Objectively Measured Sleep Duration and Health-Related Quality of Life in Older Adults with Metabolic Syndrome: A One-Year Longitudinal Analysis of the PREDIMED-Plus Cohort

Citation: Marcos-Delgado, A.; Martín-Sánchez, V.; MartínezGonzález, M.Á.; Corella, D.; Salas-Salvadó, J.; Schröder, H.; Martínez, A.; Alonso-Gómez, Á.M.; Wärnberg, J.; Vioque, J.; et al. Objectively Measured Sleep Duration and Health-Related Quality of Life in Older Adults with Metabolic Syndrome: A One-Year Longitudinal Analysis of the PREDIMED-Plus Cohort. Nutrients 2024,16, 2631. https://doi.org/10.3390/nu16162631 Academic Editor: Silvia Savastano Received: 18 July 2024 Revised: 30 July 2024 Accepted: 31 July 2024 Published: 9 August 2024 Copyright: © 2024 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/). nutrients Article Objectively Measured Sleep Duration and Health-Related Quality of Life in Older Adults with Metabolic Syndrome: A One-Year Longitudinal Analysis of the PREDIMED-Plus Cohort Alba Marcos-Delgado 1,2,3,* , Vicente Martín-Sánchez 1,2,3 , Miguel Ángel Martínez-González 4,5,6 , Dolores Corella 3,7 , Jordi Salas-Salvadó4,8,9 , Helmut Schröder 4,10 , Alfredo Martínez 4,11,12 , Ángel M. Alonso-Gómez 4,13 , Julia Wärnberg 4,14 , Jesús Vioque 3,15 , Dora Romaguera 4,16 , JoséLópez-Miranda 4,17, Ramon Estruch 4,18 , Francisco J. Tinahones 4,19, JoséM. Santos-Lozano 4,20, Jacqueline Álvarez-Pérez 4,21 , Aurora Bueno-Cavanillas 3,22,23 , Naomi Cano-Ibáñez 3,22,23 , Carmen Amezcua-Prieto 3,22 , Natalia Hernández-Segura 1,2 , Josep A. Tur 3,24 , Xavier Pintó4,25 , Miguel Delgado-Rodríguez 12,26 , Pilar Matía-Martín 27 , Josep Vidal 28,29 , Clotilde Vázquez 4,30 , Lidia Daimiel 4,31,32 , Emili Ros 4,33 , Estefanía Toledo 4,5 , Tany E. Garcidueñas-Fimbres 4,8,9 , Judith Viaplana 28,29, Eva M. Asensio 3,7 , María D. Zomeño 4,10, Antonio Garcia-Rios 4,17, Alejandro Oncina-Cánovas 14,15 , Francisco Javier Barón-López 14 , Napoleón Pérez-Farinos 14 , Carmen Sayon-Orea 4,5 , Aina M. Galmés-Panadés4,16,34 , Rosa Casas 4,18 , Lucas Tojal-Sierra 4,13 , Ana M. Gómez-Pérez 4,19 , Pilar Buil-Corsiales 5, Jesús F. García-Gavilán4,8,9 , Carolina Ortega-Azorín3,7 , Olga Castañer 10 , Patricia J. Peña-Orihuela 4,17, Sandra González-Palacios 14,15 , Nancy Babio 4,8,9 , Montse Fitó10 and Javier Nieto 35 1Faculty of Health Sciences, Department of Biomedical Sciences, Area of Preventive Medicine and Public Health, Universidad de León, 24007 León, Spain; [email protected] (V.M.-S.); [email protected] (N.H.-S.) 2The Research Group in Gene-Environment and Health Interactions, Institute of Biomedicine (IBIOMED), Universidad de León, 24007 León, Spain 3CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, 28222 Madrid, Spain; [email protected] (D.C.); [email protected] (J.V.); [email protected] (A.B.-C.); [email protected] (N.C.-I.); [email protected] (C.A.-P.); [email protected] (J.A.T.); [email protected] (E.M.A.); [email protected] (C.O.-A.) 4Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Institute of Health Carlos III, 28222 Madrid, Spain; [email protected] (M.Á.M.-G.); [email protected] (J.S.-S.); [email protected] (H.S.); [email protected] (A.M.); [email protected] (Á.M.A.-G.); [email protected] (J.W.); [email protected] (D.R.); [email protected] (J.L.-M.); restr[email protected] (R.E.); [email protected] (F.J.T.); [email protected] (J.M.S.-L.); [email protected] (J.Á.-P.); [email protected] (X.P.); [email protected] (C.V.); [email protected] (L.D.); er[email protected] (E.R.); [email protected] (E.T.); [email protected].cat (T.E.G.-F.); [email protected] (M.D.Z.); [email protected] (A.G.-R.); [email protected].es (C.S.-O.); [email protected] (A.M.G.-P.); [email protected] (R.C.); [email protected] (L.T.-S.); [email protected] (A.M.G.-P.); [email protected] (J.F.G.-G.); [email protected] (P.J.P.-O.); [email protected] (N.B.) 5Department of Preventive Medicine and Public Health, Navarra Institute for Health Research (IdiSNA), University of Navarra, 31009 Pamplona, Spain; [email protected] 6Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02124, USA 7Department of Preventive Medicine, University of Valencia, 46008 Valencia, Spain 8 Unitat de Nutrició, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, 43206 Reus, Spain 9Institut d’InvestigacióSanitària Pere Virgili (IISPV), 43204 Reus, Spain 10 Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’InvestigacióMédica (IMIM), 08003 Barcelona, Spain; [email protected] (O.C.); [email protected] (M.F.) 11 Department of Nutrition, Food Sciences, and Physiology, Center for Nutrition Research, University of Navarra, 31009 Pamplona, Spain 12 Precision Nutrition and Cardiometabolic Health Program, IMDEA Food, CEI UAM + CSIC, 28222 Madrid, Spain; [email protected] Nutrients 2024,16, 2631. https://doi.org/10.3390/nu16162631 https://www.mdpi.com/journal/nutrients Nutrients 2024,16, 2631 2 of 18 13 Bioaraba Health Research Institute, Cardiovascular, Respiratory and Metabolic Area, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 01004 Vitoria-Gasteiz, Spain 14 EpiPHAAN Research Group, School of Health Sciences, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, 29010 Málaga, Spain; [email protected] (A.O.-C.); [email protected] (F.J.B.-L.); [email protected] (N.P.-F.); [email protected] (S.G.