Next Article in Journal
Evaluating the Effects of a Progressive Kinesiotaping Treatment Protocol on Chronic Low Back Pain in Women Using Electroencephalography
Previous Article in Journal
Pregnancy and Lactation-Associated Osteoporosis: Combined Pharmacological and Rehabilitative Management
Previous Article in Special Issue
A National Study of Somatotypes in Mexican Athletes Across 43 Sports
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model

by
Pedro Forte
1,2,3,4,*,
Samuel G. Encarnação
2,3,5,
José E. Teixeira
3,4,6,7,8,
Luís Branquinho
9,10,
Tiago M. Barbosa
3,4,
António M. Monteiro
3 and
Daniel Pecos-Martín
1
1
Physiotherapy and Pain Group, Department of Physical Therapy, University of Alcala, 28801 Madrid, Spain
2
Department of Sports, Higher Institute of Educational Sciences of the Douro, 4560-708 Penafiel, Portugal
3
Research Centre for Active Living and Wellbeing (LiveWell), Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal
4
Department of Sports Sciences, Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal
5
Department of Physical Activity and Sport Sciences, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain
6
Department of Sports Sciences, Polytechnic of Guarda, 6300-559 Guarda, Portugal
7
Department of Sports Sciences, Polytechnic of Cávado and Ave., 4800-058 Guimarães, Portugal
8
SPRINT—Sport Physical Activity and Health Research & Inovation Center, 6300-559 Guarda, Portugal
9
Biosciences Higher School of Elvas, Polytechnic Institute of Portalegre, 7350-000 Portalegre, Portugal
10
Life Quality Research Center (LQRC-CIEQV), 2001-904 Santarém, Portugal
*
Author to whom correspondence should be addressed.
J. Funct. Morphol. Kinesiol. 2025, 10(3), 337; https://doi.org/10.3390/jfmk10030337
Submission received: 25 June 2025 / Revised: 29 August 2025 / Accepted: 1 September 2025 / Published: 2 September 2025

Abstract

Background: Sleep plays a crucial role in the health of older adults, and its quality is influenced by multiple physiological and functional factors. However, the relationship between sleep quality and physical fitness, body composition, and metabolic markers remains unclear. This exploratory study aimed to investigate the associations between sleep quality and physical, metabolic, and body composition variables in older adults, and to evaluate the preliminary performance of a logistic regression model in classifying sleep quality. Methods: A total of 32 subjects participated in this study, with a mean age of 69. The resting arterial pressure (systolic and diastolic), resting heart rate, anthropometrics (high waist girth), body composition (by bioimpedance), and physical fitness (Functional Fitness Test) and sleep quality (Pitsburg sleep-quality index) were evaluated. Group comparisons, associative analysis and logistic regression with 5-fold stratified cross-validation was used to classify sleep quality based on selected non-sleep-related predictors. Results: Individuals with good sleep quality showed significantly better back stretch (t = 2.592; p = 0.015; η2 = 0.239), lower limb strength (5TSTS; t = 2.564; p = 0.016; η2 = 0.476), and longer total sleep time (t = 6.882; p < 0.001; η2 = 0.675). Exploratory correlations showed that poor sleep quality was moderately associated with reduced lower-limb strength and mobility. The logistic regression model including 5TSTS and TUG achieved a mean accuracy of 0.76 ± 0.15, precision of 0.79 ± 0.18, recall of 0.83 ± 0.21, and AUC of 0.74 ± 0.16 across cross-validation folds. Conclusions: These preliminary findings suggest that physical fitness and clinical variables significantly influence sleep quality in older adults. Sleep-quality-dependent patterns suggest that interventions to improve lower limb strength may promote better sleep outcomes.

1. Introduction

Aging leads to significant changes in body composition and functional fitness [1]. These changes, notably the reduction in muscle mass and increase in adiposity, are associated with a higher risk of conditions like metabolic syndrome [1], and may lead to sarcopenia, contributing to frailty and loss of independence in older adults [2]. Physiologically, aging also affects hemodynamic parameters, including pulse pressure, arterial stiffness, and wave reflections, particularly in large arteries [3], leading to increased pulse pressure, especially in individuals over 60 years old [4].
Age-related alterations in body composition, functional fitness, resting heart rate, and arterial blood pressure are intricately linked to sleep quality [5]. In particular, obesity in older adults has been associated with reduced sleep duration, suggesting a potential interaction between metabolic and sleep-related factors [6]. Healthy behaviors may moderate this relationship, with evidence indicating that positive lifestyle factors contribute to improved sleep outcomes [7]. For example, Wu et al. [8] conducted a meta-analysis involving 197,906 participants, showing that obesity significantly shortens sleep duration. Moreover, overweight and obese individuals typically exhibit a pro-inflammatory profile with elevated levels of cytokines such as tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), and C-reactive protein (CRP) [9,10], which disrupts sleep regulation by impairing hypothalamic control of non-rapid eye movement (Non-REM) sleep [11,12].
Physical exercise and improved fitness adaptations have also been shown to positively influence sleep regulation [13,14]. Several physiological mechanisms underlie this effect, including reductions in depression and anxiety [15], improved thermoregulation following physical exertion [16,17], enhanced muscle relaxation [18], and hormonal regulation involving melatonin, cortisol, growth hormone, adenosine, ghrelin, leptin orexin, prolactin, and serotonin [19,20]. These pathways support the hypothesis that maintaining or improving physical fitness may contribute to better sleep quality in older populations.
To assess physical fitness and function in elderly individuals, various tests have been employed, including the Eurofit battery [21], the Wii Fit Balance Board [22], and the Physical Activity Scale for Elderly (PASE) [23]. More commonly used assessments include the Timed Up and Go Test (TUG) and Tinetti Gait and Balance Test, which evaluate balance and fall risk [24,25], as well as handgrip strength, an indicator of motor function and overall health [26]. Among the most used protocols, the Fullerton Functional Fitness Test (FFFT) [27], developed by Rikli and Jones, comprehensively assesses strength, flexibility, coordination, and aerobic fitness [28]. This battery includes components such as strength, balance, coordination, flexibility, and aerobic fitness [29,30]. Many studies examining physical fitness in older adults also include body composition metrics to gain a more complete health profile [31,32,33].
Despite the known links between sleep quality, physical fitness, and metabolic health, the evidence base in older populations remains limited [34]. However, there are associations between metabolic rates and sleep quality, but research in aged adults is rare [35,36,37,38,39]. The only research found was the Schilling et al. [40] study. Additionally, Kohanmoo et al. [41] and Tan et al. [42] reported an inverse relationship between fat mass and sleep quality or duration. However, most prior studies examined these domains in isolation, without integrating physical, metabolic, and sleep-related variables into a unified predictive model.
Poor sleep quality in older adults (short duration and frequent awakenings) impairs recovery, increases cortisol and anxiety levels, and perpetuates fatigue and reduced quality of life [34]. It is also associated with worse mental health and diminished performance in daily activities [43]. Considering these challenges, predictive models that integrate multiple health domains may help identify older individuals at risk of poor sleep quality. Emerging tools such as machine learning (ML) have shown promise in modulating complex health outcomes. By processing multidimensional data, ML can uncover non-linear relationships and offer preliminary classification capabilities, even in exploratory settings. However, few studies have applied ML to predict sleep quality using physical fitness and body composition variables in older adults. Therefore, the present study aimed to compare clinical and functional fitness variables by sleep quality, and to assess the predictive value of these variables using machine learning algorithms. It was hypothesized that body composition, functional fitness, and metabolic variables would significantly predict sleep quality.

