Metabolic Impact of Exercise Modalities in Inactive Obese Adults: A Randomized Controlled Trial

Metabolic Impact of Exercise Modalities in Inactive Obese Adults: A Randomized Controlled Trial | 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 Research Article Metabolic Impact of Exercise Modalities in Inactive Obese Adults: A Randomized Controlled Trial Friew Amare, Yehualaw Alemu, Mollalign Enichalew, Yalemsew Demlie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4328501/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Sep, 2024 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 4 You are reading this latest preprint version Abstract Method Twenty healthy physically inactive men (49.15 ± 2.581 years) participated in an 8-month training programme involving concurrent exercise, resistance training, and aerobic training programmes to determine the effects on glucose tolerance and plasma lipids in obese adult persons. This study adopts combining pretest posttest with a repeated measures design component to capture within-subject changes over time with a randomized control trial to assess between-group differences. Plasma glucose concentrations were measured for three hours after the ingestion of 75 g of glucose, and the total areas under the respective curves were calculated. Total plasma lipid and lipoprotein concentrations were determined during fasting. Repeated measures ANCOVA was used for primary data analysis, and the independence between blood lipid variables and group factors was tested. Result Pre- to post intervention mean values of body fat percentage, area under the curve, low-density lipoprotein, high-density lipoprotein and total cholesterol decreased in all three groups. The main effect of exercise modality on the AUC was significant. Post hoc analyses revealed that RT (-30.653 ± 6.766, p = 0.001) and CT (M=-0.896, SE = 3.347, P = 0.015) had greater effects than AT. LDL was significantly lower in the AT and CT (MD = 4.783, SE = 1.563, P = 0.002) and (M = 4.57, SE = 1.284, P = 0.008) than in the RT. There was a significant difference in TC between training modalities; AT significantly reduced TC during RT (MD= -17.716, SE = 5.705, P = 0.02). HDL and %BF were not significantly different because of the independent variables affecting these variables. Conclusion Exercise modality has emerged as a key factor in optimizing chronic lipid profiles and glycemic control. Notably, both aerobic and concurrent training demonstrated a superior ability to modulate the lipid profile, and resistance training and concurrent training were more effective in reducing the AUC. glucose tolerance lipid profile resistance training aerobic training and concurrent training Figures Figure 1 Introduction Obesity is the condition of having an abnormal or excessive accumulation of fat that poses a health risk. A body mass index (BMI) of over 30 is considered to be indicative of obesity (Hordern et al., 2012 ). The prevalence of obesity increased with age in this study, with the highest prevalence (44.3%) among middle-aged adults (40–59 years old) (Boutari & Mantzoros, 2022 ). While women and older adults are generally more likely to experience obesity, these data suggest a trend across all age groups (Lin & Li, 2021 ). The incidence of obesity has been on the rise in recent decades and has been linked to a variety of health and socioeconomic issues (Blüher, 2019 ). It is important to prioritize strategies that can help reduce the healthcare costs associated with obesity. The combined estimates of the prevalence of obesity and overweight in Middle Eastern countries were 21.17% and 33.14%, respectively (Okati-Aliabad et al., 2022 ). The combined crude prevalence rate of obesity in Nigeria was 14.3% (Adeloye et al., 2021 ). Like in many other countries, overweight and obesity are becoming increasingly prevalent public health issues in Ethiopia (Kassie et al., 2020 ). Obesity is a significant risk factor for and contributor to increased morbidity and mortality, especially as individuals age, most importantly from cardiovascular disease (CVD) and diabetes but also from cancer and chronic diseases, including osteoarthritis, liver and kidney disease, sleep apnea, and depression (Ghodeshwar et al., 2023 ; Pi-Sunyer, 2009 ). We can therefore strongly suggest that interventions improving both blood sugar control and cholesterol levels would be highly effective in preventing cardiovascular diseases (De Backer et al., 2003 ). Optimizing exercise for health goes beyond just choosing an activity. A personalized approach that considers the type of exercise is required (Ekkekakis, 2009 ). Exercise has been shown to enhance glycemic control and improve blood lipid profiles in individuals both with and without type 2 diabetes, as supported by different studies (Kim et al., 2019 ; Solomon, 2018 ). Extensive research has been conducted on the effects of aerobic training and strength modalities of exercise on various health outcomes (Doewes et al., 2023 ; Kim et al., 2019 ; Mann et al., 2014 ). Research has supported the combined benefits of AT and RT to improve glycemic control and lipid parameter health in obese older adults (Azarbayjani et al., 2014 ). During longer, low-effort workouts, the body burns fat for most of its energy and becomes better at breaking down fat stores (lipolysis) (Brown, 2002 ; Noland, 2015 ). High-intensity strength training burns mostly carbohydrates for immediate energy (Brown, 2002 ). It also triggers the release of hormones such as growth hormone and testosterone (Kraemer et al., 2020 ). By influencing the body's chemistry, hormones promote muscle growth and make it easier to access the body's ability to burn glucose derived from fat stores (lipolysis) (Loucks & Caiozzo, 2012 ). Regardless of the type or intensity of exercise, hormonal changes affecting blood lipid levels are not significantly impacted when individuals burn the same amount of calories (Crouse et al., 1997 ). To accurately assess the impact of exercise programs on health, participants need to follow a strict dietary monitoring protocol (Matthews et al., 2012 ). This involved carefully tracking the participants' food intake, often through a food frequency questionnaire, to ensure that any observed changes were primarily due to the exercise intervention itself and not influenced by significant shifts in their dietary habits (Beck et al., 2015 ). Hence, the objective of this study was to assess and compare the efficacy of various exercise modes (aerobic, resistance training, and combined exercise) and changes over time in enhancing lipid profile changes and glucose tolerance among adults who are obese. Methods and Materials Research Setting and Design This research combined pretest posttest with a repeated measures design component to capture within-subject changes over time with a randomized parallel experimental design to assess between-group differences. The study involved physically inactive persons between the ages of 45 and 60 years who had a BMI greater than 30 kg/m2. The participants were selected from a volunteer pool of inactive residents of Debre Markos town, Ethiopia and were informed through local radio and notice board postings. The inclusion criteria were as follows: (a) had a BMI > 30.0 kg/m 2, (b) were aged between 45 and 60 years, (c) volunteered to participate, (d) were physically inactive (not achieving 30–60 min per day or 150 min per week of moderate intensity exercise or 20–60 min per day (75 min per week) of vigorous intensity (SportsMedicine, 1991 ) and cleared a medical history form the physical activity readiness questionnaire, and (d) were able to perform the necessary exercises. The exclusion criteria were as follows: (a) any cardiovascular, respiratory, or muscle-skeletal disorders precluding physical exercise; (b) uncontrolled hyperglycemia (≥ 126 mg/d) or hypertension (a resting blood pressure ≥ 140/100 mm Hg); and (c) active infection, (d) acute myocardial infarction, stroke, trauma, surgery or severe liver dysfunction. The ideal sample size for our investigation was determined by using the G*Power program. We determined a sample size of 21 people, as described by Sousa et al., taking into account zero correlation among each measure TGC, Total-c, LDL-c, and HDL-c in the ATG (Sousa et al., 2014 ), a significance level (α) of 0.05, and a power of 0.8. An overall sample size of 24 was needed, accounting for a 10% nonresponse rate. Our study's intended statistical power and significance level were achieved by successfully detecting the given effect size with this sample size. After applying the inclusion criteria, 32 out of the 36 volunteers who we had initially registered as physically inactive remained. The researchers were created a final study group consisting of 24 participants via a simple random selection procedure. After that, these people were randomized at random to one of three exercise groups (each with eight participants): concurrent training, strength training, or aerobic training. The study was conducted in Debere Markos, Ethiopia, using homogeneous samples and balanced randomization (1:1:1) (Fig. 1). The data collectors were blinded to one another during the study. Before obtaining their informed consent, participants were completely told about all procedures, hazards and protocols to ensure ethical conduct. This satisfied the requirements set forth by the American College of Sports Medicine (SportsMedicine, 1991 ). Additionally, the Sport Academy research ethics committee at Debre Markos University reviewed and provided input on all procedures involving human subjects. Lastly, the study was carried out in accordance with the ethical standards provided in the 2000 revision of the Declaration of Helsinki. The first data were collected prior to the intervention, while the second data were collected at the end of the 8-week intervention. The data were collected at the Debre Markos referral hospital. We measured lipid profiles (TC, HDL and LDL) and glucose tolerance as primary outcomes and body fat percentage as a secondary outcome. The periods of follow-up (intervention) were from April 20 to 2022 to June 2022. Measurement of Study Variables Oral Glucose Tolerance Test (OGTT) The oral glucose tolerance test (OGTT) measures how well the body can breakdown and use sugar (glucose) as well as clear it from the blood stream (Bano, 2013 ). After an overnight fast, blood samples for determination of glucose concentrations were taken at 0, 30, 60, and 120 min after a standard 75 g of oral glucose dissolved in 300 ml of water was given orally, and the blood samples were allowed to drink within 5 minutes (Yassine et al., 2009 ). Blood samples were taken 48 hours after the previous training session and after a 12-hour fast. After being drawn, blood samples were centrifuged and kept at -80°C for 30 minutes. Glucose was analyzed by the hexokinase method (COBAS, Roche), and its intracoefficient of variation ranged between 1.58% (µ = 64.7 mg/dl) and 1.38% (µ = 369 mg/dl). The area under the curve of glucose was calculated using the trapezoidal rule and compared. PG-AUCs were calculated by trapezoidal approximation of PG levels (Sakaguchi et al., 2016 ). PG levels at x min were defined as PG( x ), and the PG-AUC was calculated as follows: $$\mathbf{A}\mathbf{U}\mathbf{C} (\text{m}\text{g}\text{h}/\text{d}\text{l})=\frac{\varvec{P}\varvec{G}\left(0\right)+PG\left(30\right)*2+PG\left(60\right)*3+PG\left(120\right)*2}{4}$$ Lipid profile After a 12-hour fasting period, 5 ml of blood was collected from the left median cubital vein of a seated individual at 8:00 AM. Following immediate centrifugation and refrigeration, the samples were analyzed within 24 hours. An enzymatic method utilizing an Alpha X autoanalyzer with E2HL-100 kits and a sensitivity of 0.1 mmol/dL (Hitachi, Tokyo, Japan) was employed for lipid measurement. Body Fat Percentage Abdominal, thigh, suprailiac and triceps skinfolds were measured on the right side of the body to the nearest 0.5 mm with a Lange caliper (Cambridge Scientific Instruments, Cambridge, MD, USA). All skinfolds were measured by the same technician. A reliability criterion of 2 mm was established for triplicate measurements, and the mean of these measurements was used for analytical purposes. To determine body fat percentage from skinfold measurements, we initially computed body density values using an equation specifically designed for older adults (Nevill et al., 2008 ). Body density = (0.29288 x sum of all the skinfolds) – (0.0005 x sum of all of the skinfolds squared) + (0.15845 x age) – 5.76377 The body density equations were converted into fat percentage by using the Siri equation: %BF = ((4.95/D) – 4.50) × 100 for the purpose of the analysis (Siri, 1993 ). Average daily energy intake We utilized a 24-hour interactive questionnaire with several passes that was developed and validated for use in developing countries (Gibson & Ferguson, 1999 ). The three 24-hour sessions were held on Monday, Wednesday and Saturday to capture variation in intakes throughout different days of the week. We applied the Ethiopian food composition table to estimate nutrient and energy levels from dietary data. The names of foods and drinks, their descriptions, cooking methods, and amounts from both 24-hour periods were coded and submitted to the NutriSurvey200 (Feyesa et al., 2020 ). After determining the frequency of consumption per day, we used the product sum approach to determine daily food intake. Daily food intake = ∑ (food item's stated consumption frequency, translated to times per day) * (portion size ingested of that food). The daily average energy intake was also determined as follows: ADEi = ∑daily food intake/number of data collected days. Exercise Intervention Protocol The individuals in the training groups were assigned to aerobic training (AT), resistance training (RT), or combined training (CT). The exercise regimens involved three weekly sessions over a period of eight weeks, with each session lasting 60 minutes. During each exercise session, participants engaged in a 5- to 10-minute warm-up, followed by 30 to 40 minutes of main training. Finally, they concluded with 5 to 10 minutes of cool-down. The resistance training (RT) program consisted of six exercises per session, specifically targeting the major muscle groups of the body. The exercises performed were standing plantar flexion, squatting, machine leg press, neutral reowing, bicep curl, tricep pulley, dumbbell curl, and vertical bench press. The exercise routine involved three sets per day, with 8 to 12 repetitions at an intensity of 50–75% of their one-repetition maximum (1RM). The remaining intervals between each set and between training sessions were approximately 1 to 1.5 minutes and 48 to 72 hours, respectively (Soori et al., 2014 ). The aerobic exercise involved using a treadmill at an intensity level ranging from 50–75% of the maximum heart rate (HR max). The goal was to burn approximately 500 calories per session (Asad, 2013 ). The control group (CG) engaged in a training regimen that combined the total volume of both the resistance group (RG) (3 exercises per session) and the endurance group (EG). In each session, participants performed endurance exercises before moving on to strength exercises. Details of the general training intervention approach are outlined in Table 1 .This specific exercise order was selected to explore the impact of aerobic training preceding strength training (Lepers et al., 2001 ). To minimize potential confounding factors, participants were explicitly advised not to engage in any additional resistance-type or aerobic training throughout the duration of the study. Table 1 Training detail Weeks Intensity Duration AT(HR max ) RT (1RM) CT AT (minutes) RT (3 sets) CT AT RT AT RT Week 1 &2 65% -70% 50%-55% 65% -70% 50%-55% 25 10-12 13 10-12 Week 3&4 70%-75% 55%-60% 70%-75% 55%-60% 30 10-12 15 10-12 Week 5&6 75%-80% 60%-65% 75%-80% 60%-65% 35 10-12 17 10-12 Week 7&8 80%-85% 70%-75% 80%-85% 70%-75% 40 10-12 20 10-12 Statistical analysis The data were analyzed using SPSS version 26 (SPSS Inc., Chicago, IL). A paired t test was used to examine the differences in the baseline and follow-up variables within the group because within-subject information in the RM-ANCOVA output was inaccurate (Schneider et al., 2015 ). The researchers ensured reliable results by applying a Bonferroni correction for multiple comparisons in their RM-ANCOVA, with average daily energy intake as the covariate, which was performed for HDL, LDL, TC and area under the curve (AUC) and %BF. Changes over time were compared among participants, while exercise types were compared among groups. Interactions between these factors were also investigated. All the statistical tests were two-tailed, and a p value of 0.05 or less was considered to indicate statistical significance. Results Table 2 summarizes the descriptive data characteristics at baseline and the adjusted absolute changes in BFP, AUC, HDL, LDL and TC levels during the study period. Four participants were dropped from the experiment due to exercise-induced injuries, leaving a total of 20 participants who finished the study and were included in the analysis. Table 2 Baseline and follow-up characteristics Variable ATG (Age = 49.00 ± 2.08, ADEi = 2885.00 ± 109.180) RTG (Age = 49.83 ± 3.06) ADEi = 2636.00 ± 98.82) CTG (Age = 48.71 ± 2.87) ADEi = 2753.70 ± 178.12) Baseline Follow-up Baseline Follow-up Baseline Follow-up %BF 21.07 ± 1.472 14.73 ± 0.39 22.36 ± 0.99 16.06 ± 0.55 21.91 ± 1.52 15.39 ± 0.294 AUC 294.03 ± 4.425 291.11 ± 2.7 294.33 ± 8.05 249.77 ± 3.76 294.3 ± 11.6 273.76 ± 2.004 HDL 39.94 ± 2.51 44.35 ± 1.019 39.09 ± 1.52 44.71 ± 1.417 40.69 ± 2.47 43.26 ± 0.753 LDL 128.50 ± 1.09 119.84 ± 1.32 127.19 ± 1.61 116.34 ± 1.84 128.9 ± 2.50 120.05 ± 0.979 TC 237.72 ± 8.20 170.46 ± 3.41 235.4 ± 3.19 209.91 ± 4.74 232.3 ± 4.94 187.38 ± 2.523 Note. %BF: Body fat percentage; AUC: Area under curve; HDL: High-density lipoprotein cholesterol; LDL: Low-density lipoprotein; TC: Total cholesterol. The values are presented as the means ± standard deviations. Covariates appearing in the model were evaluated at average daily energy intake (ADEi) = 2671.5620. a The average ages of the participants in the respective groups were AT = 49.00 ± 2.08, RT = 49.83 ± 3.06 and CT = 48.71 ± 2.87. The results revealed no significant differences in any of the variables among the three groups in the pretest, suggesting successful randomization of the study participants. However, there were significant differences in the %BF, AUC, HDL, LDL and TC between the pretest and posttest after 8 weeks of intervention in all three groups. The results indicated that after they received the aerobic training, body fat percentage in the aerobic training group in the AT group was t (6) = 9.306, p < 0.01; in the RT group, t(5) = 11.158, p < 0.01; and in the CT group, t(6) = 8.294, p < 0.01. The area under the curve for the older adults’ means for the pretest and posttest exams differed significantly between the aerobic training groups: t(6) = 7.054, p < 0.001; RT: t(5) = 11.904, p < 0.001; and CT: t(6) = 9.56, p < 0.001. We also found that high-density lipoprotein levels significantly improved between the pretest and posttest scores in the aerobic training group. This improvement was observed for AT (P < 0.012), RT (P < 0.013) and CT (P < 0.001). The study additionally demonstrated a statistically significant improvement in AT (t(6) = 33.806, p < 0.001), RT (t(6) = 12.504, p < 0.001) and CT (t(6) = 10.405, p < 0.001) in low-density lipoprotein (LDL) cholesterol levels between the pretest and posttest within the aerobic training group. In all three groups, the participants' total cholesterol levels decreased significantly after the training compared to before they started, as shown in Table 3 . Table 3 Test within-subject effect changes in outcomes by time points of intervention Variables AT RT CT MD(SD) t Sig. MD(SD) t Sig. MD(SD) t Sig %BF 5.39(1.53) 9.306 0.001 7.82(1.716) 11.15 0.001 6.15(1.964) 8.294 0.001 AUC 6.37(2.390) 7.054 0.001 38.99(8.023) 11.904 0.001 21.91(6.06) 9.562 0.001 HDL -4.18(1.97) -5.6 0.012 -5.97(2.133) -6.865 0.013 -2.485(1.798) -3.657 0.001 LDL 14.80(1.158) 33.806 0.011 6.12(1.198) 12.504 0.001 9.69(2.46) 10.405 0.001 TC 64.47(9.014) 18.992 0.001 30.03(2.627) 27.998 0.001 43.89(9.13) 12.707 0.001 Note: %BF: Body fat percentage; AUC: Area under curve; HDL: High-density lipoprotein cholesterol; LDL: Low-density lipoprotein; TC: Total cholesterol. MD (SD): mean (standard deviation) Table 4 shows that the main effect of exercise modality on the AUC was significant, F (2, 26) = 10.577, P = 0.001, η²=0.569. Post hoc analyses using the Bonferroni post hoc criterion for significance indicated that the area under the curve from the OGTT was significantly lower for RT (-30.653 ± 6.766, p = 0.001) and CT (M=-0.896, SE = 3.347, P = 0.015) than for AT. Table 4 Test between subject effect changes in outcomes within treatment groups Variables Between-Subjects Effects Pairwise comparison F Sig. b η² Treatment groups Mean Difference Std. Error Sig. b 95% CID Lower Bound Upper Bound %BF 1.179 NS 0.128 AT-RT -0.983 0.889 NS -3.385 1.419 AT-CT -0.671 0.44 NS -1.859 .517 RT-CT 0.312 0.739 NS -1.662 2.286 AUC 10.577 0.001 0.569 AT-RT 30.653 6.766 0.001 12.567 48.739 AT-CT 10.896 3.347 0.015 1.950 19.841 RT-CT -19.758 5.561 0.08 -34.622 -4.893 HDL 0.224 NS 0.027 AT-RT 1.436 2.149 NS -4.310 7.181 AT-CT 0.450 1.063 NS -2.392 3.292 RT-CT -0.983 1.767 NS -5.707 3.737 LDL 6.33 0.009 0.442 AT-RT 4.783 1.563 0.002 .606 8.960 AT-CT 0.213 0.773 NS -1.853 2.279 RT-CT -4.570 1.284 0.008 -8.003 -1.137 TC 4.849 0.023 0.377 AT-RT -17.716 5.705 0.02 -32.965 -2.468 AT-CT -5.570 2.822 NS -13.112 1.972 RT-CT 12.146 4.689 0.050 − .387 24.678 Note: %BF: body fat percentage; AUC: area under the curve; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein; TC: total cholesterol; NS: not significant. Covariates appearing in the model were evaluated at the following values: average daily energy intake = 2671.5620. Low-density lipoprotein F (2, 26) = 6.33, p = 0.009, η²= 0.442 parameters were significantly lower in the AT and CT groups (MD = 4.783, SE = 1.563, P = 0.002 and M = 4.57, SE = 1.284, P = 0.008, respectively) than in the RT group. There was a significant difference in total cholesterol between the training modalities, F (2, 16) = 4.849, P = 0.023, η²=0.442. Interns with IV AT showed a significant reduction in RT (MD= -17.716, SE = 5.705, P = 0.02). Despite observing a significant difference between the pretest and posttest results for high-density lipoprotein (HDL) and percentage of body fat (%BF), there was no significant difference attributable to the independent variables affecting these variables. There was a significant group × time interaction for the area under the curve from the OGTT test results (F (2, 16) = 9.002, p = 0.002), η²=0.530, high-density lipoprotein: F (2, 16) = 4.064, P = 0.037, η²=0.337, TC: F (2, 16) = 15.075, P < 0.001, η²=0.653), and no significant interaction for body fat percentage and high-density lipoprotein was detected. The results suggest that time had a different effect on the AUC, LDL and TC depending on the group, while the effect of time on %BF and HDL was the same across all groups. Discussion Over an intervention time of eight weeks, exercise emerged as a potent metabolic health intervention for obese older adult men. This program resulted in a remarkable decrease in body fat percentage and the levels of both “bad” cholesterol (LDL and TC). Simultaneously, it enhances the body’s capacity to regulate blood sugar (glucose tolerance). Intriguingly, throughout the program, “good” cholesterol (HDL) levels improved while average daily energy intake was controlled. These findings align with studies involving longer exercise durations, such as the 32-week program described (Sousa et al., 2014 ). These findings have established a physiological foundation supporting the hypothesis that resistance training may elicit favorable alterations in glucose tolerance. Similarly, other investigators have reported that strength training also reduces basal and glucose-stimulated insulin levels (Craig et al., 1989 ; Miller et al., 1984 ). In contrast, both the aerobic training (AT) and resistance training (RT) groups demonstrated improvements in glucose tolerance (Smutok et al., 1994). Our analysis indicated that compared with RE, AE and concurrent training are the most effective exercise modalities for reducing LDL and TC in older adults. The present study implemented an 8-month aerobic exercise intervention involving 111 randomly selected participants. The results indicated that the exercise group experienced a significant reduction in total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels among centrally obese older adults (Kraus et al., 2002 ). A similar study involving sedentary older adults who participated in a 16-week exercise intervention was performed. The participants were randomly assigned to one of the following groups: resistance training, aerobic walking, or combined exercise. All groups had lower lipid levels over time, including the RE group (Boardley et al., 2007 ). This discrepancy could be attributed to inadequate energy expenditure during resistance exercise compared to aerobic exercise with low to moderate intensity (Reis et al., 2017 ). The results of this study highlight that there were no significant differences in the effect on high-density lipoprotein (HDL) or percentage of body fat (%BF) levels among AT, RT and CT. These results align with the work of Moraleda et al., who similarly demonstrated that HDL cholesterol remained significantly unchanged across groups (Moraleda et al., 2013 ) and % BF(Willis et al., 2012 ). In contrast, in a previous study, resistance exercise exhibited a more robust association with HDL levels than did aerobic exercise (β = 2.56210, p < 0.0001 vs. 1.33748, p < 0.0001) (Hsu et al., 2019 ). In contrast to our results, fat mass was reduced more in the AT and CT groups than in the RT group (Willis et al., 2012 ). Conclusion Aerobic training (AT) and combined training (CT) were more effective at reducing low-density lipoprotein (LDL) and total cholesterol (TC) levels. Moreover, resistance training (RT) and combined training (CT) demonstrated greater efficacy than aerobic training (AT) in reducing glucose intolerance among previously inactive obese older adults. Interestingly, there was a significant change in the pretest and posttest levels of high-density lipoprotein (HDL) and percentage of body fat (%BF). However, when comparing aerobic training (AT), resistance training (RT), or a combination of both programs, there was no significant difference. Interestingly, the effect of time on AUC, LDL, and TC varied depending on the group, while the effect of time on %BF and HDL remained consistent across all groups. Declarations Acknowledgement The authors would like to express their gratitude to the Debere Markos University for its fund for our research and gym manager and Servicemen for their permission, Debre Markos Referral Hospital as well as to the participants, without whose cooperation this study could not have been completed. Adverse effect report No adverse effects were recorded in participants during and after the intervention. Authors’ contributions The study was conducted by F.A. who played a multifaceted role, including conceiving and designing the analysis, collecting data, contributing tools, performing the analysis, and writing the paper. Y.A. significantly improved the manuscript through contributions to research implementation, result analysis, and critical revisions. M.E. ensured proper research investigation and assisted with implementation and data collection. Y.D. provided overall project direction and planning, guaranteeing proper investigation and resolution of any accuracy or integrity concerns. Finally, S.A. ensured thorough research examination and assisted with application and data collection.. All authors read and approve the final manuscript. Funding The Debre Markos University Annual Research Funding provided assistance for this work. The financial funding from Debre Markos University has been crucial in supporting the research efforts for the project. The funding body did not participate in the study's design, data collection, analysis, or interpretation, or manuscript writing. Ethics approval and consent to participate The trial was approved by the Ethics Review Board of Debre Markos University of Sport Sciences Academy (Reference number: SPSC 05/22). All the participants were informed about the intervention and possible adverse events before the commencement of the trial and signed an informed consent form. Consent for publication Not Applicable. Competing Interests In conclusion, the authors affirm that there are no competing interests, financial or otherwise, that have influenced this research. This declaration is made with the intent to uphold the principles of academic integrity and transparency in research. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to data security before publication but are available from the corresponding author on reasonable request. References Adeloye, D., Ige-Elegbede, J. O., Ezejimofor, M., Owolabi, E. O., Ezeigwe, N., Omoyele, C., Mpazanje, R. G., Dewan, M. T., Agogo, E., & Gadanya, M. A. (2021). Estimating the prevalence of overweight and obesity in Nigeria in 2020: a systematic review and meta-analysis. Annals of medicine , 53 (1), 495-507. Asad, M. (2013). Effect of 8 weeks aerobic, resistance and concurrent training on cholestrol, LDL, HDL and cardiovascular fitness in obesity male. Applied Research in Sport Management , 1 (3), 57-64. Azarbayjani, M. A., Abedi, B., Peeri, M., Rasaee, M. J., & Stannard, S. R. (2014). Effects of combined aerobic and resistant training on lipid profile and glycemic control in sedentary men. Int Med J , 21 (2), 132-136. Bano, G. (2013). 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European guidelines on cardiovascular disease prevention in clinical practice; third joint task force of European and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of eight societies and by invited experts). European Journal of Preventive Cardiology , 10 (4), S1-S10. https://doi.org/https://doi.org/10.1097/01.hjr.0000087913.96265.e2 Doewes, R. I., Gharibian, G., Zaman, B. A., & Akhavan-Sigari, R. (2023). An updated systematic review on the effects of aerobic exercise on human blood lipid profile. Current problems in cardiology , 48 (5), 101108. Ekkekakis, P. (2009). Let them roam free? Physiological and psychological evidence for the potential of self-selected exercise intensity in public health. Sports medicine , 39 , 857-888. Feyesa, I., Endris, B. S., Habtemariam, E., Hassen, H. Y., & Gebreyesus, S. H. (2020). Development and Validation of Food Frequency Questionnaire for Food and Nutrient Intake of Adults in Butajira, Southern Ethiopia. Research Square . Ghodeshwar, G. K., Dube, A., & Khobragade, D. (2023). Impact of Lifestyle Modifications on Cardiovascular Health: A Narrative Review. Cureus , 15 (7), e42616. https://doi.org/10.7759/cureus.42616 Gibson, R. S., & Ferguson, E. L. (1999). An interactive 24-hour recall for assessing the adequacy of iron and zinc intakes in developing countries . ILSI Press Washington, DC. Hordern, M. D., Dunstan, D. W., Prins, J. B., Baker, M. K., Singh, M. A., & Coombes, J. S. (2012). Exercise prescription for patients with type 2 diabetes and pre-diabetes: a position statement from Exercise and Sport Science Australia. J Sci Med Sport , 15 (1), 25-31. https://doi.org/10.1016/j.jsams.2011.04.005 Hsu, C.