. Department of Neurology, Medical School, University of Cyprus, Cyprus; 5. Athens Association of Alzheimer’s Disease and Related Disorders, Athens, Greece; 6. 1st Department of Neurology, Aiginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece; 7. Department of Neurology, Columbia University, New York, New York, USA

Corresponding Author: Prof Demosthenes B Panagiotakos, Harokopio University, 70 El. Venizelou St., 176 71 Athens, Greece, Tel. +30 210-9549332, Email: dbpanag@hua.gr

J Frailty Aging 2022;in press
Published online April 28, 2022, http://dx.doi.org/10.14283/jfa.2022.37

 


Abstract

Background: The aging of global population has increased the scientific interest in the concept of healthy aging and its determinants. Aim: The aim of this study was to investigate the association of sleep characteristics with trajectories of healthy aging.
Design and Setting: Prospective observational study conducted in two cities, Maroussi and Larissa.
Participants: A total of 1226 older adults (≥65 years, 704 women) were selected through random sampling.
Measurements: Sleep quality was assessed with the Sleep Index II, and sleep duration was self-reported. A healthy aging metric was introduced using an Item Response Theory approach based on validated questionnaires that assessed functionality. Four healthy aging trajectories were developed based on whether the healthy aging status of the participants was above (High) or below (Low) the median at baseline and follow-up, i.e., High-High, High-Low, Low-High, and Low-Low. The association of sleep characteristics with the trajectories was investigated using a multinomial logistic regression with the Low-Low group as reference, adjusting for potential confounders.
Results: 34.3% participants classified to the High-High group, 15.7% to the High-Low, 18.6% to the Low-High, and 31.4% to the Low-Low group. Better sleep quality was associated with the probability of belonging to the High-High group (p-value<0.001); while, long sleep duration was inversely associated with likelihood of being classified in the High-High group (p-value < 0.05).
Conclusion: Poor sleep quality and long sleep duration seem to have a significant negative association with healthy aging. Public health policies are needed to raise awareness about the importance of sleep characteristics on human health.

Key words: Healthy aging, sleep duration, sleep quality, longitudinal analysis.


 

Introduction

According to the United Nations the number of people aged over 60 years is expected to have reached 2 billion by 2050 globally, and this trend is projected to persist in the following years (1). The aging of the global population has increased the interest of scientific research in defining the complex concept of healthy aging and its determinants. At first, healthy aging was regarded as equivalent to the absence of disease and disability (2). However, as many older people have one or more health conditions that, when controlled, have little influence on their wellbeing, it became clear that being free of disease or infirmity is not a requirement to achieve healthy aging. Therefore, more complex conceptualizations were proposed to describe the concept of healthy aging through the years that included the subjective feeling of satisfaction (3), emotional wellbeing (4) and maintenance of social engagement and functionality (5). The most recent definition, suggested by the World Health Organization (WHO) in 2015, states that healthy aging is “the process of developing and maintaining functional ability that enables wellbeing in older age” (6). With the emphasis on functionality, this approach sets the basis for attempts to develop objective measures, which could, in turn, be utilized to identify potential determinants.
Sleep is considered a marker of overall health and well-being. Several age-related conditions are reported to be associated with inadequate or low-quality sleep. Specifically, some studies have associated frailty with sleep disturbances (7). Previous studies have also linked sleep disturbances with cognitive decline, incident Mild Cognitive Impairment (MCI), and dementia (8-11). Furthermore, sleep disturbances have been associated with lower muscle strength (12), a significant predictor of adverse outcomes in the aging population (13). The associations mentioned above could imply a relationship of sleep disturbances with the functional capacity of older people. However, to our knowledge, there is currently no study investigating the direct link of sleep characteristics with healthy aging. Moreover, the cross-sectional design of most of the studies mentioned above does not allow for causal relationships to be established.
The aim of the present study was to explore the potential association of trajectories of healthy aging with sleep characteristics, considering both quality and duration, in a sample of middle aged and older adults.