-P.) 15 Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), 03202 Alicante, Spain 16 Health Research Institute of the Balearic Islands (IdISBa), 07012 Palma de Mallorca, Spain 17 Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 30110 Cordoba, Spain 18 Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, Institut de Recerca en Nutricióy Seguretat Alimentaria (INSA-UB), University of Barcelona, 08001 Barcelona, Spain 19 Virgen de la Victoria Hospital, Department of Endocrinology, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, 29010 Málaga, Spain 20 Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, 41006 Sevilla, Spain 21 Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, 35010 Las Palmas de Gran Canaria, Spain 22 Department of Preventive Medicine and Public Health, University of Granada, 18071 Granada, Spain 23 Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18071 Granada, Spain 24 Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands, 07012 Palma de Mallorca, Spain 25 Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge-IDIBELL, Hospitalet de Llobregat, 08901 Barcelona, Spain 26 Division of Preventive Medicine, Faculty of Medicine, University of Jaén, 23003 Jaén, Spain 27 Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; [email protected] 28 CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28222 Madrid, Spain; [email protected] (J.V.); [email protected] (J.V.) 29 Department of Endocrinology, Institut d’Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08001 Barcelona, Spain 30 Department of Endocrinology and Nutrition, Hospital Fundación Jimenez Díaz, Instituto de Investigaciones Biomédicas IISFJD, University Autonoma, 28049 Madrid, Spain 31 Nutritional Control of the Epigenome Group, Precision Nutrition and Obesity Program, IMDEA Food, CEI UAM + CSIC, 28222 Madrid, Spain 32 Departamento de Ciencias Farmacéuticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain 33 Lipid Clinic, Department of Endocrinology and Nutrition, Institut d’Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, 08001 Barcelona, Spain 34 Physical Activity and Sport Sciences Research Group (GICAFE), Institute for Educational Research and Innovation (IRIE), University of the Balearic Island, 07122 Palma, Spain 35 College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97330, USA; javier[email protected] *Correspondence: amar[email protected]; Tel.: +34-606784373 Abstract: The aim of our cross-sectional and longitudinal study is to assess the relationship between daytime and night-time sleep duration and health-related quality of life (HRQoL) in adults with metabolic syndrome after a 1-year healthy lifestyle intervention. Analysis of the data from 2119 Spanish adults aged 55–75 years from the PREDIMED-Plus study was performed. Sleep duration was assessed using a wrist-worn accelerometer. HRQoL was measured using the SF-36 questionnaire. Linear regression models adjusted for socioeconomic and lifestyle factors and morbidity were developed. In cross-sectional analyses, participants with extreme night-time sleep duration categories showed lower physical component summary scores in Models 1 and 2 [ β -coefficient (95% confidence interval) <6 h vs. 7–9 h: − 2, 3 ( − 3.8 to − 0.8); p= 0.002. >9 h vs. 7–9 h: − 1.1 ( − 2.0 to − 0.3); p= 0.01 ]. Participants who sleep less than 7 h a night and take a nap are associated with higher mental component summary scores [ β -coefficient (95% confidence interval) 6.3 (1.3 to 11.3); p= 0.01]. No differences between night-time sleep categories and 12-month changes in HRQoL were observed. In conclusion, in cross-sectional analyses, extremes in nocturnal sleep duration are related to lower Nutrients 2024,16, 2631 3 of 18 physical component summary scores and napping is associated with higher mental component summary scores in older adults who sleep less than 7 h a night. Keywords: sleep duration; daytime sleep duration; metabolic syndrome; health-related quality of life; quality of life; nap 1. Introduction Sleep habits in today’s society have changed compared with those of our ancestors. In general, we sleep less, have less quality sleep and are more unlikely to nap [ 1 ]. The main causes for this are suggested to be artificial light, using new technologies, working hours and the pace of life [1–4]. For decades, sleep-related problems have been increasing exponentially [ 5 , 6 ]; between 10 and 30% of the population reports sleeping less than 6 h a day (depending on country) [ 7 ], approximately 40% relates to having had a restful night’s sleep [ 8 , 9 ] and the consumption of sleep medications has skyrocketed [10], turning into a public health challenge. According to the Survey of Health Ageing and Retirement in Europe, 24.3% of older adults in Spain have sleep problems [ 11 ], 54.3% reported sleeping less than seven hours (male 56.2% and female 52.8%) [ 9 ] and 16% take a daily nap, a percentage that increases with flexible working hours or retirement [11]. Evidence shows that a sleep deficit is a risk factor for increased mortality and major chronic diseases like cardiovascular diseases [ 12 ], hypertension, obesity, type 2 diabetes [13,14] or metabolic syndrome (MetS) [ 15 ]. Furthermore, a strong association has been shown between sleep duration and mental illnesses such as anxiety or depression [16,17]. In recent years, the paradigm in sleep research has shifted. For most of the last few decades, research focused mostly on sleep pathologies such as sleep apnea, insomnia and narcolepsy. In recent years, however, there has been increasing attention on a broader and more holistic approach, namely on the role of sleep health in all aspects of life and health in general [ 1 , 18 ]. Health-related quality of life (HRQoL), a measure of individual well-being, has been shown to be negatively impacted by sleep deficiency, either in terms of quality or quantity. However, it is not entirely clear which HRQoL components are most affected. Furthermore, most published studies are cross-sectional, use self-reported measures of sleep duration and/or do not take into account the effect that daytime sleep (naps) may have on HRQoL [19–22]. There is no consensus in the scientific literature regarding the effects of naps on health outcomes. It seems that long naps, lasting more than 1 h, may be a symptom or consequence of a nocturnal sleep disorder and have been found to be associated with an increased risk of obesity, type 2 diabetes or MetS [ 23 , 24 ]. However, short naps between 15 and 30 min/d have been associated with an increase in cognitive function, greater memory retention, recovery from fatigue and increased alertness [25–27]. Consequently, they could be a key element in improving the population’s HRQoL. Therefore, the aim of our cross-sectional and longitudinal study is to assess the effects of daytime and night-time sleep on HRQoL in older adults with MetS after a 1-year healthy lifestyle intervention. 