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Components of total daily energy expenditure (TDEE). BMR = basal metabolic rate; NEAT = non-exercise activity thermogenesis; TEF = thermic effect of food; EAT = exercise activity thermogenesis; REE = resting energy expenditure; NREE = non-resting energy expenditure. Adapted from Maclean et al., 2011.

Components of total daily energy expenditure (TDEE). BMR = basal metabolic rate; NEAT = non-exercise activity thermogenesis; TEF = thermic effect of food; EAT = exercise activity thermogenesis; REE = resting energy expenditure; NREE = non-resting energy expenditure. Adapted from Maclean et al., 2011.

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Optimized body composition provides a competitive advantage in a variety of sports. Weight reduction is common among athletes aiming to improve their strength-to-mass ratio, locomotive efficiency, or aesthetic appearance. Energy restriction is accompanied by changes in circulating hormones, mitochondrial efficiency, and energy expenditure that serv...

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... For the second meta-analysis, comparisons were made for changes in exercise performance in athletes with ≥ 2 markers of LEA at the end of the training block. Markers of LEA included a diagnosis of functional hypothalamic amenorrhea (FHA) as per clinical practice guidelines [20], an Table 1 Markers of low energy availability used for study inclusion RMR resting metabolic rate, FFM fat-free mass, BMD bone mineral density, DEXA dual-energy x-ray absorptiometry, T 3 triiodothyronine, IGF-1 insulin-like growth factor 1, EA energy availability Two of the following markers Functional hypothalamic amenorrhea as per clinical practice guidelines [20] EA < 30 kcal/kg FFM/day or evidence of an energy deficit from food and training records [21] Decreased BMD compared with prior DEXA scan or Z-score less than − 1.0 [22] Score ≥ 8 on the Low Energy Availability in Females Questionnaire [23] Decreased body mass, or fat mass measured by DEXA or sum of skin-folds [24] Suppressed RMR ratio [25] or decreased RMR relative to FFM [8,27] Decreased resting muscle glycogen content [30,31] Decreased levels of leptin [29,38,39], T 3 [28,40], testosterone (males only) [41], insulin [29,32,39], IGF-1 [42], osteocalcin [42], procollagen 1 N-terminal propeptide [33,42] Increased levels of hepcidin [34,35], cross-linked C-terminal telopeptide of type 1 collagen [28,42], growth hormone [32], β-hydroxybutyrate [32], and/or cortisol [32,36,37] Markers only needed to be measured to meet the inclusion criteria for review (independent of direction of change). All markers have been shown to be altered by low energy availability but it is uncertain how they may be affected by training stress alone energy availability < 30 kcal/kg FFM/day or evidence of an energy deficit from food and training records [21], decreased BMD compared with prior dual-energy X-ray absorptiometry (DEXA) scan or Z-score less than − 1.0 [22], score ≥ 8 on the Low Energy Availability in Females Questionnaire [23], decreased body mass or fat mass [24], a suppressed resting metabolic rate (RMR) ratio [25,26], decreased RMR relative to FFM [8,27], decreased resting muscle glycogen content [30,31], and changes in select biochemical markers that have been demonstrated to be altered in periods of LEA (see Table 1) [24,[28][29][30][32][33][34][35][36][37][38][39][40][41][42]. ...
