Effects of Intra-Session Exercise Sequence and Circadian Rhythms on Physical Capacity and Sleep Quality in Obese Women

Effects of Intra-Session Exercise Sequence and Circadian Rhythms on Physical Capacity and Sleep Quality in Obese Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Effects of Intra-Session Exercise Sequence and Circadian Rhythms on Physical Capacity and Sleep Quality in Obese Women Yeong-Hyun Cho, Tae-Beom Seo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3936001/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to determine the effects of exercise orders and circadian rhythms on body composition, blood lipids, physical fitness and upper- and lower-extremity muscular functions in adult obese women. Forty-four women with obesity were divided into the obesity control group (OCG), aerobic-resistance exercise in the morning group (ARMG), resistance-aerobic exercise in the morning group (RAMG), aerobic-resistance exercise in the evening group (AREG), and resistance-aerobic exercise in the evening group (RAEG). The combined exercise program consisted of treadmill exercise and weight training, and all participants performed the exercise for 8 weeks. Body weight, body mass, body mass index and fasting glucose were significantly decreased in the RAMG at post. while other body compositions and blood lipids did not change at the post compared the baseline. Upper-and lower-extremity muscular functions and Pittsburgh Sleep Quality Index value was significantly lower in all exercise groups versus OCG. Our findings provide new evidence that resistance-aerobic exercise order in the morning might positively improve body weight, body mass, body mass index and fasting glucose in obese women. In addition, physical fitness and upper- and lower-extremity muscular functions and sleep quality might be improved by performing regular exercise programs regardless of exercise order and timing. Health sciences/Diseases Health sciences/Health care Figures Figure 1 Introduction Obesity is a serious social problem that increases the prevalence of metabolic diseases such as diabetes, high blood pressure, dyslipidemia, hyperinsulinemia, and metabolic syndrome 1 . In recent studies, morning, and evening exercises as well as concurrent aerobic and resistance exercise were highlighted as effective obesity prevention and management methods 2,3 . The circadian rhythms are governed by the body’s rhythm in the suprachiasmatic nucleus within the hypothalamus of the human brain 4 . This rhythm is closely related to various physiological changes such as heart rate, stress, metabolism, sleep quality, immunity, and obesity-related hormone secretion 5,6 suggesting a more effective approach to preventing and managing obesity 7,8 . According to exercise physiology and sports medicine researchers, regular aerobic and/or anaerobic exercise is the most economical and effective way to manage obesity. Aerobic and resistance exercises are mainly used to treat obesity, but the two induce different physical changes 9 . Aerobic exercise is used to reduce body fat by promoting fatty acid oxidation and improving cardiovascular endurance 10 , whereas resistant exercise is known to increase muscle mass and strength by activating anabolic hormones such as testosterone and insulin-like growth factor-1, which synthesize proteins in the skeletal muscles 11 . Many previous studies have applied concurrent aerobic and resistance training programs to improve physical fitness and body composition in obese people and reported its efficacy at regulating body fat, muscle mass, blood lipid concentrations, and anabolic hormones 12 . However, several studies have reported the interference effects of the sequence of concurrent exercises can counteract the positive effects induced by a single exercise 13,14 . In terms of circadian rhythms and exercise, evening exercise significantly increases the secretion of neurotransmitters and muscle functions compared to morning exercise 15,16 . Previous study observed that anaerobic exercise capacity was higher in the afternoon than in the morning and recommended exercising in the afternoon accordingly 17 . In contrast, other studies reported that exercise performance was more effective at 6:00 AM than at 6:00 PM. Based on these results, circadian rhythms and exercise order are believed to be therapeutic methods for preventing obesity. However, the effect of concurrent exercise sequence in the morning or evening on obesity improvement and prevention remains unclear. We hypothesized that exercise performance order in the morning or evening could change the quality of life of obese women by promoting body composition, muscular functions, and sleep quality. Therefore, this study aimed to investigate the effect of intra-session exercise sequence and circadian rhythms on physical fitness, upper- and lower-extremity muscle functions, and sleep quality in obese women. Results Change in anthropometric characteristics and body composition To examine the differences in body composition according to circadian rhythms and exercise order, we identified anthropometric characteristics, body composition, and hip and waist circumferences (Table 3). In comparison between pre-experimental groups, anthropometric characteristics, body composition, and hip and waist circumferences were no significant (all baseline p-values >0.05). At Post, body weight (p<0.003), body fat mass (p<0.010) and body mass index (p<0.002) were significantly decreased in the RAMG, while anthropometric characteristics, body composition, and hip and waist circumferences did not change significantly in the OCG. In addition, % fat, fat-free mass, waist circumference, hip circumference, and waist-hip ratio were not significantly different all groups. There was a significant group × time interaction in the body weight (p<0.004), body fat mass (p<0.042) and body mass index (p<0.003) except for percent body fat (p<0.984), free fat mass (p<0.275), waist and hip circumference (p<0.731, p<0.991, respectively), and waist-hip ratio (p<0.169). Change in basic physical fitness We measured various physical fitness parameters to identify maximal strength, muscular endurance, flexibility, and cardiorespiratory endurance according to circadian rhythms and exercise order (Table 4). In comparison between pre-experimental groups, physical fitness parameters were no significant (all baseline p-values >0.05). At Post, back strength was significantly increased in the ARMG (p<0.048), RAMG (p<0.021), AREG (p<0.004). Sit up was significantly increased in the AREG (p<0.001) and RAEG (p<0.006). Physical efficiency index was significantly increased in the RAMG (p<0.025) and RAEG (p<0.050), while physical fitness parameters did not change significantly in the OCG. There was a significant group × time interaction in the sit up (p<0.046) and sit and reach (p<0.049) except for grip and back strength (p<0.827, p<0.377, respectively) and physical efficiency index (p<0.088). Change in Upper and lower extremity muscle functions To examine upper- and lower-extremity muscle function, we performed the FAPT, SAPT, TFET, vertical jump, and sit-to-stand tests (Table 5). There was no significant difference at baseline between groups in upper- and lower-extremity muscle functions (all baseline p-values >0.05). At Post, front abdominal and right-side abdominal power test were significantly increased in the ARMG (p<0.031, p<0.031, respectively), RAMG (p<0.003, p<0.022, respectively), AREG (p<0.003, p<0.034, respectively). Left side abdominal power test, trunk flexor endurance test and sit to stand test were significantly increased in ARMG (p<0.041, p<0.016, p<0.008, respectively), RAMG (p<0.006, p<0.001, p<0.001, respectively), AREG (p<0.006, p<0.013, p<0.001, respectively), RAEG (p<0.001, p<0.011, p<0.013, respectively), while all upper- and lower-extremity muscle functions did not change in the OCG. There was a significant group × time interaction in the front abdominal power test (p<0.018), trunk flexor endurance test (p<0.006) and sit to stand (p<0.024) except for left and right abdominal power test (p<0.373, p<0.925, respectively) and vertical jump (p<0.372). Change in blood lipids and Pittsburgh sleep quality index We measured the levels of TC, TG, HDL-C, LDL-C, and FG in the blood and PSQI values to identify blood lipids and Pittsburgh sleep quality index according to circadian rhythms and exercise order (Table 6). There was no significant difference at baseline between groups in blood lipid and sleep quality index (all baseline p-values >0.05). At Post, Pittsburgh sleep quality index was significantly decreased in the ARMG (p<0.003), RAMG (p<0.010), AREG (p<0.008), RAEG (p<0.010), while OCG did not change significantly different. All blood lipid s was no significantly different in all groups except for fasting glucose. fasting glucose was significantly decreased only the ARMG (p0.127) except for Pittsburgh sleep quality index (p<0.015). Disscusion Recent clinical studies on the effect of obesity prevention reported that exercise order and circadian rhythms can improve body composition, physical fitness, sleep quality, cardiovascular disease–related factors, and inflammation 18 , 19 . However, there is somewhat controversial. Thus, we attempted to demonstrate the effect of exercise order and circadian rhythms on health-related physical fitness and cardiovascular disease–related risk factors in obese adult women. The major findings indicated that resistance-aerobic exercise order in the morning was improved body composition. In addition, physical fitness and upper-and lower-extremity muscular functions were increased regardless of exercise order and circadian rhythms. Our study confirmed the effects of anthropometric characteristics and body composition, hip- and waist-circumference according to circadian rhythms and exercise order. Percent body fat, fat free mass, hip and waist circumference, and waist-to-hip ratio were not significantly improved all groups. While body weight, body fat mass and body mass index were significantly decreased in the RAMG. Several previous studies suggested that morning exercise reduces body weight, body fat mass and body mass index in overweight/obese women compared to evening exercise 20 , 21 . In additional, early exercise (sessions completed between 7:00 and 11:59 am) was significantly decreased more body weight than late exercise (sessions completed between 3:00 and 7:00 pm) 10 months after applying exercise intervention in female with overweight and obese 22 . Resistance exercise performed before aerobic exercise has been reported to increase fat oxidation and energy consumption 23 . It has also been shown to increase the concentrations of fatty acids, glycerol, and growth hormone 24 . The results of these previous studies support our findings that aerobic exercise performed after resistance exercise in the morning improved body weight, body fat mass, and body mass index. This cause can be attributed that fasting aerobic exercise in the morning accelerated fatty acid oxidation compared to the evening exercise group performed after carbohydrate intake 25 , 26 . Moreover, resistance exercise before aerobic exercise is thought to contribute to increased body temperature and promotion of fatty acid oxidation by excess post oxygen consumption (EPOC) 27 . However, the study found no oxygen intake, body temperature, or fatty acids. Therefore, further research is needed to overcome the limitations of this study. Our study confirmed the effects of basic physical fitness and upper-and lower-extremity muscular functions according to circadian rhythms and exercise order. Strength, endurance and upper-and lower- extremity muscular functions tests were significantly improved almost all exercise groups after intervention. Meta-analysis study reported that concurrent endurance and resistance exercises had a negative effect on muscle hypertrophy, strength, and power depending on frequency and duration 28 . In contrast, alteration of intra-session exercise sequence (endurance exercise after resistance exercise) has been reported to have a positive effect on changes in physical fitness, muscle strength, and insulin sensitivity 29 , and exercise capacity and performance are higher in the evening than those in the morning because body temperature and heart rate are increased to an appropriate level 30 . In another previous studies, morning versus evening exercise training was reported to lead to improvement of physical fitness of subjects, but there was no significant time effect 31 . These previous studies consistent with our findings that regular exercise improved physical strength and upper and lower extremity muscle functions regardless of exercise order and timing. However, changes in physical fitness and muscular functions according to intra-session exercise sequence and circadian rhythms are still controversial. These causes are not well documented that exercise sequence during day and night may activate protein metabolism including AMPK and mTOR signaling pathway and hormone secretion including cortisol and melatonin 16 , 32 . Thus, the limitation of this study is that the molecular biological interference effect according to the exercise sequence and the hormonal change during day and night could not be confirmed. In future study, we believe