2. Materials and Methods

2.1. Study Design

This study followed a cross-sectional observational design, to explore associations between body composition, functional fitness, and sleep quality in older adults. A total of 32 community-dwelling individuals aged 60 years or older were assessed in a controlled, single-session setting. Participants were recruited from a local health and exercise program, using a convenient sampling approach. Therefore, all results should be interpreted as exploratory and hypothesis-generating, laying the groundwork for future studies with larger and more representative samples. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), a validated subjective tool, while body composition was estimated using a bioimpedance scale (Tanita BC-601). The functional fitness test was used, and other parameters, including the sit-to-stand test, handgrip strength, and walking speed, were evaluated. All procedures were conducted by trained researchers following standardized protocols to ensure consistency across measurements. This study adhered to key elements of the STROBE checklist for cross-sectional studies, including clear reporting of variables, participant characteristics, statistical methods, and limitations.

2.2. Sample

Thirty-two subjects participated in this study; twenty-six were females, and six were males. The sample mean age was 69 years. The convenience sample was recruited in the Bragança Municipality. All the participants were aged community people. All the procedures were in agreement with Helsinki’s declaration. The research project received approval by the Ethical Committee of the Instituto Politécnico de Bragança (number: 2576). The participants were instructed to maintain normal daily activities to prevent physical inactivity. The participants were asked to complete a sample characterization questionnaire during the first visit. The inclusion criteria were: (i) being aged 60 years or older, (ii) maintaining functional independence in daily activities; (iii) not having severe chronic diseases or taking sleep-related medication; (iv) not having any significant cardiovascular, musculoskeletal, metabolic, or joint conditions that could interfere with the assessments; (v) not having developed any new illness or begun any new medication during the study period that could affect sleep or physical function; (vi) being a non-smoker; (vii) not having undertaken long-distance travel during the study period that could cause jet lag and affect sleep quality. Given the small sample size, the results are exploratory in nature and not generalizable.

2.3. Procedures

2.3.1. Anthropometrics, Body Composition, and Metabolic Variables

Anthropometrics was evaluated by stature and body mass. Additionally, the body composition was estimated with a digital bioimpedance scale (Tanita BC-601, Arlington Heights, IL, USA), which is validated and used for research [44]; however, this equipment does not provide the estimation algorithms. The computed variables of body composition were lean mass, percentage of fat mass, bone mineral density, visceral fat, total body mass, muscle mass, fat mass, and bone mineral density. The Tanita BC-601 device uses proprietary algorithms to estimate body composition and does not provide raw impedance values. These estimates may not be fully validated for all populations, especially older adults with atypical body composition. Therefore, results should be interpreted with caution. The participants made the evaluations wearing light clothing and without shoes and socks during the morning and before breakfast. It is important to note that the use of BIA in elderly populations presents inherent limitations, as age-related alterations in fluid balance and body tissue conductivity may compromise measurement accuracy. The stature was evaluated standing with the head in the Frankfurt plane. Waist and hip circumferences were also evaluated. The cardiovascular measures variables were the arterial pressure (systolic and diastolic) and resting heart rate measured with an OMRON (M2 HEM-7143-E, Omron, Kyoto, Japan), which is also validated to be used in research [44]. The metabolic rate was estimated by bioimpedance with the Tanita.

2.3.2. Arterial Blood Pressure, Resting Heart Rate and Sleep Quality

Arterial systolic blood pressure (SBP), diastolic blood pressure (DBP), and resting heart rate (RHR) were measured following the 2018 European Society of Cardiology and the European Society of Hypertension (ESC/ESH) Guidelines for the management of arterial hypertension [45]. Two measurements were performed, and the average between metrics was calculated.
Sleep quality was verified through the use of the Pitsburg sleep-quality index (PSQI), a 19-item questionnaire [46], and validated for the Portuguese population [47], used in this research. The PSQI items are subdivided into the following components: (1) subjective sleep quality, (2) sleep latency, (3) sleep duration, (4) habitual sleep efficiency, (5) sleep disturbances, (6) use of sleeping medication, and (7) daytime dysfunction. Each component is scored from 0 to 3, with higher scores indicating poorer sleep quality. A global score greater than 5 indicates poor sleep quality [47].

2.3.3. Handgrip Strength

Handgrip strength was assessed using a digital palmar dynamometer (CAMRY®, Lisbon, Portugal), with the maximum kilograms-force (kgf) achieved using a palm grip as the measurement. The participants stood with their arms away from their bodies and, upon the researcher’s signal, exerted maximum palm grip force on the dynamometer for four seconds [29]. Each participant was given two attempts, and the evaluator noted the highest recorded result.

2.3.4. Functional Fitness

The Functional Fitness Test by Rikli and Jones was used to assess the main physical parameters associated with functional mobility [28]. The battery was composed of the 2 min Step Test and the Seat To Stand, where each participant was positioned standing up in front of a 43 cm highchair. In the arm curl test, the participant was positioned in a chair 43 cm high, holding a 2 kg dumbbell. The Time-Up-and-Go Test was conducted with the participant seated in a chair 43 cm high, facing a cone at 2.44 m, and the time was recorded in seconds after two trials. Finally, the Sit and Reach and in the Back Scratch tests were applied.

2.3.5. Relative Lower Limb Muscle Power

The lower limb muscle power was measured through the five-time sit-to-stand (5TSTS) test. The test was performed in a standardized chair of 0.49 m in height. The evaluator encouraged the participants throughout the test to ensure they always perform the maximum movement speed and preserve the technique. Two attempts were performed with an interval of 60 s, and the shortest time was noted. Shorter completion time indicates greater lower-limb muscle power [48].