... Markers of LEA included a diagnosis of functional hypothalamic amenorrhea (FHA) as per clinical practice guidelines [20], an Table 1 Markers of low energy availability used for study inclusion RMR resting metabolic rate, FFM fat-free mass, BMD bone mineral density, DEXA dual-energy x-ray absorptiometry, T 3 triiodothyronine, IGF-1 insulin-like growth factor 1, EA energy availability Two of the following markers Functional hypothalamic amenorrhea as per clinical practice guidelines [20] EA < 30 kcal/kg FFM/day or evidence of an energy deficit from food and training records [21] Decreased BMD compared with prior DEXA scan or Z-score less than − 1.0 [22] Score ≥ 8 on the Low Energy Availability in Females Questionnaire [23] Decreased body mass, or fat mass measured by DEXA or sum of skin-folds [24] Suppressed RMR ratio [25] or decreased RMR relative to FFM [8,27] Decreased resting muscle glycogen content [30,31] Decreased levels of leptin [29,38,39], T 3 [28,40], testosterone (males only) [41], insulin [29,32,39], IGF-1 [42], osteocalcin [42], procollagen 1 N-terminal propeptide [33,42] Increased levels of hepcidin [34,35], cross-linked C-terminal telopeptide of type 1 collagen [28,42], growth hormone [32], β-hydroxybutyrate [32], and/or cortisol [32,36,37] Markers only needed to be measured to meet the inclusion criteria for review (independent of direction of change). All markers have been shown to be altered by low energy availability but it is uncertain how they may be affected by training stress alone energy availability < 30 kcal/kg FFM/day or evidence of an energy deficit from food and training records [21], decreased BMD compared with prior dual-energy X-ray absorptiometry (DEXA) scan or Z-score less than − 1.0 [22], score ≥ 8 on the Low Energy Availability in Females Questionnaire [23], decreased body mass or fat mass [24], a suppressed resting metabolic rate (RMR) ratio [25,26], decreased RMR relative to FFM [8,27], decreased resting muscle glycogen content [30,31], and changes in select biochemical markers that have been demonstrated to be altered in periods of LEA (see Table 1) [24,[28][29][30][32][33][34][35][36][37][38][39][40][41][42]. Given that FFM is the greatest contributor to RMR [43], we assessed RMR relative to FFM rather than absolute RMR. ...
... Finally, the power section was modified such that a study was given 1 point if it provided a power calculation and 0 points if it did not. The highest possible score was 28, with studies categorized as excellent (26)(27)(28), good (20)(21)(22)(23)(24)(25), fair (15)(16)(17)(18)(19), and poor (≤ 14). For studies where selection bias did not apply, the 6 points from this section were removed so that the highest possible score was 22. Corresponding levels were excellent (20)(21)(22), good , fair , and poor (≤ 8). ...
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Background: Overreaching is the transient reduction in performance that occurs following training overload and is driven by an imbalance between stress and recovery. Low energy availability (LEA) may drive underperformance by compounding training stress; however, this has yet to be investigated systematically. Objective: The aim of this study was to quantify changes in markers of LEA in athletes who demonstrated underperformance, and exercise performance in athletes with markers of LEA. Methods: Studies using a ≥ 2-week training block with maintained or increased training loads that measured exercise performance and markers of LEA were identified using a systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Changes from pre- to post-training were analyzed for (1) markers of LEA in underperforming athletes and (2) performance in athletes with ≥ 2 markers of LEA. Results: From 56 identified studies, 14 separate groups of athletes demonstrated underperformance, with 50% also displaying ≥ 2 markers of LEA post-training. Eleven groups demonstrated ≥ 2 markers of LEA independent of underperformance and 37 had no performance reduction or ≥ 2 markers of LEA. In underperforming athletes, fat mass (d = - 0.29, 95% confidence interval [CI] - 0.54 to - 0.04; p = 0.02), resting metabolic rate (d = - 0.63, 95% CI - 1.22 to - 0.05; p = 0.03), and leptin (d = - 0.72, 95% CI - 1.08 to - 0.35; p < 0.0001) were decreased, whereas body mass (d = - 0.04, 95% CI - 0.21 to 0.14; p = 0.70), cortisol (d = - 0.06, 95% CI - 0.35 to 0.23; p = 0.68), insulin (d = - 0.12, 95% CI - 0.43 to 0.19; p = 0.46), and testosterone (d = - 0.31, 95% CI - 0.69 to 0.08; p = 0.12) were unaltered. In athletes with ≥ 2 LEA markers, performance was unaffected (d = 0.09, 95% CI - 0.30 to 0.49; p = 0.6), and the high heterogeneity in performance outcomes (I2 = 84.86%) could not be explained by the performance tests used or the length of the training block. Conclusion: Underperforming athletes may present with markers of LEA, but overreaching is also observed in the absence of LEA. The lack of a specific effect and high variability of outcomes with LEA on performance suggests that LEA is not obligatory for underperformance.