-S., Chang, S.-T., Nfor, O. N., Lee, K.-J., Lee, S.-S., & Liaw, Y.-P. (2019). Effects of regular aerobic exercise and resistance training on high-density lipoprotein cholesterol levels in Taiwanese adults. International journal of environmental research and public health , 16 (11), 2003. Kassie, A. M., Abate, B. B., & Kassaw, M. W. (2020). Prevalence of overweight/obesity among the adult population in Ethiopia: a systematic review and meta-analysis. BMJ open , 10 (8). Kim, K.-B., Kim, K., Kim, C., Kang, S.-J., Kim, H. J., Yoon, S., & Shin, Y.-A. (2019). Effects of exercise on the body composition and lipid profile of individuals with obesity: a systematic review and meta-analysis. Journal of obesity & metabolic syndrome , 28 (4), 278. Kraemer, W. J., Ratamess, N. A., Hymer, W. C., Nindl, B. C., & Fragala, M. S. (2020). Growth hormone (s), testosterone, insulin-like growth factors, and cortisol: roles and integration for cellular development and growth with exercise. Frontiers in endocrinology , 11 , 513110. https://doi.org/https://doi.org/10.3389/fendo.2020.00033 Kraus, W. E., Houmard, J. A., Duscha, B. D., Knetzger, K. J., Wharton, M. B., McCartney, J. S., Bales, C. W., Henes, S., Samsa, G. P., & Otvos, J. D. (2002). Effects of the amount and intensity of exercise on plasma lipoproteins. New England Journal of Medicine , 347 (19), 1483-1492. Lepers, R., Millet, G. Y., & Maffiuletti, N. A. (2001). Effect of cycling cadence on contractile and neural properties of knee extensors. Medicine & Science in Sports & Exercise , 33 (11), 1882-1888. Lin, X., & Li, H. (2021). Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front Endocrinol (Lausanne) , 12 , 706978. https://doi.org/10.3389/fendo.2021.706978 Loucks, A. B., & Caiozzo, M. (2012). The endocrine system: integrated influences on metabolism, growth, and reproduction. Acsm’s advanced exercise physiology. 2nd ed. Philadelphia (PA): Wolters Kluwer Lippincott Williams & Wilkins , 466-506. Mann, S., Beedie, C., & Jimenez, A. (2014). Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. Sports medicine , 44 , 211-221. Matthews, C. E., Hagströmer, M., Pober, D. M., & Bowles, H. R. (2012). Best practices for using physical activity monitors in population-based research. Medicine and science in sports and exercise , 44 (1 Suppl 1), S68. Miller, W., Sherman, W., & Ivy, J. (1984). Effect of strength training on glucose tolerance and post-glucose insulin response. Medicine and science in sports and exercise , 16 (6), 539-543. Moraleda, B. R., Morencos, E., Peinado, A. B., Bermejo, L., Candela, C. G., Benito, P. J., & Group, P. S. (2013). Can the exercise mode determine lipid profile improvements in obese patients? Nutrición hospitalaria , 28 (3), 607-617. Nevill, A. M., Metsios, G. S., Jackson, A. S., Wang, J., Thornton, J., & Gallagher, D. (2008). Can we use the Jackson and Pollock equations to predict body density/fat of obese individuals in the 21st century? Int J Body Compos Res , 6 (3), 114-121. Noland, R. C. (2015). Exercise and regulation of lipid metabolism. Progress in molecular biology and translational science , 135 , 39-74. Okati-Aliabad, H., Ansari-Moghaddam, A., Kargar, S., & Jabbari, N. (2022). Prevalence of obesity and overweight among adults in the middle east countries from 2000 to 2020: a systematic review and meta-analysis. Journal of Obesity , 2022 . Pi-Sunyer, X. (2009). The medical risks of obesity. Postgraduate medicine , 121 (6), 21-33. https://doi.org/https://doi.org/10.3810/pgm.2009.11.2074 Reis, V. M., Garrido, N. D., Vianna, J., Sousa, A. C., Alves, J. V., & Marques, M. C. (2017). Energy cost of isolated resistance exercises across low-to high-intensities. PloS one , 12 (7), e0181311. Sakaguchi, K., Takeda, K., Maeda, M., Ogawa, W., Sato, T., Okada, S., Ohnishi, Y., Nakajima, H., & Kashiwagi, A. (2016). Glucose area under the curve during oral glucose tolerance test as an index of glucose intolerance. Diabetol Int , 7 (1), 53-58. https://doi.org/10.1007/s13340-015-0212-4 Schneider, B. A., Avivi-Reich, M., & Mozuraitis, M. (2015). A cautionary note on the use of the Analysis of Covariance (ANCOVA) in classification designs with and without within-subject factors. Frontiers in psychology , 6 , 135022. Siri, W. E. (1993). Body composition from fluid spaces and density: analysis of methods. 1961. Nutrition , 9 (5), 480-491; discussion 480, 492. Solomon, T. P. (2018). Sources of inter-individual variability in the therapeutic response of blood glucose control to exercise in type 2 diabetes: going beyond exercise dose. Frontiers in Physiology , 9 , 896. Soori, R., Ravasi, A., & Ranjbar, K. (2014). The comparison of between endurance and resistance training on vaspin and adiponectin in obese middle-age men. Sport Physiology , 5 (20), 97-114. Sousa, N., Mendes, R., Abrantes, C., Sampaio, J., & Oliveira, J. (2014). A randomized study on lipids response to different exercise programs in overweight older men. International journal of sports medicine , 1106-1111. https://doi.org/http://dx.doi.org/ 10.1055/s-0034-1374639 SportsMedicine, A. (1991). Guidelines for exercise testing and prescription . Williams & Wilkins. Willis, L. H., Slentz, C. A., Bateman, L. A., Shields, A. T., Piner, L. W., Bales, C. W., Houmard, J. A., & Kraus, W. E. (2012). Effects of aerobic and/or resistance training on body mass and fat mass in overweight or obese adults. Journal of applied physiology , 113 (12), 1831-1837. https://doi.org/10.1152/japplphysiol.01370.2011 Yassine, H. N., Marchetti, C. M., Krishnan, R. K., Vrobel, T. R., Gonzalez, F., & Kirwan, J. P. (2009). Effects of exercise and caloric restriction on insulin resistance and cardiometabolic risk factors in older obese adults—a randomized clinical trial. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences , 64 (1), 90-95. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Sep, 2024 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 02 May, 2024 Editor assigned by journal 02 May, 2024 Submission checks completed at journal 30 Apr, 2024 First submitted to journal 26 Apr, 2024 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-4328501","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297922204,"identity":"d5bf0803-ccdd-4658-be27-897008d5b5eb","order_by":0,"name":"Friew Amare","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PMQrCMBSA4ciDZHnSNaD0DIWACApexS6d0huIIIVM4txjODkXg3XpARxcpODgpAguYrHN5NTWTTD/kBdCPkgIsdl+MBaZMcaJhqTc8H4jQW1G4JK9mlYEm0lixlaQLPPMQTNh3VOOs8RfxPJ+OcyGSJjerWsJMCEwPfoRDzcjmZYPwyA41JEJUNqT9OyrkghJS8JxUEsQKHvKQvtLLs9CFu0IhVBpwTGDPFTtCPReq8D1mBpAuOJIm/6CTtq5xY8xegD5XT7mrsN0Wks+o9ysba9XwfWb2zabzfY/vQGjYkG1sH1XNwAAAABJRU5ErkJggg==","orcid":"","institution":"Debre Markos University","correspondingAuthor":true,"prefix":"","firstName":"Friew","middleName":"","lastName":"Amare","suffix":""},{"id":297922205,"identity":"820d75d4-9297-4bbb-a447-dc95425c2be4","order_by":1,"name":"Yehualaw Alemu","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Yehualaw","middleName":"","lastName":"Alemu","suffix":""},{"id":297922207,"identity":"bef44829-e693-4a53-a7b3-d189b1195748","order_by":2,"name":"Mollalign Enichalew","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Mollalign","middleName":"","lastName":"Enichalew","suffix":""},{"id":297922209,"identity":"be680dc7-af79-45eb-a63c-d762968565f0","order_by":3,"name":"Yalemsew Demlie","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Yalemsew","middleName":"","lastName":"Demlie","suffix":""},{"id":297922211,"identity":"9a702e48-8750-42a1-8d0c-bba390559af2","order_by":4,"name":"Solomon Adamu","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Solomon","middleName":"","lastName":"Adamu","suffix":""}],"badges":[],"createdAt":"2024-04-26 09:10:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4328501/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4328501/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13102-024-00982-7","type":"published","date":"2024-09-11T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56027837,"identity":"ea88d79e-c53e-4005-8916-fba93bc39663","added_by":"auto","created_at":"2024-05-07 17:24:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":249460,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant description chart\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4328501/v1/9f4699521ea16e6e08e6bef5.jpg"},{"id":64619399,"identity":"f7e49189-3afc-4487-bb09-6be8702e7218","added_by":"auto","created_at":"2024-09-16 16:14:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4328501/v1/6e10f5b0-db96-4072-b92f-ab54e1f47c26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMetabolic Impact of Exercise Modalities in Inactive Obese Adults: A Randomized Controlled Trial\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is the condition of having an abnormal or excessive accumulation of fat that poses a health risk. A body mass index (BMI) of over 30 is considered to be indicative of obesity (Hordern et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The prevalence of obesity increased with age in this study, with the highest prevalence (44.3%) among middle-aged adults (40\u0026ndash;59 years old) (Boutari \u0026amp; Mantzoros, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While women and older adults are generally more likely to experience obesity, these data suggest a trend across all age groups (Lin \u0026amp; Li, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The incidence of obesity has been on the rise in recent decades and has been linked to a variety of health and socioeconomic issues (Bl\u0026uuml;her, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is important to prioritize strategies that can help reduce the healthcare costs associated with obesity.