2.4. Statistical Analysis

The Kolmogorov–Smirnov test, kurtosis (<±3), and Skewness (−2 to +2 criteria) values allowed us to assess the normality of the distribution, and Levene’s test assessed the homogeneity. Thus, the t-test allowed the comparison of variables’ sleep quality, and Pearson’s correlation test allowed the association between the variables. The test was carried out at a significance level of 5%. The effect size (eta square − η2) was computed and interpreted as without effect (if 0 < η2 ≤ 0.04), minimum (if 0.04 < η2 ≤ 0.25), moderate (if 0.25 < η2 ≤ 0.64), and strong (if η2 > 0.64). The logistic regression Machine Learning algorithm was developed using two lower-body functional performance variables: the Five-Time Sit-to-Stand Test (5TSTS1) and the Timed Up and Go Test (TUG), with only two predictors to avoid overfitting (hence, the sample size still limits the power and generalizability of our results). The model was evaluated using 5-fold stratified cross-validation, with standardization of input variables applied within each fold. Performance metrics (accuracy, precision, recall, and ROC AUC) were computed for each fold and reported as mean ± standard deviation. Due to the limited sample size, default hyperparameters were used, and no optimization procedures (e.g., grid search) were performed to avoid overfitting. The 5-fold stratified cross-validation procedure was used to assess model performance. Evaluation metrics included accuracy, precision, recall, and the area under the receiver operating characteristic curve (ROC AUC), reported as mean ± standard deviation. The machine learning procedure was conducted in agreement with the literature for analysis over 20 participants [46]. All statistical analyses were performed using JASP version 0.18 (JASP Team, Amsterdam, The Netherlands), and the machine learning models were developed using Python 3.11 with the Scikit-learn library

3. Results

The analyses conducted in this study were structured to address two main objectives: (1) to compare clinical and functional variables according to sleep quality classification; and (2) to evaluate whether these variables could predict sleep quality using machine learning models. Descriptive statistics and group comparisons (t-tests and effect sizes) were used to explore differences by sleep quality status. Correlational analyses were performed to examine the relationships between physical fitness, body composition, and sleep quality components. Finally, supervised machine learning models were applied to assess the predictive value of selected variables for classifying sleep quality, providing a preliminary test of their potential in data-driven risk identification.

3.1. Descriptives

The study evaluated various health and fitness parameters among 32 individuals, distinguishing between those with good (15) and poor (17) sleep quality. The mean age was 69.28 years. Those with poor sleep demonstrated slightly better performance in the 5-time sit-to-stand test (6.75 s) than those with good sleep quality (7.69 s). These descriptive results provide the context for the subsequent analyses, beginning with comparisons between good and poor sleep quality.

3.2. Sleep Quality Comparisons

Descriptives (means and standard deviations) for good and poor sleep quality are presented in Table 1. Poor sleep quality was associated with lower total sleep (3.35 h) compared to good sleep quality (6.13 h). Individuals with poor sleep also had higher mean ages (73.24 years) and visceral fat (8.29) compared to those with good sleep (69.47 years and 7.47 visceral fat). Despite these differences, both groups had similar heart rates (72.38 vs. 71.82 bpm) and total fat percentages (29.93% vs. 32.13%). Comparing the variables between poor and good sleep quality, it is possible to find that the TUG (t = 2.564; p = 0.016; η2 = 0.476), total fat (kg) (t = 2.592; p = 0.015; η2 = 0.239), and total sleep (t = 6.882; p < 0.001; η2 = 0.675) time significantly differed between groups, where the persons with good sleep quality presented higher scores.
Beyond group differences, we then examined associations between the studied variables to better understand the interrelationships underlying sleep quality.

3.3. Associative Analysis and Machine Learning

Based on the Pearsons correlation test, the total sleep scores presented significant associations with 5TSTS (r = 0.442; p = 0.011), TUG (r = 0.411; p = 0.019), and back stretch (r = 0.406; p = 0.021). Finally, to assess the potential predictive capacity of selected measures, we applied an exploratory logistic regression model with cross-validation.
Given the limited sample size (n = 32), the machine learning analyses were conducted in an exploratory manner to assess whether selected functional and physiological variables could provide preliminary classification of sleep quality. The logistic regression model using 5TSTS and TUG achieved a mean accuracy of 0.76 (±0.15), precision of 0.79 (±0.18), recall of 0.83 (±0.21), and ROC/AUC of 0.74 (±0.16) across 5-fold cross-validation. While the data showed a moderate association between functional lower-limb performance and sleep quality, the findings are tentative given the small and specific sample.

4. Discussion

This study aimed to understand the differences between sleep quality, physical fitness, body composition, cardiovascular measures, and their interplay with sleep quality. It was hypothesized that the variables of functional fitness, body composition, and cardiovascular measures explain sleep quality and its dependency. The results of the present study confirmed the hypothesis; however, while the findings revealed potential associations, they should be interpreted with caution, given the limited statistical power and small sample size. Different variables also explained the total sleep time.

4.1. Sleep Quality Comparisons

When comparing the participants between people with poor and good sleep quality, it is possible to find that the 5TSTS, Back Stretch, and total sleep time significantly differed between groups, where the people with good sleep quality presented higher scores of functional fitness. These exploratory results suggest a positive association between physical fitness levels, particularly in lower body strength and flexibility, and sleep quality, which may contribute to improved sleep patterns. The literature provides supportive information about the relationship between functional fitness and sleep quality. Studies have shown that improvements in functional fitness, including lower limb strength, have been associated with improved sleep outcomes [47,49,50]. Interventions that target lower limb strength, such as resistance training and specific exercises like yoga, have been proven to enhance sleep quality and overall wellbeing [51,52,53]. Lower limb strength plays an important role in mobility, and independence is highlighted as an important factor in supporting a healthy sleep pattern [47,54,55].

4.2. Associative Analysis and Machine Learning

The associative analysis allowed us to highlight the variables that primarily explain the total sleep time. For the total sleep, the Back Stretch, waist girth, 5TSTS, and Visceral Fat explained the sleep time. The sleep quality relationships with waist girth, visceral fat, and total fat can be explained by the body composition interplay with hormonal regulation, inflammation, and metabolic health [56,57]. Additionally, variables like 5TSTS, Back Stretch, and Seat and Reach test reflect aspects of functional fitness and flexibility, and again may influence comfort and relaxation [56,58,59].
The negative impact of body water is possibly explained by the increased urine output, resulting in the need for frequent urination during the night [60,61,62]. This negative effect on the circadian rhythm due to urine production can interfere with sleep continuity, resulting in poor sleep quality [62,63]. As for the 2MW, the literature presents evidence of the positive effects of aerobic exercise on arterial pressure and cardiovascular health [64,65]. Lately, aerobic exercise seems related to good sleep quality, time, efficiency, and latency [66,67,68].
The logistic regression model using 5TSTS and TUG achieved a mean accuracy of 0.76, precision of 0.79, recall of 0.83, and ROC/AUC of 0.74. These results suggest that functional fitness variables, particularly strength-related metrics, may serve as possible meaningful predictors of poor versus good sleep quality in older adults. This is aligned with the literature where body composition [56,57] and functional fitness [56,58,59] seem to be associated with good sleep quality and wellbeing. However, it was not possible to find studies with machine learning analysis to predict sleep quality based on anthropometrics, body composition, and functional fitness. For those reasons, comparisons with other studies regarding the algorithm’s scores were difficult to perform. Anyway, other studies with accelerometers and electrodermal instruments reported that machine learning algorithms may predict sleep quality with a percentage of accuracy between 78% and 84% [69,70], aligned with the present study. The use of a limited number of predictors in the machine learning models allowed for greater interpretability and reduced the risk of overfitting given the small sample size [71]. However, this parsimony may overlook important variables and complex interactions, limiting predictive accuracy. Thus, the findings are preliminary and hypothesis-generating, requiring validation in larger, more diverse samples with broader variable sets.