... The maintenance of a healthy body mass, or the loss of body mass during dieting, is generally framed in terms of the balance between energy intake and energy expenditure. On the expenditure side, RMR is the largest contributor, and the least modifiable component [44], while exercise associated thermogenesis (EAT), non-exercise associated thermogenesis (NEAT), and the thermic effect of food (TEF) are more modifiable. Since NEAT is the largest of those three components, a major aspect of our experimental design was to reduce the confounding effect of changes in NEAT by monitoring physical activity and encouraging the participants to maintain their normal levels of NEAT and EAT. ...
... Since NEAT is the largest of those three components, a major aspect of our experimental design was to reduce the confounding effect of changes in NEAT by monitoring physical activity and encouraging the participants to maintain their normal levels of NEAT and EAT. That intervention was especially important because metabolic compensation following weight-loss can result in a decrease in NEAT and a decrease in the motivation to exercise, which will lead to a decrease in EAT [44]. At the same time, our experimental design ensured that energy intake was constant at each participant's habitual daily energy consumption. ...
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Background: The ketogenic diet (KD) has been shown to result in body mass loss in people with disease as well as healthy people, yet the effect of the KD on thyroid function and metabolism are unknown. Objective: We aimed to determine the effects of a KD, compared with an isocaloric high-carbohydrate low-fat (HCLF) diet, on resting metabolic rate and thyroid function in healthy individuals. Design: Eleven healthy, normal-weight participants (mean(SD) age: 30(9) years) completed this randomized crossover-controlled study. For a minimum of three weeks on each, participants followed two isocaloric diets: a HCLF diet (55%carbohydrate, 20%fat, 25%protein) and a KD (15%carbohydrate, 60%fat, 25% protein), with a one-week washout period in-between. Importantly, while on the KD, the participants were required to remain in a state of nutritional ketosis for three consecutive weeks. Crossover analyses and linear mixed models were used to assess effect of diet on body mass, thyroid function and resting metabolic rate. Results: Both dietary interventions resulted in significant body mass loss (p<0.05) however three weeks of sustained ketosis (KD) resulted in a greater loss of body mass (mean (95%CI): -2.9 (-3.5, -2.4) kg) than did three weeks on the HCLF diet (-0.4 (-1.0, 0.1) kg, p < 0.0001). Compared to pre-diet levels, the change in plasma T3 concentration was significantly different between the two diets (p = 0.003), such that plasma T3 concentration was significantly lower following the KD diet (4.1 (3.8, 4.4) pmol/L, p<0.0001) but not different following the HCLF diet (4.8 (4.5, 5.2) pmol/L, p = 0.171. There was a significant increase in T4 concentration from pre-diet levels following the KD diet (19.3 (17.8, 20.9) pmol/L, p < 0.0001), but not following the HCLF diet (17.3 (15.7, 18.8) pmol.L, p = 0.28). The magnitude of change in plasma T4 concentration was not different between the two diets (p = 0.4). There was no effect of diet on plasma thyroid stimulating hormone concentration (p = 0.27). There was a significantly greater T3:T4 ratio following the HCLF diet (0.41 (0.27, 0.55), p < 0.0001) compared to pre-diet levels but not following the KD diet (0.25 (0.12, 0.39), p = 0.80). Conclusions: Although the diets were isocaloric and physical activity and resting metabolic rate remained constant, the participants lost more mass after the KD than after the HCLF diet. The observed significant changes in triiodothyronine concentration suggest that unknown metabolic changes occur in nutritional ketosis, changes that warrant further investigation. Trial registration: Pan African Clinical Trial Registry: PACTR201707002406306 URL: https://pactr.samrc.ac.za/.
... Total energy expenditure of most athletes is expected to be greater compared to general population because of training, and changes in metabolism and body composition [1]. At the same time, estimating energy needs is crucial in diet planning to improve sport performance and manage body mass in weight-category sports [2,3]. Further, under-or over-estimating athletes' energy requirements might result in unwanted changes in fat-free mass (FFM), and/or fat mass (FM), impaired performance and health concerns, for instance increased risk of injuries or cardiovascular diseases [1,2,4]. ...