 

Methods

Design and Participants

The Hellenic Longitudinal Investigation in Aging and Diet (HELIAD) is an observational, epidemiological cohort study, being conducted in two cities, Maroussi, which is in Athens Metropolitan area and Larissa, which is located in central Greece. Free living individuals (i.e., not living in nursing homes or other health assisted centers), aged 65 years or older, were eligible to participate. Participants were selected through random sampling (see below for details) from the municipality records of the cities. A total of 1226 participants with complete available information for the assessment of healthy aging were included in the present analysis; of them n = 704 (57.4%) were women (mean age 72.1 + 5.2 years) and 522 (42.6%) men (mean age 74.2 + 5.2 years).

Bioethics

The study protocol has been approved by the Ethics Review Boards of the National and Kapodistrian University of Athens and the University of Thessaly. All participants have provided written informed consent prior to entering the study.

Measurements

Various demographic (age, sex), social (years of education, mean annual income), nutritional (dietary habits), lifestyle (smoking, exercise), environmental, clinical (including medical history), and neuropsychological information of the participants was evaluated at baseline examination, as well as at three years after enrollment (follow-up examination). The assessments were made by qualified physicians-neurologists, neuropsychologists, and dieticians, who conducted face-to-face interviews with each participant. The caregivers of the participants assisted in case a participant was unable to provide all the information requested.

Healthy Aging Assessment

The development of the Healthy Aging Index (HAI) introduced in this study was based on the World Health Organization’s conceptualization of health status with emphasis on the functionality (14). Following a methodology similar to that proposed by Caballero et al. (15), information from several validated instruments: Blessed Dementia-Scale (16), Instrumental Activities of Daily Living (17), and Extended Instrumental Activities of Daily Living (18) were combined in a hybrid Item Response Theory (IRT) model (i.e., one parameter logistic for binary items and graded response model for ordinal items). Latent theta scores of each participant were extracted and rescaled on a 1 to 100 scale, with greater values indicating a higher level of functionality. In the current analysis, HAI was also used as a binary variable (Low or High) with the median as the cut off value. Then, based on their HAI scores, both at baseline and 3-year follow up, the participants were classified in four trajectories of healthy aging, i.e., “Low-Low (i.e., started from low and remained at low healthy index score in the follow-up evaluation), Low-High, High-Low, High-High”.

Sleep Characteristics Assessment

Information about the sleep characteristics of the participants was obtained through the Sleep Scale from the Medical Outcomes Study (MOS), consisting of 12 self-reported items (19, 20). Sleep quality was then evaluated through the Sleep Index II proposed by Hays et al. (21) in the manual of the specific scale by summing the following questions referring to the previous 4-week period: [1] How long did it usually take for you to fall asleep? [2] Feel that your sleep was not quiet (moving restlessly, feeling tense, speaking etc., while sleeping)? [3] Get enough sleep to feel rested upon waking in the morning? [4] Awaken short of breath or with a headache? (5) Feel drowsy or sleepy during the day? [6] Have trouble falling asleep? [7] Awaken during your sleep time and have trouble falling asleep again? [8] Have trouble staying awake during the day? and [9] Get the amount of sleep you need? Each of the questions has a possible rating of 1 to 6, based on the frequency of the sleep problem. Thus, Sleep Index II ranged between 1 and 54, with higher scores indicating sleep dysfunction (i.e., the lower the better). Furthermore, to examine sleep duration, participants were asked to report how many hours they slept each night during the past four weeks.
The following steps were taken to ensure reliability and validity of the MOS sleep scale in a Greek population: first, a process of translation and back translation was followed before finalizing the questionnaire form that was administered to our participants. Regarding sleep quality, the MOS Sleep Index II was associated with the equivalent answers from the Neuropsychiatric Inventory (NPI), while sleep duration questions of the MOS were associated with the one from the Athens Physical Activity Questionnaire (APAQ) (22). The associations between the two measures were examined using bivariate correlations. The correlation between the two sleep duration variables was statistically significant (r=0.716, p-value ≤0.0001) The correlation between the two sleep quality variables was also statistically significant (r=0.357, p≤0.0001). Thus, both sets of correlations support the evidence of convergent validity with regard to the MOS scale.
Moreover, the use of sleep-related medications was also recorded; these medications included narcotics, hypnotics, anxiolytics, antipsychotics, anticholinergics, or phenobarbital.