4.3. Psychophysiological Remarks

Considering a psychophysiological approach, the interplay between lower limb strength and sleep quality can be explained by different mechanisms. Lower limb strength and consequent independence may promote physical activity and tiredness, resulting in relaxation and somnolence [72,73]. Second, the lower limbs’ strength, relation to balance and stability may contribute to a perception of safety due to the reduced risk of falls and injuries during sleep, contributing to better sleep quality [72,74] because lower limb-related physical fitness is associated with reduced levels of pain and discomfort that may disrupt sleep [73]. Altogether, psychologically, these implications may also result in positive mental health and relaxation, resulting in better sleep quality [47,75]. Finally, physical activity and exercise regulate circadian rhythms and release endorphins, which are known to enhance sleep patterns [52,76].

4.4. Strengths, Limitations and Future Studies

This study presents a novel and exploratory approach to understanding the relationship between body composition, functional fitness, and sleep quality in older adults. A key strength lies in the integration of traditional statistical methods with a machine learning algorithm (logistic regression), which provides complementary perspectives on the data and enhances analytical robustness. The study uses validated tools such as the Pittsburgh Sleep Quality Index and BIA-based body composition estimates, alongside functional fitness tests, offering a multidimensional profile of the participants. Furthermore, the use of cross-validation and transparent model evaluation metrics (e.g., ROC AUC, accuracy, precision, recall) strengthens the internal consistency of the machine learning analysis and reflects a commitment to methodological rigor despite the exploratory nature of the work.
It is also possible to point out some of the limitations of this study: (i) this is not an interventional study comparing exercise base therapeutics; (ii) this study did not control biochemical, psychological, or physiological variables; (iii) the daily life routines including physical activity and nutritional habits were not evaluated or controlled; (iv) the sample size and the number of males and the exploratory characteristics of the study did not allow us to conduct sex comparisons with precise outputs. Although logistic regression modeling was performed with only two predictors to avoid overfitting, still limited the power and generalizability of our results, additionally no factor analysis were made to select important variables; (v) the reliance on estimated data from bioimpedance and subjective sleep measures (PSQI) introduces potential measurement bias, additionally, body composition estimates were derived from BIA using proprietary algorithms, which may lack validation across diverse populations, reducing reproducibility compared to reference methods such as DEXA; (vi) the machine learning algorithms were technically valid for application in this dataset. Further, future studies should (i) evaluate the effects of a regular training program on sleep quality; (ii) analyze biochemical, psychological, and physiological variables as well as body composition and physical fitness; (iii) assess the effects of active living and wellbeing lifestyles in sleep quality; (iiii) performing more robust approaches, such as classificatory machine learning approach, that can deal with several characteristics of predictors in the same set of analysis; (iv) recruit larger samples; (iv) mitigate the lack of data collection about bruxism incidence in the participants, which has been reported as a significant co-factor in sleep worsening; (v) employ precise instruments like DEXA and accelerometers to evaluate body composition and sleep quality.

5. Conclusions

This exploratory analysis provides early insight into potential functional predictors of sleep quality, which may inform future confirmatory research with larger samples that physical fitness and body composition may be important in sleep quality. The lower limbs’ strength and upper limbs’ flexibility seem to explain the sleep quality. This study suggests that improving muscle strength and managing body fat levels through regular physical activity may contribute to better sleep quality in older adults. Integrating simple functional fitness assessments (such as the 5TSTS) into routine geriatric evaluations may provide a practical pathway to identify older adults at higher risk of poor sleep quality and associated health outcomes.