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Background An accurate estimation of athletes’ energy needs is crucial in diet planning to improve sport performance and to maintain an appropriate body composition. This study aimed to develop and validate in elite athletes new equations for estimating resting energy expenditure (REE) based on anthropometric parameters as well as bioimpedance analysis (BIA)-derived raw variables and to validate the accuracy of selected predictive equations. Methods Adult elite athletes aged 18–40 yrs were studied. Anthropometry, indirect calorimetry and BIA were performed in all subjects. The new predictive equations were generated using different regression models. The accuracy of the new equations was assessed at the group level (bias) and at the individual level (precision accuracy), and then compared with the one of five equations used in the general population or three athletes-specific formulas. Results One-hundred and twenty-six male athletes (age 26.9 ± 9.1 yrs; weight 71.3 ± 10.9 kg; BMI 22.8 ± 2.7 kg/m ² ) from different sport specialties were randomly assigned to the calibration ( n = 75) or validation group ( n = 51). REE was directly correlated with individual characteristics, except for age, and raw BIA variables. Most of the equations from the literature were reasonably accurate at the population level (bias within ±5%). The new equations showed a mean bias −0.3% (Eq. A based on anthropometric parameters) and −0.6% (Eq. B based on BIA-derived raw variables). Precision accuracy (individual predicted-measured differences within ±5%) was ~75% in six out of eight of the selected equations and even higher for Eq. A (82.4%) and Eq. B (92.2%). Conclusion In elite athletes, BIA-derived phase angle is a significant predictor of REE. The new equations have a very good prediction accuracy at both group and individual levels. The use of phase angle as predictor of REE requires further research with respect to different sport specialties, training programs and training level.
... During weight loss, the energy expenditure exceeds energy intake resulting in a negative energy balance. Metabolic adaptation will be directed towards energy sparing, which is the lowering of RMR to compensate for the negative energy balance [12,13]. A low RMR is regarded as a risk factor for weight gain [14,15], and metabolic adaptation after weight loss is also a predictor of weight regain [16]. ...
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Background: Branched chain amino acids (BCAA) supplementation is reported to aid in lean mass preservation, which may in turn minimize the reduction in resting metabolic rate (RMR) during weight loss. Our study aimed to examine the effect of BCAA supplementation to a hypocaloric diet on RMR and substrate utilization during a weight loss intervention. Methods: A total of 111 Chinese subjects comprising 55 males and 56 females aged 21 to 45 years old with BMI between 25 and 36 kg/m2 were randomized into three hypocaloric diet groups: (1) standard-protein (14%) with placebo (CT), (2) standard-protein with BCAA, and (3) high-protein (27%) with placebo. Indirect calorimetry was used to measure RMR, carbohydrate, and fat oxidation before and after 16 weeks of dietary intervention. Results: RMR was reduced from 1600 ± 270 kcal/day to 1500 ± 264 kcal/day (p < 0.0005) after weight loss, but no significant differences in the change of RMR, respiratory quotient, and percentage of fat and carbohydrate oxidation were observed among the three diet groups. Subjects with BCAA supplementation had an increased postprandial fat (p = 0.021) and decreased postprandial carbohydrate (p = 0.044) oxidation responses compared to the CT group after dietary intervention. Conclusions: BCAA-supplemented standard-protein diet did not significantly attenuate reduction of RMR compared to standard-protein and high-protein diets. However, the postprandial fat oxidation response increased after BCAA-supplemented weight loss intervention.
... For example, it has been reported that EAT and NEAT would be suppressed as a response to bodyweight decrease in obese individuals [14][15][16][17]. The relative magnitude of the TEF would not change with energy restriction, but the overall reduced energy intake would still decrease the absolute magnitude of the TEF [18,19]. Among the variables comprising total daily energy expenditure, RMR has been the most studied in the physique competitors. ...
... One of the potential benefits of incorporating an IER dietary strategy is to offset some of the adverse physiological effects that CER can exert. Although research investigating IER in physique athletes is in its infancy, there are some data and optimism to suggest that such strategies could prevent RED-S [2,3,18]. Chappell's team reported that 10 out of 32 male competitors and 8 out of 16 female competitors consumed periodic "cheat meals" during their contest preparation, furthermore, one of the 32 males and 4 of the 16 females used refeed strategies during their contest preparation [3]. ...
... There are also limits to this conjecture. First, the researchers did not analyze hormonal changes [1,18,59], and second, self-reported errors also could not be ignored. Therefore, caution should be exercised in practice. ...