Other measurements

Education level was measured in years of formal education. Physical activity status was evaluated using the validated Athens Physical Activity Questionnaire (APAQ) (22) in which a specific metabolic equivalent value is given for each activity. For the present analysis, physical activity status was evaluated as “High/adequate” and “Low/inadequate”. Information about smoking status (current and or past) was also retrieved. Adherence to the Mediterranean diet was evaluated using a specific, validated diet quality index, the MedDietScore (24). Body Mass Index (BMI) was calculated by dividing participants’ measured weight in kilograms by height in meters squared. A leveled platform scale and a wall-mounted stadiometer were used to calculate weight and height to the nearest 0.5 kg and 0.5 cm, respectively.
Additionally, frailty and cognitive impairment were also evaluated; frailty was assessed using the Frailty Index (FI) (25) which has been developed by Rockwood and Mitnitski (26, 27), and defines frailty as the ratio of deficits presented in a person to the total number of deficits considered in a medical evaluation. In the HELIAD study, 61 variables regarding age-related deficiencies, diseases, syndromes, functioning in activities of daily living, cognitive decline, mood disorders, and performance on physical activities were included to assess frailty (27). According to this index, the cutoff point for frailty is considered a score of 0.25, with higher scores indicating a greater degree of frailty. FI was chosen as it can be used as phenotype index (frail or non-frail) but also as an index of the accumulation of deficits, providing more information about the functional capacity of the participants. Frailty was also assessed with different tools, including the Tilburg Frailty Indicator (28), the Groningen Frailty Indicator (29) and the Fried frailty (30) phenotype in confirmatory analyses, to test whether the use of an alternative definition differentiates the observed impact of frailty. Moreover, extensive literature suggests that cognitive impairment is associated with sleep disturbances in both demented and non-demented older adults (31, 32). Thus, clinical diagnoses of dementia or Mild Cognitive Impairment (MCI), were also included in the fully adjusted models. Neurologic diagnoses were reached through consensus meetings including all main investigators. Diagnosis of dementia was based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR) (33). MCI diagnosis was based on the Petersen criteria (34).

Statistical analysis

Continuous variables are presented as mean value (and standard deviation), and categorical variables are presented as absolute and relative (%) frequencies. Associations between categorical variables were tested with the chi-square test; analysis of variance (ANOVA) was used to evaluate group mean differences for the normally distributed continuous variables. Additional post-hoc t-tests with Bonferroni adjustments were performed when a significant difference between groups was detected. A multinomial logistic regression with trajectory group as the dependent variable was applied to evaluate the impact of sleep quality and sleep duration on the probability of belonging to each trajectory group versus the “Low-Low” trajectory that was set as the comparison class. Sleep quality and duration were separately introduced in the models as continuous and categorical variables. Sleep quality was also classified as “High” (Sleep Index score <13), “Regular” (Sleep Index Score between 14 and 20), and “Low” (Sleep Index Score >21). Sleep duration was categorized as short (less than six hours), Regular (six to eight hours) or Long (more than eight hours). The categorical variables of sleep quality and duration were entered in the models as dummy variables. To account for protentional residual confounding, factors that could be related to both healthy aging and sleep characteristics, as they have been reported in the literature, i.e., age, sex, education, physical activity, dietary patterns assessed with MedDietScore, history of smoking, use of sleep-related medication, frailty status based on FI and clinical diagnoses of MCI and dementia were introduced as covariates in the fully adjusted logistic regression models. Collinearity between independent variables was tested using variance inflation factor (VIF, values >4 indicate serious collinearity); Pearson criterion was used to evaluate models’ goodness-of-fit. The impact of sleep characteristics on healthy aging was further explored using a General Linear Model. Moreover, the course of the HAI score through the three-year interval between the two waves of assessment was also evaluated in relation to sleep characteristics, using random effects generalized linear models. Path analysis using structural equation modeling was additionally applied to reveal links between lifestyle, clinical and biological characteristics of the participants, in relation to sleep characteristics (quality and duration) and the four healthy aging trajectories. Age, years of formal education, physical activity and adherence to the Mediterranean diet were introduced as extrinsic predictor variables. Frailty, dementia and MCI were introduced as potential mediators of the association of sleep characteristics with healthy aging. Path analysis was performed using sem command in STATA software (STATA Corp., TX, USA). To evaluate model’s goodness-of-fit the chi-square to degrees of freedom index (χ2/df) was used (values <3 considered as good fit), as well as the root mean square error of approximation (RMSEA) criterion (with score ≤0.05 indicating good and <0.10 indicating adequate fit). All other analyses were performed using the SPSS 25 software package (IBM Corp, Chicago, IL, USA).