that the perspective of protein and hormone synthesis on intra-session exercise sequence and circadian rhythms are needed to analyze. Our study investigated blood lipids and sleep quality according to exercise orders and circadian rhythms. Specifically, TC, TG, HDL-C, LDL-C levels were not significantly changed in all groups except for fasting glucose. Fasting glucose was significantly decreased only the ARMG. However, the fasting glucose level is within the normal range and does not mean much. In addition, Pittsburgh sleep quality index was improved in all exercise groups compared the obesity control group. Looking at previous studies that confirmed changes in blood lipids according to exercise sequence and circadian rhythms, exercise ameliorated blood lipids with no difference between morning and evening exercise 33 . Also, regular exercise tends to improve blood lipid regardless of the exercise sequence 34 . It has been known that sleep quality can be changed by circadian rhythm and exercise. Previous study reported that combined endurance and resistance exercise was improved the PSQI value 35 . Also, regular exercise did not differ sleep quality between the two groups (morning vs evening) 31 . The results of previous studies support our findings that concurrent aerobic and resistance exercise performed in the morning or evening might effectively improve sleep quality in obese women. The reason is that circadian rhythms and physical activity are closely related to the sleep-wake cycle, bring about a positive effect on melatonin and cortisol curves 36 . Given the findings reported in previous and present studies, circadian rhythms and exercise order play an important role in improving body composition, physical fitness and sleep quality in obese women. However, this study has two limitations. First, we could not confirm hormonal and molecular biological interference effects and second, we could not recruit enough participants to generalize the study’s results. Thus, in future studies, it will be necessary to perform new experiments by mechanism study and recruiting sufficient subjects to enable generalization of the findings. Conclusions We investigated whether the sequence of aerobic and resistance exercises according to circadian rhythms could improve body composition, upper- and lower-extremity muscle functions, blood lipids and Pittsburgh sleep quality index in obese women. Our findings provide new evidence that resistance exercise in the morning followed by aerobic exercise might promote fat metabolism. Furthermore, physical fitness, upper and lower extremity muscular functions, and quality of sleep might be improved by performing regular exercise regardless of order and timing. Material & Methods Participants and Study Design The study design is presented in Fig. 1. This study recruited obese women (n = 55) who had a body fat percentage of 30% or more and had not been diagnosed with cardiovascular or musculoskeletal diseases in the previous 6 months. Ultimately, fifty participations participated in this study according to the exclusion criteria. As shown in Table 1, the participants were randomly divided into the aerobic-resistance exercise in the morning group (ARMG, n = 10), resistance-aerobic exercise in the morning group (RAMG, n = 10), aerobic-resistance exercise in the evening group (AREG, n = 10), resistance-aerobic exercise in the evening group (RAEG, n = 10), and the obesity control group (OCG, n = 10). Three participants in the ARMG (n = 2) and RAMG (n = 1) withdrew from the study for personal reasons. Three participants (two in the AREG, one in the RAEG) with an exercise participation rate of less than 80% were excluded from the analysis. In the baseline-test, body weight (F = .113, p = .977), body fat mass (F = .410, p = .800), % body fat (F = .751, p = .563), fat-free mass (F = .041, p = .997), or body mass index (F = .277, p = .891) were not significantly difference between groups. Written informed consent was obtained from all participants after they were informed about the study aims and the potential risks derived from the tests and methods. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Jeju National University Institutional Review Board (JJNU-IRB-2021-045-001). Combined exercise intervention This study applied a combined exercise program that was performed 3 days a week for 8 weeks, and the different groups followed different orders of aerobic and resistance exercise in the morning or evening (Table 2). All participants had a 1-week adaptation exercise period before the main experiment, following which the maximum heart rate and 1 repetition max (1RM) were measured to set the exercise intensity for participants before starting the exercise program. The aerobic exercise intensity was set using the Karvonen formula (206.9 - (0.67 × age)), and treadmill exercise was performed for 30 min at the most effective 60–70% maximum heart rate (HRmax) intensity for fat burning 37 . The resistance exercise intensity was set up using the 1RM indirect estimation formula (1RM = wt + (wt × 0.025 × reps)). Resistance exercise was performed by applying the progressive overload principle at 0–4 weeks (60–80% 1RM) and 5–8 weeks (70–80% 1RM). The resistance exercise consisted of exercises involving push-and-pull and weight bearing, and all participants performed three sets (8–15 RM) with a 2-min rest between sets using concentric contraction for 1 s and eccentric contraction for 2 s. Intensities in treadmill and resistance exercises were equally applied to all exercise groups. Resistance exercises used weight training machines to minimize the possibility of injury. Dynamic stretching using foam rollers was performed for 5 min during warm-up and cool-down to induce the best condition and rapid muscle fatigue recovery, respectively. The morning exercise group performed an exercise program from 7 AM to 08:30 AM, while the evening exercise group performed an exercise program from 7 PM to 08:30 PM under the supervision of a professional trainer. Measurements Anthropometric and body composition measurements Physique and body composition were measured using a height and weight auto measuring scale (DS-103M, Dong San Jenix, Seoul, Korea) and a body composition analyzer (Inbody 770; Inbody, Seoul, Korea). The participants visited the laboratory for pre-prandial analyses at 9 AM three times during the study (before and 4 and 8 weeks after) for 8 h. Waist circumference and hip circumference were measured using flexible metric measuring tape. Hip circumference was measured at the level of the maximum extension of the buttocks posteriorly in a horizontal plane and waist circumference was measured around the abdomen at the level of the umbilicus. Waist- hip ratio (WHR) was calculated as waist circumference divided by hip circumference. Basic physical fitness Grip and back strengths were measured using a digital dynamometer (T.K.K. 5101 and T.K.K. 5402; TAKEI, Tokyo, Japan). It was measured twice, and the maximum value was recorded in kilograms. Muscular endurance and flexibility were assessed using a sit-up device (T.K.K. 5505; TAKEI). Cardiorespiratory endurance was measured using the physical efficiency index (PEI) score through the Havard step test. The PEI calculation using the long form equation - fitness index formula is as follows: . Blood lipids The blood lipids including fasting glucose (FG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), were measured at baseline and post-exercise. FG levels were analyzed using a disposable blood collection needle (Accu-Chek®Softclix; Roche, Mannheim, Germany) and a blood glucose meter (Accu-Chek®Guide; Roche). TC, TG, HDL-C, and LDL-C levels were measured using a Mission Cholesterol Meter (Acon Laboratories, Inc., San Diego, CA, USA). Upper extremity muscle function The abdominal power test was performed according to a previously reported method 38 . This test consists of front and side abdominal power tests (FAPT and SAPT). The FAPT was performed as follows: the participant laid down while holding the medicine ball (2% body weight) with the knee bent at 90° and both arms spread on the mat. The SAPT was performed as follows: the participant sat on the mat (knee angle, 90°; hip angle, 45°), held the medicine ball (2% body weight), and the measurements were taken twice by turning the upper body 90° outward in the opposite direction and then inward again to throw the medicine ball from the knee line. The maximum distance between the FAPT and SAPT was measured from the toe of the participant to the point where the ball fell. The trunk flexor endurance test (TFET) was performed to evaluate abdominal muscle endurance. As described previously 39 , the ankle was fixed to the floor with the hip and knee bent at 90° and the arms crossed in front of the chest. Thereafter, the time (in seconds) required to remove the support and maintain the waist angle at 60° was recorded. Lower extremity muscle function The vertical jump test was performed twice using a digital vertical jump device (DW771A; SKARO, Seoul, Korea), and the maximum height (in centimeters) was measured. Fatigue was minimized by taking a break of approximately 2 min after the first vertical jump as previously described 40 . For the measurement, the participants sat on a 43-cm-high chair, crossed their arms at chest level, put their palms on their shoulders, and sat down and stood up for 30 s until their knees were completely straight. The number of repetitions performed in 30 s was recorded. Morningness-eveningness questionnaire and Pittsburgh sleep quality index The identification of circadian rhythm types developed previously was translated into Korean (MEQ-K) and applied in the present study 41,42 . The Cronbach's α coefficient for the item internal consistency of the MEQ-K was 0.77, and the reliability and validity were verified with a sample fit of 0.847. The MEQ consists of 19 questions, and a score of 0–6 points is assigned to each question. The results were divided into five categories according to the scores received. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) before and 4 and 8 weeks after the experiment. The PSQI subjectively evaluates sleep quality, including amount, depth, and comfort. This study used the Korean version of the PSQI (K-PSQI) 43 . The K-PSQI showed high reliability in terms of internal consistency (Cronbach’s α = 0.84). The PSQI is divided into seven subdomains (subjective sleep quality, sleep latency, sleep time, usual sleep efficiency, sleep disturbance, use of sleeping pills, and daytime dysfunction), each with a score of 0–3. Therefore, the higher the average score over the seven domains, the poorer the sleep quality. Statistical analysis The data were analyzed using SPSS version 23 (IBM, Armonk, NY, USA), with significance threshold of p < 0.05. All data are presented as mean ± standard deviations. To test if five groups presented similar characteristics at baseline, a between-group comparison was conducted using one-way ANOVA. Anthropometric characteristics and body composition, physical fitness, Upper and lower extremity muscle functions, and blood lipid and Pittsburgh sleep quality index data were analyzed with a two-way repeated-measures ANOVA that included between-subjects factor of groups and the within-subjects’ factors of time (Baseline vs Post repeated- measures analysis). Declarations Acknowledgments The authors have no acknowledgment. Author contributions Y.H.C. contributed to the conceptualization, data analysis & interpretation, and writing of the manuscript. T.B.S. takes responsibility for data integrity and contributed to the study design, interpretation of the data and revision of the manuscript. Y.H.C. conceptualized, interpreted the data, and revised the manuscript. All authors have reviewed and the approved the final version of the manuscript. Data availability The datasets generated and/or analyzed during the current study are available on Figshare. The URL is https://figshare.com/s/ef211a68a712bf1c639e. The DOI is http://doi.org/https://doi.org/10.6084/m9.figshare.25241926. References Consitt LA, Bell JA, Houmard JA. Intramuscular lipid metabolism, insulin action, and obesity. IUBMB Life. 2009;61(1):47-55. Brooker PG, Gomersall SR, King NA, Leveritt MD. The feasibility and acceptability of morning versus evening exercise for overweight and obese adults: A randomized controlled trial. Contemp Clin Trials Commun. 2019;14:100320. Wheeler MJ, Dunstan DW, Ellis KA, et al. Effect of Morning Exercise With or Without Breaks in Prolonged Sitting on Blood Pressure in Older Overweight/Obese Adults Evidence for Sex Differences. Hypertension. 2019;73(4):859-867. Engin A. Circadian Rhythms in Diet-Induced Obesity. Adv Exp Med Biol. 2017;960:19-52. Panda S. Circadian physiology of metabolism. Science. 2016;354(6315):1008-1015. Peek CB, Levine DC, Cedernaes J, et al. Circadian Clock Interaction with HIF1alpha Mediates Oxygenic Metabolism and Anaerobic Glycolysis in Skeletal Muscle. Cell Metab. 2017;25(1):86-92. Broussard JL, Van Cauter E. Disturbances of sleep and circadian rhythms: novel risk factors for obesity. Curr Opin Endocrinol Diabetes Obes. 2016;23(5):353-359. Li Y, Ma J, Yao K, et al. Circadian rhythms and obesity: Timekeeping governs lipid metabolism. J Pineal Res. 2020;69(3):e12682. Villareal DT, Aguirre L, Gurney AB, et al. Aerobic or resistance exercise, or both, in dieting obese older adults. New England Journal of Medicine. 