2. Materials and Methods 2.1. Study Design and Participants The PREDIMED-Plus study is a 6-year ongoing, multicenter, controlled, randomized intervention study with two parallel groups for the primary prevention of cardiovascular disease, involving 6874 people recruited in 23 Spanish centers. The study methods have been reported elsewhere [ 28 ] and are available on the PREDIMED-Plus website (http:// www.predimedplus.com, accessed on 1 May 2024). Eligible participants were communitydwelling men (55–75 years old) and women (60–75 years old), with a body mass index Nutrients 2024,16, 2631 4 of 18 (BMI) between ≥ 27 and <40 kg/m 2 and who met at least 3 components of the MetS definition [ 29 ]. The intervention group received an intense intervention with an energyrestricted traditional Mediterranean diet (erMedDiet), physical activity (PA) promotion and motivational support to lose weight. The control group received general recommendations about the Mediterranean diet and healthy guidelines. Out of 6874 participants, data derived from accelerometry were available in a subsample of 2223 participants. According to protocol, accelerometers were provided to a subset of participants (50% of participants in the intensive intervention group and 20% of those in the control group) in order to quantify PA and sleep. One hundred and four participants were excluded owing to incomplete sleep or covariate data. The final sample size was 2119 participants at baseline and 1-year follow-up (Supplemental Figure S1). The trial was approved by the Institutional Review Board of all recruitment centers where the study was conducted, according to the ethical standards of the Declaration of Helsinki. The trial was retrospectively registered in the International Standard Randomized Controlled Trial registry (ISRCTN: http://www.isrctn.com/ISRCTN89898870, accessed on 1 May 2024). All participants provided written informed consent. 2.2. Principal Predictor Variable: Objectively Assessed Sleep by Accelerometry The participants wore a wrist-worn accelerometer on their nondominant wrist for 8 consecutive 24 h days (GENEActiv, ActivInsights Ltd., Kimbolton, UK). The GENEActiv contains a triaxial accelerometer capturing accelerations in a range of ± 8 G’s and it was set at a sampling rate of 40 Hz. The monitor is totally waterproof, and they were asked to not remove it during water-based activities (i.e., showering, bathing or swimming). Raw data were downloaded at each study center using the GENEActiv PC software 1.2 (ActivInsights Ltd., Cambridgeshire, UK) as binary files (bin) and were uploaded to a common server at the study coordinating center at the University of Malaga. All raw data files were processed on an ongoing basis with the open-source R package GGIR v. 2.4–3 (https://cran.r-project.org, accessed on 1 May 2024) [30]. The sleep detection algorithm HDCZA (the Heuristic Algorithm looking at Distribution of Change in Z-Angle), available as part of the GGIR package [ 31 ], was used to identify the sleep period time window (SPT window, which refers to the time window starting at sleep onset and ending when the person wakes up after the last sleep episode of the night). Open-source software such as GGIR allows raw data to be processed in an identical manner regardless of monitors, and equivalent values for sleep estimates have been shown between the most common monitors used in epidemiologic studies [ 32 ]. Daytime napping was estimated as a period of sustained inactivity during the day, detected as the absence of change in arm angle greater than 5 degrees for at least 5 min [31]. Days when the accelerometer registered a valid night record were considered as valid, and only results from participants with at least two valid record days (and nights) were included in the analyses. 2.3. Outcome Variable: Health-Related Quality of Life (HRQoL) The dependent variable was HRQoL at baseline and 1-year follow-up, measured using the Spanish version of the SF-36 questionnaire [ 33 , 34 ]. This questionnaire consisted of 36 items that assessed eight dimensions or scales: physical function (PF), physical role (RP), bodily pain (BP), general health (GH), vitality (VT), social function (SF), emotional role (RE) and mental health (MH). These dimensions were used to define two health component summaries: the physical component summary (PCS) and the mental component summary (MCS). Each item received a numerical score that was encoded, summed up and put on a scale from 0 to 100. The higher the score, the better quality of life in the analyzed field [ 35 ]. The reliability of the scale used to determine the values of the Spanish population of ≥ 60 years was higher than the proposed standard of Cronbach’s α , 0.7 [ 36 ], and has been previously used to measure HRQoL in older adults [37,38]. Nutrients 2024,16, 2631 5 of 18 2.4. Covariates At baseline, self-reported