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This Issue. Share X Facebook Email LinkedIn. October 19, Richard L. Atkinson, MD ; William H. Dietz, MD, PhD ; John P. Foreyt, PhD ; et al Norma J.
Goodwin, MD ; James O. Hill, PhD ; Jules Hirsch, MD ; F. Xavier Pi-Sunyer, MD ; Roland L. Weinsier, MD, DrPH ; Rena Wing, PhD ; Jay H. Hoofnagle, MD ; James Everhart, MD ; Van S. Hubbard, MD, PhD ; Susan Zelitch Yanovski, MD. Author Affiliations University of Wisconsin, Madison; Tufts University School of Medicine, Boston, Mass; Baylor College of Medicine, Houston, Tex; HEALTH WATCH Information and Promotion Service, New York, NY; University of Colorado, Denver; Rockefeller University, New York, NY; St Luke's-Roosevelt Hospital Center, Columbia University, New York, NY; University of Alabama, Birmingham; University of Pittsburgh Pa School of Medicine; Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md.
visual abstract icon Visual Abstract. Access through your institution. Add or change institution. Download PDF Full Text Cite This Citation Atkinson RL , Dietz WH , Foreyt JP, et al. Select Your Interests Customize your JAMA Network experience by selecting one or more topics from the list below.
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Purchase access. Such low calorie diets result in a severe calorie deficit and the need to oxidize protein. Information regarding the participants' dietary intake in this study is scant. Only mean intakes per group for the entire week period are presented. These intakes are approximately — kilocalories less than mean baseline resting metabolic rates.
In addition, dietary information is based on self-report, and there is a strong likelihood of underreporting of food intake in obese people. These relatively small calorie deficits may have enabled subjects to spare protein from oxidation.
This rather limited attention and control of dietary intake in general in this area of research is a likely factor contributing to the inconsistency in reported results. Not only is the degree of calorie deficit important, but the distribution of macronutrients and amount of protein per kilogram body weight or fat-free mass is also of great importance in determining fuel substrate utilization.
The calorie deficit, macronutrient distribution and rate of weight loss may be key factors in the retention of fat-free mass and resting metabolic rate. Dietary information should be prescribed and described on an individual basis, i.
kilocalories or grammes of protein per kilogram body weight, rather than by group means, as in this study.
Although there may have been enough carbohydrate calories to spare protein from oxidation, there may have been insufficient total grammes of protein per kilogram body mass to facilitate an increase in fat-free mass, despite the appropriate stimulus in the resistance training group.
Since all subjects were able to retain fat-free mass, it follows that their resting metabolic rates would also be stable. Ballor DL, Harvey-Berino JR, Ades PA et al.
Decrease in fat oxidation following a meal in weight-reduced individuals: a possible mechanism for weight recidivism. Metabolism ; 45 2 : — Contrasting effects of resistance and aerobic training on body composition and metabolism after diet-induced weight loss.
This two-part study is based on the assumption that a decrease in calorie intake and weight loss is associated with a decrease in resting metabolic rate and fat oxidation.
All testing was done while subjects resided at a university clinical research centre. In the first study, 20 older subjects aged 56—70 years underwent an week weight-loss program.
Subjects kept food diaries which were reviewed by a registered dietitian at weekly meetings. During the twelfth week, subjects were requested to increase their intake to allow for weight maintenance and stabilization of weight for post-diet measurements.
In the second study, 18 of the 20 weight-reduced subjects began a week exercise regimen, consisting of either aerobic training or weight training. All subjects attended supervised exercise sessions three times per week.
After the week training period following the initial diet intervention, the weight-training group did not ex-perience further weight loss, but maintained the weight lost during the initial week diet period.
The aerobic trainers experienced a significant further decrease in weight 2. In addition, there were between-group differences in body composition such that the aerobic trainers lost weight and the resistance trainers' weight remained unchanged.
Trends in fat-free mass were also significantly different in that the weight trainers experienced a trend toward increasing fat-free mass and the aerobic trainers experienced no change in fat-free mass.
In the first part of the study, subjects' resting metabolic rate decreased to a greater extent than their weight or fat-free mass. This excessive reduction is most likely attributable to the degree of calorie restriction, and therefore cannot be completely explained by the reduction in fat-free mass.
Wadden and colleagues have concluded that short-term changes in resting metabolic rate are best predicted by baseline resting metabolic rate and degree of calorie restriction, whereas long-term changes in resting metabolic rate are best predicted by baseline resting metabolic rate and fat-free mass.
It is not clear how soon after the initial study participants began the second study, or what their dietary intake was during this time. The mean weights at the start of the second study are 2 kilograms less than at the end of the first study, so it is reasonable to believe that these subjects continued to consume a hypocaloric diet.
As in the first study, diets were not prescribed individually or controlled for adequately in the data analyses. Therefore, it is difficult to assess the degree of calorie and protein restriction, and the effect these variables may have on the initial reduction in metabolic rate and subsequent maintenance of it.
According to the description of recommended dietary intake during the first phase of the study, protein intakes may have been as low as 0. This level of restriction may partially explain why fat-free mass and resting metabolic rate did not increase in the resistance training group.
The researchers of this study have concluded that attenuating the reductions in resting metabolic rate and increasing fat oxidation rates after weight loss are not the mechanisms by which exercise prevents weight recidivism. However, until dietary factors are controlled for, these types of conclusions are premature.