2017;376(20):1943-1955. Chapman SB, Aslan S, Spence JS, et al. Shorter term aerobic exercise improves brain, cognition, and cardiovascular fitness in aging. Front Aging Neurosci. 2013;5:75. Mitchell CJ, Churchward-Venne TA, Bellamy L, Parise G, Baker SK, Phillips SM. Muscular and Systemic Correlates of Resistance Training-Induced Muscle Hypertrophy. Plos One. 2013;8(10). Wilson JM, Marin PJ, Rhea MR, Wilson SM, Loenneke JP, Anderson JC. Concurrent training: a meta-analysis examining interference of aerobic and resistance exercises. J Strength Cond Res. 2012;26(8):2293-2307. Rosa C, Vilaca-Alves J, Fernandes HM, Saavedra FJ, Pinto RS, dos Reis VM. Order effects of combined strength and endurance training on testosterone, cortisol, growth hormone, and IGF-1 binding protein 3 in concurrently trained men. J Strength Cond Res. 2015;29(1):74-79. Drummond MJ, Vehrs PR, Schaalje GB, Parcell AC. Aerobic and resistance exercise sequence affects excess postexercise oxygen consumption. J Strength Cond Res. 2005;19(2):332-337. Kim HK, Konishi M, Takahashi M, et al. Effects of Acute Endurance Exercise Performed in the Morning and Evening on Inflammatory Cytokine and Metabolic Hormone Responses. Plos One. 2015;10(9):e0137567. Kuusmaa M, Schumann M, Sedliak M, et al. Effects of morning versus evening combined strength and endurance training on physical performance, muscle hypertrophy, and serum hormone concentrations. Appl Physiol Nutr Metab. 2016;41(12):1285-1294. Souissi M, Chtourou H, Zrane A, et al. Effect of time-of-day of aerobic maximal exercise on the sleep quality of trained subjects. Biological Rhythm Research. 2012;43(3):323-330. Chen T, Lin J, Lin Y, et al. Effects of aerobic exercise and resistance exercise on physical indexes and cardiovascular risk factors in obese and overweight school-age children: A systematic review and meta-analysis. Plos One. 2021;16(9):e0257150. Liu H, Liu S, Wang K, et al. Time-Dependent Effects of Physical Activity on Cardiovascular Risk Factors in Adults: A Systematic Review. Int J Environ Res Public Health. 2022;19(21). Arciero PJ, Ives SJ, Mohr AE, et al. Morning Exercise Reduces Abdominal Fat and Blood Pressure in Women; Evening Exercise Increases Muscular Performance in Women and Lowers Blood Pressure in Men. Front Physiol. 2022;13:893783. Alizadeh Z, Younespour S, Rajabian Tabesh M, Haghravan S. Comparison between the effect of 6 weeks of morning or evening aerobic exercise on appetite and anthropometric indices: a randomized controlled trial. Clinical obesity. 2017;7(3):157-165. Willis EA, Creasy SA, Honas JJ, Melanson EL, Donnelly JE. The effects of exercise session timing on weight loss and components of energy balance: midwest exercise trial 2. Int J Obes (Lond). 2020;44(1):114-124. Kang J, Rashti SL, Tranchina CP, Ratamess NA, Faigenbaum AD, Hoffman JR. Effect of preceding resistance exercise on metabolism during subsequent aerobic session. Eur J Appl Physiol. 2009;107(1):43-50. Goto K, Ishii N, Sugihara S, Yoshioka T, Takamatsu K. Effects of resistance exercise on lipolysis during subsequent submaximal exercise. Med Sci Sports Exerc. 2007;39(2):308-315. Bennard P, Doucet E. Acute effects of exercise timing and breakfast meal glycemic index on exercise-induced fat oxidation. Appl Physiol Nutr Metab. 2006;31(5):502-511. De Bock K, Richter EA, Russell AP, et al. Exercise in the fasted state facilitates fibre type-specific intramyocellular lipid breakdown and stimulates glycogen resynthesis in humans. J Physiol. 2005;564(Pt 2):649-660. Kang J, Ratamess N. Which comes first? Resistance before aerobic exercise or vice versa? ACSM's Health & Fitness Journal. 2014;18(1):9-14. Wilkinson SB, Phillips SM, Atherton PJ, et al. Differential effects of resistance and endurance exercise in the fed state on signalling molecule phosphorylation and protein synthesis in human muscle. J Physiol. 2008;586(15):3701-3717. Donges CE, Duffield R, Guelfi KJ, Smith GC, Adams DR, Edge JA. Comparative effects of single-mode vs. duration-matched concurrent exercise training on body composition, low-grade inflammation, and glucose regulation in sedentary, overweight, middle-aged men. Appl Physiol Nutr Metab. 2013;38(7):779-788. Ezagouri S, Zwighaft Z, Sobel J, et al. Physiological and Molecular Dissection of Daily Variance in Exercise Capacity. Cell Metab. 2019;30(1):78-91 e74. Saidi O, Colin E, Rance M, Doré E, Pereira B, Duché P. Effect of morning versus evening exercise training on sleep, physical activity, fitness, fatigue and quality of life in overweight and obese adults. Chronobiol Int. 2021;38(11):1537-1548. Fyfe JJ, Bishop DJ, Stepto NK. Interference between Concurrent Resistance and Endurance Exercise: Molecular Bases and the Role of Individual Training Variables. Sports Med. 2014;44(6):743-762. Moholdt T, Parr EB, Devlin BL, Giskeodegard GF, Hawley JA. Effect of high-fat diet and morning or evening exercise on lipoprotein subfraction profiles: secondary analysis of a randomised trial. Sci Rep-Uk. 2023;13(1). Eklund D, Hakkinen A, Laukkanen JA, Balandzic M, Nyman K, Hakkinen K. Fitness, body composition and blood lipids following 3 concurrent strength and endurance training modes. Appl Physiol Nutr Metab. 2016;41(7):767-774. Bonato M, La Torre A, Saresella M, Marventano I, Merati G, Vitale JA. Salivary cortisol concentration after high-intensity interval exercise: Time of day and chronotype effect. Chronobiol Int. 2017;34(6):698-707. Taheri M, Irandoust K. The exercise-induced weight loss improves self-reported quality of sleep in obese elderly women with sleep disorders. Sleep Hypn. 2018;20(1):54-59. Gellish RL, Goslin BR, Olson RE, McDonald A, Russi GD, Moudgil VK. Longitudinal modeling of the relationship between age and maximal heart rate. Med Sci Sport Exer. 2007;39(5):822-829. Cowley PM, Swensen TC. Development and reliability of two core stability field tests. J Strength Cond Res. 2008;22(2):619-624. Arman N, Tokgoz G, Seyit H, Karabulut M. The effects of core stabilization exercise program in obese people awaiting bariatric surgery: A randomized controlled study. Complement Ther Clin Pract. 2021;43:101342. Martinez-Hernandez U, Dehghani-Sanij AA. Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. Pattern Recogn Lett. 2019;118:32-41. Horne JA, Ostberg O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int J Chronobiol. 1976;4(2):97-110. Lee JH, Kim SJ, Lee SY, Jang KH, Kim IS, Duffy JF. Reliability and validity of the Korean version of Morningness-Eveningness Questionnaire in adults aged 20-39 years. Chronobiol Int. 2014;31(4):479-486. Sohn SI, Kim DH, Lee MY, Cho YW. The reliability and validity of the Korean version of the Pittsburgh Sleep Quality Index. Sleep Breath. 2012;16(3):803-812. Tables Table 3. Baseline to post-intervention comparison of body composition and waist and hip circumference Parameter Baseline Post p-value G T G×T Body weight (kg) OCG 68.55±17.41 68.34±17.87 .977 .001 .004 ARMG 65.07±12.39 64.11±12.02 RAMG 68.31±8.13 64.81±7.64* AREG 66.61±14.62 66.41±15.22 RAEG 65.85±9.79 64.94±9.85 Body fat mass (kg) OCG 27.25±11.37 27.04±11.72 .795 .001 .042 ARMG 23.22±6.70 22.53±6.12 RAMG 25.55±5.49 23.06±5.69* AREG 25.16±7.79 24.76±8.10 RAEG 23.70±6.27 22.90±6.12 Percent body fat (%) OCG 38.35±5.70 38.30±5.96 .551 .214 .984 ARMG 35.37±4.21 34.80±3.95 RAMG 35.82±5.17 35.15±4.38 AREG 37.27±4.02 36.72±4.41 RAEG 35.67±5.40 35.10±5.08 Fat-free mass (kg) OCG 41.30±6.39 41.37±6.41 .997 .235 .275 ARMG 41.85±6.76 41.57±6.86 RAMG 42.76±3.08 41.86±2.77 AREG 41.45±7.60 41.65±7.75 RAEG 42.15±5.35 42.03±5.19 Body mass index (kg∙m 2 ) OCG 25.65±5.88 25.56±6.03 .898 .001 .003 ARMG 24.73±3.50 24.37±3.46 RAMG 26.01±3.01 24.65±3.02* AREG 26.06±5.11 26.48±5.29 RAEG 24.90±3.52 24.54±3.43 Waist circumference (cm) OCG 83.10±13.71 80.80±15.03 .951 .001 .731 ARMG 80.95±8.08 75.36±9.93 RAMG 81.83±5.50 76.72±7.88 AREG 85.20±13.75 77.37±16.92 RAEG 82.03±8.78 77.46±11.70 Hip circumference (cm) OCG 100.65±9.70 98.16±12.09 .955 .018 .991 ARMG 98.23±8.36 94.65±10.38 RAMG 99.62±7.16 95.18±11.11 AREG 100.28±8.72 97.57±13.24 RAEG 100.67±5.98 97.20±11.45 Waist-hip ratio (%) OCG 0.82±0.08 0.81±0.07 .980 .002 .169 ARMG 0.82±0.06 0.79±0.03 RAMG 0.82±0.04 0.81±0.08 AREG 0.84±0.07 0.78±0.08 RAEG 0.81±0.06 0.79±0.04 Values are presented as mean± standard deviation. OCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G×T, group× time effect. Significantly different from Baseline, *P<0.05 Table 4. Baseline to post-intervention comparison of basic physical fitness Parameter Baseline Post p-value G T G×T Grip strength (kg) OCG 23.95±2.65 24.03±2.83 .764 .027 .827 ARMG 24.69±5.23 25.98±3.89 RAMG 25.63±2.81 26.91±2.60 AREG 25.46±6.51 26.64±6.05 RAEG 25.75±4.99 26.46±5.89 Back strength (kg) OCG 55.31±13.11 60.51±7.12 .763 .001 .377 ARMG 48.78±13.79 60.85±13.03* RAMG 58.09±16.23 69.46±10.82* AREG 52.43±16.88 70.53±19.34* RAEG 57.29±27.24 63.51±19.41 Sit up (reps) OCG 15.00±9.32 15.66±8.09 .104 .001 .046 ARMG 18.87±6.03 22.00±5.90 RAMG 15.62±8.88 19.62±6.99 AREG 22.25±8.90 29.25±6.92* RAEG 16.77±13.05 25.27±8.87* Physical efficiency index (score) OCG 47.22±2.75 47.21±2.86 .564 .159 .088 ARMG 46.90±2.17 47.34±4.90 RAMG 47.28±2.14 49.12±2.30* AREG 46.54±1.89 48.78±3.90 RAEG 44.68±4.44 46.86±5.62* Values are presented as mean± standard deviation. OCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G×T, group×time effect. Significantly different from Baseline, *P<0.05 Table 5 . Baseline to post-intervention comparison of upper and lower extremity muscle functions Parameter Baseline Post p-value G T G×T Front abdominal power test (cm) OCG 1796.11±678.29 1749.44±740.90 .853 .001 .018 ARMG 1506.25±530.31 2083.12±810.48* RAMG 1683.12±514.12 2165.00±501.82* AREG 1767.50±576.95 2253.43±755.89* RAEG 1988.88±896.22 2126.38±766.03 Left side abdominal power test (cm) OCG 1968.33±620.00 2147.33±335.28 .470 .001 .373 ARMG 1998.75±460.51 2470.00±670.91* RAMG 2095.00±668.77 2538.75±396.60* AREG 2363.12±702.54 2688.43±661.50* RAEG 2076.11±594.19 2549.94±403.18*** Right side abdominal power test (cm) OCG 1836.66±528.75 2188.88±359.75 .277 .001 .925 ARMG 2096.25±456.10 2490.00±662.26* RAMG 2063.75±570.83 2432.87±470.75* AREG 2345.00±757.92 2820.31±639.09* RAEG 2107.77±708.78 2391.77±426.76 Trunk flexor endurance test (cm) OCG 37.02±13.07 33.69±13.88 .031 .001 .006 ARMG 46.40±26.11 88.65±39.00* RAMG 34.93±15.81 66.73±30.62** AREG 60.34±51.27 116.41±63.77* RAEG 50.19±39.10 106.73±76.40* Vertical Jump (cm) OCG 23.61±4.10 23.58±5.55 .852 .001 .372 ARMG 23.62±3.17 25.75±2.76* RAMG 23.43±2.73 25.68±2.83 AREG 24.43±4.96 26.62±3.43 RAEG 22.61±4.18 25.36±5.45 Sit to stand (reps) OCG 24.66±5.51 26.85±5.16 .838 .001 .024 ARMG 21.37±3.50 27.37±2.32* RAMG 21.75±3.59 30.66±5.40** AREG 25.50±5.45 34.62±4.68** RAEG 23.88±4.59 31.55±6.28* Values are presented as mean± standard deviation. OCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G×T, group×time effect. Significantly different from Baseline, *P<0.05; ** P<0.01, ** P<0.001 Table 6. Baseline to post-intervention comparison of blood rapid and Pittsburgh sleep quality index Parameter Baseline Post p-value G T G×T Total cholesterol (mg/dL) OCG 195.22±23.43 194.03±29.60 .672 .009 .461 ARMG 237.62±68.80 193.37±33.53 RAMG 211.37±49.44 193.00±46.18 AREG 226.25±72.78 209.00±40.01 RAEG 224.77±56.51 206.66±26.48 Triglyceride (mg/dL) OCG 167.11±66.22 152.55±81.55 .312 .732 .790 ARMG 175.37±73.86 141.37±63.87 RAMG 210.50±96.52 209.48±86.86 AREG 146.62±96.06 172.12±68.73 RAEG 145.00±97.30 144.22±87.25 High-density lipoprotein cholesterol (mg/dL) OCG 54.66±11.74 59.44±15.83 .020 .004 .826 ARMG 60.25±8.41 74.37±19.07 RAMG 50.87±13.16 59.87±15.34 AREG 64.62±20.96 70.62±6.63 RAEG 69.11±18.07 76.00±15.36 Low-density lipoprotein cholesterol (mg/dL) OCG 100.00±25.15 83.13±28.12 .366 .664 .127 ARMG 121.75±53.21 104.85±32.43 RAMG 109.12±46.95 96.45±35.76 AREG 116.37±38.14 127.20±66.86 RAEG 104.16±21.61 126.82±32.04 Fasting glucose (mg/dL) OCG 99.66±7.64 101.77±15.88 .073 .162 .594 ARMG 100.12±6.08 94.12±5.22* RAMG 94.75±8.65 91.50±6.36 AREG 94.50±7.52 91.75±8.39 RAEG 93.77±7.77 92.22±6.88 Pittsburgh sleep quality index (score) OCG 7.66±1.49 7.66±1.41 .003 .001 .015 ARMG 5.37±2.13 3.87±1.80* RAMG 7.62±1.99 5.00±1.11* AREG 6.50±1.69 4.37±1.40* RAEG 6.88±2.57 4.77±1.48* Values are presented as mean± standard deviation. OCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G×T, group×time effect. Significantly different from Baseline, *P<0.05; ** P<0.01, ** P<0.001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3936001","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274654068,"identity":"18385b92-48cd-48da-88d3-198b51ee5e7e","order_by":0,"name":"Yeong-Hyun Cho","email":"","orcid":"","institution":"Jeju National University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yeong-Hyun","middleName":"","lastName":"Cho","suffix":""},{"id":274654069,"identity":"58af8164-1dda-4c8f-8668-19a1b629ccfd","order_by":1,"name":"Tae-Beom Seo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACZoYKNgbGBihPgggdjM0MZ0jWwtiGxCWoRb69+fjjwnl8cswzco9JMNTYMUjOPoBfi8GZY4nNM7exGTPOyEuTYDiWzCDNl0BAi0SOYTPvNrbExhk5ZhIMbAcY5HgIOWz+G6CWOWz1EC3/iNDCcIMHqKWBLYERpIWx7QCDNCEtBmfSEmfPOMZm2NjzxtgisS+ZR7KHkMPaDx/4XFBzTN6wPcfwxodvdnISZwg5DAKOMRg2AKkEBgaCPoGBGgZ5YpWOglEwCkbByAMAlp46tJd82KkAAAAASUVORK5CYII=","orcid":"","institution":"Jeju National University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tae-Beom","middleName":"","lastName":"Seo","suffix":""}],"badges":[],"createdAt":"2024-02-07 06:29:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3936001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3936001/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51770293,"identity":"ff5cf4c9-fbe4-41ec-a943-07ac4bf1f1bb","added_by":"auto","created_at":"2024-02-28 19:24:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":559534,"visible":true,"origin":"","legend":"\u003cp\u003eThe Experimental Design. OCG, obesity control group; ARMG. aerobic-resistance exercise in the morning group; RAMG, resistance-aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group\u003c/p\u003e","description":"","filename":"Fig.1.Experimentldesign.