information was obtained by interview for sociodemographic variables: sex (men/women), age (years), marital status (married or living with a partner, divorced or widowed or single), labor status (active, retired, unemployed or household work) and educational level ( ≤ primary, secondary or university). The lifestyle covariates of smoking status (current, former or never smoker), caffeine drinks/day (mg/d), alcohol drinks/day (g/d) and leisure time spent watching TV (h/wk) were also collected through the baseline questionnaire. The Mediterranean diet adherence was measured through a 17-item questionnaire by a trained interviewer [39]. Body mass index (kg/m 2 ) was calculated from weight and height, measured under standardized conditions with light clothing and no shoes, using electronic scales and portable extendable stadiometers. Mean values of the two measurements were used for the analyses according to the PREDIMED-Plus protocol. Physical activity intensities were objectively estimated by accelerometry and calculated and classified using a previously proposed threshold for ENMO (Euclidean Norm Minus One) in the nondominant wrist: inactivity (<45 mg), light activity (45–99.9 mg) and moderate-to-vigorous activity (MVPA, >100 mg) [40]. PA was categorized, according to WHO recommendations, as ≥ 150 min/week (active/non active) [41]. Finally, the following physician-diagnosed diseases were self-reported: hypertension (yes/no), type 2 diabetes mellitus (yes/no), sedative treatment (yes/no), depression (yes/no), sleep apnea (yes/no) and chronic obstructive pulmonary disease (yes/no). 2.5. Statistical Analysis 2.5.1. Cross-Sectional Analysis First, the analysis of baseline characteristics was carried out in the entire sample according to four categories of night-time sleep duration, as defined in previous studies (<6, ≥ 6–<7, ≥ 7–<9 and ≥ 9 h/day (h/d)) [ 19 , 21 ]. Means (standard deviation, SD) or medians (interquartile range, IQ) were used for quantitative variables with normal or nonnormal distribution, respectively, and absolute and relative frequencies (n, %) were used for qualitative variables. p-Values for differences between categories of night-time sleep duration were calculated using a chi-squared test or ANOVA for categorical and continuous variables, respectively. In cases of non-normally distributed continuous variables, we performed a Kruskal–Wallis test. Second, linear regression models were developed with HRQoL after 1-year follow-up as the dependent variable and sleep duration as the main independent variable (using ≥ 7–<9 h of sleep as reference category). Model 1 was a linear model adjusted for age and sex; Model 2 was Model 1 plus marital status, labor status and educational level; Model 3 was Model 2 plus lifestyle factors (smoking status, caffeine drinks/day, alcohol drinks/day, leisure time spent watching TV, adherence to a Mediterranean diet, BMI, PA and daytime sleep duration); and Model 4 was Model 3 plus morbidity (hypertension, type 2 diabetes, sedative treatment, depression, sleep apnea and chronic obstructive pulmonary disease) and treatment assignment. The Benjamini–Hochberg procedure (BH) was used as a correction test. Because, in most cases, the results from Models 1 and 2 were virtually identical, only the latter are shown in some tables. Stratified analyses by sex were also performed (Supplementary Material). Third, linear regression models were used to study the associations between daytime sleep duration and HRQoL, stratified by night-time sleep duration. The categories for daytime sleep duration were <15, ≥ 15–<60 and ≥ 60 min/day (min/d). Analyses using 4 categories—splitting the middle category in two using 30 min/d as the cut-off—yield similar results. The category <15 min/d of daytime sleep was used as the reference. The categories for night-time sleep duration were <7, ≥ 7–<9 and ≥ 9 h/d. We could not maintain four sleep categories due to the small number of participants who slept <6 h/d at night Nutrients 2024,16, 2631 6 of 18 and had <15 min/d of daytime sleep. Linear regression models were as described above, removing daytime sleep duration in Model 3. It was not possible to perform stratified analyses by sex due to insufficient samples in the categories of interest. 2.5.2. Longitudinal Analysis To examine whether nightand daytime sleep duration predicted changes in HRQoL, linear regression was carried out, where the dependent variable was differences in HRQoL between baseline and one year after the intervention and night/day sleep duration was the main independent variable. Twelve-month changes in HRQoL were expressed as means (SD) and β -regression coefficients and used to assess the 1-year average changes in HRQoL associated with nightand daytime sleep duration at baseline. A positive β -coefficient means an improvement in HRQoL, while a negative coefficient means a worsening. We defined “clinically significant” changes in HRQoL when there was at least a 5% change in the magnitude of the SF-36 score. Logistic regression models were carried out with this cut-off point to define the dependent variable. In these models, odds ratios (ORs) were calculated: an OR above 1 quantifies the likelihood of a clinically significant improvement in participants’ HRQoL, while an OR less than 1 indicates the risk of that change being for the worse. The categories of nightand daytime sleep were as described above, as were the models and potential confounders for which the analyses were adjusted. Stratified analyses by sex, age, BMI and PA were also performed except for analyses according to daytime sleep categories due to insufficient sample size. The analysis was performed with StataCorp (College Station, TX, USA) 2019, Stata Statistical Software: Release 16. 