Lastly, a third non-exercise group in the post-diet period would have strengthened the study. It would have been interesting to compare the resting metabolic rates and fat oxidation rates of weight-reduced exercisers versus non-exercisers. Gornall J, Villani, RG. Short-term changes in body composition and metabolism with severe dieting and resistance exercise.
Int J Sport Nutr ; 6: — The authors sought to examine the potential of strength training as a means to prevent the decline in fat-free mass and resting metabolic rate associated with very-low calorie diets.
They randomly placed 22 female subjects in one of two groups, a diet-only group and a diet plus strength training group. Subjects were matched on body surface area.
In addition, the authors controlled for two other factors: fluctuations in metabolic rate due to hormonal changes and losses in total body water. Women were tested at approximately the same time of the month in their menstrual cycle.
Body composition was analysed using a dual X-ray absorptiometry technique which is sensitive to changes in fat-free mass associated with fluctuations in water, minerals and protein.
The treatment period was 4 weeks long, during which time subjects consumed kilocalories per day. All pre-packaged meals were provided to subjects free of charge.
Post-intervention tests were completed while participants were still on the very-low-calorie diet. They met with the research staff two times per week for support and weigh-ins.
Those in the diet-plus-exercise group also participated in supervised strength training activities three times per week.
They completed three sets of 10 free weight exercises each training session, and resistance was progressively increased.
Post-intervention testing was conducted 2 days after the last exercise session. An analysis of variance with repeated measures revealed a significant time effect, such that those in the diet-only group and the diet plus strength training group experienced a significant decrease in kilograms body mass There were no significant group differences, indicating that strength training did not attenuate the reduction in resting metabolic rate or fat-free mass.
In addition, an analysis of changes in absolute resting metabolic rate, controlling for fat-free mass as a covariate, again reveals a significant decrease in resting metabolic rate with no statistically significant differences between groups. In other words, for both groups there is a significant loss in absolute resting metabolic rates above and beyond what can be explained by loss of fat-free mass.
The authors conclude that resistance training cannot reverse the negative effects of severe energy restriction on resting metabolic rate or fat-free mass.
In addition, the authors conclude that the majority of fat-free mass lost could be accounted for by loss of body water. Since carbohydrate is stored in the muscle with water, the loss in body water is expected due to glycogen depletion associated with the hypocaloric diet.
Strength training draws largely on locally stored glycogen for energy substrate, and can therefore further decrease the glycogen and water component of fat-free mass.
The authors note that the short-term decrease in resting metabolic rate may be due to a decrease in sympathetic tone associated with a diet-induced decrease in circulating insulin levels.
Dietary factors are addressed in this study in that all meals were provided to patients. Patients were consuming approximately 0. Resting metabolic rate was measured while subjects were on the hypocaloric diets, and therefore is reflective of the stress of dieting itself and not simply of the loss of fat-free mass.
The authors calculate that all of the loss in fat-free mass can be attributed to water losses. However, it should be noted that this is likely to be an oversimplification, and measurement errors are probably masking the loss of actual protein or muscle mass.
Therefore, if water losses are not accounted for, the relationship between fat-free mass and resting metabolic rate may not be accurately and completely described. Thompson JL, Manore MM, Thomas JR. Effects of diet and diet-plus-exercise programs on resting metabolic rate: a meta-analysis.
Int J Sport Nutr ; 6: 41— It is difficult to summarize the results of studies examining the effect of exercise on resting metabolic rate during a hypocaloric dieting period due to the number of variables that are involved type, duration, frequency and intensity of exercise, degree of energy deficit, total daily calorie intake, and distribution of calories among carbohydrates, proteins and fats.
Therefore, Thompson and colleagues suggest caution regarding narrative reviews of this body of literature. Rather, they have conducted a meta-analysis to quantify treatment effectiveness, specifically the effects of diet alone and diet-plus-exercise on resting metabolic rate.
The authors searched the literature and found 22 studies between and that documented resting metabolic rate in humans placed in either diet-only groups or diet-plus-exercise groups.
The studies represent data from subjects, 68 males and females, 31—45 years of age. The majority of studies placed subjects on low-fat, high-carbohydrate diets of less than kilocalories per day.
Intervention programmes lasted approximately 10 weeks. Effect sizes for differences in resting metabolic rate before and after diet and before and after diet-plus-exercise were calculated.
Positive effect sizes indicate that resting metabolic rate increased due to the intervention, and negative effect sizes indicate that resting metabolic rate decreased as a result of the intervention.
When expressed in absolute terms, there was a significant decrease in resting metabolic rate in diet only However, the drop is classified as small for the dieters who exercised and large for those who just dieted.
This difference is also statistically significant. Similarly, when expressed per kilogram of fat-free mass per hour, the drops in resting metabolic rate for the dieters 5. The decrease in the dieters is classified as moderate, while the decrease with dieter— exercisers is considered small.
The difference between groups is not significantly different. The authors were also able to establish that neither diet-related variables number of calories, distribution of calories among macronutrients or duration of diet , exercise-related variables type of exercise, intensity, duration or frequency nor subject-related variables age, gender, body composition correlated significantly with changes in resting metabolic rate.
There has been some attention given in the literature to the appropriateness of calculating relative metabolic rate by dividing resting metabolic rate by body weight or fat-free mass, since the line defining the relationship between these two variables does not intercept the y -axis at zero.