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3936001/v1/d6c2e04b377877c3a920d3e0.jpg"},{"id":57470820,"identity":"e8c5efdf-2a26-4430-817a-a083a3559af0","added_by":"auto","created_at":"2024-05-31 06:29:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1550642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3936001/v1/b3ed4acf-1576-4220-a801-5bc6cdd7f8e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Intra-Session Exercise Sequence and Circadian Rhythms on Physical Capacity and Sleep Quality in Obese Women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a serious social problem that increases the prevalence of metabolic diseases such as diabetes, high blood pressure, dyslipidemia, hyperinsulinemia, and metabolic syndrome \u003csup\u003e1\u003c/sup\u003e. In recent studies, morning, and evening exercises as well as concurrent aerobic and resistance exercise were highlighted as effective obesity prevention and management methods\u003csup\u003e2,3\u003c/sup\u003e. The circadian rhythms are governed by the body\u0026rsquo;s rhythm in the suprachiasmatic nucleus within the hypothalamus of the human brain \u003csup\u003e4\u003c/sup\u003e. This rhythm is closely related to various physiological changes such as heart rate, stress, metabolism, sleep quality, immunity, and obesity-related hormone secretion \u003csup\u003e5,6\u003c/sup\u003e suggesting a more effective approach to preventing and managing obesity\u003csup\u003e7,8\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to exercise physiology and sports medicine researchers, regular aerobic and/or anaerobic exercise is the most economical and effective way to manage obesity. Aerobic and resistance exercises are mainly used to treat obesity, but the two induce different physical changes \u003csup\u003e9\u003c/sup\u003e. Aerobic exercise is used to reduce body fat by promoting fatty acid oxidation and improving cardiovascular endurance \u003csup\u003e10\u003c/sup\u003e, whereas resistant exercise is known to increase muscle mass and strength by activating anabolic hormones such as testosterone and insulin-like growth factor-1, which synthesize proteins in the skeletal muscles \u003csup\u003e11\u003c/sup\u003e. Many previous studies have applied concurrent aerobic and resistance training programs to improve physical fitness and body composition in obese people and reported its efficacy at regulating body fat, muscle mass, blood lipid concentrations, and anabolic hormones \u003csup\u003e12\u003c/sup\u003e. However, several studies have reported the interference effects of the sequence of concurrent exercises can counteract the positive effects induced by a single exercise \u003csup\u003e13,14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn terms of circadian rhythms and exercise, evening exercise significantly increases the secretion of neurotransmitters and muscle functions compared to morning exercise \u003csup\u003e15,16\u003c/sup\u003e. Previous study observed that anaerobic exercise capacity was higher in the afternoon than in the morning and recommended exercising in the afternoon accordingly \u003csup\u003e17\u003c/sup\u003e. In contrast, other studies reported that exercise performance was more effective at 6:00 AM than at 6:00 PM.\u003c/p\u003e\n\u003cp\u003eBased on these results, circadian rhythms and exercise order are believed to be therapeutic methods for preventing obesity. However, the effect of concurrent exercise sequence in the morning or evening on obesity improvement and prevention remains unclear. We hypothesized that exercise performance order in the morning or evening could change the quality of life of obese women by promoting body composition, muscular functions, and sleep quality. Therefore, this study aimed to investigate the effect of intra-session exercise sequence and circadian rhythms on physical fitness, upper- and lower-extremity muscle functions, and sleep quality in obese women.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eChange in anthropometric characteristics and body composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the differences in body composition according to circadian rhythms and exercise order, we identified anthropometric characteristics, body composition, and hip and waist circumferences (Table 3). In comparison between pre-experimental groups, anthropometric characteristics, body composition, and hip and waist circumferences were no significant (all baseline p-values \u0026gt;0.05). At Post, body weight (p\u0026lt;0.003), body fat mass (p\u0026lt;0.010) and body mass index (p\u0026lt;0.002) were significantly decreased in the RAMG, while anthropometric characteristics, body composition, and hip and waist circumferences did not change significantly in the OCG. In addition, % fat, fat-free mass, waist circumference, hip circumference, and waist-hip ratio were not significantly different all groups. There was a significant group \u0026times; time interaction in the body weight (p\u0026lt;0.004), body fat mass (p\u0026lt;0.042) and body mass index (p\u0026lt;0.003) except for percent body fat (p\u0026lt;0.984), free fat mass (p\u0026lt;0.275), waist and hip circumference (p\u0026lt;0.731, p\u0026lt;0.991, respectively), and waist-hip ratio (p\u0026lt;0.169).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChange in basic physical fitness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe measured various physical fitness parameters to identify maximal strength, muscular endurance, flexibility, and cardiorespiratory endurance according to circadian rhythms and exercise order (Table 4). In comparison between pre-experimental groups, physical fitness parameters were no significant (all baseline p-values \u0026gt;0.05). At Post, back strength was significantly increased in the ARMG (p\u0026lt;0.048), RAMG (p\u0026lt;0.021), AREG (p\u0026lt;0.004). Sit up was significantly increased in the AREG (p\u0026lt;0.001) and RAEG (p\u0026lt;0.006). Physical efficiency index was significantly increased in the RAMG (p\u0026lt;0.025) and RAEG (p\u0026lt;0.050), while physical fitness parameters did not change significantly in the OCG. There was a significant group \u0026times; time interaction in the sit up (p\u0026lt;0.046) and sit and reach (p\u0026lt;0.049) except for grip and back strength (p\u0026lt;0.827, p\u0026lt;0.377, respectively) and physical efficiency index (p\u0026lt;0.088).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChange in Upper and lower extremity muscle functions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine upper- and lower-extremity muscle function, we performed the FAPT, SAPT, TFET, vertical jump, and sit-to-stand tests (Table 5). There was no significant difference at baseline between groups in upper- and lower-extremity muscle functions (all baseline p-values \u0026gt;0.05). At Post, front abdominal and right-side abdominal power test were significantly increased in the ARMG (p\u0026lt;0.031, p\u0026lt;0.031, respectively), RAMG (p\u0026lt;0.003, p\u0026lt;0.022, respectively), AREG (p\u0026lt;0.003, p\u0026lt;0.034, respectively). Left side abdominal power test, trunk flexor endurance test and sit to stand test were significantly increased in ARMG (p\u0026lt;0.041, p\u0026lt;0.016, p\u0026lt;0.008, respectively), RAMG (p\u0026lt;0.006, p\u0026lt;0.001, p\u0026lt;0.001, respectively), AREG (p\u0026lt;0.006, p\u0026lt;0.013, p\u0026lt;0.001, respectively), RAEG (p\u0026lt;0.001, p\u0026lt;0.011, p\u0026lt;0.013, respectively), while all upper- and lower-extremity muscle functions did not change in the OCG. There was a significant group \u0026times; time interaction in the front abdominal power test (p\u0026lt;0.018), trunk flexor endurance test (p\u0026lt;0.006) and sit to stand (p\u0026lt;0.024) except for left and right abdominal power test (p\u0026lt;0.373, p\u0026lt;0.925, respectively) and vertical jump (p\u0026lt;0.372).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChange in blood lipids and Pittsburgh sleep quality index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe measured the levels of TC, TG, HDL-C, LDL-C, and FG in the blood and PSQI values to identify blood lipids and Pittsburgh sleep quality index according to circadian rhythms and exercise order (Table 6). There was no significant difference at baseline between groups in blood lipid and sleep quality index (all baseline p-values \u0026gt;0.05). At Post, Pittsburgh sleep quality index was significantly decreased in the ARMG (p\u0026lt;0.003), RAMG (p\u0026lt;0.010), AREG (p\u0026lt;0.008), RAEG (p\u0026lt;0.010), while OCG did not change significantly different. All blood lipid s was no significantly different in all groups except for fasting glucose. fasting glucose was significantly decreased only the ARMG (p\u0026lt;0.019). All groups were no significant group \u0026times; time interaction in the blood lipid parameters (p\u0026gt;0.127) except for Pittsburgh sleep quality index (p\u0026lt;0.015).\u003c/p\u003e"},{"header":"Disscusion","content":"\u003cp\u003eRecent clinical studies on the effect of obesity prevention reported that exercise order and circadian rhythms can improve body composition, physical fitness, sleep quality, cardiovascular disease\u0026ndash;related factors, and inflammation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, there is somewhat controversial. Thus, we attempted to demonstrate the effect of exercise order and circadian rhythms on health-related physical fitness and cardiovascular disease\u0026ndash;related risk factors in obese adult women. The major findings indicated that resistance-aerobic exercise order in the morning was improved body composition. In addition, physical fitness and upper-and lower-extremity muscular functions were increased regardless of exercise order and circadian rhythms.\u003c/p\u003e \u003cp\u003eOur study confirmed the effects of anthropometric characteristics and body composition, hip- and waist-circumference according to circadian rhythms and exercise order. Percent body fat, fat free mass, hip and waist circumference, and waist-to-hip ratio were not significantly improved all groups. While body weight, body fat mass and body mass index were significantly decreased in the RAMG. Several previous studies suggested that morning exercise reduces body weight, body fat mass and body mass index in overweight/obese women compared to evening exercise \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In additional, early exercise (sessions completed between 7:00 and 11:59 am) was significantly decreased more body weight than late exercise (sessions completed between 3:00 and 7:00 pm) 10 months after applying exercise intervention in female with overweight and obese \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Resistance exercise performed before aerobic exercise has been reported to increase fat oxidation and energy consumption \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It has also been shown to increase the concentrations of fatty acids, glycerol, and growth hormone \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The results of these previous studies support our findings that aerobic exercise performed after resistance exercise in the morning improved body weight, body fat mass, and body mass index. This cause can be attributed that fasting aerobic exercise in the morning accelerated fatty acid oxidation compared to the evening exercise group performed after carbohydrate intake \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Moreover, resistance exercise before aerobic exercise is thought to contribute to increased body temperature and promotion of fatty acid oxidation by excess post oxygen consumption (EPOC) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, the study found no oxygen intake, body temperature, or fatty acids. Therefore, further research is needed to overcome the limitations of this study.