\u003c/p\u003e \u003cp\u003eThe combined estimates of the prevalence of obesity and overweight in Middle Eastern countries were 21.17% and 33.14%, respectively (Okati-Aliabad et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The combined crude prevalence rate of obesity in Nigeria was 14.3% (Adeloye et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Like in many other countries, overweight and obesity are becoming increasingly prevalent public health issues in Ethiopia (Kassie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObesity is a significant risk factor for and contributor to increased morbidity and mortality, especially as individuals age, most importantly from cardiovascular disease (CVD) and diabetes but also from cancer and chronic diseases, including osteoarthritis, liver and kidney disease, sleep apnea, and depression (Ghodeshwar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pi-Sunyer, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe can therefore strongly suggest that interventions improving both blood sugar control and cholesterol levels would be highly effective in preventing cardiovascular diseases (De Backer et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Optimizing exercise for health goes beyond just choosing an activity. A personalized approach that considers the type of exercise is required (Ekkekakis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Exercise has been shown to enhance glycemic control and improve blood lipid profiles in individuals both with and without type 2 diabetes, as supported by different studies (Kim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Solomon, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Extensive research has been conducted on the effects of aerobic training and strength modalities of exercise on various health outcomes (Doewes et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Research has supported the combined benefits of AT and RT to improve glycemic control and lipid parameter health in obese older adults (Azarbayjani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring longer, low-effort workouts, the body burns fat for most of its energy and becomes better at breaking down fat stores (lipolysis) (Brown, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Noland, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). High-intensity strength training burns mostly carbohydrates for immediate energy (Brown, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). It also triggers the release of hormones such as growth hormone and testosterone (Kraemer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By influencing the body's chemistry, hormones promote muscle growth and make it easier to access the body's ability to burn glucose derived from fat stores (lipolysis) (Loucks \u0026amp; Caiozzo, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Regardless of the type or intensity of exercise, hormonal changes affecting blood lipid levels are not significantly impacted when individuals burn the same amount of calories (Crouse et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). To accurately assess the impact of exercise programs on health, participants need to follow a strict dietary monitoring protocol (Matthews et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This involved carefully tracking the participants' food intake, often through a food frequency questionnaire, to ensure that any observed changes were primarily due to the exercise intervention itself and not influenced by significant shifts in their dietary habits (Beck et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHence, the objective of this study was to assess and compare the efficacy of various exercise modes (aerobic, resistance training, and combined exercise) and changes over time in enhancing lipid profile changes and glucose tolerance among adults who are obese.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eResearch Setting and Design\u003c/h2\u003e\n\u003cp\u003eThis research combined pretest posttest with a repeated measures design component to capture within-subject changes over time with a randomized parallel experimental design to assess between-group differences.\u003c/p\u003e\n\u003cp\u003eThe study involved physically inactive persons between the ages of 45 and 60 years who had a BMI greater than 30 kg/m2. The participants were selected from a volunteer pool of inactive residents of Debre Markos town, Ethiopia and were informed through local radio and notice board postings.\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were as follows: (a) had a BMI\u0026thinsp;\u0026gt;\u0026thinsp;30.0 kg/m\u003csup\u003e2,\u003c/sup\u003e (b) were aged between 45 and 60 years, (c) volunteered to participate, (d) were physically inactive (not achieving 30\u0026ndash;60 min per day or 150 min per week of moderate intensity exercise or 20\u0026ndash;60 min per day (75 min per week) of vigorous intensity (SportsMedicine, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e) and cleared a medical history form the physical activity readiness questionnaire, and (d) were able to perform the necessary exercises. The exclusion criteria were as follows: (a) any cardiovascular, respiratory, or muscle-skeletal disorders precluding physical exercise; (b) uncontrolled hyperglycemia (\u0026ge;\u0026thinsp;126 mg/d) or hypertension (a resting blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/100 mm Hg); and (c) active infection, (d) acute myocardial infarction, stroke, trauma, surgery or severe liver dysfunction.\u003c/p\u003e\n\u003cp\u003eThe ideal sample size for our investigation was determined by using the G*Power program. We determined a sample size of 21 people, as described by Sousa et al., taking into account zero correlation among each measure TGC, Total-c, LDL-c, and HDL-c in the ATG (Sousa et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), a significance level (\u0026alpha;) of 0.05, and a power of 0.8. An overall sample size of 24 was needed, accounting for a 10% nonresponse rate. Our study's intended statistical power and significance level were achieved by successfully detecting the given effect size with this sample size.\u003c/p\u003e\n\u003cp\u003eAfter applying the inclusion criteria, 32 out of the 36 volunteers who we had initially registered as physically inactive remained. The researchers were created a final study group consisting of 24 participants via a simple random selection procedure. After that, these people were randomized at random to one of three exercise groups (each with eight participants): concurrent training, strength training, or aerobic training. The study was conducted in Debere Markos, Ethiopia, using homogeneous samples and balanced randomization (1:1:1) (Fig.\u0026nbsp;1). The data collectors were blinded to one another during the study.\u003c/p\u003e\n\u003cp\u003eBefore obtaining their informed consent, participants were completely told about all procedures, hazards and protocols to ensure ethical conduct. This satisfied the requirements set forth by the American College of Sports Medicine (SportsMedicine, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e). Additionally, the Sport Academy research ethics committee at Debre Markos University reviewed and provided input on all procedures involving human subjects. Lastly, the study was carried out in accordance with the ethical standards provided in the 2000 revision of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eThe first data were collected prior to the intervention, while the second data were collected at the end of the 8-week intervention. The data were collected at the Debre Markos referral hospital. We measured lipid profiles (TC, HDL and LDL) and glucose tolerance as primary outcomes and body fat percentage as a secondary outcome. The periods of follow-up (intervention) were from April 20 to 2022 to June 2022.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eMeasurement of Study Variables\u003c/h2\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003eOral Glucose Tolerance Test (OGTT)\u003c/h2\u003e\n\u003cp\u003eThe oral glucose tolerance test (OGTT) measures how well the body can breakdown and use sugar (glucose) as well as clear it from the blood stream (Bano, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). After an overnight fast, blood samples for determination of glucose concentrations were taken at 0, 30, 60, and 120 min after a standard 75 g of oral glucose dissolved in 300 ml of water was given orally, and the blood samples were allowed to drink within 5 minutes (Yassine et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Blood samples were taken 48 hours after the previous training session and after a 12-hour fast. After being drawn, blood samples were centrifuged and kept at -80\u0026deg;C for 30 minutes. Glucose was analyzed by the hexokinase method (COBAS, Roche), and its intracoefficient of variation ranged between 1.58% (\u0026micro;\u0026thinsp;=\u0026thinsp;64.7 mg/dl) and 1.38% (\u0026micro;\u0026thinsp;=\u0026thinsp;369 mg/dl). The area under the curve of glucose was calculated using the trapezoidal rule and compared. PG-AUCs were calculated by trapezoidal approximation of PG levels (Sakaguchi et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). PG levels at \u003cem\u003ex\u003c/em\u003e min were defined as PG(\u003cem\u003ex\u003c/em\u003e), and the PG-AUC was calculated as follows:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\mathbf{A}\\mathbf{U}\\mathbf{C} (\\text{m}\\text{g}\\text{h}/\\text{d}\\text{l})=\\frac{\\varvec{P}\\varvec{G}\\left(0\\right)+PG\\left(30\\right)*2+PG\\left(60\\right)*3+PG\\left(120\\right)*2}{4}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eLipid profile\u003c/h2\u003e\n\u003cp\u003eAfter a 12-hour fasting period, 5 ml of blood was collected from the left median cubital vein of a seated individual at 8:00 AM. Following immediate centrifugation and refrigeration, the samples were analyzed within 24 hours. An enzymatic method utilizing an Alpha X autoanalyzer with E2HL-100 kits and a sensitivity of 0.1 mmol/dL (Hitachi, Tokyo, Japan) was employed for lipid