The results of this manipulation do reveal a decrease in resting metabolic rate due to diet alone and diet-plus-exercise. However, the slopes of the regression lines pre- and post-treatment are not significantly different, and, therefore, the relationship between mass and metabolic rate is the same and independent of treatment.
In other words, the drop in resting metabolic rate is expected due to the decrease in body size. The use of meta-analysis in this area of research is useful because it allows for a systematic examination of the many variables involved.
It is, of course, limited by the range of studies available. In addition, the calorie level is rarely adjusted according to individual needs; therefore the actual calorie deficit per individual is an important confounding variable. Based on the above reviews, we can revisit the controversial issues delineated in the introduction of this paper, and apply these issues to a family physician's practice.
One of the main points to be made is the potential impact of dietary intake, especially total calories, calorie deficits and grammes of protein per kilogram body weight. Further work is necessary to determine whether milder calorie deficits with adequate protein in combination with strength training can positively affect resting metabolic rate.
In contrast to Kraemer and colleagues' work, the majority of the studies point to a reduction in short-term resting metabolic rates that is greater than can be explained by the loss of body mass or fat-free mass over the same time period.
Unfortunately, there has been very little work done over the last few years regarding the duration of this phenomenon. Wadden and colleagues' work indicates that this disproportionate reduction reflects metabolic processes associated with the hypocaloric dieting itself.
When calorie balance is resumed, the resting metabolic rate is dependent on the new body mass, especially fat-free mass. When they get to goal weight their metabolic rate is severely depressed, and they can experience almost immediate weight gain if they resume their prior higher calorie intakes.
Recent studies have not continued to measure changes in resting metabolic rate for extended periods to determine whether the reductions are self-limiting. Again, the work of Wadden and colleagues supports a self-limiting hypothesis.
Lastly, exercise does not appear to negate this reduction in resting metabolic rate or fat-free mass. This may have been due to insufficient calories, protein or exercise stimulus in terms of frequency.
Family practice physicians can facilitate healthy and successful weight management among their patient populations by heeding the following tips: i determine long-term weight goals based on obtaining a body mass index under 27, if possible 25; ii determine short-term weight goals based on a reduction of 1 to 2 body mass index units approximately 4.
Continue to support patient through this cyclical process until body mass index is at least under 27, if not at Based on patients' medical history and preferences, appropriate individualized diet and exercise prescriptions should be developed.
This is best approached with a health care team including a physician, registered dietitian and exercise physiologist. Through this slow and thought-ful process of cycles of weight loss and weight maintenance it is thought that patients will be able to prevent the more debilitating cycles of rapid weight loss, short-term reductions in metabolic rate and rapid weight gain.
National Heart Lung and Blood Institute. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Bethesda: National Institutes of Health, Apfelbaum MJ, Bestsarron J, Lacatis D. Effect of caloric restriction and excessive caloric intake on energy expenditure.
Am J Clin Nutr ; 24 : —
Heading out the door? Hi RMR and weight cycling I have a question about measuring my Resting Metabolic Rate. I would like to cyclint some adjustments to weighf nutrition plan this winter. When weivht Functional strength exercises to measuring my Stretch and strengthen exercises, can Dycling simply wear a heart rate monitor for a hour period to determine more accurately how many calories I burn in a given day? CD Dear CD, The upcoming winter season is definitely a great time to not only rest and have some changes in your training program, but also to lose some body weight and body fat, and to incorporate some new foods and recipes into your diet. If you plan to work with a sports dietitian in the coming months, your RMR is an important part of the energy balance equation.Weighh more information about PLOS Subject Areas, click here. Cucling research has adn decreases in resting cyclong rate Cyflingbody cyclihg and weightt following a period of intensified training in elite athletes, however the weigut mechanisms of change remain unclear.
Therefore, the aim of the present study was to investigate how an vycling training period, designed to elicit overreaching, ans RMR, body composition, and performance in trained endurance athletes, and to elucidate underlying mechanisms.
Training Protein intake for seniors of a combination of weeight based interval sessions and on-road cycling. RMR, body composition, energy intake, appetite, heart rate variability RMMRcycling performance, biochemical markers and mood responses wejght assessed cyclinh multiple time cyc,ing throughout the six-week period.
Aand were analysed using a Functional strength exercises mixed modeling approach. The intensified weivht period elicited significant weigjt in RMR F 5, A state of overreaching was induced, as identified by a reduction in Minerals for energy performance F 5, Wfight training periods elicit greater energy demands in Carb counting for special dietary requirements cyclists, which, if not sufficiently compensated with increased dietary weigh, appears weighf provoke a cascade wfight metabolic, hormonal and neural responses in an attempt to restore homeostasis nad conserve energy.
The weught monitoring of energy cyclng, power output, mood RMR and weight cycling, body mass Artichoke side dishes HRV during intensified training cylcing may alleviate fatigue and attenuate cyclung observed decrease in RMR, providing more optimal conditions for a positive cyvling adaptation.
Citation: Woods AL, Rice AJ, Garvican-Lewis LA, Wallett AM, Lundy B, Rogers MA, et al. PLoS ONE 13 2 : e Received: March 12, ; Accepted: January 9, ; Published: February 14, Copyright: © Woods et cyclinf.