\u003c/p\u003e \u003cp\u003eOur study confirmed the effects of basic physical fitness and upper-and lower-extremity muscular functions according to circadian rhythms and exercise order. Strength, endurance and upper-and lower- extremity muscular functions tests were significantly improved almost all exercise groups after intervention. Meta-analysis study reported that concurrent endurance and resistance exercises had a negative effect on muscle hypertrophy, strength, and power depending on frequency and duration \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In contrast, alteration of intra-session exercise sequence (endurance exercise after resistance exercise) has been reported to have a positive effect on changes in physical fitness, muscle strength, and insulin sensitivity \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and exercise capacity and performance are higher in the evening than those in the morning because body temperature and heart rate are increased to an appropriate level \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In another previous studies, morning versus evening exercise training was reported to lead to improvement of physical fitness of subjects, but there was no significant time effect \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These previous studies consistent with our findings that regular exercise improved physical strength and upper and lower extremity muscle functions regardless of exercise order and timing. However, changes in physical fitness and muscular functions according to intra-session exercise sequence and circadian rhythms are still controversial. These causes are not well documented that exercise sequence during day and night may activate protein metabolism including AMPK and mTOR signaling pathway and hormone secretion including cortisol and melatonin \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Thus, the limitation of this study is that the molecular biological interference effect according to the exercise sequence and the hormonal change during day and night could not be confirmed. In future study, we believe that the perspective of protein and hormone synthesis on intra-session exercise sequence and circadian rhythms are needed to analyze.\u003c/p\u003e \u003cp\u003eOur study investigated blood lipids and sleep quality according to exercise orders and circadian rhythms. Specifically, TC, TG, HDL-C, LDL-C levels were not significantly changed in all groups except for fasting glucose. Fasting glucose was significantly decreased only the ARMG. However, the fasting glucose level is within the normal range and does not mean much. In addition, Pittsburgh sleep quality index was improved in all exercise groups compared the obesity control group. Looking at previous studies that confirmed changes in blood lipids according to exercise sequence and circadian rhythms, exercise ameliorated blood lipids with no difference between morning and evening exercise \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Also, regular exercise tends to improve blood lipid regardless of the exercise sequence \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. It has been known that sleep quality can be changed by circadian rhythm and exercise. Previous study reported that combined endurance and resistance exercise was improved the PSQI value \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Also, regular exercise did not differ sleep quality between the two groups (morning vs evening) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The results of previous studies support our findings that concurrent aerobic and resistance exercise performed in the morning or evening might effectively improve sleep quality in obese women. The reason is that circadian rhythms and physical activity are closely related to the sleep-wake cycle, bring about a positive effect on melatonin and cortisol curves \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Given the findings reported in previous and present studies, circadian rhythms and exercise order play an important role in improving body composition, physical fitness and sleep quality in obese women. However, this study has two limitations. First, we could not confirm hormonal and molecular biological interference effects and second, we could not recruit enough participants to generalize the study\u0026rsquo;s results. Thus, in future studies, it will be necessary to perform new experiments by mechanism study and recruiting sufficient subjects to enable generalization of the findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe investigated whether the sequence of aerobic and resistance exercises according to circadian rhythms could improve body composition, upper- and lower-extremity muscle functions, blood lipids and Pittsburgh sleep quality index in obese women. Our findings provide new evidence that resistance exercise in the morning followed by aerobic exercise might promote fat metabolism. Furthermore, physical fitness, upper and lower extremity muscular functions, and quality of sleep might be improved by performing regular exercise regardless of order and timing.\u003c/p\u003e"},{"header":"Material \u0026 Methods","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eParticipants and Study Design\u003c/h2\u003e\n \u003cp\u003eThe study design is presented in Fig. 1. This study recruited obese women (n\u0026thinsp;=\u0026thinsp;55) who had a body fat percentage of 30% or more and had not been diagnosed with cardiovascular or musculoskeletal diseases in the previous 6 months. Ultimately, fifty participations participated in this study according to the exclusion criteria. As shown in Table 1, the participants were randomly divided into the aerobic-resistance exercise in the morning group (ARMG, n\u0026thinsp;=\u0026thinsp;10), resistance-aerobic exercise in the morning group (RAMG, n\u0026thinsp;=\u0026thinsp;10), aerobic-resistance exercise in the evening group (AREG, n\u0026thinsp;=\u0026thinsp;10), resistance-aerobic exercise in the evening group (RAEG, n\u0026thinsp;=\u0026thinsp;10), and the obesity control group (OCG, n\u0026thinsp;=\u0026thinsp;10). Three participants in the ARMG (n\u0026thinsp;=\u0026thinsp;2) and RAMG (n\u0026thinsp;=\u0026thinsp;1) withdrew from the study for personal reasons. Three participants (two in the AREG, one in the RAEG) with an exercise participation rate of less than 80% were excluded from the analysis. In the baseline-test, body weight (F\u0026thinsp;=\u0026thinsp;.113, p\u0026thinsp;=\u0026thinsp;.977), body fat mass (F\u0026thinsp;=\u0026thinsp;.410, p\u0026thinsp;=\u0026thinsp;.800), % body fat (F\u0026thinsp;=\u0026thinsp;.751, p\u0026thinsp;=\u0026thinsp;.563), fat-free mass (F\u0026thinsp;=\u0026thinsp;.041, p\u0026thinsp;=\u0026thinsp;.997), or body mass index (F\u0026thinsp;=\u0026thinsp;.277, p\u0026thinsp;=\u0026thinsp;.891) were not significantly difference between groups. Written informed consent was obtained from all participants after they were informed about the study aims and the potential risks derived from the tests and methods. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Jeju National University Institutional Review Board (JJNU-IRB-2021-045-001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eCombined exercise intervention\u003c/h2\u003e\n \u003cp\u003eThis study applied a combined exercise program that was performed 3 days a week for 8 weeks, and the different groups followed different orders of aerobic and resistance exercise in the morning or evening (Table 2). All participants had a 1-week adaptation exercise period before the main experiment, following which the maximum heart rate and 1 repetition max (1RM) were measured to set the exercise intensity for participants before starting the exercise program. The aerobic exercise intensity was set using the Karvonen formula (206.9 - (0.67 \u0026times; age)), and treadmill exercise was performed for 30 min at the most effective 60\u0026ndash;70% maximum heart rate (HRmax) intensity for fat burning \u003csup\u003e37\u003c/sup\u003e. The resistance exercise intensity was set up using the 1RM indirect estimation formula (1RM\u0026thinsp;=\u0026thinsp;wt + (wt \u0026times; 0.025 \u0026times; reps)). Resistance exercise was performed by applying the progressive overload principle at 0\u0026ndash;4 weeks (60\u0026ndash;80% 1RM) and 5\u0026ndash;8 weeks (70\u0026ndash;80% 1RM). The resistance exercise consisted of exercises involving push-and-pull and weight bearing, and all participants performed three sets (8\u0026ndash;15 RM) with a 2-min rest between sets using concentric contraction for 1 s and eccentric contraction for 2 s. Intensities in treadmill and resistance exercises were equally applied to all exercise groups. Resistance exercises used weight training machines to minimize the possibility of injury. Dynamic stretching using foam rollers was performed for 5 min during warm-up and cool-down to induce the best condition and rapid muscle fatigue recovery, respectively. The morning exercise group performed an exercise program from 7 AM to 08:30 AM, while the evening exercise group performed an exercise program from 7 PM to 08:30 PM under the supervision of a professional trainer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eMeasurements\u003c/h2\u003e\n \u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eAnthropometric and body composition measurements\u003c/h2\u003e\n \u003cp\u003ePhysique and body composition were measured using a height and weight auto measuring scale (DS-103M, Dong San Jenix, Seoul, Korea) and a body composition analyzer (Inbody 770; Inbody, Seoul, Korea). The participants visited the laboratory for pre-prandial analyses at 9 AM three times during the study (before and 4 and 8 weeks after) for 8 h. Waist circumference and hip circumference were measured using flexible metric measuring tape. Hip circumference was measured at the level of the maximum extension of the buttocks posteriorly in a horizontal plane and waist circumference was measured around the abdomen at the level of the umbilicus. Waist- hip ratio (WHR) was calculated as waist circumference divided by hip circumference.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eBasic physical fitness\u003c/h2\u003e\n \u003cp\u003eGrip and back strengths were measured using a digital dynamometer (T.K.K. 5101 and T.K.K. 5402; TAKEI, Tokyo, Japan). It was measured twice, and the maximum value was recorded in kilograms. Muscular endurance and flexibility were assessed using a sit-up device (T.K.K. 5505; TAKEI). Cardiorespiratory endurance was measured using the physical efficiency index (PEI) score through the Havard step test. The PEI calculation using the long form equation - fitness index formula is as follows: \u0026lt;Long Form Equation - Fitness Index = (100 \u0026times; test duration in seconds) divided by (2 \u0026times; sum of heartbeats in the recovery period)\u0026gt;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eBlood lipids\u003c/h2\u003e\n \u003cp\u003eThe blood lipids including fasting glucose (FG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), were measured at baseline and post-exercise. FG levels were analyzed using a disposable blood collection needle (Accu-Chek\u0026reg;Softclix; Roche, Mannheim, Germany) and a blood glucose meter (Accu-Chek\u0026reg;Guide; Roche). TC, TG, HDL-C, and LDL-C levels were measured using a Mission Cholesterol Meter (Acon Laboratories, Inc., San Diego, CA, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eUpper extremity muscle function\u003c/h2\u003e\n \u003cp\u003eThe abdominal power test was performed according to a previously reported method \u003csup\u003e38\u003c/sup\u003e. This test consists of front and side abdominal power tests (FAPT and SAPT). The FAPT was performed as follows: the participant laid down while holding the medicine ball (2% body weight) with the knee bent at 90\u0026deg; and both arms spread on the mat. The SAPT was performed as follows: the participant sat on the mat (knee angle, 90\u0026deg;; hip angle, 45\u0026deg;), held the medicine ball (2% body weight), and the measurements were taken twice by turning the upper body 90\u0026deg; outward in the opposite direction and then inward again to throw the medicine ball from the knee line. The maximum distance between the FAPT and SAPT was measured from the toe of the participant to the point where the ball fell. The trunk flexor endurance test (TFET) was performed to evaluate abdominal muscle endurance. As described previously \u003csup\u003e39\u003c/sup\u003e, the ankle was fixed to the floor with the hip and knee bent at 90\u0026deg; and the arms crossed in front of the chest. Thereafter, the time (in seconds) required to remove the support and maintain the waist angle at 60\u0026deg; was recorded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eLower extremity muscle function\u003c/h2\u003e\n \u003cp\u003eThe vertical jump test was performed twice using a digital vertical jump device (DW771A; SKARO, Seoul, Korea), and the maximum height (in centimeters) was measured. Fatigue was minimized by taking a break of approximately 2 min after the first vertical jump as previously described \u003csup\u003e40\u003c/sup\u003e. For the measurement, the participants sat on a 43-cm-high chair, crossed their arms at chest level, put their palms on their shoulders, and sat down and stood up for 30 s until their knees were completely straight. The number of repetitions performed in 30 s was recorded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eMorningness-eveningness questionnaire and Pittsburgh sleep quality index\u003c/h2\u003e\n \u003cp\u003eThe identification of circadian rhythm types developed previously was translated into Korean (MEQ-K) and applied in the present study \u003csup\u003e41,42\u003c/sup\u003e. The Cronbach\u0026apos;s \u0026alpha; coefficient for the item internal consistency of the MEQ-K was 0.77, and the reliability and validity were verified with a sample fit of 0.847. The MEQ consists of 19 questions, and a score of 0\u0026ndash;6 points is assigned to each question. The results were divided into five categories according to the scores received.