Author Contributions

Conceptualization, P.F. and A.M.M.; methodology, T.M.B. and D.P.-M.; software, S.G.E.; validation, D.P.-M. and A.M.M.; formal analysis, P.F., J.E.T. and L.B.; investigation, P.F. and S.G.E.; resources, A.M.M.; data curation, J.E.T., L.B. and D.P.-M.; writing—original draft preparation, P.F.; writing—review and editing, J.E.T., L.B., T.M.B., S.G.E., A.M.M. and D.P.-M.; visualization, S.G.E.; supervision, T.M.B., A.M.M. and D.P.-M.; project administration, A.M.M.; funding acquisition, A.M.M. and T.M.B. 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 and the research project received approval by the Ethical Committee of the Instituto Politécnico de Bragança (number: 2576, approved 25 October 2023).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to possible participants identification.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Al-Sofiani, M.E.; Ganji, S.; Kalyani, R.R. Body Composition Changes in Diabetes and Aging. J. Diabetes Complicat. 2019, 33, 451–459. [Google Scholar] [CrossRef]
  2. Picca, A.; Landi, F.; Cesari, M. Editorial: Age-Related Changes in Body Composition: Mechanisms, Clinical Implications and Possible Treatments. Front. Med. 2020, 7, 230. [Google Scholar] [CrossRef] [PubMed]
  3. Steppan, J.; Barodka, V.; Berkowitz, D.E.; Nyhan, D. Vascular Stiffness and Increased Pulse Pressure in the Aging Cardiovascular System. Cardiol. Res. Pract. 2011, 2011, 263585. [Google Scholar] [CrossRef]
  4. Uangpairoj, P. Shibata Evaluation of Vascular Wall Elasticity of Human Digital Arteries Using Alternating Current-Signal Photoplethysmography. Vasc. Health Risk Manag. 2013, 9, 283–295. [Google Scholar] [CrossRef]
  5. Kawasaki, Y.; Kitamura, E.; Kasai, T. Impact of Body Composition on Sleep and Its Relationship with Sleep Disorders: Current Insights. Nat. Sci. Sleep 2023, 15, 375–388. [Google Scholar] [CrossRef]
  6. De Moraes Coelho, V.; De Oliveira Sinhoroto, C.; Magnabosco, P.; Nasser Figueiredo, V.; Pereira de Almeida Neto, O. Sleep Quality and Body Composition in a Nursing Team. Biosci. J. 2022, 38, e38082. [Google Scholar] [CrossRef]
  7. Casas, R.S.; Pettee Gabriel, K.K.; Kriska, A.M.; Kuller, L.H.; Conroy, M.B. Association of Leisure Physical Activity and Sleep with Cardiovascular Risk Factors in Postmenopausal Women. Menopause 2012, 19, 413–419. [Google Scholar] [CrossRef] [PubMed]
  8. Wu, Y.; Zhai, L.; Zhang, D. Sleep Duration and Obesity among Adults: A Meta-Analysis of Prospective Studies. Sleep Med. 2014, 15, 1456–1462. [Google Scholar] [CrossRef]
  9. Wang, M.; Wei, J.; Yang, X.; Ni, P.; Wang, Y.; Zhao, L.; Deng, W.; Guo, W.; Wang, Q.; Li, T.; et al. The Level of IL-6 Was Associated with Sleep Disturbances in Patients with Major Depressive Disorder. Neuropsychiatr. Dis. Treat. 2019, 15, 1695–1700. [Google Scholar] [CrossRef]
  10. Dolsen, E.A.; Harvey, A.G. IL-6, sTNF-R2, and CRP in the Context of Sleep, Circadian Preference, and Health in Adolescents with Eveningness Chronotype: Cross-Sectional and Longitudinal Treatment Effects. Psychoneuroendocrinology 2021, 129, 105241. [Google Scholar] [CrossRef]
  11. Krueger, J.M.; Obál, F.J.; Fang, J.; Kubota, T.; Taishi, P. The Role of Cytokines in Physiological Sleep Regulation. Ann. N. Y. Acad. Sci. 2001, 933, 211–221. [Google Scholar] [CrossRef] [PubMed]
  12. Majde, J.A.; Krueger, J.M. Links between the Innate Immune System and Sleep. J. Allergy Clin. Immunol. 2005, 116, 1188–1198. [Google Scholar] [CrossRef]
  13. De Nys, L.; Anderson, K.; Ofosu, E.F.; Ryde, G.C.; Connelly, J.; Whittaker, A.C. The Effects of Physical Activity on Cortisol and Sleep: A Systematic Review and Meta-Analysis. Psychoneuroendocrinology 2022, 143, 105843. [Google Scholar] [CrossRef] [PubMed]
  14. Gao, X.; Qiao, Y.; Chen, Q.; Wang, C.; Zhang, P. Effects of Different Types of Exercise on Sleep Quality Based on Pittsburgh Sleep Quality Index in Middle-Aged and Older Adults: A Network Meta-Analysis. J. Clin. Sleep Med. JCSM Off. Publ. Am. Acad. Sleep Med. 2024, 20, 1193–1204. [Google Scholar] [CrossRef]
  15. Piva, T.; Masotti, S.; Raisi, A.; Zerbini, V.; Grazzi, G.; Mazzoni, G.; Belvederi Murri, M.; Mandini, S. Exercise Program for the Management of Anxiety and Depression in Adults and Elderly Subjects: Is It Applicable to Patients with Post-COVID-19 Condition? A Systematic Review and Meta-Analysis. J. Affect. Disord. 2023, 325, 273–281. [Google Scholar] [CrossRef]
  16. Caldwell, H.G.; Coombs, G.B.; Howe, C.A.; Hoiland, R.L.; Patrician, A.; Lucas, S.J.E.; Ainslie, P.N. Evidence for Temperature-Mediated Regional Increases in Cerebral Blood Flow during Exercise. J. Physiol. 2020, 598, 1459–1473. [Google Scholar] [CrossRef]
  17. Aritake-Okada, S.; Tanabe, K.; Mochizuki, Y.; Ochiai, R.; Hibi, M.; Kozuma, K.; Katsuragi, Y.; Ganeko, M.; Takeda, N.; Uchida, S. Diurnal Repeated Exercise Promotes Slow-Wave Activity and Fast-Sigma Power during Sleep with Increase in Body Temperature: A Human Crossover Trial. J. Appl. Physiol. 2019, 127, 168–177. [Google Scholar] [CrossRef]
  18. Alparslan, G.B.; Orsal, Ö.; Unsal, A. Assessment of Sleep Quality and Effects of Relaxation Exercise on Sleep Quality in Patients Hospitalized in Internal Medicine Services in a University Hospital: The Effect of Relaxation Exercises in Patients Hospitalized. Holist. Nurs. Pract. 2016, 30, 155–165. [Google Scholar] [CrossRef]
  19. Stokes, K.A.; Sykes, D.; Gilbert, K.L.; Chen, J.-W.; Frystyk, J. Brief, High Intensity Exercise Alters Serum Ghrelin and Growth Hormone Concentrations but Not IGF-I, IGF-II or IGF-I Bioactivity. Growth Horm. IGF Res. Off. J. Growth Horm. Res. Soc. Int. IGF Res. Soc. 2010, 20, 289–294. [Google Scholar] [CrossRef] [PubMed]
  20. Athanasiou, N.; Bogdanis, G.C.; Mastorakos, G. Endocrine Responses of the Stress System to Different Types of Exercise. Rev. Endocr. Metab. Disord. 2023, 24, 251–266. [Google Scholar] [CrossRef]
  21. Kreivėnaitė, L.; Streckis, V.; Visagurskienė, K.; Buliuolis, A.; Lileikienė, A. Dynamics of Variation in Physical Capacity of Elderly People. Balt. J. Sport Health Sci. 2018, 4. [Google Scholar] [CrossRef]
  22. Chang, W.-D.; Chang, W.-Y.; Lee, C.; Feng, C.-Y. Validity and Reliability of Wii Fit Balance Board for the Assessment of Balance of Healthy Young Adults and the Elderly. J. Phys. Ther. Sci. 2013, 25, 1251–1253. [Google Scholar] [CrossRef]
  23. Jayawardena, R.; Wickramawardhane, P.; Dalpadadu, C.; Hills, A.P.; Ranasignhe, P. The Impact of an Oral Nutritional Supplement on Body Weight Gain in Older Adults With Malnutrition: An Open-Label Randomized Controlled Trial. Trials 2023, 24, 625. [Google Scholar] [CrossRef]
  24. Jiang, S.; Li, P. Current Development in Elderly Comprehensive Assessment and Research Methods. BioMed Res. Int. 2016, 2016, 1–10. [Google Scholar] [CrossRef]
  25. Rodrigues, F.; Teixeira, J.E.; Forte, P. The Reliability of the Timed Up and Go Test among Portuguese Elderly. Healthcare 2023, 11, 928. [Google Scholar] [CrossRef]
  26. Nakagaichi, M.; Anan, Y.; Hikiji, Y.; Uratani, S. Developing an Assessment Based on Physical Fitness Age to Evaluate Motor Function in Frail and Healthy Elderly Women. Clin. Interv. Aging 2018, 13, 179–184. [Google Scholar] [CrossRef] [PubMed]
  27. Honório, S.; Batista, M.; Paulo, R.; Mendes, P.; Serrano, J.; Petrica, J.; Faustino, A.; Santos, J.; Martins, J. Functional Fitness and Nutritional Status of Institutionalized Elderly. Med. Sport 2017, 70, 200–211. [Google Scholar] [CrossRef]
  28. Rikli, R.; Jones, J. Senior Fitness Test Manual, 2nd ed.; Human Kinetics: Champaign, IL, USA, 2013; 200p. [Google Scholar]