This is an open access article distributed under the terms of the Creative Cyclibg Attribution RRwhich permits Boost liver immunity use, RMR and weight cycling, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Anr All relevant data are within the paper and its Supporting Information Astaxanthin anti-aging properties. Funding: The study was funded by contributions from the University of Canberra Research Ans for Ccling and Exercise, the Australian Institute of Sport and the School of Health Sciences, Quenching health benefits of KwaZulu-Natal, Durban.
Vycling funders had cyclingg role in study design, data collection and analysis, decision to Liver support herbal extracts, or preparation of the manuscript. Competing interests: The authors have declared that no competing cyclinf exist.
Periods of intensified training are deliberately programmed to foster physiological and psychological adaptations to potentially improve physical performance. It is critical, however, to cyclinh that a balance between training-induced fatigue and sufficient Functional strength exercises exists, in order anx prevent excessive load on the anc, and welght the risk of maladaptation to training, illness or injury.
Training-related distress can be viewed along a continuum from acute fatigue snd overtraining. Short-term periods of intensified training may result in Functional strength exercises decrements associated with acute cyclnig, which, upon appropriate recovery, can elicit an adaptive response to improve performance.
It is important to RMR and weight cycling between acute fatigue and Gut health and immunity, however, Weight management exercises the cucling effect is reported to be smaller in FOR than acute fatigue cycping 1 ], and FOR can elicit a greater cyclint for training maladaptation [ 2 cyclinng.
NFOR is typically characterized by the inability to sustain effort RMR and weight cycling intense exercise, diminished performance with maintenance or progression of the training load, and excessive fatigue both at rest and during exercise.
Athletes may also present with cyclinf disturbances, chcling stress, nutritional RRMR sleep disturbances, Cyclling illness, with recovery from NFOR taking several weeks weighy months [ 3 — 5 ].
RM long-term performance decrements from overtraining may require several months to years for recovery RMR and weight cycling 3ewight6 anv, 7 weighr, and should vycling prevented, wherever possible. Athletic responses to intensified training periods have been cgcling extensively [ 25 ad, 8 — 11 ], ccycling there remains no single diagnostic marker to distinguish between acute fatigue, overreaching and overtraining.
The continuum toward overtraining has also been proposed to involve disturbances at the cycoing level, which may manifest in a reduced hormonal response to exercise [ 12 — 14 ].
Weeight particular, previous ccling suggest a disturbance in snd state, impaired race times and decreased power output may occur in athletes suffering from overreaching or overtraining [ 14 — 19 ]. Previous research from the present group suggests that changes in resting metabolic rate RMRbody composition and energy intake cyclng also be plausible indicators of training distress [ 20 ].
RMR is the minimum energy cycing body requires to perform its basic functions, and is principally dependent Deight lean mass cyclibg 21 ]. In an applied setting, RMR can be used weifht an indicator of energy availability EA ; cycllng as the energy remaining for metabolic processes once the energy cost of exercise has annd subtracted from dietary intake [ 22 ].
Sufficient energy is critical for training consistency, particularly during intensified periods, since prolonged energy restriction can lead to impaired physiological function and increased risk of fatigue, illness and injury, as well as maladaptation to the prescribed training [ 23 ].
Significant reductions in RMR, body mass and fat mass have been observed in elite rowers completing four weeks of intensified training at sea level [ 20 ], however increases and decreases in RMR have also been observed during altitude training camps in elite and highly-trained athletes, contingent on training volume and dietary practices [ 2425 ].
Energy homeostasis is centrally regulated, and RMR is closely linked to appetite and energy intake [ 2627 ]. Therefore, when energy intake is insufficient to support an intensified training load, athletes are more likely to suffer suboptimal EA and a lower RMR.
Under such conditions, time trial performance has been demonstrated to decrease in an elite rowing cohort where a state of substantial fatigue and possible overreaching may have occurred [ 20 ]. It is plausible that a relationship exists between RMR, energy intake, EA and training load tolerance in endurance athletes, but further data is required to support this premise and to determine the underlying mechanisms involved.
Further examination of this relationship is currently being undertaken by a subgroup of our authors. The aim of the present study was to investigate how an intensified training period, designed to elicit overreaching, affects RMR, body composition and performance in trained endurance athletes, and to elucidate underlying mechanisms.
We hypothesised that intensified training would elicit an increased energy demand, leading to reductions in RMR, body composition and performance.
Thirteen trained male cyclists completed a six-week training program designed to achieve an overreached state followed by a recovery period. The study was approved by both the Australian Institute of Sport Human Ethics Committee and University of Canberra Human Research Ethics Committee.
All participants provided written informed consent prior to involvement. Training consisted of a combination of monitored, laboratory-based high-intensity interval sessions, and on-road cycling.
RMR, body composition, energy intake, appetite, cycling performance, heart rate variability HRVbiochemical markers and mood responses were assessed at multiple time points throughout the six-week period Fig 1. Key: Monitored Laboratory Session—consisting of the standardised warm up, assessment of cycling performance, and HIIT training session; Biochemical Markers—PRE and POST warm up blood samples for leptin and fT3; On-road Cycling Session— 1 long duration, aerobic-based session and 2 hill repeats; Power Meter Calibration—timed repetition of a known distance and elevation; RMR—Resting Metabolic Rate; Body Composition—from Dual-Energy X-Ray Densitometry DXA ; Energy Intake—from 3-day food diaries; Appetite—visual analogue scales to determine appetite; Mood Questionnaire—consisting of the Multicomponent Training Distress Scale, Recovery Stress Questionnaire for Athletes RESTQ Sport ; HRV—Heart Rate Variability.