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Objective: To compare the effects of continuous energy restriction (CER) and intermittent energy restriction (IER) in bodyweight loss plan in sedentary individuals with normal bodyweight and explore the influence factors of effect and individual retention. Methods: 26 participants were recruited in this randomized controlled and double-blinded trial and allocated to CER and IER groups. Bodyweight (BW), body mass index (BMI), and resting metabolic rate (RMR) would be collected before and after a 4-week (28 days) plan which included energy restriction (CER or IER) and moderate-intensity exercise. Daily intake of three major nutrients (protein, carbohydrate, fat) and calories were recorded. Results: A significant decrease in BW and BMI were reported within each group. No statistically significant difference in the change of RMR in CERG. No statistically significant difference was reported in the effect between groups, neither as well the intake of total calories, three major nutrients, and individual plan retention. The influence factors of IER and CER are different. Conclusion: Both CER and IER are effective and safe energy restriction strategies in the short term. Daily energy intake and physical exercise are important to both IER and CER.
... This fact, added to the physiological adaptations that reduce daily energy expenditure [35], facilitates the appearance of the so-called plateau in weight loss. Some of these adaptations are highlighted in the review of Trexler et al. [36], who concluded that after the implementation of a caloric deficit in order to lose fat, the body activates different mechanisms to minimize this weight loss. These include a reduction in daily energy expenditure (mainly due to the loss of body mass itself and the decrease in energy expenditure associated with physical activities other than exercise, the so-called non-exercise activity thermogenesis or NEAT), greater mitochondrial efficiency in the use of energy and changes in circulating hormone levels. ...
... To avoid the ergolytic potential of low glycogen storage, in addition to opting for a diet with a higher daily CHO content, the strategy known as "Refeed" or "carbohydrate loading" can also be used. This consists of increasing dietary CHO and calories to levels equal to or higher than maintenance in a timely and scheduled manner within the planning [36]. Generally, in bodybuilding, the protocols used last 24 h, once or twice a week [95], although the trials that have studied refeed protocols used them for at least three days per week [96,97]. ...
... Generally, in bodybuilding, the protocols used last 24 h, once or twice a week [95], although the trials that have studied refeed protocols used them for at least three days per week [96,97]. The supposed objective of this strategy is to temporarily increase the circulating leptin levels and stimulate the metabolic rate [36]. There is evidence that leptin is sensitive to brief periods of refeeding with CHO, but not with fats (FAT) [96]. ...
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Managing the body composition of athletes is a common practice in the field of sports nutrition. The loss of body weight (BW) in resistance-trained athletes is mainly conducted for aesthetic reasons (bodybuilding) or performance (powerlifting or weightlifting). The aim of this review is to provide dietary–nutritional strategies for the loss of fat mass in resistance-trained athletes. During the weight loss phase, the goal is to reduce the fat mass by maximizing the retention of fat-free mass. In this narrative review, the scientific literature is evaluated, and dietary–nutritional and supplementation recommendations for the weight loss phase of resistance-trained athletes are provided. Caloric intake should be set based on a target BW loss of 0.5–1.0%/week to maximize fat-free mass retention. Protein intake (2.2–3.0 g/kgBW/day) should be distributed throughout the day (3–6 meals), ensuring in each meal an adequate amount of protein (0.40–0.55 g/kgBW/meal) and including a meal within 2–3 h before and after training. Carbohydrate intake should be adapted to the level of activity of the athlete in order to training performance (2–5 g/kgBW/day). Caffeine (3–6 mg/kgBW/day) and creatine monohydrate (0.08–0.10 g/kgBW/day) could be incorporated into the athlete’s diet due to their ergogenic effects in relation to resistance training. The intake of micronutrients complexes should be limited to special situations in which there is a real deficiency, and the athlete cannot consume through their diet.
... While these behaviors can facilitate rapid weight loss, they may also compromise sport-specific performance 4 and contribute to metabolic adaptations that promote increased adiposity and weight regain 5 in excess of the amount of weight originally lost 6 . This may lead to multiple bouts of weight cycling that, over the long-term, increase risk of chronic diseases such as obesity, hypertension and type 2 diabetes 2, 6,7,8,9,10 . ...