\u003c/p\u003e\n \u003cp\u003eSleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI) before and 4 and 8 weeks after the experiment. The PSQI subjectively evaluates sleep quality, including amount, depth, and comfort. This study used the Korean version of the PSQI (K-PSQI) \u003csup\u003e43\u003c/sup\u003e. The K-PSQI showed high reliability in terms of internal consistency (Cronbach\u0026rsquo;s \u0026alpha;\u0026thinsp;=\u0026thinsp;0.84). The PSQI is divided into seven subdomains (subjective sleep quality, sleep latency, sleep time, usual sleep efficiency, sleep disturbance, use of sleeping pills, and daytime dysfunction), each with a score of 0\u0026ndash;3. Therefore, the higher the average score over the seven domains, the poorer the sleep quality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eThe data were analyzed using SPSS version 23 (IBM, Armonk, NY, USA), with significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. To test if five groups presented similar characteristics at baseline, a between-group comparison was conducted using one-way ANOVA. Anthropometric characteristics and body composition, physical fitness, Upper and lower extremity muscle functions, and blood lipid and Pittsburgh sleep quality index data were analyzed with a two-way repeated-measures ANOVA that included between-subjects factor of groups and the within-subjects\u0026rsquo; factors of time (Baseline vs Post repeated- measures analysis).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no acknowledgment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.H.C. contributed to the conceptualization, data analysis \u0026amp; interpretation, and writing of the manuscript. T.B.S. takes responsibility for data integrity and contributed to the study design, interpretation of the data and revision of the manuscript. Y.H.C. conceptualized, interpreted the data, and revised the manuscript. All authors have reviewed and the approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available on Figshare. The URL is https://figshare.com/s/ef211a68a712bf1c639e. The DOI is http://doi.org/https://doi.org/10.6084/m9.figshare.25241926.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eConsitt LA, Bell JA, Houmard JA. Intramuscular lipid metabolism, insulin action, and obesity. \u003cem\u003eIUBMB Life. \u003c/em\u003e2009;61(1):47-55.\u003c/li\u003e\n\u003cli\u003eBrooker PG, Gomersall SR, King NA, Leveritt MD. The feasibility and acceptability of morning versus evening exercise for overweight and obese adults: A randomized controlled trial. \u003cem\u003eContemp Clin Trials Commun. \u003c/em\u003e2019;14:100320.\u003c/li\u003e\n\u003cli\u003eWheeler MJ, Dunstan DW, Ellis KA, et al. Effect of Morning Exercise With or Without Breaks in Prolonged Sitting on Blood Pressure in Older Overweight/Obese Adults Evidence for Sex Differences. \u003cem\u003eHypertension. \u003c/em\u003e2019;73(4):859-867.\u003c/li\u003e\n\u003cli\u003eEngin A. Circadian Rhythms in Diet-Induced Obesity. \u003cem\u003eAdv Exp Med Biol. \u003c/em\u003e2017;960:19-52.\u003c/li\u003e\n\u003cli\u003ePanda S. Circadian physiology of metabolism. \u003cem\u003eScience. \u003c/em\u003e2016;354(6315):1008-1015.\u003c/li\u003e\n\u003cli\u003ePeek CB, Levine DC, Cedernaes J, et al. Circadian Clock Interaction with HIF1alpha Mediates Oxygenic Metabolism and Anaerobic Glycolysis in Skeletal Muscle. \u003cem\u003eCell Metab. \u003c/em\u003e2017;25(1):86-92.\u003c/li\u003e\n\u003cli\u003eBroussard JL, Van Cauter E. Disturbances of sleep and circadian rhythms: novel risk factors for obesity. \u003cem\u003eCurr Opin Endocrinol Diabetes Obes. \u003c/em\u003e2016;23(5):353-359.\u003c/li\u003e\n\u003cli\u003eLi Y, Ma J, Yao K, et al. Circadian rhythms and obesity: Timekeeping governs lipid metabolism. \u003cem\u003eJ Pineal Res. \u003c/em\u003e2020;69(3):e12682.\u003c/li\u003e\n\u003cli\u003eVillareal DT, Aguirre L, Gurney AB, et al. Aerobic or resistance exercise, or both, in dieting obese older adults. \u003cem\u003eNew England Journal of Medicine. \u003c/em\u003e2017;376(20):1943-1955.\u003c/li\u003e\n\u003cli\u003eChapman SB, Aslan S, Spence JS, et al. Shorter term aerobic exercise improves brain, cognition, and cardiovascular fitness in aging. \u003cem\u003eFront Aging Neurosci. \u003c/em\u003e2013;5:75.\u003c/li\u003e\n\u003cli\u003eMitchell CJ, Churchward-Venne TA, Bellamy L, Parise G, Baker SK, Phillips SM. Muscular and Systemic Correlates of Resistance Training-Induced Muscle Hypertrophy. \u003cem\u003ePlos One. \u003c/em\u003e2013;8(10).\u003c/li\u003e\n\u003cli\u003eWilson JM, Marin PJ, Rhea MR, Wilson SM, Loenneke JP, Anderson JC. Concurrent training: a meta-analysis examining interference of aerobic and resistance exercises. \u003cem\u003eJ Strength Cond Res. \u003c/em\u003e2012;26(8):2293-2307.\u003c/li\u003e\n\u003cli\u003eRosa C, Vilaca-Alves J, Fernandes HM, Saavedra FJ, Pinto RS, dos Reis VM. Order effects of combined strength and endurance training on testosterone, cortisol, growth hormone, and IGF-1 binding protein 3 in concurrently trained men. \u003cem\u003eJ Strength Cond Res. \u003c/em\u003e2015;29(1):74-79.\u003c/li\u003e\n\u003cli\u003eDrummond MJ, Vehrs PR, Schaalje GB, Parcell AC. Aerobic and resistance exercise sequence affects excess postexercise oxygen consumption. \u003cem\u003eJ Strength Cond Res. \u003c/em\u003e2005;19(2):332-337.\u003c/li\u003e\n\u003cli\u003eKim HK, Konishi M, Takahashi M, et al. Effects of Acute Endurance Exercise Performed in the Morning and Evening on Inflammatory Cytokine and Metabolic Hormone Responses. \u003cem\u003ePlos One. \u003c/em\u003e2015;10(9):e0137567.\u003c/li\u003e\n\u003cli\u003eKuusmaa M, Schumann M, Sedliak M, et al. Effects of morning versus evening combined strength and endurance training on physical performance, muscle hypertrophy, and serum hormone concentrations. \u003cem\u003eAppl Physiol Nutr Metab. \u003c/em\u003e2016;41(12):1285-1294.\u003c/li\u003e\n\u003cli\u003eSouissi M, Chtourou H, Zrane A, et al. Effect of time-of-day of aerobic maximal exercise on the sleep quality of trained subjects. \u003cem\u003eBiological Rhythm Research. \u003c/em\u003e2012;43(3):323-330.\u003c/li\u003e\n\u003cli\u003eChen T, Lin J, Lin Y, et al. Effects of aerobic exercise and resistance exercise on physical indexes and cardiovascular risk factors in obese and overweight school-age children: A systematic review and meta-analysis. \u003cem\u003ePlos One. \u003c/em\u003e2021;16(9):e0257150.\u003c/li\u003e\n\u003cli\u003eLiu H, Liu S, Wang K, et al. Time-Dependent Effects of Physical Activity on Cardiovascular Risk Factors in Adults: A Systematic Review. \u003cem\u003eInt J Environ Res Public Health. \u003c/em\u003e2022;19(21).\u003c/li\u003e\n\u003cli\u003eArciero PJ, Ives SJ, Mohr AE, et al. Morning Exercise Reduces Abdominal Fat and Blood Pressure in Women; Evening Exercise Increases Muscular Performance in Women and Lowers Blood Pressure in Men. \u003cem\u003eFront Physiol. \u003c/em\u003e2022;13:893783.\u003c/li\u003e\n\u003cli\u003eAlizadeh Z, Younespour S, Rajabian Tabesh M, Haghravan S. Comparison between the effect of 6 weeks of morning or evening aerobic exercise on appetite and anthropometric indices: a randomized controlled trial. \u003cem\u003eClinical obesity. \u003c/em\u003e2017;7(3):157-165.\u003c/li\u003e\n\u003cli\u003eWillis EA, Creasy SA, Honas JJ, Melanson EL, Donnelly JE. The effects of exercise session timing on weight loss and components of energy balance: midwest exercise trial 2. \u003cem\u003eInt J Obes (Lond). \u003c/em\u003e2020;44(1):114-124.\u003c/li\u003e\n\u003cli\u003eKang J, Rashti SL, Tranchina CP, Ratamess NA, Faigenbaum AD, Hoffman JR. Effect of preceding resistance exercise on metabolism during subsequent aerobic session. \u003cem\u003eEur J Appl Physiol. \u003c/em\u003e2009;107(1):43-50.\u003c/li\u003e\n\u003cli\u003eGoto K, Ishii N, Sugihara S, Yoshioka T, Takamatsu K. Effects of resistance exercise on lipolysis during subsequent submaximal exercise. \u003cem\u003eMed Sci Sports Exerc. \u003c/em\u003e2007;39(2):308-315.\u003c/li\u003e\n\u003cli\u003eBennard P, Doucet E. Acute effects of exercise timing and breakfast meal glycemic index on exercise-induced fat oxidation. \u003cem\u003eAppl Physiol Nutr Metab. \u003c/em\u003e2006;31(5):502-511.\u003c/li\u003e\n\u003cli\u003eDe Bock K, Richter EA, Russell AP, et al. Exercise in the fasted state facilitates fibre type-specific intramyocellular lipid breakdown and stimulates glycogen resynthesis in humans. \u003cem\u003eJ Physiol. \u003c/em\u003e2005;564(Pt 2):649-660.\u003c/li\u003e\n\u003cli\u003eKang J, Ratamess N. Which comes first? Resistance before aerobic exercise or vice versa? \u003cem\u003eACSM\u0026apos;s Health \u0026amp; Fitness Journal. \u003c/em\u003e2014;18(1):9-14.\u003c/li\u003e\n\u003cli\u003eWilkinson SB, Phillips SM, Atherton PJ, et al. Differential effects of resistance and endurance exercise in the fed state on signalling molecule phosphorylation and protein synthesis in human muscle. \u003cem\u003eJ Physiol. \u003c/em\u003e2008;586(15):3701-3717.\u003c/li\u003e\n\u003cli\u003eDonges CE, Duffield R, Guelfi KJ, Smith GC, Adams DR, Edge JA. Comparative effects of single-mode vs. duration-matched concurrent exercise training on body composition, low-grade inflammation, and glucose regulation in sedentary, overweight, middle-aged men. \u003cem\u003eAppl Physiol Nutr Metab. \u003c/em\u003e2013;38(7):779-788.\u003c/li\u003e\n\u003cli\u003eEzagouri S, Zwighaft Z, Sobel J, et al. Physiological and Molecular Dissection of Daily Variance in Exercise Capacity. \u003cem\u003eCell Metab. \u003c/em\u003e2019;30(1):78-91 e74.\u003c/li\u003e\n\u003cli\u003eSaidi O, Colin E, Rance M, Dor\u0026eacute; E, Pereira B, Duch\u0026eacute; P. Effect of morning versus evening exercise training on sleep, physical activity, fitness, fatigue and quality of life in overweight and obese adults. \u003cem\u003eChronobiol Int. \u003c/em\u003e2021;38(11):1537-1548.\u003c/li\u003e\n\u003cli\u003eFyfe JJ, Bishop DJ, Stepto NK. Interference between Concurrent Resistance and Endurance Exercise: Molecular Bases and the Role of Individual Training Variables. \u003cem\u003eSports Med. \u003c/em\u003e2014;44(6):743-762.\u003c/li\u003e\n\u003cli\u003eMoholdt T, Parr EB, Devlin BL, Giskeodegard GF, Hawley JA. Effect of high-fat diet and morning or evening exercise on lipoprotein subfraction profiles: secondary analysis of a randomised trial. \u003cem\u003eSci Rep-Uk. \u003c/em\u003e2023;13(1).\u003c/li\u003e\n\u003cli\u003eEklund D, Hakkinen A, Laukkanen JA, Balandzic M, Nyman K, Hakkinen K. Fitness, body composition and blood lipids following 3 concurrent strength and endurance training modes. \u003cem\u003eAppl Physiol Nutr Metab. \u003c/em\u003e2016;41(7):767-774.\u003c/li\u003e\n\u003cli\u003eBonato M, La Torre A, Saresella M, Marventano I, Merati G, Vitale JA. Salivary cortisol concentration after high-intensity interval exercise: Time of day and chronotype effect. \u003cem\u003eChronobiol Int. \u003c/em\u003e2017;34(6):698-707.\u003c/li\u003e\n\u003cli\u003eTaheri M, Irandoust K. The exercise-induced weight loss improves self-reported quality of sleep in obese elderly women with sleep disorders. \u003cem\u003eSleep Hypn. \u003c/em\u003e2018;20(1):54-59.\u003c/li\u003e\n\u003cli\u003eGellish RL, Goslin BR, Olson RE, McDonald A, Russi GD, Moudgil VK. Longitudinal modeling of the relationship between age and maximal heart rate. \u003cem\u003eMed Sci Sport Exer. \u003c/em\u003e2007;39(5):822-829.\u003c/li\u003e\n\u003cli\u003eCowley PM, Swensen TC. Development and reliability of two core stability field tests. \u003cem\u003eJ Strength Cond Res. \u003c/em\u003e2008;22(2):619-624.\u003c/li\u003e\n\u003cli\u003eArman N, Tokgoz G, Seyit H, Karabulut M. The effects of core stabilization exercise program in obese people awaiting bariatric surgery: A randomized controlled study. \u003cem\u003eComplement Ther Clin Pract. \u003c/em\u003e2021;43:101342.\u003c/li\u003e\n\u003cli\u003eMartinez-Hernandez U, Dehghani-Sanij AA. Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor. \u003cem\u003ePattern Recogn Lett. \u003c/em\u003e2019;118:32-41.\u003c/li\u003e\n\u003cli\u003eHorne JA, Ostberg O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. \u003cem\u003eInt J Chronobiol. \u003c/em\u003e1976;4(2):97-110.\u003c/li\u003e\n\u003cli\u003eLee JH, Kim SJ, Lee SY, Jang KH, Kim IS, Duffy JF. Reliability and validity of the Korean version of Morningness-Eveningness Questionnaire in adults aged 20-39 years. \u003cem\u003eChronobiol Int. \u003c/em\u003e2014;31(4):479-486.\u003c/li\u003e\n\u003cli\u003eSohn SI, Kim DH, Lee MY, Cho YW. The reliability and validity of the Korean version of the Pittsburgh Sleep Quality Index. \u003cem\u003eSleep Breath. \u003c/em\u003e2012;16(3):803-812.