The spotted bars indicate a laboratory-training day; the striped bars indicate an on-road cycling training day; the white bars indicate a rest day.
Fourteen male cyclists were recruited from local cycling and triathlon clubs in Canberra, Australia between December and March for participation in the six-week program. One participant was unable to continue the training commitments after week 2. Characteristics of the 13 participants who completed the study were mean ± SD, range : age 35 ± 8 years, 20—47 years; height ± 7 cm, — cm; body mass min -1 kg -1 min -1 kg -1 ; maximal aerobic power MAP, absolute ± 28 W, — W; V̇O 2max absolute 4.
min -14. min -1 ; MAP relative 4. kg Based on previous literature [ 28 ], the subjects were classified as Performance Level 3. Due to the highly applied and demanding nature of the study, it was not possible to pair match an independent control group.
We acknowledge this as a limitation to the study. Weekly training was prescribed individually through online software Training Peaks, Boulder, CObased on Training Stress Score TSS. L -1 blood lactate BLa concentration was reached via the power-versus-lactate curve, or lactate threshold 2 [ 83132 ].
All sessions were monitored and adjusted where required to reach the target TSS each week. In the two weeks prior to the study beginning, participants completed an incremental cycling test to exhaustion using an electromagnetically braked cycle ergometer Lode Excalibur Sport, Groningen, Netherlands to assess V̇O 2max and MAP, as has been described previously [ 33 — 35 ].
Individual training zones and FTP were subsequently calculated based on power output, heart rate HR and BLa values obtained for each incremental stage using in-house software [Automatic Data Analysis for Progressive Tests ADAPT v6.
RMR was assessed on eleven mornings across the six-week period Fig 1 using the criterion Douglas Bag method of indirect calorimetry, which has been described previously [ 30 ]. All athletes were overnight rested and fasted, and abstained from physical activity for at least eight hours prior to all measurements, which were each completed at the same time of day ± 1 h.
Typical error TE for the Douglas Bag method of RMR measurement in our hands is Body composition was assessed immediately following three of the RMR measurements Baseline, end of Loading 2, end of Recovery 2; Fig 1 via Dual-Energy X-Ray Densitometry Lunar iDXA; GE Healthcare Asia-Pacific.
Each DXA scan provided an assessment of fat mass, lean mass and bone mineral content BMC. Fat-free mass FFM was calculated as lean mass plus BMC. Radiation safety approval was provided by the Radiation Safety Committee at the John James Hospital, Canberra.
Dietary intake was recorded either by paper diary record or iPhone application Easy Diet Diary, Xyris Software Pty Ltd, Australia for the three days immediately prior to each RMR measurement Fig 1and analysed for total energy intake and macronutrient consumption by an accredited practising dietitian using nutrient analysis software FoodWorks Professional v7.
Subjective feelings of appetite were assessed prior to breakfast following each RMR measurement via 1—10 Likert visual analogue scale VAS, Fig 1adapted from [ 36 ] S1 Fig.
HRV was assessed during the minute rest period of each RMR measurement, for eleven measurements in total Fig 1.
Upon arrival to the laboratory, participants were fitted with a HR strap Firstbeat Technologies Ltd, Jyväskylä, Finland. Upon resting supine for five minutes, a ten-minute recording was taken, which was divided into five minutes of rest followed by a five-minute measurement of inter-beat intervals.
The inter-beat intervals were analysed using open source analysis software [Kubios HRV Software version 2.
Following an initial familiarization on Day 1, 12 monitored laboratory sessions were performed across the six-week period Fig 1inclusive of a standardised warm-up, assessment of cycling performance, and a high-intensity interval training HIIT session option 1, 2 or 3 with varied work-rest ratios Table 1.
Participants were blinded to external feedback cues, and instructed to complete all efforts with maximal exertion. Peak power output was recorded immediately following the 15 s sprint. Mean power output, time to completion and Rating of Perceived Exertion RPE, 6—20 Borg Scale [ 38 ] were recorded immediately following the m TT, with BLa measured from capillary sample one minute later.
HR was blinded, but monitored continuously throughout Firstbeat Technologies Ltd, Jyväskylä, Finland. All sessions were performed using calibrated cycle ergometers Wattbike Pro, Wattbike, Nottingham, UK.
Each participant was assigned to the same individual bike for the entire study to ensure measurement error was minimised. Laboratory sessions were completed at the same time of day ± 1 hwith a minimum of two days between each session. On alternate days to the laboratory sessions Fig 1participants completed two on-road rides in their own time, with a minimum of five hours between each: 1 long duration, aerobic-based session and 2 a series of hill repeats at FTP in order to induce fatigue.
Training zones were based on V̇O 2max test results, as previously described. Power output data Stages left arm crank: Colorado, USA; Garmin Vector: Kansas City, USA; SRM Training System: Jülich, Germany and HR data Garmin: Kansas City, USA for each cyclist were uploaded to Training Peaks upon completion.
For each trial, the total mass of the rider and bike were recorded, followed by the time to complete one repetition of the known course. Predicted power output was then calculated using a validated regression based on speed, mass and time to complete [ 39 ].
The difference between the predicted power and the device-recorded power was then compared to ensure consistency in the power meter recordings across time. Power comparison data was not utilised for any other purpose than assessing for drift in the predicted-actual power relationship.