measurement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eBody Fat Percentage\u003c/h2\u003e\n\u003cp\u003eAbdominal, thigh, suprailiac and triceps skinfolds were measured on the right side of the body to the nearest 0.5 mm with a Lange caliper (Cambridge Scientific Instruments, Cambridge, MD, USA). All skinfolds were measured by the same technician. A reliability criterion of 2 mm was established for triplicate measurements, and the mean of these measurements was used for analytical purposes. To determine body fat percentage from skinfold measurements, we initially computed body density values using an equation specifically designed for older adults (Nevill et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody density\u003c/strong\u003e = (0.29288 x sum of all the skinfolds) \u0026ndash; (0.0005 x sum of all of the skinfolds squared) + (0.15845 x age) \u0026ndash; 5.76377\u003c/p\u003e\n\u003cp\u003eThe body density equations were converted into fat percentage by using the Siri equation: %BF = ((4.95/D) \u0026ndash; 4.50) \u0026times; 100 for the purpose of the analysis (Siri, \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eAverage daily energy intake\u003c/h2\u003e\n\u003cp\u003eWe utilized a 24-hour interactive questionnaire with several passes that was developed and validated for use in developing countries (Gibson \u0026amp; Ferguson, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). The three 24-hour sessions were held on Monday, Wednesday and Saturday to capture variation in intakes throughout different days of the week. We applied the Ethiopian food composition table to estimate nutrient and energy levels from dietary data. The names of foods and drinks, their descriptions, cooking methods, and amounts from both 24-hour periods were coded and submitted to the NutriSurvey200 (Feyesa et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). After determining the frequency of consumption per day, we used the product sum approach to determine daily food intake. Daily food intake = \u0026sum; (food item's stated consumption frequency, translated to times per day) * (portion size ingested of that food). The daily average energy intake was also determined as follows: ADEi = \u0026sum;daily food intake/number of data collected days.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eExercise Intervention Protocol\u003c/h2\u003e\n\u003cp\u003eThe individuals in the training groups were assigned to aerobic training (AT), resistance training (RT), or combined training (CT). The exercise regimens involved three weekly sessions over a period of eight weeks, with each session lasting 60 minutes. During each exercise session, participants engaged in a 5- to 10-minute warm-up, followed by 30 to 40 minutes of main training. Finally, they concluded with 5 to 10 minutes of cool-down. The resistance training (RT) program consisted of six exercises per session, specifically targeting the major muscle groups of the body. The exercises performed were standing plantar flexion, squatting, machine leg press, neutral reowing, bicep curl, tricep pulley, dumbbell curl, and vertical bench press. The exercise routine involved three sets per day, with 8 to 12 repetitions at an intensity of 50\u0026ndash;75% of their one-repetition maximum (1RM). The remaining intervals between each set and between training sessions were approximately 1 to 1.5 minutes and 48 to 72 hours, respectively (Soori et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The aerobic exercise involved using a treadmill at an intensity level ranging from 50\u0026ndash;75% of the maximum heart rate (HR max). The goal was to burn approximately 500 calories per session (Asad, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The control group (CG) engaged in a training regimen that combined the total volume of both the resistance group (RG) (3 exercises per session) and the endurance group (EG). In each session, participants performed endurance exercises before moving on to strength exercises. Details of the general training intervention approach are outlined in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.This specific exercise order was selected to explore the impact of aerobic training preceding strength training (Lepers et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). To minimize potential confounding factors, participants were explicitly advised not to engage in any additional resistance-type or aerobic training throughout the duration of the study.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 1\u003c/p\u003e\n\u003cp\u003eTraining detail\u003c/p\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eWeeks\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"300\"\u003e\n\u003cp\u003e\u003cstrong\u003eIntensity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"202\"\u003e\n\u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"78\"\u003e\n\u003cp\u003eAT(HR \u003csub\u003emax\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"72\"\u003e\n\u003cp\u003eRT (1RM)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"150\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"67\"\u003e\n\u003cp\u003eAT (minutes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"48\"\u003e\n\u003cp\u003eRT (3 sets)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"87\"\u003e\n\u003cp\u003eCT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003eRT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36\"\u003e\n\u003cp\u003eAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003eRT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eWeek 1 \u0026amp;2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e65% -70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e50%-55%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e65% -70%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e50%-55%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eWeek 3\u0026amp;4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e70%-75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e55%-60%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e70%-75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e55%-60%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eWeek 5\u0026amp;6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e75%-80%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e60%-65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e75%-80%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e60%-65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eWeek 7\u0026amp;8\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e80%-85%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e70%-75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e80%-85%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e70%-75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"48\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"36\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"51\"\u003e\n\u003cp\u003e10-12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eThe data were analyzed using SPSS version 26 (SPSS Inc., Chicago, IL). A paired t test was used to examine the differences in the baseline and follow-up variables within the group because within-subject information in the RM-ANCOVA output was inaccurate (Schneider et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The researchers ensured reliable results by applying a Bonferroni correction for multiple comparisons in their RM-ANCOVA, with average daily energy intake as the covariate, which was performed for HDL, LDL, TC and area under the curve (AUC) and %BF. Changes over time were compared among participants, while exercise types were compared among groups. Interactions between these factors were also investigated. All the statistical tests were two-tailed, and a p value of 0.05 or less was considered to indicate statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the descriptive data characteristics at baseline and the adjusted absolute changes in BFP, AUC, HDL, LDL and TC levels during the study period. Four participants were dropped from the experiment due to exercise-induced injuries, leaving a total of 20 participants who finished the study and were included in the analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline and follow-up characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eATG (Age\u0026thinsp;=\u0026thinsp;49.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08, ADEi\u0026thinsp;=\u0026thinsp;2885.00\u0026thinsp;\u0026plusmn;\u0026thinsp;109.180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRTG (Age\u0026thinsp;=\u0026thinsp;49.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06)\u003c/p\u003e \u003cp\u003eADEi\u0026thinsp;=\u0026thinsp;2636.00\u0026thinsp;\u0026plusmn;\u0026thinsp;98.82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCTG (Age\u0026thinsp;=\u0026thinsp;48.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87)\u003c/p\u003e \u003cp\u003eADEi\u0026thinsp;=\u0026thinsp;2753.70\u0026thinsp;\u0026plusmn;\u0026thinsp;178.12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%BF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e22.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e16.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e21.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e15.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e294.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e291.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e294.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e249.77\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e294.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e273.76\u0026thinsp;\u0026plusmn;\u0026thinsp;2.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e39.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e39.09 \u0026plusmn; 1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e44.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e40.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e43.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e128.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e119.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e127.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e116.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e128.