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cimg 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\" width=\"555\" height=\"781\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eBaseline to post-intervention comparison of body composition and waist and hip circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.35042735042735%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.29059829059829%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.23931623931624%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.11965811965812%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.98198198198198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.98198198198198%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.03603603603604%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u0026times;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody weight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e68.55\u0026plusmn;17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e68.34\u0026plusmn;17.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e65.07\u0026plusmn;12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e64.11\u0026plusmn;12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e68.31\u0026plusmn;8.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e64.81\u0026plusmn;7.64*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e66.61\u0026plusmn;14.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e66.41\u0026plusmn;15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e65.85\u0026plusmn;9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e64.94\u0026plusmn;9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody fat mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e27.25\u0026plusmn;11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e27.04\u0026plusmn;11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e23.22\u0026plusmn;6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e22.53\u0026plusmn;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e25.55\u0026plusmn;5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e23.06\u0026plusmn;5.69*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e25.16\u0026plusmn;7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e24.76\u0026plusmn;8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e23.70\u0026plusmn;6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e22.90\u0026plusmn;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent body fat (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e38.35\u0026plusmn;5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e38.30\u0026plusmn;5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e35.37\u0026plusmn;4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e34.80\u0026plusmn;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e35.82\u0026plusmn;5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e35.15\u0026plusmn;4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e37.27\u0026plusmn;4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e36.72\u0026plusmn;4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e35.67\u0026plusmn;5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e35.10\u0026plusmn;5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eFat-free mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e41.30\u0026plusmn;6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e41.37\u0026plusmn;6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e41.85\u0026plusmn;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e41.57\u0026plusmn;6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e42.76\u0026plusmn;3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e41.86\u0026plusmn;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e41.45\u0026plusmn;7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e41.65\u0026plusmn;7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e42.15\u0026plusmn;5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e42.03\u0026plusmn;5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass index (kg∙m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e25.65\u0026plusmn;5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e25.56\u0026plusmn;6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e24.73\u0026plusmn;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e24.37\u0026plusmn;3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e26.01\u0026plusmn;3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e24.65\u0026plusmn;3.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e26.06\u0026plusmn;5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e26.48\u0026plusmn;5.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e24.90\u0026plusmn;3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e24.54\u0026plusmn;3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e83.10\u0026plusmn;13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e80.80\u0026plusmn;15.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e80.95\u0026plusmn;8.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e75.36\u0026plusmn;9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e81.83\u0026plusmn;5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e76.72\u0026plusmn;7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e85.20\u0026plusmn;13.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e77.37\u0026plusmn;16.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e82.03\u0026plusmn;8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e77.46\u0026plusmn;11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eHip circumference (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e100.65\u0026plusmn;9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e98.16\u0026plusmn;12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e98.23\u0026plusmn;8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e94.65\u0026plusmn;10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e99.62\u0026plusmn;7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e95.18\u0026plusmn;11.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e100.28\u0026plusmn;8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e97.57\u0026plusmn;13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e100.67\u0026plusmn;5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e97.20\u0026plusmn;11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist-hip ratio (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.890222984562607%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.382504288164665%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.353344768439108%\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.295025728987994%\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.178387650085764%\" rowspan=\"5\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.722126929674099%\" rowspan=\"5\"\u003e\n \u003cp\u003e.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e0.78\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.87301587301587%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.96825396825397%\"\u003e\n \u003cp\u003e0.81\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.158730158730158%\"\u003e\n \u003cp\u003e0.79\u0026plusmn;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eValues are presented as mean\u0026plusmn; standard deviation.\u003c/p\u003e\n \u003cp\u003eOCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G\u0026times;T, group\u0026times; time effect. Significantly different from Baseline, *P\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eBaseline to post-intervention comparison of basic physical fitness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.3781512605042%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.983193277310924%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.327731092436974%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.3109243697479%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u0026times;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrip strength (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.38655462184874%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.983193277310924%\"\u003e\n \u003cp\u003e23.95\u0026plusmn;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.327731092436974%\"\u003e\n \u003cp\u003e24.03\u0026plusmn;2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e24.69\u0026plusmn;5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e25.98\u0026plusmn;3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e25.63\u0026plusmn;2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e26.91\u0026plusmn;2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e25.46\u0026plusmn;6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e26.64\u0026plusmn;6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e25.75\u0026plusmn;4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e26.46\u0026plusmn;5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eBack strength (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.38655462184874%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.983193277310924%\"\u003e\n \u003cp\u003e55.31\u0026plusmn;13.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.327731092436974%\"\u003e\n \u003cp\u003e60.51\u0026plusmn;7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e48.78\u0026plusmn;13.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e60.85\u0026plusmn;13.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e58.09\u0026plusmn;16.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e69.46\u0026plusmn;10.82*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e52.43\u0026plusmn;16.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e70.53\u0026plusmn;19.34*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e57.29\u0026plusmn;27.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e63.51\u0026plusmn;19.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSit up (reps)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.38655462184874%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.983193277310924%\"\u003e\n \u003cp\u003e15.00\u0026plusmn;9.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.327731092436974%\"\u003e\n \u003cp\u003e15.66\u0026plusmn;8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e18.87\u0026plusmn;6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e22.00\u0026plusmn;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e15.62\u0026plusmn;8.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e19.62\u0026plusmn;6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e22.25\u0026plusmn;8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e29.25\u0026plusmn;6.92*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e16.77\u0026plusmn;13.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e25.27\u0026plusmn;8.87*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical efficiency index (score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.38655462184874%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.983193277310924%\"\u003e\n \u003cp\u003e47.22\u0026plusmn;2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.327731092436974%\"\u003e\n \u003cp\u003e47.21\u0026plusmn;2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.436974789915967%\" rowspan=\"5\"\u003e\n \u003cp\u003e.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e46.90\u0026plusmn;2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e47.34\u0026plusmn;4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e47.28\u0026plusmn;2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e49.12\u0026plusmn;2.30*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e46.54\u0026plusmn;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e48.78\u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73134328358209%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.940298507462686%\"\u003e\n \u003cp\u003e44.68\u0026plusmn;4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.32835820895522%\"\u003e\n \u003cp\u003e46.86\u0026plusmn;5.62*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eValues are presented as mean\u0026plusmn; standard deviation.\u003c/p\u003e\n \u003cp\u003eOCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G\u0026times;T, group\u0026times;time effect. Significantly different from Baseline, *P\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e. Baseline to post-intervention comparison of upper and lower extremity muscle functions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.823529411764707%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.016806722689076%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.705882352941178%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.45378151260504%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u0026times;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eFront abdominal power test\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e1796.11\u0026plusmn;678.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e1749.44\u0026plusmn;740.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e1506.25\u0026plusmn;530.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2083.12\u0026plusmn;810.48*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e1683.12\u0026plusmn;514.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2165.00\u0026plusmn;501.82*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e1767.50\u0026plusmn;576.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2253.43\u0026plusmn;755.89*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e1988.88\u0026plusmn;896.