  29. Marques, E.A.; Baptista, F.; Santos, R.; Vale, S.; Santos, D.A.; Silva, A.M.; Mota, J.; Sardinha, L.B. Normative Functional Fitness Standards and Trends of Portuguese Older Adults: Cross-Cultural Comparisons. J. Aging Phys. Act. 2014, 22, 126–137. [Google Scholar] [CrossRef] [PubMed]
  30. Monteiro, A.M.; Bartolomeu, R.F.; Forte, P.; Carvalho, J. The Effects of Three Different Types of Training in Functional Fitness and Body Composition in Older Women. J. Sport Health Res. 2019, 11, 289–304. [Google Scholar]
  31. Monteiro, A.M.; Rodrigues, S.; Matos, S.; Teixeira, J.E.; Barbosa, T.M.; Forte, P. The Effects of 32 Weeks of Multicomponent Training with Different Exercises Order in Elderly Women’s Functional Fitness and Body Composition. Medicina 2022, 58, 628. [Google Scholar] [CrossRef]
  32. Zhuang, M.; Shi, J.; Liu, J.; He, X.; Chen, N. Comparing the Efficacy of Low-Load Resistance Exercise Combined with Blood Flow Restriction versus Conventional-Load Resistance Exercise in Chinese Community-Dwelling Older People with Sarcopenic Obesity: A Study Protocol for a Randomised Controlled Trial. BMC Geriatr. 2023, 23, 874. [Google Scholar] [CrossRef]
  33. Jiang, Y.; Tan, S.; Wang, Z.; Guo, Z.; Li, Q.; Wang, J. Aerobic Exercise Training at Maximal Fat Oxidation Intensity Improves Body Composition, Glycemic Control, and Physical Capacity in Older People with Type 2 Diabetes. J. Exerc. Sci. Fit. 2020, 18, 7–13. [Google Scholar] [CrossRef]
  34. Alnawwar, M.A.; Alraddadi, M.I.; Algethmi, R.A.; Salem, G.A.; Salem, M.A.; Alharbi, A.A. The Effect of Physical Activity on Sleep Quality and Sleep Disorder: A Systematic Review. Cureus 2023, 15, e43595. [Google Scholar] [CrossRef]
  35. Aggio, D.A.; Sartini, C.; Papacosta, O.; Lennon, L.T.; Ash, S.; Whincup, P.H.; Wannamethee, S.G.; Jefferis, B.J. Cross-Sectional Associations of Objectively Measured Physical Activity and Sedentary Time with Sarcopenia and Sarcopenic Obesity in Older Men. Prev. Med. 2016, 91, 264–272. [Google Scholar] [CrossRef]
  36. Hernandez-Martinez, J.; González-Castillo, C.; Herrera-Valenzuela, T.; Muñoz-Vásquez, C.; Magnani Branco, B.H.; Valdés-Badilla, P. Association between Physical Activity Habits with Cardiometabolic Variables, Body Composition, and Physical Performance in Chilean Older Women. Int. J. Environ. Res. Public Health 2023, 20, 6688. [Google Scholar] [CrossRef] [PubMed]
  37. Buckinx, F.; Peyrusqué, É.; Granet, J.; Aubertin-Leheudre, M. Impact of Current or Past Physical Activity Level on Functional Capacities and Body Composition among Elderly People: A Cross-Sectional Analysis from the YMCA Study. Arch. Public Health 2021, 79, 50. [Google Scholar] [CrossRef] [PubMed]
  38. Pedrero-Chamizo, R.; Gómez-Cabello, A.; Mélendez, A.; Vila-Maldonado, S.; Espino, L.; Gusi, N.; Villa, G.; Casajús, J.A.; González-Gross, M.; Ara, I. Higher Levels of Physical Fitness Are Associated with a Reduced Risk of Suffering Sarcopenic Obesity and Better Perceived Health among the Elderly. The EXERNET Multi-Center Study. J. Nutr. Health Aging 2015, 19, 211–217. [Google Scholar] [CrossRef] [PubMed]
  39. Shimoda, T.; Suzuki, T.; Tsutsumi, K.; Samukawa, M.; Yoshimura, S.; Ogasawara, K. Association between Physical Activity Levels and Body Composition among Healthy Older Japanese Adults during a Snowy Winter: A Cross-Sectional Study. Int. J. Environ. Res. Public. Health 2020, 17, 5316. [Google Scholar] [CrossRef]
  40. Schilling, R.; Schmidt, S.C.E.; Fiedler, J.; Woll, A. Associations between Physical Activity, Physical Fitness, and Body Composition in Adults Living in Germany: A Cross-Sectional Study. PLoS ONE 2023, 18, e0293555. [Google Scholar] [CrossRef]
  41. Kohanmoo, A.; Kazemi, A.; Zare, M.; Akhlaghi, M. Gender-Specific Link between Sleep Quality and Body Composition Components: A Cross-Sectional Study on the Elderly. Sci. Rep. 2024, 14, 8113. [Google Scholar] [CrossRef]
  42. Tan, X.; Titova, O.E.; Lindberg, E.; Elmståhl, S.; Lind, L.; Schiöth, H.B.; Benedict, C. Association Between Self-Reported Sleep Duration and Body Composition in Middle-Aged and Older Adults. J. Clin. Sleep Med. 2019, 15, 431–435. [Google Scholar] [CrossRef] [PubMed]
  43. Xu, X.; Li, W.; Zou, S.; Li, Y.; Wang, H.; Yan, X.; Du, X.; Zhang, L.; Zhang, Q.; Cheung, T.; et al. Sleep Disturbances and Their Association With Quality of Life in Older Psychiatric Patients During the COVID-19 Pandemic. J. Geriatr. Psychiatry Neurol. 2022, 35, 229–236. [Google Scholar] [CrossRef] [PubMed]
  44. Topouchian, J.; Agnoletti, D.; Blacher, J.; Youssef, A.; Ibanez, I.; Khabouth, J.; Khawaja, S.; Beaino, L.; Asmar, R. Validation of Four Automatic Devices for Self-Measurement of Blood Pressure According to the International Protocol of the European Society of Hypertension. Vasc. Health Risk Manag. 2011, 7, 709–717. [Google Scholar] [CrossRef]
  45. Williams, B.; Mancia, G.; Spiering, W.; Rosei, E.A.; Azizi, M.; Burnier, M.; Clement, D.L.; Coca, A.; De Simone, G.; Dominiczak, A.; et al. 2018 ESC/ESH Guidelines for the Management of Arterial Hypertension. Eur. Heart J. 2018, 39, 3021–3104. [Google Scholar] [CrossRef]
  46. Rajput, D.; Wang, W.-J.; Chen, C.-C. Evaluation of a Decided Sample Size in Machine Learning Applications. BMC Bioinform. 2023, 24, 48. [Google Scholar] [CrossRef]
  47. Fraga, A.; Rodrigues, L.; Catarino, B. Correlation of Lower Limb Muscle Strength with Functional Mobility and Quality of Life in Patients with Multiple Sclerosis. Med. Res. Arch. 2022, 10. [Google Scholar] [CrossRef]
  48. Alcazar, J.; Losa-Reyna, J.; Rodriguez-Lopez, C.; Alfaro-Acha, A.; Rodriguez-Mañas, L.; Ara, I.; García-García, F.J.; Alegre, L.M. The Sit-to-Stand Muscle Power Test: An Easy, Inexpensive and Portable Procedure to Assess Muscle Power in Older People. Exp. Gerontol. 2018, 112, 38–43. [Google Scholar] [CrossRef]
  49. Zhang, H.; Chen, X.; Han, P.; Ma, W.; Zhang, Y.; Song, P.; Wu, Y.; Zhu, Y.; Jiang, Z.; Cai, M.; et al. Mediating Effects of Lower Extremity Function on the Relationship between Night Sleep Duration and Cardiovascular Disease Risk: A Cross-Sectional Study in Elderly Chinese without Cardiovascular Diseases. BMJ Open 2021, 11, e046015. [Google Scholar] [CrossRef]
  50. Antônio Gomes de Resende, N.; Matheus Amarante do, N.; Danilo Rodrigues Pereira da, S.; Raquel Simões Mendes, N.; Josimari Melo de, S.; Marzo Edir Da Silva, G. Effects of Multicomponent Training on Functional Fitness and Quality of Life in Older Women: A Randomized Controlled Trial. Int. J. Sports Exerc. Med. 2019, 5, 126. [Google Scholar] [CrossRef]
  51. Sivaramakrishnan, D.; Fitzsimons, C.; Kelly, P.; Ludwig, K.; Mutrie, N.; Saunders, D.H.; Baker, G. The Effects of Yoga Compared to Active and Inactive Controls on Physical Function and Health Related Quality of Life in Older Adults- Systematic Review and Meta-Analysis of Randomised Controlled Trials. Int. J. Behav. Nutr. Phys. Act. 2019, 16, 33. [Google Scholar] [CrossRef]
  52. Oshima-Saeki, C.; Taniho, Y.; Arita, H.; Fujimoto, E. Lower-Limb Warming Improves Sleep Quality in Elderly People Living in Nursing Homes. Sleep Sci. 2017, 10, 87–91. [Google Scholar] [CrossRef]
  53. Corrêa, H.L.; Moura, S.R.G.; Neves, R.V.P.; Tzanno-Martins, C.; Souza, M.K.; Haro, A.S.; Costa, F.; Silva, J.A.B.; Stone, W.; Honorato, F.S.; et al. Resistance Training Improves Sleep Quality, Redox Balance and Inflammatory Profile in Maintenance Hemodialysis Patients: A Randomized Controlled Trial. Sci. Rep. 2020, 10, 11708. [Google Scholar] [CrossRef]
  54. Azimizadeh, M.J.; Tabatabai Asl, S.M.; Hoseini, S.H. The Effects of an Eight-Week Cawthorne-Cooksey Training Program on Balance and Lower Limb Strength in the Elderly. J. Sport Biomech. 2021, 7, 68–77. [Google Scholar] [CrossRef]