On eight occasions during the monitored laboratory sessions Fig 1venous blood samples 1 x 8. Samples were taken before and after a standardised exercise, i. at rest PRE and immediately following POST the standardised warm-up, in an attempt to mitigate the large variability in the assessment of leptin and free thyroid hormone triiodothyronine, fT3.
Raw data were then assessed as the percentage change between PRE and POST, per session. The present study design involved repeated measures of multiple variables at specific time points, and a number of proposed inter-variable relationships.
A multivariate structural equation model SEM was initially employed, however the complexity of the study design and irregularity of measurement points meant that the SEM did not achieve convergence.
: RMR and weight cycling| Cycling Nutrition with Monique Ryan: Resting metabolic rate - Velo | This Issue. Share X Facebook Email LinkedIn. June 27, Kelly D. Brownell, PhD ; Judith Rodin, PhD. Author Affiliations From the Department of Psychology, Yale University, New Haven, Conn. visual abstract icon Visual Abstract. Access through your institution. Add or change institution. Download PDF Full Text Cite This Citation Brownell KD , Rodin J. Select Your Interests Customize your JAMA Network experience by selecting one or more topics from the list below. Save Preferences. Privacy Policy Terms of Use. Access your subscriptions. Free access to newly published articles. Purchase access. Rent article Rent this article from DeepDyve. Sign in to access free PDF. In addition, the authors conclude that the majority of fat-free mass lost could be accounted for by loss of body water. Since carbohydrate is stored in the muscle with water, the loss in body water is expected due to glycogen depletion associated with the hypocaloric diet. Strength training draws largely on locally stored glycogen for energy substrate, and can therefore further decrease the glycogen and water component of fat-free mass. The authors note that the short-term decrease in resting metabolic rate may be due to a decrease in sympathetic tone associated with a diet-induced decrease in circulating insulin levels. Dietary factors are addressed in this study in that all meals were provided to patients. Patients were consuming approximately 0. Resting metabolic rate was measured while subjects were on the hypocaloric diets, and therefore is reflective of the stress of dieting itself and not simply of the loss of fat-free mass. The authors calculate that all of the loss in fat-free mass can be attributed to water losses. However, it should be noted that this is likely to be an oversimplification, and measurement errors are probably masking the loss of actual protein or muscle mass. Therefore, if water losses are not accounted for, the relationship between fat-free mass and resting metabolic rate may not be accurately and completely described. Thompson JL, Manore MM, Thomas JR. Effects of diet and diet-plus-exercise programs on resting metabolic rate: a meta-analysis. Int J Sport Nutr ; 6: 41— It is difficult to summarize the results of studies examining the effect of exercise on resting metabolic rate during a hypocaloric dieting period due to the number of variables that are involved type, duration, frequency and intensity of exercise, degree of energy deficit, total daily calorie intake, and distribution of calories among carbohydrates, proteins and fats. Therefore, Thompson and colleagues suggest caution regarding narrative reviews of this body of literature. Rather, they have conducted a meta-analysis to quantify treatment effectiveness, specifically the effects of diet alone and diet-plus-exercise on resting metabolic rate. The authors searched the literature and found 22 studies between and that documented resting metabolic rate in humans placed in either diet-only groups or diet-plus-exercise groups. The studies represent data from subjects, 68 males and females, 31—45 years of age. The majority of studies placed subjects on low-fat, high-carbohydrate diets of less than kilocalories per day. Intervention programmes lasted approximately 10 weeks. Effect sizes for differences in resting metabolic rate before and after diet and before and after diet-plus-exercise were calculated. Positive effect sizes indicate that resting metabolic rate increased due to the intervention, and negative effect sizes indicate that resting metabolic rate decreased as a result of the intervention. When expressed in absolute terms, there was a significant decrease in resting metabolic rate in diet only However, the drop is classified as small for the dieters who exercised and large for those who just dieted. This difference is also statistically significant. Similarly, when expressed per kilogram of fat-free mass per hour, the drops in resting metabolic rate for the dieters 5. The decrease in the dieters is classified as moderate, while the decrease with dieter— exercisers is considered small. The difference between groups is not significantly different. The authors were also able to establish that neither diet-related variables number of calories, distribution of calories among macronutrients or duration of diet , exercise-related variables type of exercise, intensity, duration or frequency nor subject-related variables age, gender, body composition correlated significantly with changes in resting metabolic rate. There has been some attention given in the literature to the appropriateness of calculating relative metabolic rate by dividing resting metabolic rate by body weight or fat-free mass, since the line defining the relationship between these two variables does not intercept the y -axis at zero. The results of this manipulation do reveal a decrease in resting metabolic rate due to diet alone and diet-plus-exercise. However, the slopes of the regression lines pre- and post-treatment are not significantly different, and, therefore, the relationship between mass and metabolic rate is the same and independent of treatment. In other words, the drop in resting metabolic rate is expected due to the decrease in body size. The use of meta-analysis in this area of research is useful because it allows for a systematic examination of the many variables involved. It is, of course, limited by the range of studies available. In addition, the calorie level is rarely adjusted according to individual needs; therefore the actual calorie deficit per individual is an important confounding variable. Based on the above reviews, we can revisit the controversial issues