3. Results 3.1. Baseline Descriptive Characteristics Baseline characteristics in the total sample and according to four categories of nighttime sleep duration are shown in Table 1. The night-time sleep duration mean (SD) in the total sample (n= 2119) was 8.0 (1.3) h/d, the mean age (SD) was 65.0 (4.9) years and 47.4% of participants were female. Those participants sleeping ≥ 9 h were older and more likely to be women, retired, have a university degree and never have smoked. They were also more likely to have lower light and moderate–vigorous PA levels, higher prevalence of depression and higher sedative treatment use. The individuals sleeping <6 h showed higher caffeine and alcohol consumption, spent more time watching TV on the weekends and had higher daytime sleep duration. No significant differences in marital status, adherence to energy-restricted MedDiet, hypertension, type 2 diabetes, sleep apnea and chronic lung disease were found. Regarding the SF-36 score, lower scores are observed for extreme sleep durations, obtaining statistically significant differences in all scales except for RE. Table 1. Baseline characteristics of the PREDIMED-Plus study participants across categories of night-time sleep duration. Categories of Night-Time Sleep Duration (h/d) p-Value Total <6 ≥6–<7 ≥7–<9 ≥9 n= 2119 n= 129 n= 316 n= 1247 n= 427 Sleep parameters Night-time sleep duration, Min–max, h/d 3.1–14.2 3.1–5.9 6.0–6.9 7.0–8.0 9.0–14.2 <0.001 Night-time sleep duration, mean (SD), h/d 8.0 (1.3) 5.2 (0.7) 6.6 (0.3) 8.0 (0.5) 9.8 (0.7) <0.001 Napping duration, median (IQ), min/d 61.2 (37.8–91.2) 90 (69.0–139.8) 68.7 (42.0–103.2) 55.8 (33.0–81.6) 64.8 (40.8–97.2) <0.001 Age, mean (SD), years 65.0 (4.9) 64.7 (5.3) 64.0 (5.1) 64.9 (4.9) 66.3 (4.4) <0.001 Nutrients 2024,16, 2631 7 of 18 Table 1. Cont. Categories of Night-Time Sleep Duration (h/d) p-Value Total <6 ≥6–<7 ≥7–<9 ≥9 n= 2119 n= 129 n= 316 n= 1247 n= 427 Female, n(%) 1005 (47.4) 25 (19.4) 106 (33.5) 616 (33.5) 258 (60.4) <0.001 Labor status, n(%) Retired 1219 (57.5) 77 (59.7) 162 (51.3) 698 (56.0) 282 (66.0) <0.001 Educational level, n(%) ≤Primary education 1062 (50.1) 56 (43.4) 129 (40.8) 611 (49.0) 66 (15.5) <0.001 University education 463 (21.9) 40 (31.0) 95 (30.1) 262 (21.0) 266 (62.3) Smoking, n(%) Never 919 (43.4) 36 (27.9) 112 (35.4) 555 (44.5) 216 (50.6) <0.001 Caffeine drinks/day, median (IQ), mg/day 21.4 (0–50) 21.4 (0–125) 7.1 (0–50) 21.4 (0–50) 3.3 (0–50) <0.001 Alcohol drinks/day, median (IQ), g/day 5.1 (0.7–14.8) 7.4 (1.5–28.4) 7.3 (1.5–18.6) 5.1 (0.7–14.7) 2.9 (0.0–11.8) <0.001 Leisure time spent watching TV, mean (SD), h/day Non-labor days 3.9 (3.3) 4.6 (7.7) 4.2 (5.2) 3.7 (2.0) 3.8 (1.9) 0.01 Sedative treatment, n(%) 514 (24.3) 29 (22.5) 52 (16.5) 295 (23.7) 138 (32.3) <0.001 Depression, n(%) 472 (22.3) 23 (17.8) 51 (16.1) 274 (22.0) 124 (29.0) <0.001 BMI, mean (SD), kg/m232.6 (3.5) 33.4 (3.5) 32.9 (3.4) 32.4 (3.4) 32.8 (3.6) 0.004 PA, mean (SD) IPA 7.2 (1.7) 8.9 (2.6) 7.6 (1.7) 7.1 (1.6) 6.6 (1.4) <0.001 LPA 2.6 (1.1) 2.6 (1.3) 2.8 (1.1) 2.6 (1.1) 2.2 (0.9) <0.001 MVPA 40.2 (32.2) 40.1 (34.6) 42.1 (33.4) 41.8 (32.5) 34.0 (28.9) <0.001 HRQoL, SF-36 score, mean (SD), points (1-year follow-up) PF 79.3 (18.6) 78.3 (19.2) 80.9 (17.5) 80.2 (17.9) 75.7 (20.9) <0.001 RF 81.3 (33.3) 82.2 (32.4) 84.2 (30.6) 82.1 (32.6) 76.6 (36.9) 0.01 BP 65.9 (25.1) 66.3 (24.6) 67.7 (23.6) 66.9 (24.9) 61.6 (26.5) 0.001 GH 64.3 (17.0) 63.8 (16.7) 64.9 (16.4) 64.9 (17.0) 62.0 (17.2) 0.02 VT 65.1 (19.3) 65.2 (17.6) 67.1 (17.7) 65.7 (19.1) 61.6 (20.9) <0.001 SF 85.6 (19.3) 85.9 (20.4) 88.1 (17.5) 86.0 (19.2) 82.7 (20.0) 0.001 RE 90.3 (26.0) 92.2 (22.6) 91.1 (24.0) 90.6 (25.8) 88.2 (28.9) 0.27 MH 75.4 (17.8) 77.1 (16.0) 78.0 (15.5) 75.4 (17.7) 73.2 (19.6) 0.002 PCS 46.3 (8.4) 45.9 (8.5) 46.9 (8.0) 46.7 (8.3) 44.8 (9.1) <0.001 MCS 51.5 (9.3) 52.3 (8.7) 52.4 (8.2) 51.4 (9.3) 50.6 (10.2) 0.05 BMI: body mass index; BP: bodily pain; GH: general health; h/d: hours/day; HRQoL: health-related quality of life; IPA: inactive physical activity (h/average day)—cut-off intensity level used for inactivity (excluding SIBs) < 40 mg (<1.5 Mets); LPA: light physical activity in bouts of at least 1 min (accumulated min/average day)—cut-off intensity level for LPA is >40 mg (1.5 Mets) and <100 mg (3 Mets); MVPA: moderate–vigorous physical activity in bouts of at least 1 min (accumulated min/day)—cut-off intensity level for MVPA is >100 mg (3 Mets); MH: mental health; MCS: aggregated mental dimensions; PA: physical activity; PCS: aggregated physical dimensions; RE: emotional role; RF: role function; SF: social function; VT: vitality. p-Value for differences between categories of night-time sleep duration was calculated by chi-squared test or ANOVA for categorical and continuous variables, respectively. In cases of non-normally distributed variables, we performed Kruskal–Wallis test. 3.2. Cross-Sectional Analysis 3.2.1. Night-Time Sleep Duration Compared with those in the ≥ 7–<9 h category, participants in categories outside the recommended range of sleep duration (<6 h and ≥ 9 h) showed lower PF, BP, VT, SF, GH and PCS scores in the multivariable-adjusted Models 1 and 2 (Table 2and Figure 1). When confounding lifestyle variables were included in the model (Model 3), statistically significant association was lost in all dimensions except for BP and PCS, although there was still a decrease in scores in those individuals who sleep more than 9 h. In the fully adjusted model (Model 4), the association between sleep duration and HRQoL dimensions was lost. Nutrients 2024,16, 2631 8 of 18 Nutrients 2024, 16, x FOR PEER REVIEW 8 of 18 Figure 1. Multivariable-adjusted