  55. Kato, T.; Ikezoe, T.; Tabara, Y.; Matsuda, F.; Tsuboyama, T.; Ichihashi, N. Differences in Lower Limb Muscle Strength and Balance Ability between Sarcopenia Stages Depend on Sex in Community-Dwelling Older Adults. Aging Clin. Exp. Res. 2022, 34, 527–534. [Google Scholar] [CrossRef] [PubMed]
  56. McCoy, T.; Sochan, A.J.; Spaeth, A.M. The Relationship between Sleep and Physical Activity by Age, Race, and Gender. Rev. Cardiovasc. Med. 2024, 25, 378. [Google Scholar] [CrossRef]
  57. Knobbe, T.J.; Kremer, D.; Eisenga, M.F.; van Londen, M.; Annema, C.; Bültmann, U.; Kema, I.P.; Navis, G.J.; Berger, S.P.; Bakker, S.J.L.; et al. Sleep Quality, Fatigue, Societal Participation and Health-Related Quality of Life in Kidney Transplant Recipients: A Cross-Sectional and Longitudinal Cohort Study. Nephrol. Dial. Transplant. 2023, 39, 74–83. [Google Scholar] [CrossRef]
  58. Li, C.-H.; Chung, M.-H.; Liao, C.-H.; Su, C.-C.; Lin, Y.; Liao, Y.-M. Urinary Incontinence and Sleep Quality in Older Women with Type 2 Diabetes: A Cross-Sectional Study. Int. J. Environ. Res. Public. Health 2022, 19, 15642. [Google Scholar] [CrossRef]
  59. Zhang, J.; Wang, C.; Gong, W.; Peng, H.; Tang, Y.; Li, C.C.; Zhao, W.; Ye, Z.; Lou, T. Association between Sleep Quality and Cardiovascular Damage in Pre-Dialysis Patients with Chronic Kidney Disease. BMC Nephrol. 2014, 15, 131. [Google Scholar] [CrossRef] [PubMed]
  60. Vu, H.M.; Tran, V.T.H.; Hoang, H.Q.; Han, B.; Hoang, B.X. Efficacy and Tolerability of Ich Nieu Khang Dietary Supplement for Overactive Bladder. J. Med. Food 2023, 26, 262–269. [Google Scholar] [CrossRef] [PubMed]
  61. Torimoto, K.; Uchimura, N.; Roitmann, E.; Marumoto, M.; Hirakata, T.; Burtea, T. A Large Survey of Nocturia Related to Sleep Quality and Daytime Quality of Life in a Young Japanese Population: NOCTURNE Study. Neurourol. Urodyn. 2021, 40, 340–347. [Google Scholar] [CrossRef]
  62. Kanammit, P.; Boonchan, T.; Sirisreetreerux, P.; Viseshsindh, W.; Kochakarn, W. Nocturia and Effect on the Quality of Life. A Study at Ramathibodi Hospital. Insight Urol. 2021, 42, 144–153. [Google Scholar] [CrossRef]
  63. Udo, Y.; Nakao, M.; Honjo, H.; Ukimura, O.; Kitakoji, H.; Miki, T. Sleep Duration Is an Independent Factor in Nocturia: Analysis of Bladder Diaries. BJU Int. 2009, 104, 75–79. [Google Scholar] [CrossRef]
  64. Akazawa, N.; Ra, S.; Sugawara, J.; Maeda, S. Influence of Aerobic Exercise Training on Post-Exercise Responses of Aortic Pulse Pressure and Augmentation Pressure in Postmenopausal Women. Front. Physiol. 2015, 6, 268. [Google Scholar] [CrossRef] [PubMed]
  65. Schroeder, E.C.; Ranadive, S.M.; Yan, H.; Lane-Cordova, A.D.; Kappus, R.M.; Cook, M.D.; Fernhall, B. Effect of Acute Maximal Exercise on Vasodilatory Function and Arterial Stiffness in African-American and White Adults. J. Hypertens. 2019, 37, 1262–1268. [Google Scholar] [CrossRef] [PubMed]
  66. Ezati, M.; Keshavarz, M.; Barandouzi, Z.A.; Montazeri, A. The Effect of Regular Aerobic Exercise on Sleep Quality and Fatigue among Female Student Dormitory Residents. BMC Sports Sci. Med. Rehabil. 2020, 12, 44. [Google Scholar] [CrossRef]
  67. Brupbacher, G.; Zander-Schellenberg, T.; Straus, D.; Porschke, H.; Infanger, D.; Gerber, M.; von Känel, R.; Schmidt-Trucksäss, A. The Acute Effects of Aerobic Exercise on Sleep in Patients with Unipolar Depression: A Randomized Controlled Trial. Sleep 2021, 44, zsab177. [Google Scholar] [CrossRef]
  68. Abd El-Kader, S.M.; Al-Jiffri, O.H. Aerobic Exercise Modulates Cytokine Profile and Sleep Quality in Elderly. Afr. Health Sci. 2019, 19, 2198–2207. [Google Scholar] [CrossRef]
  69. Piccini, J.; August, E.; Óskarsdóttir, M.; Arnardóttir, E.S. Using the Electrodermal Activity Signal and Machine Learning for Diagnosing Sleep. Front. Sleep 2023, 2, 1127697. [Google Scholar] [CrossRef]
  70. Sundararajan, K.; Georgievska, S.; te Lindert, B.H.W.; Gehrman, P.R.; Ramautar, J.; Mazzotti, D.R.; Sabia, S.; Weedon, M.N.; van Someren, E.J.W.; Ridder, L.; et al. Sleep Classification from Wrist-Worn Accelerometer Data Using Random Forests. Sci. Rep. 2021, 11, 24. [Google Scholar] [CrossRef] [PubMed]
  71. Han, M.; Cai, D.; Huo, Z.; Shen, Z.; Tang, L.; Yang, S.; Wang, C. Reducing Overfitting Risk in Small-Sample Learning with ANN: A Case of Predicting Graduate Admission Probability. In Communications in Computer and Information Science, Proceedings of the Artificial Intelligence and Machine Learning, Nanjing, China, 25–27 November 2023; Jin, H., Pan, Y., Lu, J., Eds.; Springer Nature: Singapore, 2024; pp. 404–419. [Google Scholar]
  72. Zhang, Y.; Zhao, M.; Zhang, B.; Zhang, K.; Zhou, Z. Acupuncture as an Adjunctive Treatment for Post-Stroke Epilepsy: Protocol for a Randomized Controlled Trial. Front. Neurol. 2021, 12, 711390. [Google Scholar] [CrossRef]
  73. Szlejf, C.; Suemoto, C.K.; Drager, L.F.; Griep, R.H.; Fonseca, M.J.M.; Diniz, M.F.H.S.; Lotufo, P.A.; Benseãor, I.M. Association of Sleep Disturbances with Sarcopenia and Its Defining Components: The ELSA-Brasil Study. Braz. J. Med. Biol. Res. 2021, 54, e11539. [Google Scholar] [CrossRef] [PubMed]
  74. Cauley, J.A.; Hovey, K.M.; Stone, K.L.; Andrews, C.A.; Barbour, K.E.; Hale, L.; Jackson, R.D.; Johnson, K.C.; LeBlanc, E.S.; Li, W.; et al. Characteristics of Self-Reported Sleep and the Risk of Falls and Fractures: The Women’s Health Initiative (WHI). J. Bone Miner. Res. 2019, 34, 464–474. [Google Scholar] [CrossRef] [PubMed]
  75. Fábrega-Cuadros, R.; Aibar-Almazán, A.; Martínez-Amat, A.; Hita-Contreras, F. Impact of Psychological Distress and Sleep Quality on Balance Confidence, Muscle Strength, and Functional Balance in Community-Dwelling Middle-Aged and Older People. J. Clin. Med. 2020, 9, 3059. [Google Scholar] [CrossRef]
  76. Delgado-Floody, P.; Caamaño Navarrete, F.; Chirosa-Ríos, L.; Martínez-Salazar, C.; Vargas, C.A.; Guzmán-Guzmán, I.P. Exercise Training Program Improves Subjective Sleep Quality and Physical Fitness in Severely Obese Bad Sleepers. Int. J. Environ. Res. Public. Health 2022, 19, 13732. [Google Scholar] [CrossRef] [PubMed]
Table 1. Comparisons between sleep quality.
Table 1. Comparisons between sleep quality.
VariablesGood Sleep Quality (n = 15)Poor Sleep Quality (n = 17)Sleep Quality Comparison
Mean (±SD)Mean (±SD)tpη2
Age (yo)69.47 (±5.99)73.24 (±7.34)−1.7240.0950.456
Mass (kg)64.73 (±11.87)67.44 (±9.25)−0.7240.4740.002
Stature (cm)157.49 (±4.30)159.16 (±5.58)−0.9380.3560.013
Rest Heart rate (Bpm)71.82 (±6.26)72.38 (±8.91)1.7420.1970.411
Hand grip (kg)21.67 (±8.61)26.12 (±5.46)0.4110.5260.002
Arm curl (Repetition)21.29 (±4.75)23.30 (±4.00)−0.2040.8400.408
Waist circumference (cm)88.00 (±10.23)89.05 (±9.34)−1.7660.0880.178
Hip circumference (cm)101.53 (±9.68)99.62 (±4.86)−1.3020.2030.634
5TSTS (seconds)7.69 (±1.00)6.75 (±1.08)−0.3030.7640.164
CS30 (repetitions)21.14 (±4.05)22.84 (±3.30)0.6910.4970.138
TUG (seconds)5.96 (±1.04)5.36 (±0.67)2.5640.016 *0.476
Seat and Reach (cm)−2.14 (±7.39)−0.15 (±9.92)−1.3090.2000.733
Back Stretch (cm)−5.21 (±8.40)−14.49 (±11.40)1.9570.0600.188
2MST (Repetitions)180.52 (±22.38)186.69 (±35.45)−0.6370.5290.453
Total Fat (kg)20.94 (±7.33)20.05 (±5.27)2.5920.015 *0.239
Total Fat (%)32.13 (±6.79)29.93 (±5.78)−0.5800.5660.148
Lean Mass (kg)41.23 (±5.60)44.54 (±6.37)0.9900.3300.337
Lean Mass (%)63.73 (±7.54)64.88 (±7.49)−1.5520.1310.157
Body Water (%)47.95 (±4.70)49.33 (±3.83)−0.4300.6700.431
Visceral Fat7.47 (±2.23)8.29 (±3.42)−0.9110.3690.344
MET [KJ]5405.20 (±709.26)5648.75 (±599.06)−0.7920.4350.000
MET [Kcal]1411.87 (±521.07)1477.87 (±463.82)−0.3790.7070.438
Total Sleep6.13 (±1.46)3.35 (±0.61)6.882<0.001 *0.675
Note: yo: years old; Bpm: beats per minute; 5TSTS: Five Times Sit to Stand Test; CS30: Sit to stand test 30 s; TUG: time up and go-test; 2MST: 2 min step test; Kcal: kilocalories; * p < 0.05.
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.