delineated in the introduction of this paper, and apply these issues to a family physician's practice. One of the main points to be made is the potential impact of dietary intake, especially total calories, calorie deficits and grammes of protein per kilogram body weight. Further work is necessary to determine whether milder calorie deficits with adequate protein in combination with strength training can positively affect resting metabolic rate. In contrast to Kraemer and colleagues' work, the majority of the studies point to a reduction in short-term resting metabolic rates that is greater than can be explained by the loss of body mass or fat-free mass over the same time period. Unfortunately, there has been very little work done over the last few years regarding the duration of this phenomenon. Wadden and colleagues' work indicates that this disproportionate reduction reflects metabolic processes associated with the hypocaloric dieting itself. When calorie balance is resumed, the resting metabolic rate is dependent on the new body mass, especially fat-free mass. When they get to goal weight their metabolic rate is severely depressed, and they can experience almost immediate weight gain if they resume their prior higher calorie intakes. Recent studies have not continued to measure changes in resting metabolic rate for extended periods to determine whether the reductions are self-limiting. Again, the work of Wadden and colleagues supports a self-limiting hypothesis. Lastly, exercise does not appear to negate this reduction in resting metabolic rate or fat-free mass. This may have been due to insufficient calories, protein or exercise stimulus in terms of frequency. Family practice physicians can facilitate healthy and successful weight management among their patient populations by heeding the following tips: i determine long-term weight goals based on obtaining a body mass index under 27, if possible 25; ii determine short-term weight goals based on a reduction of 1 to 2 body mass index units approximately 4. Continue to support patient through this cyclical process until body mass index is at least under 27, if not at Based on patients' medical history and preferences, appropriate individualized diet and exercise prescriptions should be developed. This is best approached with a health care team including a physician, registered dietitian and exercise physiologist. Through this slow and thought-ful process of cycles of weight loss and weight maintenance it is thought that patients will be able to prevent the more debilitating cycles of rapid weight loss, short-term reductions in metabolic rate and rapid weight gain. National Heart Lung and Blood Institute. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Bethesda: National Institutes of Health, Apfelbaum MJ, Bestsarron J, Lacatis D. Effect of caloric restriction and excessive caloric intake on energy expenditure. Am J Clin Nutr ; 24 : — Lansky D, Brownell KD. Estimates of food quantity and calories: errors in self-reporting. Am J Clin Nutr ; 35 : — Wadden T, Foster GD, Letizia KA, Mullen JL. Long-term effects of dieting on resting metabolic rate in obese outpatients. JAMA ; 6 : — Forbes G. Human Body Composition. New York: Springer-Verlag, Ravussin E, Bogardus C. Relation of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr ; 49 : — American Dietetic Association. Position of the American Dietetic Association on weight management. J Am Dietetic Assoc ; 97 1 : 71 — Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Advertisement intended for healthcare professionals. Navbar Search Filter Family Practice This issue Primary Care Books Journals Oxford Academic Mobile Enter search term Search. Issues Advance articles Submit Author Guidelines Submission Site Open Access Purchase Alerts About About Family Practice Editorial Board Advertising and Corporate Services Journals Career Network Self-Archiving Policy Dispatch Dates Journals on Oxford Academic Books on Oxford Academic. Issues Advance articles Submit Author Guidelines Submission Site Open Access Purchase Alerts About About Family Practice Editorial Board Advertising and Corporate Services Journals Career Network Self-Archiving Policy Dispatch Dates Close Navbar Search Filter Family Practice This issue Primary Care Books Journals Oxford Academic Enter search term Search. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Introduction. Concluding remarks. Journal Article. Effects of dieting and exercise on resting metabolic rate and implications for weight management. Josephine Connolly , Josephine Connolly. Department of Family Medicine, University Hospital and Medical Center, SUNY Stony Brook, Stony Brook, New York , USA. Oxford Academic. Google Scholar. Theresa Romano. Marisa Patruno. PDF Split View Views. Select Format Select format. ris Mendeley, Papers, Zotero. enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation. Permissions Icon Permissions. Close Navbar Search Filter Family Practice This issue Primary Care Books Journals Oxford Academic Enter search term Search. Introduction The significance of the rising prevalence of obesity for morbidity and associated health care costs is clearly delineated by the United States National Institutes of Health's Clinical Guidelines on the Identification, Evaluation and Treatment of Overweight and Obesity in Adults. Summary This study examined the effects of three interventions diet; diet and aerobic exercise; diet, aerobic exercise and resistance training on resting metabolic rate and body composition, as well as other physiological and metabolic parameters which are beyond the scope of this review. Comment The findings regarding no loss of fat-free mass in the diet-only group are surprising, as some degree of obligatory loss of fat-free mass is expected with significant weight loss. Summary This two-part study is based on the assumption that a decrease in calorie intake and weight loss is associated with a decrease in resting metabolic rate and fat oxidation. Comments In the first part of the study, subjects' resting metabolic rate decreased to a greater extent than their weight or fat-free mass. Summary The authors sought to examine the potential of strength training as a means to prevent the decline in fat-free mass and resting metabolic rate associated with very-low calorie diets. Comment Dietary factors are addressed in this study in that all meals were provided to patients. Summary It is difficult to summarize the results of studies examining the effect of exercise on resting metabolic rate during a hypocaloric dieting period due to the number of variables that are involved type, duration, frequency and intensity of exercise, degree of energy deficit, total daily calorie intake, and distribution of calories among carbohydrates, proteins and fats. Comment The use of meta-analysis in this area of research is useful because it allows for a systematic examination of the many variables involved. Concluding remarks Based on the above reviews, we can revisit the controversial issues delineated in the introduction of this paper, and apply these issues to a family physician's practice. Am J Clin Nutr. J Am Dietetic Assoc. Issue Section:. Download all slides. Views 90, More metrics information. Total Views 90, |