Model 3 and 4 β -coefficients (95% confidence interval) of dimensions of health-related quality of life according to night-time sleep duration categories. Nutrients 2024,16, 2631 9 of 18 Table 2. Multivariable-adjusted β -coefficients (95% confidence interval) of health-related quality of life according to night-time sleep duration categories. Categories of Night-Time Sleep Duration (h/d) <6 ≥6–<7 ≥7–<9 ≥9 n= 129 n= 316 n= 1247 n= 427 HRQoL β-Coefficients (95% CI) p-Value β-Coefficients (95% CI) p-Value β-Coefficients (95% CI) p-Value β-Coefficients (95% CI) p-Value PF −5.4 (−8.6 to −2.3) 0.001 −1.7 (−3.9 to 0.5) 0.12 0 (ref.) −2.2 (−4.1 to −0.2) 0.03 RF −4.3 (−10.2 to 1.6) 0.16 −0.6 (−4.7 to 3.4) 0.77 0 (ref.) −3.2 (−6.8 to 0.4) 0.08 BP −5.0 (−9.4 to −0.6) 0.03 −1.8 (−4.8 to 1.2) 0.25 0 (ref.) −3.4 (−6.1 to −0.7) 0.01 GH −3.6 (−6.6 to −0.5) 0.02 −1.6 (−3.6 to 0.5) 0.14 0 (ref.) −1.7 (−3.5 to 0.1) 0.07 VT −4.1 (−7.5 to −0.7) 0.02 −0.5 (−2.8 to 1.8) 0.68 0 (ref.) −2.9 (−5.9 to −0.9) 0.005 SF −3.4 (−6.7 to 0.03) 0.05 0.4 (−1.9 to 2.7) 0.74 0 (ref.) −2.1 (−4.2 to −0.08) 0.04 RE −0.8 (−5.5 to 3.9) 0.73 −0.7 (−3.9 to 2.5) 0.66 0 (ref.) −1.6 (−4.4 to 1.3) 0.29 MH −1.5 (−4.6 to 1.6) 0.36 1.1 (−1.0 to 3.2) 0.30 0 (ref.) −1.2 (−3.1 to 0.7) 0.23 PCS −2.3 (−3.8 to −0.8) 0.002 −0.8 (−1.8 to 0.2) 0.10 0 (ref.) −1.1 (−2.0 to −0.3) 0.01 MCS −0.3 (−2.0 to 1.3) 0.70 0.5 (−0.6 to 1.6) 0.40 0 (ref.) −0.6 (−1.6 to 0.4) 0.26 BP: bodily pain; CI: confidence interval; GH: general health; h/d: hours/day; HRQoL: health-related quality of life; MCS: aggregated mental dimensions; MH: mental health; PCS: aggregated physical dimensions; PF: physical function; RE: emotional role; RF: role function; SF: social function; VT: vitality. The data shown are those corresponding to Model 2 of the linear regression adjusted by sociodemographic variables: age, sex, marital status (married or living with a partner, divorced or widowed or single), labor status (active, retirement and unemployed or household work) and educational level (≤primary, secondary or university). Model 3 was a linear model adjusted for sociodemographic variables: age, sex, marital status (married or living with a partner, divorced or widowed or single), labor status (active, retirement and unemployed or household work), educational level ( ≤ primary, secondary and university) and lifestyle factors, such as smoking status (current, former or never smoker), caffeine drinks/day (mg/d), alcohol drinks/day (g/d), leisure time spent watching TV (h/wk), adherence to a Mediterranean diet measured through a 17-item questionnaire (continuous), BMI (kg/m 2 ) and MVPA recommendations (active/inactive). Model 4 was a linear model adjusted in the same way as Model 3 and for morbidities, such as hypertension (yes/no), type 2 diabetes mellitus (yes/no), sedative treatment (yes/no), depression (yes/no), sleep apnea (yes/no) and chronic obstructive pulmonary disease (yes/no), daytime sleep (min/day) and treatment assignment, stratified by daytime sleep duration. In the analysis stratified by sex, the women who slept <6 h or ≥ 9 had a lower score in the PCS (Models 1, 2 and 3), but when adjusted for morbidity, only those women who sleep less than 6 h have their PCS affected. In men, only sleeping <6 h was associated with a worse PCS in Model 1 ( Table S1). 3.2.2. Daytime Sleep Duration Those participants who sleep less than 7 h a night and take a nap greater than or equal to 15 min/d show an improvement in MCS, while for those who sleep 7 h or more at night, no statistically significant association between napping and HRQoL was found (Table 3 and Figure 2). Model 4 was a linear model adjusted for age, sex, marital status (married or living with a partner, divorced or widowed or single), labor status (active, retirement and unemployed or household work), educational level ( ≤ primary, secondary or university), lifestyle factors, such as smoking status (current, former or never smoker), caffeine drinks/day (mg/d), alcohol drinks/day (g/d), leisure time spent watching TV (h/wk), adherence to a Mediterranean diet assessed using a 17-item questionnaire (continuous), BMI (kg/m 2 ), MVPA recommendations (active/inactive), and for morbidities, such as hypertension (yes/no), Nutrients 2024,16, 2631 16 of 18 had no role in the design of the study; in the collection, analyses or interpretation of the data; in the writing of the article or in the decision to publish the results. Institutional Review Board Statement: Research Ethics Committees from all recruitment centers approved the study protocol (between 1 October 2013 to 31 December 2016), according to the ethical standards of the Declaration of Helsinki. Details on the protocol can be found at http://predimedplus. com (accessed on 1 May 2024). The trial was retrospectively registered in the International Standard Randomized Controlled Trial registry (ISRCTN: http://www.isrctn.com/ISRCTN89898870, accessed on 1 May 2024). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper. Data Availability Statement: The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Bakour, C.; Nieto, F.J.; Petersen, D.J. Foundations of Sleep Health, 1st ed.; Elsevier: Amsterdam, The Netherlands, 2022. Available online: https://www.thensf.org/foundations-of-sleep-health/ (accessed on 13 June 2023). 