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e120.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e237.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e170.46\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e235.4 \u0026plusmn; 3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e209.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e232.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e187.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote. %BF: Body fat percentage; AUC: Area under curve; HDL: High-density lipoprotein cholesterol; LDL: Low-density lipoprotein; TC: Total cholesterol. The values are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Covariates appearing in the model were evaluated at average daily energy intake (ADEi)\u0026thinsp;=\u0026thinsp;2671.5620.\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe average ages of the participants in the respective groups were AT\u0026thinsp;=\u0026thinsp;49.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08, RT\u0026thinsp;=\u0026thinsp;49.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06 and CT\u0026thinsp;=\u0026thinsp;48.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87. The results revealed no significant differences in any of the variables among the three groups in the pretest, suggesting successful randomization of the study participants.\u003c/p\u003e \u003cp\u003eHowever, there were significant differences in the %BF, AUC, HDL, LDL and TC between the pretest and posttest after 8 weeks of intervention in all three groups. The results indicated that after they received the aerobic training, body fat percentage in the aerobic training group in the AT group was t (6)\u0026thinsp;=\u0026thinsp;9.306, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; in the RT group, t(5)\u0026thinsp;=\u0026thinsp;11.158, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; and in the CT group, t(6)\u0026thinsp;=\u0026thinsp;8.294, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. The area under the curve for the older adults\u0026rsquo; means for the pretest and posttest exams differed significantly between the aerobic training groups: t(6)\u0026thinsp;=\u0026thinsp;7.054, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RT: t(5)\u0026thinsp;=\u0026thinsp;11.904, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and CT: t(6)\u0026thinsp;=\u0026thinsp;9.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. We also found that high-density lipoprotein levels significantly improved between the pretest and posttest scores in the aerobic training group. This improvement was observed for AT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.012), RT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.013) and CT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The study additionally demonstrated a statistically significant improvement in AT (t(6)\u0026thinsp;=\u0026thinsp;33.806, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RT (t(6)\u0026thinsp;=\u0026thinsp;12.504, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CT (t(6)\u0026thinsp;=\u0026thinsp;10.405, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in low-density lipoprotein (LDL) cholesterol levels between the pretest and posttest within the aerobic training group. In all three groups, the participants' total cholesterol levels decreased significantly after the training compared to before they started, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest within-subject effect changes in outcomes by time points of intervention\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMD(SD)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSig.\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMD(SD)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSig.\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eMD(SD)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eSig\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%BF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.39(1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.82(1.716)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.15(1.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.37(2.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.99(8.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.91(6.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.18(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.97(2.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.485(1.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-3.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.80(1.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.12(1.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.69(2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.47(9.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.03(2.627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.89(9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote: %BF: Body fat percentage; AUC: Area under curve; HDL: High-density lipoprotein cholesterol; LDL: Low-density lipoprotein; TC: Total cholesterol. MD (SD): mean (standard deviation)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that the main effect of exercise modality on the AUC was significant, F (2, 26)\u0026thinsp;=\u0026thinsp;10.577, P\u0026thinsp;=\u0026thinsp;0.001, η\u0026sup2;=0.569. Post hoc analyses using the Bonferroni post hoc criterion for significance indicated that the area under the curve from the OGTT was significantly lower for RT (-30.653\u0026thinsp;\u0026plusmn;\u0026thinsp;6.766, p\u0026thinsp;=\u0026thinsp;0.001) and CT (M=-0.896, SE\u0026thinsp;=\u0026thinsp;3.347, P\u0026thinsp;=\u0026thinsp;0.015) than for AT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest between subject effect changes in outcomes within treatment groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBetween-Subjects Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c10\" namest=\"c5\"\u003e \u003cp\u003ePairwise comparison\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eη\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e95% CID\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e%BF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e10.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-19.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-34.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-4.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-RT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-17.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-32.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-13.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: %BF: body fat percentage; AUC: area under the curve; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein; TC: total cholesterol; NS: not significant. Covariates appearing in the model were evaluated at the following values: average daily energy intake\u0026thinsp;=\u0026thinsp;2671.5620.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLow-density lipoprotein F (2, 26)\u0026thinsp;=\u0026thinsp;6.33, p\u0026thinsp;=\u0026thinsp;0.009, η\u0026sup2;= 0.442 parameters were significantly lower in the AT and CT groups (MD\u0026thinsp;=\u0026thinsp;4.783, SE\u0026thinsp;=\u0026thinsp;1.563, P\u0026thinsp;=\u0026thinsp;0.002 and M\u0026thinsp;=\u0026thinsp;4.57, SE\u0026thinsp;=\u0026thinsp;1.284, P\u0026thinsp;=\u0026thinsp;0.008, respectively) than in the RT group. There was a significant difference in total cholesterol between the training modalities, F (2, 16)\u0026thinsp;=\u0026thinsp;4.849, P\u0026thinsp;=\u0026thinsp;0.023, η\u0026sup2;=0.442. Interns with IV AT showed a significant reduction in RT (MD= -17.716, SE\u0026thinsp;=\u0026thinsp;5.705, P\u0026thinsp;=\u0026thinsp;0.02). Despite observing a significant difference between the pretest and posttest results for high-density lipoprotein (HDL) and percentage of body fat (%BF), there was no significant difference attributable to the independent variables affecting these variables.\u003c/p\u003e \u003cp\u003eThere was a significant group \u0026times; time interaction for the area under the curve from the OGTT test results (F (2, 16)\u0026thinsp;=\u0026thinsp;9.002, p\u0026thinsp;=\u0026thinsp;0.002), η\u0026sup2;=0.530, high-density lipoprotein: F (2, 16)\u0026thinsp;=\u0026thinsp;4.064, P\u0026thinsp;=\u0026thinsp;0.037, η\u0026sup2;=0.337, TC: F (2, 16)\u0026thinsp;=\u0026thinsp;15.075, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u0026sup2;=0.653), and no significant interaction for body fat percentage and high-density lipoprotein was detected. The results suggest that time had a different effect on the AUC, LDL and TC depending on the group, while the effect of time on %BF and HDL was the same across all groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver an intervention time of eight weeks, exercise emerged as a potent metabolic health intervention for obese older adult men. This program resulted in a remarkable decrease in body fat percentage and the levels of both \u0026ldquo;bad\u0026rdquo; cholesterol (LDL and TC). Simultaneously, it enhances the body\u0026rsquo;s capacity to regulate blood sugar (glucose tolerance). Intriguingly, throughout the program, \u0026ldquo;good\u0026rdquo; cholesterol (HDL) levels improved while average daily energy intake was controlled. These findings align with studies involving longer exercise durations, such as the 32-week program described (Sousa et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings have established a physiological foundation supporting the hypothesis that resistance training may elicit favorable alterations in glucose tolerance. Similarly, other investigators have reported that strength training also reduces basal and glucose-stimulated insulin levels (Craig et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Miller et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). In contrast, both the aerobic training (AT) and resistance training (RT) groups demonstrated improvements in glucose tolerance (Smutok et al., 1994).