... Soldiers who reported weight cycling were more likely to report higher BMI and adopting various weight management behaviors prior to body weight screening. Though causality cannot be demonstrated with these data, the association between weight cycling and BMI is consistent with robust data from civilian populations linking weight cycling with weight gain and higher BMI 1,6,7,8,9,10,43 . Notably, the observed association between weight cycling and BMI was independent of years of service in the Army. ...
Article
Weight cycling is prevalent in sports/professions with body composition standards. It has been associated with weight management behaviors that may contribute to suboptimal diet quality and weight gain. US Army Soldiers may be at increased risk of weight cycling relative to civilians due to mandated body composition standards. However, the relationship between weight cycling, weight management behaviors, body mass index (BMI), and diet quality among Soldiers is unknown. In this cross-sectional study, 575 Soldiers (89% enlisted, 90% male, 23±4yr) at Army installations at Joint Base Elmendorf-Richardson, AK, Joint-Base Lewis McChord, WA, and Fort Campbell, KY completed questionnaires on food frequency, health-related behaviors, and history of weight cycling (≥3 weight fluctuations ≥5% body weight). Weight cycling was reported by 33% of Soldiers. Those who reported weight cycling reported higher BMI (27±4 vs. 25±3 kg/m ² , p <0.001) and higher prevalence of engaging in weight management behaviors prior to body weight screening, but did not report lower dietary quality (Healthy Eating Index-2015 (HEI) scores 59±10 vs 59±11, p =0.46) relative to those who did not report weight cycling. Results of mediation analyses suggested that weight cycling may affect BMI both directly ( c’ = 1.19, 95% CI: 0.62, 1.75) and indirectly ( ab =0.45, 95% CI: 0.19, 0.75), and HEI scores indirectly ( ab =0.69, 95% CI: 0.20, 1.35) through the adoption of weight management behaviors. Weight cycling is common in Soldiers and is associated with higher BMI and higher prevalence of engaging in weight management behaviors that mediate associations between weight cycling, BMI and diet quality.
... Such outcomes support the contention that some view weight loss as a transient period of time and do not recognize the necessity for permanent lifestyle and dietary habit change. Dietrestricted weight loss creates a host of distinct biological adaptations, including but not limited to increased hunger, decreased satiety, suppressed energy expenditure, and altered levels of circulating hormones known to influence weight loss and maintenance [2,3]. These adaptations inevitably cause weight regain if permanent lifestyle changes are not created. ...
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Background The purpose of this study was to compare a flexible vs. rigid diet on weight loss and subsequent weight regain in resistance-trained (RT) participants in a randomized, parallel group design. Methods Twenty-three males and females (25.6 ± 6.1 yrs; 170 ± 8.1 cm; 75.4 ± 10.3 kg) completed the 20-week intervention (consisting of a 10-week diet phase and a 10-week post-diet phase). Participants were randomized to a flexible diet (FLEX) comprised of non-specific foods or a rigid diet (RIGID) comprised of specific foods. Participants adhered to an ~20%kcal reduction during the first 10-weeks of the intervention and were instructed to eat ad libitum for the final 10-weeks. Body composition and resting metabolic rate were assessed 5 times: (baseline, 5, 10 [end of diet phase], 16, and 20 weeks). Results During the 10-week diet phase, both groups significantly reduced bodyweight (FLEX: baseline = 76.1 ± 8.4kg, post-diet = 73.5 ± 8.8 kg, ▲2.6 kg; RIGID: baseline = 74.9 ± 12.2 kg, post-diet = 71.9 ± 11.7 kg, ▲3.0 kg, p < 0.001); fat mass (FLEX: baseline = 14.8 ± 5.7 kg, post-diet = 12.5 ± 5.0 kg, ▲2.3 kg; RIGID: baseline = 18.1 ± 6.2 kg, post-diet = 14.9 ± 6.5 kg, ▲3.2 kg p < 0.001) and body fat% (FLEX: baseline = 19.4 ± 8.5%, post-diet = 17.0 ± 7.1%, ▲2.4%; RIGID: baseline = 24.0 ± 6.2%, post-diet = 20.7 ± 7.1%, ▲3.3%; p < 0.001). There were no significant differences between the two groups for any variable during the diet phase. During the post-diet phase, a significant diet x time interaction ( p < 0.001) was observed for FFM with the FLEX group gaining a greater amount of FFM (+1.7 kg) in comparison with the RIGID group (−0.7 kg). Conclusions A flexible or rigid diet strategy is equally effective for weight loss during a caloric restriction diet in free-living, RT individuals. While post-diet FFM gains were greater in the FLEX group, there were no significant differences in the amount of time spent in resistance and aerobic exercise modes nor were there any significant differences in protein and total caloric intakes between the two diet groups. In the absence of a clear physiological rationale for increases in FFM, in addition to the lack of a standardized diet during the post-diet phase, we refrain from attributing the increases in FFM in the FLEX group to their diet assignment during the diet phase of the investigation. We recommend future research investigate additional physiological and psychological effects of flexible diets and weight regain in lean individuals.