 

Results

Healthy aging trajectories

The mean value of Healthy Aging Index (HAI, theoretical range 0-100) was 53.7 + 13.1 (observed range:1-95) at baseline in men and 52.21 + 13.5 (observed range:1-95) in women (p for sex differences at baseline = 0.13), as well as 52.3 + 15.7 (observed range: 0-94) at follow up in men and 49.8 + 17.0 (observed range:0-95) in women (p for sex differences at follow-up = 0.01). As it can be seen, HAI score was higher at baseline examination compared to the follow-up (p<0.001). According to the healthy aging trajectories classification presented above, 31.3% of the participants were classified to the Low-Low trajectory between baseline and follow-up examination, 15.5% to the Low-High, 18.4% to the High-Low and 34.8% to the High-High trajectory. Table 1 summarizes the baseline characteristics of the study’s participants by trajectory status.

Table 1. Distribution of HELIAD Study participants’ characteristics according to their classification in the healthy aging trajectories

P-values represent significance levels of ANOVA for continuous variables and chi-square for numeric. Post-hoc comparisons (vs. “Low-Low group) were performed using t-test and corresponding p-values were corrected using the Bonferroni rule; *p<0.05, **p<0.01

 

Sleep quality and sleep duration

Overall, mean sleep quality index (theoretical range 0 to 54) was 16.4 + 6.8 for men and 17.8 + 7.9 for women, suggesting a moderate quality in both genders. Sleep duration was 6.4 + 1.5 hours for men and 6.5 + 1.5 hours for women, whereas almost 1 out of 4 (i.e., 24%) participants reported short (i.e., <6 hours/day) sleep duration. Sleep Index score was also negatively associated with years of formal education (rho = -0,10, p-value<0.001). and diet quality, as measured through MedDietScore (rho = -0.11, p-value<0.001).

Sleep quality and sleep duration in relation to healthy aging trajectories

Crude data analysis revealed that sleep quality was significantly associated with the trajectories of healthy aging. In particular, every 1/54-unit increase in the Sleep index scale (i.e., towards worse sleep quality) was associated with 8% lower likelihood of an individual being classified in the High-High group (Odds ratio, OR=0.92; 95% Confidence Interval, CI: 0.90, 0.94; p-value<0.001). Similarly, Sleep Quality index scale was inversely associated with the probability of belonging to the Low-High group (OR= 0.96, 95% CI:0.94, 0.98, p-value=0.002), suggesting that the worse the sleep quality of an individual, the less the likelihood of improving the healthy aging status from low to high. Moreover, introducing sleep quality as a categorical variable (i.e., low, regular and high) provided similar findings, as participants that were classified as “high”- or “regular”-quality sleepers presented a 3.0- and a 1.9-time higher likelihood of belonging to the High-High group, respectively, compared to those categorized as poor sleepers (i.e., “low” quality) (p-value<0.001 and =0.002 respectively).
Regarding sleep duration, the results revealed that participants who categorized either as short or long sleepers presented a significantly lower likelihood of belonging to the High-High trajectory group, as compared to the regular-duration (i.e., six to eight hours) sleepers (OR= 0.67, 95% CI:0.62, 0.73, p-value=0.04 and OR=0.33, 95% CI:0.32, 0.37 and <0.001, respectively).

Multi-adjusted analysis of sleep quality and sleep duration in relation to healthy aging trajectories and general linear models

Age, years of formal education, adherence to the Mediterranean dietary pattern measured with the MedDietScore and daily energy expenditure in physical activities, were introduced as continuous covariates, while sex, history of smoking, FI, use of sleep-related medication and clinical diagnoses of dementia and MCI were included in the models as categorical covariates. The association of sleep quality and trajectories of healthy aging remained significant in all these models (between-subjects effect of sleep quality and duration on HAI, b coef. = 6.03, p-value<0.001, and b coef. = -6.02, p-value<0.001 for high sleep quality and long sleep duration, respectively). Moreover, as it can be seen in Table 2, each unit increase in sleep quality index (i.e., worse quality) was associated with a 7% lower likelihood of being classified to the High-High healthy aging trajectory (OR = 0.93, p-value<0.001), while “high”- and “regular”-quality sleepers presented approximately 80% higher likelihood (ORs =1.86 and 1.83, respectively) of belonging to the High-High healthy aging trajectory, as compared to “poor” quality sleepers. Regarding sleep duration, only long-duration was associated with a 62% lower likelihood of belonging to the High-High healthy aging trajectory, as compared to regular duration (OR =0.38, p-value < 0.05). Assessing frailty with TFI, GFI and Fried frailty phenotype, provided similar results in the confirmatory models, without significant differentiations between the assessment tools (data not shown).