\u003c/p\u003e \u003cp\u003eOur analysis indicated that compared with RE, AE and concurrent training are the most effective exercise modalities for reducing LDL and TC in older adults. The present study implemented an 8-month aerobic exercise intervention involving 111 randomly selected participants. The results indicated that the exercise group experienced a significant reduction in total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels among centrally obese older adults (Kraus et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). A similar study involving sedentary older adults who participated in a 16-week exercise intervention was performed. The participants were randomly assigned to one of the following groups: resistance training, aerobic walking, or combined exercise. All groups had lower lipid levels over time, including the RE group (Boardley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This discrepancy could be attributed to inadequate energy expenditure during resistance exercise compared to aerobic exercise with low to moderate intensity (Reis et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of this study highlight that there were no significant differences in the effect on high-density lipoprotein (HDL) or percentage of body fat (%BF) levels among AT, RT and CT. These results align with the work of Moraleda et al., who similarly demonstrated that HDL cholesterol remained significantly unchanged across groups (Moraleda et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and % BF(Willis et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, in a previous study, resistance exercise exhibited a more robust association with HDL levels than did aerobic exercise (β\u0026thinsp;=\u0026thinsp;2.56210, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 vs. 1.33748, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Hsu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast to our results, fat mass was reduced more in the AT and CT groups than in the RT group (Willis et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAerobic training (AT) and combined training (CT) were more effective at reducing low-density lipoprotein (LDL) and total cholesterol (TC) levels. Moreover, resistance training (RT) and combined training (CT) demonstrated greater efficacy than aerobic training (AT) in reducing glucose intolerance among previously inactive obese older adults. Interestingly, there was a significant change in the pretest and posttest levels of high-density lipoprotein (HDL) and percentage of body fat (%BF). However, when comparing aerobic training (AT), resistance training (RT), or a combination of both programs, there was no significant difference. Interestingly, the effect of time on AUC, LDL, and TC varied depending on the group, while the effect of time on %BF and HDL remained consistent across all groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the Debere Markos University for its fund for our research and gym manager and Servicemen for their permission, Debre Markos Referral Hospital as well as to the participants, without whose cooperation this study could not have been completed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdverse effect report\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo adverse effects were recorded in participants during and after the intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted by F.A. who played a multifaceted role, including conceiving and designing the analysis, collecting data, contributing tools, performing the analysis, and writing the paper. Y.A. significantly improved the manuscript through contributions to research implementation, result analysis, and critical revisions. M.E. ensured proper research investigation and assisted with implementation and data collection. Y.D. provided overall project direction and planning, guaranteeing proper investigation and resolution of any accuracy or integrity concerns. Finally, S.A. ensured thorough research examination and assisted with application and data collection.. All authors read and approve the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Debre Markos University Annual Research Funding provided assistance for this work. The financial funding from Debre Markos University has been crucial in supporting the research efforts for the project. The funding body did not participate in the study\u0026apos;s design, data collection, analysis, or interpretation, or manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trial was approved by the Ethics Review Board of Debre Markos University of Sport Sciences Academy (Reference number: SPSC 05/22). All the participants were informed about the intervention and possible adverse events before the commencement of the trial and signed an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, the authors affirm that there are no competing interests, financial or otherwise, that have influenced this research. This declaration is made with the intent to uphold the principles of academic integrity and transparency in research.\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 not publicly available due to data security before publication but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeloye, D., Ige-Elegbede, J. O., Ezejimofor, M., Owolabi, E. O., Ezeigwe, N., Omoyele, C., Mpazanje, R. G., Dewan, M. T., Agogo, E., \u0026amp; Gadanya, M. A. (2021). Estimating the prevalence of overweight and obesity in Nigeria in 2020: a systematic review and meta-analysis. \u003cem\u003eAnnals of medicine\u003c/em\u003e,\u003cem\u003e 53\u003c/em\u003e(1), 495-507.\u003c/li\u003e\n\u003cli\u003eAsad, M. (2013). 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(1991). \u003cem\u003eGuidelines for exercise testing and prescription\u003c/em\u003e. Williams \u0026amp; Wilkins.\u003c/li\u003e\n\u003cli\u003eWillis, L. H., Slentz, C. A., Bateman, L. A., Shields, A. T., Piner, L. W., Bales, C. W., Houmard, J. A., \u0026amp; Kraus, W. E. (2012). Effects of aerobic and/or resistance training on body mass and fat mass in overweight or obese adults. \u003cem\u003eJournal of applied physiology\u003c/em\u003e,\u003cem\u003e 113\u003c/em\u003e(12), 1831-1837. https://doi.org/10.1152/japplphysiol.01370.2011\u003c/li\u003e\n\u003cli\u003eYassine, H. N., Marchetti, C. M., Krishnan, R. K., Vrobel, T. R., Gonzalez, F., \u0026amp; Kirwan, J. P. (2009). Effects of exercise and caloric restriction on insulin resistance and cardiometabolic risk factors in older obese adults\u0026mdash;a randomized clinical trial. \u003cem\u003eJournals of Gerontology Series A: Biomedical Sciences and Medical Sciences\u003c/em\u003e,\u003cem\u003e 64\u003c/em\u003e(1), 90-95.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"glucose tolerance, lipid profile, resistance training, aerobic training and concurrent training","lastPublishedDoi":"10.21203/rs.3.rs-4328501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4328501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eTwenty healthy physically inactive men (49.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.581 years) participated in an 8-month training programme involving concurrent exercise, resistance training, and aerobic training programmes to determine the effects on glucose tolerance and plasma lipids in obese adult persons. This study adopts combining pretest posttest with a repeated measures design component to capture within-subject changes over time with a randomized control trial to assess between-group differences. Plasma glucose concentrations were measured for three hours after the ingestion of 75 g of glucose, and the total areas under the respective curves were calculated. Total plasma lipid and lipoprotein concentrations were determined during fasting. Repeated measures ANCOVA was used for primary data analysis, and the independence between blood lipid variables and group factors was tested.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003ePre- to post intervention mean values of body fat percentage, area under the curve, low-density lipoprotein, high-density lipoprotein and total cholesterol decreased in all three groups. The main effect of exercise modality on the AUC was significant. Post hoc analyses revealed that RT (-30.653\u0026thinsp;\u0026plusmn;\u0026thinsp;6.766, p\u0026thinsp;=\u0026thinsp;0.001) and CT (M=-0.896, SE\u0026thinsp;=\u0026thinsp;3.347, P\u0026thinsp;=\u0026thinsp;0.015) had greater effects than AT. LDL was significantly lower in the AT and CT (MD\u0026thinsp;=\u0026thinsp;4.783, SE\u0026thinsp;=\u0026thinsp;1.563, P\u0026thinsp;=\u0026thinsp;0.002) and (M\u0026thinsp;=\u0026thinsp;4.57, SE\u0026thinsp;=\u0026thinsp;1.284, P\u0026thinsp;=\u0026thinsp;0.008) than in the RT. There was a significant difference in TC between training modalities; AT significantly reduced TC during RT (MD= -17.716, SE\u0026thinsp;=\u0026thinsp;5.705, P\u0026thinsp;=\u0026thinsp;0.02). HDL and %BF were not significantly different because of the independent variables affecting these variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eExercise modality has emerged as a key factor in optimizing chronic lipid profiles and glycemic control. Notably, both aerobic and concurrent training demonstrated a superior ability to modulate the lipid profile, and resistance training and concurrent training were more effective in reducing the AUC.\u003c/p\u003e","manuscriptTitle":"Metabolic Impact of Exercise Modalities in Inactive Obese Adults: A Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 17:24:41","doi":"10.21203/rs.3.rs-4328501/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-02T10:11:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-02T09:53:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-01T00:13:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2024-04-26T09:03:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a5588216-ee36-43bd-a6a7-8ede4c3748f3","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:07:17+00:00","versionOfRecord":{"articleIdentity":"rs-4328501","link":"https://doi.org/10.1186/s13102-024-00982-7","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2024-09-11 15:57:14","publishedOnDateReadable":"September 11th, 2024"},"versionCreatedAt":"2024-05-07 17:24:41","video":"","vorDoi":"10.1186/s13102-024-00982-7","vorDoiUrl":"https://doi.org/10.1186/s13102-024-00982-7","workflowStages":[]},"version":"v1","identity":"rs-4328501","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4328501","identity":"rs-4328501","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}