2. Böhmer, M.N.; Hamers, P.C.M.; Bindels, P.J.E.; Oppewal, A.; Someren, E.J.W.; van Festen, D.A.M. Are we still in the dark? A systematic review on personal daily light exposure, sleep-wake rhythm, and mood in healthy adults from the general population. Sleep Health 2021,7, 610–630. [CrossRef] [PubMed] 3. Samson, D.R.; Crittenden, A.N.; Mabulla, I.A.; Mabulla, A.Z.P.; Nunn, C.L. Chronotype variation drives night-time sentinel-like behaviour in hunter–gatherers. Proc. R. Soc. B Biol. Sci. 2017,284, 20170967. [CrossRef] [PubMed] 4. Faulkner, S.M.; Dijk, D.J.; Drake, R.J.; Bee, P.E. Adherence and acceptability of light therapies to improve sleep in intrinsic circadian rhythm sleep disorders and neuropsychiatric illness: A systematic review. Sleep Health 2020,6, 690–701. [CrossRef] [PubMed] 5. Alexander, M.; Ray, M.A.; Hébert, J.R.; Youngstedt, S.D.; Zhang, H.; Steck, S.E.; Bogan, R.K.; Burch, J.B. The National Veteran Sleep Disorder Study: Descriptive Epidemiology and Secular Trends, 2000–2010. Sleep 2016,39, 1399–1410. [CrossRef] 6. Ferrie, J.E.; Kumari, M.; Salo, P.; Singh-Manoux, A.; Kivimaki, M. Sleep epidemiology—A rapidly growing field. Int. J. Epidemiol. 2011,40, 1431–1437. [CrossRef] [PubMed] 7. Hoyos, C.; Glozier, N.; Marshall, N.S. Recent Evidence on Worldwide Trends on Sleep Duration. Curr. Sleep Med. Rep. 2015,1, 195–204. [CrossRef] 8. Kerkhof, G.A. Epidemiology of sleep and sleep disorders in The Netherlands. Sleep Med. 2017,30, 229–239. [CrossRef] [PubMed] 9. Madrid-Valero, J.J.; Martínez-Selva, J.M.; Ribeiro do Couto, B.; Sánchez-Romera, J.F.; Ordoñana, J.R. Age and gender effects on the prevalence of poor sleep quality in the adult population. Gac. Sanit. 2017,31, 18–22. [CrossRef] [PubMed] 10. Silva, J.; Vieira, P.; Gomes, A.A.; Roth, T.; de Azevedo, M.H.P.; Marques, D.R. Sleep difficulties and use of prescription and non-prescription sleep aids in Portuguese higher education students. Sleep Epidemiol. 2021,1, 100012. [CrossRef] 11. Andréu, M.M.; de Larrinaga, A.R.; Pérez, J.A.M.; Martínez, M.M.; Cuesta, F.J.P.; Guerra, A.J.A.; Santo-Tomás, O.R.; Luque, M.J.J.; Isern, F.J.S.; Sanz, T.C.; et al. Sueño saludable: Evidencias y guías de actuación. Documento oficial de la Sociedad Española de Sueño. Rev. Neurol. 2016,63, 1. [CrossRef] 12. Fan, M.; Sun, D.; Zhou, T.; Heianza, Y.; Lv, J.; Li, L.; Qi, L. Sleep patterns, genetic susceptibility, and incident cardiovascular disease: A prospective study of 385,292 UK biobank participants. Eur. Heart J. 2020,41, 1182–1189. [CrossRef] [PubMed] 13. Rosique-Esteban, N.; Papandreou, C.; Romaguera, D.; Warnberg, J.; Corella, D.; Martínez-González, M.; Díaz-López, A.; Estruch, R.; Vioque, J.; Arós, F.; et al. Cross-sectional associations of objectively-measured sleep characteristics with obesity and type 2 diabetes in the PREDIMED-Plus trial. Sleep 2018,41, zsy190. [CrossRef] [PubMed] 14. Johnson, S.T.; Thiel, D.; Al Sayah, F.; Mundt, C.; Qiu, W.; Buman, M.P.; Vallance, J.K.; Johnson, J.A. Objectively measured sleep and health-related quality of life in older adults with type 2 diabetes: A cross-sectional study from the Alberta’s Caring for Diabetes Study. Sleep Health 2017,3, 102–106. [CrossRef] [PubMed] 15. Smiley, A.; King, D.; Bidulescu, A. The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients 2019,11, 2582. [CrossRef] [PubMed] 16. Stickley, A.; Leinsalu, M.; DeVylder, J.E.; Inoue, Y.; Koyanagi, A. Sleep problems and depression among 237 023 communitydwelling adults in 46 lowand middle-income countries. Sci. Rep. 2019,9, 12011. [CrossRef] [PubMed] 17. Zhai, L.; Zhang, H.; Zhang, D. Sleep Duration and Depression Among Adults: A Meta-Analysis of Prospective Studies. Depress. Anxiety 2015,32, 664–670. [CrossRef] [PubMed] 18. Hale, L. An introduction and invitation to join our sleep health community. Sleep Health 2015,1, 1–2. [CrossRef] [PubMed] 19. Liu, Y.; Wheaton, A.G.; Croft, J.B.; Xu, F.; Cunningham, T.J.; Greenlund, K.J. Relationship between sleep duration and self-reported health-related quality of life among US adults with or without major chronic diseases, 2014. Sleep Health 2018,4, 265–272. [CrossRef] [PubMed] Nutrients 2024,16, 2631 17 of 18 20. Goldman, S.E.; Stone, K.L.; Ancoli-Israel, S.; Blackwell, T.; Ewing, S.K.; Boudreau, R.; Cauley, J.A.; Hall, M.; Matthews, K.A.; Newman, A.B. Poor Sleep is Associated with Poorer Physical Performance and Greater Functional Limitations in Older Women. Sleep 2007,30, 1317–1326. [CrossRef] 21. Faubel, R.; Lopez-Garcia, E.; Guallar-Castillón, P.; Balboa-Castillo, T.; Gutiérrez-Fisac, J.L.; Banegas, J.R.; Rodríguez-Artalejo, F. Sleep Duration and Health-Related Quality of Life among Older Adults: A Population-Based Cohort in Spain. Sleep 2009,32, 1059–1068. 22. Xiao, Q.; Chaput, J.-P.; Olds, T.; Fogelholm, M.; Hu, G.; Lambert, E.V.; Maher, C.; Maia, J.; Onywera, V.; Sarmiento, O.L.; et al. Sleep characteristics and health-related quality of life in 9to 11-year-old children from 12 countries. Sleep Health 2020,6, 4–14. [CrossRef] [PubMed] 23. Li, W.; Kondracki, A.J.; Sun, N.; Gautam, P.; Kalan, M.E.; Jebai, R.; Gbadamosi, S.O.; Sun, W. Nighttime sleep duration, daytime napping, and metabolic syndrome: Findings from the China Health and Retirement Longitudinal Study. Sleep Breath 2022,26, 1427–1435. [CrossRef] [PubMed] 24. Papandreou, C.; Díaz-López, A.; Babio, N.; Martínez-González, M.A.; Bulló, M.; Corella, D.; Fitó, M.; Romaguera, D.; Vioque, J.; Alonso-Gómez, Á.M.; et al. Long Daytime Napping Is Associated with Increased Adiposity and Type 2 Diabetes in an Elderly Population with Metabolic Syndrome. J. Clin. Med. 2019,8, 1053. [CrossRef] [PubMed] 25. Dutheil, F.; Danini, B.; Bagheri, R.; Fantini, M.L.; Pereira, B.; Moustafa, F.; Trousselard, M.; Navel, V. Effects of a Short Daytime Nap on the Cognitive Performance: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2021,18, 10212. [CrossRef] [PubMed] 26. Fan, F.; McPhillips, M.V.; Li, J. 0720 Daytime Napping and Cognition in Older Adults. Sleep 2018,41 (Suppl. S1), A267–A268. [CrossRef] 27. Faraut, B.; Andrillon, T.; Vecchierini, M.F.; Leger, D. Napping: A public health issue. From epidemiological to laboratory studies. Sleep Med. Rev. 2017,35, 85–100. [CrossRef] [PubMed] 28. Martínez-González, M.A.; Buil-Cosiales, P.; Corella, D.; Bulló, M.; Fitó, M.; Vioque, J.; Romaguera, D.; Martínez, J.A.; Wärnberg, J.; López-Miranda, J.; et al. Cohort Profile: Design and methods of the PREDIMED-Plus randomized trial. Int. J. Epidemiol. C. [CrossRef] [PubMed] 29. Alberti, K.G.M.M.