Table 2. Results from fully adjusted, nested nominal logistic regression models (Odds Ratio and 95% Confidence Intervals) that evaluated sleep quality and sleep duration in relation to the likelihood of being in a specific healthy aging trajectory from baseline to follow-up (i.e., High-High, High-Low, Low-High) vs. Low-Low trajectory

All models were adjusted for age, sex, years of education, BMI, MedDietScore, physical activity, smoking status, use of sleep-related medication, frailty status, and clinical diagnoses of mild cognitive impairment and dementia.

 

Paths between sleep quality and sleep duration in relation to healthy aging trajectories

Path analysis was additionally applied to further explore the tested hypotheses. Figure 1 illustrates the path model that was revealed from the data. A significant direct association of sleep characteristics with healthy aging trajectories was observed. Sleep quality was directly associated with all three trajectory groups (versus the reference Low-Low group). In particular, higher score of the Sleep Quality Index (i.e., worse quality) was negatively associated with Low-High (i.e., improvement in healthy aging score during follow-up) and High-High trajectories, and positively associated with High-Low trajectory (i.e., worsening in healthy aging score during follow-up). Moreover, sleep duration (long vs. regular) was directly and negatively associated with the High-High trajectory only. However, significant indirect associations of sleep characteristics on healthy aging, mediated by frailty and MCI, were also observed. Specifically, long sleep duration was positively associated with history of frailty and dementia, which, thereinafter they were associated with High-Low (positively) and High-High (negatively) trajectories, respectively. In addition, sleep quality was positively associated with history of frailty, and, in turn, frailty was negatively associated with Low-High and High-High healthy aging trajectories. Moreover, presence of MCI was negatively associated with Low-High and High-High trajectories, irrespective of sleep quality or duration.

Figure 1. Conceptual theoretical framework and results from path-analysis that evaluated the role of sleep quality and duration on trajectories of healthy aging (High-High, High-Low and Low-High vs. Low-Low), through demographic, lifestyle and clinical characteristics of the participants

Only significant (p-value < 0.05) associations are presented through solid arrows. Values indicate the beta coefficients of the structured equation models applied (model’s performance : χ2/df was 2 <3 and considered as good fit, RMSEA was 0.07 ≤0.11 indicating adequate fit).

 