... Basal metabolism thermogenesis is the largest component of the changeable aspects of metabolism. [34] If the basal metabolism decreases too much during weight loss, the weight would rebound quickly and even get higher, similar to a yo-yo ball. In this study, participants were instructed to maintain their original exercise level, that is, to neither reduce their activity due to diet changes, nor deliberately increase their exercise level. ...
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Evidence from trials demonstrating the benefits and risks of low-glycemic index and fat-restricted diets in weight loss and blood lipid profile changes is unclear. This study aimed to assess the implemented and effects of a fat-restricted low-glycemic index diet on weight control and blood lipid profile changes in in overweight/obese Southwest Chinese individualst.This prospective pilot study enrolled overweight/obese subjects at the People's Hospital of Sichuan Province between February and July 2019. The daily energy intake was reduced by 300 to 500 kcal according to the participant's weight and activity level, with low-glycemic index carbohydrate- and fat-energy ratios < 45% and 25% to 30%, respectively. Participants received guidance for 3 months by telephone follow-up, internet interaction, or WeChat. Changes in weight, body composition, and blood profile were measured.A total of 254 patients were finally analyzed, including 101 males and 153 females. After adjusting for potential confounders, weight (P < .001), body mass index (P < .001), waist circumference (P < .001), waist-hip ratio (P < .001), body fat percentage (P < .001), visceral fat area (P < .001), basal metabolism (P = .002), cholesterol (P < .001), and triglycerides (P < .001) were significantly reduced after the 3-month intervention. The above indexes showed no significant differences between men and women.Regardless of gender, fat-restricted low-glycemic index diet might be helpful for controlling weight and lowering blood cholesterol and triglycerides in overweight/obese individuals in Southwest China.
... Indeed, studies have shown that some of the effects of LEA are mediated via reduced CHO availability [22], which appears to have effects on especially leptin and T3 concentrations [60,61]. These in turn affect RMR, thus facilitating or suppressing metabolism during periods of LEA [62]. Meanwhile, increased protein intake in the face of LEA allows maintain increased muscle protein synthesis [63] lean mass and health during weight loss [59]. ...
Article
Decades of laboratory research have shown impairments to several body systems after only 4-5 days of strictly controlled consistent low energy availability (LEA); where energy availability (EA) = Energy Intake (EI) – Exercise Energy Expenditure (EEE) / Fat-Free Mass. Meanwhile, cross-sectional reports exist on the interrelatedness of LEA, menstrual dysfunction and impaired bone health in females (the Female Athlete Triad). These findings have demonstrated that LEA is the key underpinning factor behind a broader set of health and performance outcomes, recently termed as Relative Energy Deficiency in Sport (RED-S). There is utmost importance of early screening and diagnosis of RED-S to avoid the development of severe negative health and performance outcomes. However, a significant gap exists between short-term laboratory studies and cross-sectional reports, or clinically field-based situations, of long-term/chronic LEA and no definitive, validated diagnostic tests for RED-S exist. This review aims to highlight methodological challenges related to the assessment of the components of EA equation in the field (e.g. challenges with EI and EEE measures). Due to the uncertainty of these parameters, we propose the use of more chronic “objective” markers of LEA (i.e. blood markers). However, we note that direct extrapolations of laboratory-based outcomes into the field are likely to be problematic due to potentially poor ecological validity and the extreme variability in most athlete’s daily EI and EEE. Therefore, we provide a critical appraisal of the scientific literature, highlighting research gaps, and a potential set of leading objective RED-S markers while working in the field.