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.T.; Loria, C.M.; Smith, S.C., Jr. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009,120, 1640–1645. [CrossRef] 30. Migueles, J.H.; Rowlands, A.V.; Huber, F.; Sabia, S.; van Hees, V.T. GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data. J. Meas. Phys. Behav. 2019,2, 188–196. [CrossRef] 31. van Hees, V.T.; Sabia, S.; Jones, S.E.; Wood, A.R.; Anderson, K.N.; Kivimäki, M.; Frayling, T.M.; Pack, A.I.; Bucan, M.; Trenell, M.I.; et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci. Rep. 2018,8, 12975. [CrossRef] 32. Plekhanova, T.; Rowlands, A.V.; Yates, T.; Hall, A.; Brady, E.M.; Davies, M.; Khunti, K.; Edwardson, C.L. Equivalency of Sleep Estimates: Comparison of Three Research-Grade Accelerometers. J. Meas. Phys. Behav. 2020,3, 294–303. [CrossRef] 33. Alonso, J.; Regidor, E.; Barrio, G.; Prieto, L.; Rodríguez, C.; De La Fuente, L. Population reference values of the Spanish version of the Health Questionnaire SF-36. Med. Clin. 1998,111, 410–416. 34. Vilagut, G.; Ferrer, M.; Rajmil, L.; Rebollo, P.; Permanyer-Miralda, G.; Quintana, J.M.; Santed, R.; Valderas, J.M.; Ribera, A.; Domingo-Salvany, A.; et al. The Spanish version of the Short Form 36 Health Survey: A decade of experience and new developments. Gac. Sanit. 2005,19, 135–150. [CrossRef] [PubMed] 35. Alonso, J. Manual de Puntuación de la Versión Española del Cuestionario SF-36. Published Online 2000. Available online: http://www.imim.es (accessed on 1 May 2024). 36. López-García, E.; Banegas, J.R.; Graciani Pérez-Regadera, A.; Gutiérrez-Fisac, J.L.; Alonso, J.; Rodríguez-Artalejo, F. Populationbased reference values for the Spanish version of the SF-36 Health Survey in the elderly. Med. Clin. 2003,120, 568–573. [CrossRef] [PubMed] 37. Walters, S.J.; Munro, J.F.; Brazier, J.E. Using the SF-36 with older adults: A cross-sectional community-based survey. Age Ageing 2001,30, 337–343. [CrossRef] [PubMed] 38. Balboa-Castillo, T.; León-Muñoz, L.M.; Graciani, A.; Rodríguez-Artalejo, F.; Guallar-Castillón, P. Longitudinal association of physical activity and sedentary behavior during leisure time with health-related quality of life in community-dwelling older adults. Health Qual. Life Outcomes 2011,9, 47. [CrossRef] 39. Schröder, H.; Fitó, M.; Estruch, R.; Martínez-González, M.A.; Corella, D.; Salas-Salvadó, J.; Lamuela-Raventós, R.; Ros, E.; Salaverría, I.; Fiol, M.; et al. A Short Screener Is Valid for Assessing Mediterranean Diet Adherence among Older Spanish Men and Women. J. Nutr. 2011,141, 1140–1145. [CrossRef] [PubMed] 40. Hildebrand, M.; Hansen, B.H.; van Hees, V.T.; Ekelund, U. Evaluation of raw acceleration sedentary thresholds in children and adults. Scand. J. Med. Sci. Sports 2017,27, 1814–1823. [CrossRef] [PubMed] 41. Organización Mundial de la Salud. Actividad Física. Available online: https://www.who.int/es/news-room/fact-sheets/detail/ physical-activity (accessed on 29 November 2021). Nutrients 2024,16, 2631 18 of 18 42. Sleep Foundation. How Much Sleep Do We Really Need? Sleep Foundation. 9 March 2021. Available online: https://www. sleepfoundation.org/how-sleep-works/how-much-sleep-do-we-really-need (accessed on 13 December 2021). 43. Ghafouri, M.; Teymourzadeh, A.; Nakhostin-Ansari, A.; Sepanlou, S.G.; Dalvand, S.; Moradpour, F.; Bavarsad, A.H.; Boogar, S.S.; Dehghan, M.; Ostadrahimi, A.; et al. Prevalence and predictors of low back pain among the Iranian population: Results from the Persian cohort study. Ann. Med. Surg. 2022,74, 103243. [CrossRef] 44. Park, H.M.; Kwon, Y.J.; Kim, H.S.; Lee, Y.J. Relationship between Sleep Duration and Osteoarthritis in Middle-Aged and Older Women: A Nationwide Population-Based Study. J. Clin. Med. 2019,8, 356. [CrossRef] 45. Chen, X.; Gelaye, B.; Williams, M.A. Sleep characteristics and health-related quality of life among a national sample of American young adults: Assessment of possible health disparities. Qual. Life Res. 2014,23, 613–625. [CrossRef] [PubMed] 46. Diekelmann, S.; Born, J. The memory function of sleep. Nat. Rev. Neurosci. 2010,11, 114–126. [CrossRef] [PubMed] 47. Gribble, A.K.; Sayón-Orea, C.; Bes-Rastrollo, M.; Kales, S.N.; Shirahama, R.; Martínez-González, M.; Fernandez-Montero, A. Risk of Developing Metabolic Syndrome Is Affected by Length of Daily Siesta: Results from a Prospective Cohort Study. Nutrients 2021,13, 4182. [CrossRef] [PubMed] 48. Papandreou, C.; Bulló, M.; Díaz-López, A.; Martínez-González, M.A.; Corella, D.; Castañer, O.; Vioque, J.; Romaguera, D.; Martínez, A.J.; Pérez-Farinós, N.; et al. High sleep variability predicts a blunted weight loss response and short sleep duration a reduced decrease in waist circumference in the PREDIMED-Plus Trial. Int. J. Obes. 2020,44, 330–339. [CrossRef] [PubMed] 49. Afolalu, E.F.; Ramlee, F.; Tang, N.K.Y. Effects of sleep changes on pain-related health outcomes in the general population: A systematic review of longitudinal studies with exploratory meta-analysis. Sleep Med. Rev. 2018,39, 82–97. [CrossRef] 50. Jung, K.I.; Song, C.H.; Ancoli-Israel, S.; Barrett-Connor, E. Gender differences in nighttime sleep and daytime napping as predictors of mortality in older adults: The Rancho Bernardo Study. Sleep Med. 2013,14, 12–19. [CrossRef] 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.