Discussion

To our knowledge, this is the first study investigating the relationship of sleep characteristics with trajectories of healthy aging. The presented results revealed a significant, direct, as well as indirect, association of sleep quality and duration with healthy aging in a sample of older adults. Despite the limitations of the present study, due to its observational design, the aforementioned findings underline the role of adequate and of good quality of sleep in a healthy aging process. According to WHO declaration, “every person, in every place in the world should have the opportunity to live a long and healthy life”. What it has been achieved until now is to consistently extend the past decades life expectancy in most of the countries around the world; however, whether people enjoy a healthy aging process is still questioned. Healthy ageing is about creating the environments and opportunities that enable people to be and do what they value throughout their lives. Being free of disease or infirmity is not a requirement for healthy ageing, but wellbeing is, and sleep characteristics have not been well studied and understood in the process of a healthy ageing path through life.
In this work, sleep quality showed an independent and consistent association with healthy aging trajectories, as higher sleep quality was correlated with a significantly higher likelihood of belonging to the, favorable, High-High group; i.e., individuals who were classified at high level of healthy aging during the baseline examination, remained high at follow-up, suggesting that sleep quality confers to achieving a healthy aging path. These findings are in line with some previous studies that have presented associations of subjective sleep quality with fitness levels, that could be considered as a proxy of healthy aging (35). Similarly, studies that used objective sleep quality measures, e.g., polysomnography, sleep EEG, actigraphy, also reported significant correlations between sleep quality and physical function in older adults (36, 37). Regarding sleep duration and its association with healthy aging trajectories the results showed that compared to regular sleep duration, short and long sleep duration was significantly associated with lower likelihood of belonging to the favorable High-High trajectory, with long duration presenting a greater effect size. Unfortunately, there are also very few studies that have associated sleep duration with healthy aging determinants. In particular, some studies investigating the association of frailty with sleep duration have reported that short and long sleep duration might constitute risk factors for frailty (38, 39), while others reported that only long-duration presents a significant association (40, 41). Considering the above, our study’s findings imply that both long and short sleep duration might be associated with unfavorable healthy aging trajectories, with the evidence regarding long duration being more consistent and with a greater size effect.
The exact mechanism via which sleep characteristics influence the trajectories of aging is not totally understood. In previous analyses from the HELIAD study, sleep disturbances were associated with frailty and cognitive performance, which was hypothesized in path analysis that could mediate the effect on healthy aging observed in our study (42-44). However, the findings of the present study suggested a clear link of sleep characteristics with healthy aging, which was independent of frailty and cognitive impairment, as despite introducing these variables in the fully adjusted models, most associations preserved their significance and path analysis revealed additional direct associations of sleep characteristics with healthy aging trajectories next to the ones mediated by frailty and cognitive impairment. Additionally, sleep disturbances have also been associated with reduced physical strength (45) and sarcopenia (46), which are in turn associated with a decline in functional capacity (47). Moreover, sleep disturbances have been associated with various hormone secretion disorders as they have been presented to disrupt circadian rhythms (48) and the hypothalamic regulation while they also have a direct impact on the secretion of certain hormones. Specifically, it has been presented that inadequate or low-quality sleep induces the secretion of catabolic hormones such as cortisol and Thyroid Stimulating Hormone (TSH), while it lowers the secretion of anabolic hormones, such as growth hormone and testosterone (49). Furthermore, inadequate sleep has been associated with increased inflammation (50) and insulin resistance (51). All the associations mentioned above are key elements in the process of aging, with complex interplays between them, that may set the molecular basis of the impact of sleep on the trajectories of healthy aging. Additionally, the link between healthy aging and sleep may also be bidirectional, and sleep disturbances might constitute an indicator of an underlying subclinical process.
The findings of the present study should be viewed in light of its limitations. Firstly, information about sleep quality and duration was self-reported by the participants, and as a result, the possibility of misreporting cannot be ruled out. However, the information was collected by specialized personnel via face-to-face interviews in order to minimize this risk. Additionally, despite the study’s prospective design, a clear causal relationship cannot be established, as the association might work the other way around or even be bidirectional, especially if we consider that the factors that determine the aging trajectory that one is set upon might start acting at a significantly younger age than that of the study’s participants. Moreover, although a number of potential confounders was taken into account in the fully adjusted models, residual confounding cannot be totally excluded.
This study also has several strengths. The simultaneous exploration of the effects of both sleep quality and duration provides a better understanding of the role of sleep, in relation to healthy aging process. Published literature suggests that short and long sleepers report sleep disturbances more frequently (52). As a result, these two characteristics are interrelated (53) and should be investigated in the same models to reveal their synergistic effects. In addition, the prospective approach of this study provides stronger and more plausible associations concerning their potential cause-and-effect nature compared to cross-sectional studies. Finally, the large sample of the HELIAD study that includes participants from both urban and rural areas can be considered representative of the older Greek population.

 

Conclusion

The findings of the present study revealed a significant relationship of sleep characteristics with trajectories of healthy aging. Specifically, lower sleep quality and long sleep were associated with less favorable trajectories of healthy aging path of middle aged and older adults. Taking into account the continuous aging of the global population, and the fact that almost one quarter of the population in several societies are already considered older adults, awareness should be raised about the role of sleep on aging process, especially among health professionals, with the aim to promote a healthier lifestyle, within a friendly environment that ensures high quality and undisturbed sleep.

 

Ethical standard: The study protocol has been approved by the Ethics Review Boards of the National and Kapodistrian University of Athens and the University of Thessaly. All participants have provided written informed consent prior to entering the study,

Conflicts of Interest: The authors have none to declare.

 

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