Share and Cite

MDPI and ACS Style

Forte, P.; Encarnação, S.G.; Teixeira, J.E.; Branquinho, L.; Barbosa, T.M.; Monteiro, A.M.; Pecos-Martín, D. Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model. J. Funct. Morphol. Kinesiol. 2025, 10, 337. https://doi.org/10.3390/jfmk10030337

AMA Style

Forte P, Encarnação SG, Teixeira JE, Branquinho L, Barbosa TM, Monteiro AM, Pecos-Martín D. Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model. Journal of Functional Morphology and Kinesiology. 2025; 10(3):337. https://doi.org/10.3390/jfmk10030337

Chicago/Turabian Style

Forte, Pedro, Samuel G. Encarnação, José E. Teixeira, Luís Branquinho, Tiago M. Barbosa, António M. Monteiro, and Daniel Pecos-Martín. 2025. "Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model" Journal of Functional Morphology and Kinesiology 10, no. 3: 337. https://doi.org/10.3390/jfmk10030337

APA Style

Forte, P., Encarnação, S. G., Teixeira, J. E., Branquinho, L., Barbosa, T. M., Monteiro, A. M., & Pecos-Martín, D. (2025). Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model. Journal of Functional Morphology and Kinesiology, 10(3), 337. https://doi.org/10.3390/jfmk10030337

Article Metrics

Back to TopTop