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2126.38\u0026plusmn;766.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft side abdominal power test\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e1968.33\u0026plusmn;620.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e2147.33\u0026plusmn;335.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e1998.75\u0026plusmn;460.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2470.00\u0026plusmn;670.91*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2095.00\u0026plusmn;668.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2538.75\u0026plusmn;396.60*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2363.12\u0026plusmn;702.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2688.43\u0026plusmn;661.50*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2076.11\u0026plusmn;594.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2549.94\u0026plusmn;403.18***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eRight side abdominal power test\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e1836.66\u0026plusmn;528.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e2188.88\u0026plusmn;359.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2096.25\u0026plusmn;456.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2490.00\u0026plusmn;662.26*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2063.75\u0026plusmn;570.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2432.87\u0026plusmn;470.75*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2345.00\u0026plusmn;757.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2820.31\u0026plusmn;639.09*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e2107.77\u0026plusmn;708.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e2391.77\u0026plusmn;426.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrunk flexor endurance test\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e37.02\u0026plusmn;13.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e33.69\u0026plusmn;13.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e46.40\u0026plusmn;26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e88.65\u0026plusmn;39.00*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e34.93\u0026plusmn;15.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e66.73\u0026plusmn;30.62**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e60.34\u0026plusmn;51.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e116.41\u0026plusmn;63.77*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e50.19\u0026plusmn;39.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e106.73\u0026plusmn;76.40*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eVertical Jump (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e23.61\u0026plusmn;4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e23.58\u0026plusmn;5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e23.62\u0026plusmn;3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e25.75\u0026plusmn;2.76*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e23.43\u0026plusmn;2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e25.68\u0026plusmn;2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e24.43\u0026plusmn;4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e26.62\u0026plusmn;3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e22.61\u0026plusmn;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e25.36\u0026plusmn;5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eSit to stand (reps)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e24.66\u0026plusmn;5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e26.85\u0026plusmn;5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e21.37\u0026plusmn;3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e27.37\u0026plusmn;2.32*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e21.75\u0026plusmn;3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e30.66\u0026plusmn;5.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e25.50\u0026plusmn;5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e34.62\u0026plusmn;4.68**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e23.88\u0026plusmn;4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e31.55\u0026plusmn;6.28*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eValues are presented as mean\u0026plusmn; standard deviation.\u003c/p\u003e\n \u003cp\u003eOCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G\u0026times;T, group\u0026times;time effect. Significantly different from Baseline, *P\u0026lt;0.05; ** P\u0026lt;0.01, ** P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e Baseline to post-intervention comparison of blood rapid and Pittsburgh sleep quality index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.823529411764707%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.016806722689076%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.705882352941178%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.45378151260504%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u0026times;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e195.22\u0026plusmn;23.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e194.03\u0026plusmn;29.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e237.62\u0026plusmn;68.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e193.37\u0026plusmn;33.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e211.37\u0026plusmn;49.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e193.00\u0026plusmn;46.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e226.25\u0026plusmn;72.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e209.00\u0026plusmn;40.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e224.77\u0026plusmn;56.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e206.66\u0026plusmn;26.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e167.11\u0026plusmn;66.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e152.55\u0026plusmn;81.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e175.37\u0026plusmn;73.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e141.37\u0026plusmn;63.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e210.50\u0026plusmn;96.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e209.48\u0026plusmn;86.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e146.62\u0026plusmn;96.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e172.12\u0026plusmn;68.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e145.00\u0026plusmn;97.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e144.22\u0026plusmn;87.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-density lipoprotein cholesterol\u0026nbsp;(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e54.66\u0026plusmn;11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e59.44\u0026plusmn;15.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e60.25\u0026plusmn;8.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e74.37\u0026plusmn;19.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e50.87\u0026plusmn;13.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e59.87\u0026plusmn;15.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e64.62\u0026plusmn;20.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e70.62\u0026plusmn;6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e69.11\u0026plusmn;18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e76.00\u0026plusmn;15.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-density lipoprotein cholesterol\u0026nbsp;(mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e100.00\u0026plusmn;25.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e83.13\u0026plusmn;28.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e121.75\u0026plusmn;53.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e104.85\u0026plusmn;32.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e109.12\u0026plusmn;46.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e96.45\u0026plusmn;35.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e116.37\u0026plusmn;38.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e127.20\u0026plusmn;66.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e104.16\u0026plusmn;21.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e126.82\u0026plusmn;32.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting glucose (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e99.66\u0026plusmn;7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e101.77\u0026plusmn;15.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e100.12\u0026plusmn;6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e94.12\u0026plusmn;5.22*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e94.75\u0026plusmn;8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e91.50\u0026plusmn;6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e94.50\u0026plusmn;7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e91.75\u0026plusmn;8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e93.77\u0026plusmn;7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e92.22\u0026plusmn;6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003ePittsburgh sleep quality index (score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.397306397306397%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.457912457912458%\"\u003e\n \u003cp\u003eOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.053872053872055%\"\u003e\n \u003cp\u003e7.66\u0026plusmn;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.747474747474747%\"\u003e\n \u003cp\u003e7.66\u0026plusmn;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.447811447811448%\" rowspan=\"5\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eARMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e5.37\u0026plusmn;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e3.87\u0026plusmn;1.80*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e7.62\u0026plusmn;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e5.00\u0026plusmn;1.11*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eAREG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e6.50\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e4.37\u0026plusmn;1.40*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.022727272727273%\"\u003e\n \u003cp\u003eRAEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.21590909090909%\"\u003e\n \u003cp\u003e6.88\u0026plusmn;2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.76136363636363%\"\u003e\n \u003cp\u003e4.77\u0026plusmn;1.48*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eValues are presented as mean\u0026plusmn; standard deviation.\u003c/p\u003e\n \u003cp\u003eOCG, obesity control group; ARMG, aerobic-resistance exercise in the morning group; RAMG, resistance- aerobic exercise in the morning group; AREG, aerobic-resistance exercise in the evening group; RAEG, resistance-aerobic exercise in the evening group. G, group effect; T, time effect; G\u0026times;T, group\u0026times;time effect. Significantly different from Baseline, *P\u0026lt;0.05; ** P\u0026lt;0.01, ** P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3936001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3936001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to determine the effects of exercise orders and circadian rhythms on body composition, blood lipids, physical fitness and upper- and lower-extremity muscular functions in adult obese women. Forty-four women with obesity were divided into the obesity control group (OCG), aerobic-resistance exercise in the morning group (ARMG), resistance-aerobic exercise in the morning group (RAMG), aerobic-resistance exercise in the evening group (AREG), and resistance-aerobic exercise in the evening group (RAEG). The combined exercise program consisted of treadmill exercise and weight training, and all participants performed the exercise for 8 weeks. Body weight, body mass, body mass index and fasting glucose were significantly decreased in the RAMG at post. while other body compositions and blood lipids did not change at the post compared the baseline. Upper-and lower-extremity muscular functions and Pittsburgh Sleep Quality Index value was significantly lower in all exercise groups versus OCG. Our findings provide new evidence that resistance-aerobic exercise order in the morning might positively improve body weight, body mass, body mass index and fasting glucose in obese women. In addition, physical fitness and upper- and lower-extremity muscular functions and sleep quality might be improved by performing regular exercise programs regardless of exercise order and timing.\u003c/p\u003e","manuscriptTitle":"Effects of Intra-Session Exercise Sequence and Circadian Rhythms on Physical Capacity and Sleep Quality in Obese Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 19:24:23","doi":"10.21203/rs.3.rs-3936001/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30e8f65c-21e3-4200-8ff7-9065fd2956f8","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28951082,"name":"Health sciences/Diseases"},{"id":28951083,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2024-05-31T06:21:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 19:24:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3936001","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3936001","identity":"rs-3936001","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}