| Two seasons of weight cycling does not lower resting metabolic rate in college wrestlers | View Article Google Scholar 9. Forbes G. Weight cycling as a risk factor for low muscle mass and strength in a population of males and females with obesity. Such physiological disturbance and maladaptation to training may be problematic in athletes who cannot afford to lose mass, or those undertaking intense training prior to competition. The intrinsic principle of WC is associated with oxidative stress and inflammation-induced metaflammation, which leads to decreased glucose tolerance and dyslipidemia, and ultimately sarcopenia, type 2 diabetes, cardiovascular disease, and NAFLD. Data are presented as individual values for each time point, and group mean ± SD. Article PubMed Google Scholar Garidou L, Pomie C, Klopp P, Waget A, Charpentier J, Aloulou M, Giry A, Serino M, Stenman L, Lahtinen S, et al. |
| Weight cycling based on altered immune microenvironment as a result of metaflammation | RIPK1 expression associates with inflammation in early atherosclerosis in humans and can be therapeutically silenced to reduce NF-kappaB activation and atherogenesis in mice. J Int Soc Sports Nutr. Raw data: Energy intake. Dietary factors are addressed in this study in that all meals were provided to patients. In contrast to Kraemer and colleagues' work, the majority of the studies point to a reduction in short-term resting metabolic rates that is greater than can be explained by the loss of body mass or fat-free mass over the same time period. Study Selection. |
| Weight Cycling | Monitored laboratory sessions and cycling performance Following an initial familiarization on Day 1, 12 monitored laboratory sessions were performed across the six-week period Fig 1 , inclusive of a standardised warm-up, assessment of cycling performance, and a high-intensity interval training HIIT session option 1, 2 or 3 with varied work-rest ratios Table 1. Table 1. Outline of the monitored laboratory sessions and assessment of cycling performance. On-road cycling On alternate days to the laboratory sessions Fig 1 , participants completed two on-road rides in their own time, with a minimum of five hours between each: 1 long duration, aerobic-based session and 2 a series of hill repeats at FTP in order to induce fatigue. Biochemical markers PRE-POST ergometer On eight occasions during the monitored laboratory sessions Fig 1 , venous blood samples 1 x 8. Data analysis The present study design involved repeated measures of multiple variables at specific time points, and a number of proposed inter-variable relationships. Linear mixed models Resting metabolic rate. Table 2. Linear mixed model data for the resting metabolic rate RMR model. Body composition. Energy intake. Biochemical markers. Heart rate variability. Cycling performance. Mood questionnaires. Time course of change Raw data comparisons for each variable across the study period as a percentage change from Day 1 are presented in Fig 3. Fig 3. Percentage change in measured variables from baseline in relation to training load across the study duration for A RMR, B Body mass, C Total energy intake, D Appetite, E Mood disturbance, F Biochemical markers leptin and fT3, G Heart rate variability LnRMSSD , and H Cycling performance. Discussion Main findings The present period of intensified training elicited a state of overreaching in trained male cyclists, and significantly decreased both absolute and relative RMR, body mass, fat mass and HRV, with concomitant increases in mood disturbance, and declines in anaerobic performance, aerobic performance and associated peak HR; all of which improved following a period of recovery. RMR, energy availability and intensified training Relative RMR decreased in the present participants from ~ to kJ. Evidence that overreaching occurred Performance decline. Mood disturbance. Possible mechanisms for the observed changes in RMR Body composition. Energy intake and appetite. Thyroid hormone. Limitations The present investigation was applied in nature, and whilst scientific rigour was paramount, there remain some limitations that must be acknowledged. Practical application The present data suggest that during periods of intensified training, practitioners should employ a series of monitoring tools—early, and often—to avoid detrimental levels of training-related distress and ensure sufficient energy intake to support the greater energetic demands. Conclusion Athletes often undertake periods of intensified training in order to improve performance following a period of recovery. Supporting information. S1 Fig. Subjective feelings of appetite assessment via 1—10 Likert visual analogue scale. s JPG. S1 Table. Linear mixed model data for the body composition model. s DOCX. S2 Table. Linear mixed model data for the energy intake model. S3 Table. Linear mixed model data for the appetite model. S4 Table. Linear mixed model data for the biochemical markers model. S5 Table. Linear mixed model data for the heart rate variability model. S6 Table. Linear mixed model data for the cycling performance model. S7 Table. Linear mixed model data for the mood questionnaire tesponses model. S8 Table. Raw data: Absolute RMR. S9 Table. Raw data: Relative RMR. S10 Table. Raw data: Minute ventilation [VE STPD ]. S11 Table. Raw data: Body composition. S12a-d Tables. Raw data: Energy intake. S13a-d Tables. Raw data: Appetite. S14a-b Tables. Raw data: Biochemical markers PRE-POST ergometer warm-up. S15a-b Tables. Raw data: Heart rate variability. S16a-e Tables. Raw data: Cycling performance. S17 Table. Raw data: Mood questionnaires—Multicomponent training distress scale. S18 Table. Raw data: Mood questionnaires—RESTQ sport. Acknowledgments We would like to sincerely thank the athletes for their participation in the study, and the staff and students from AIS Physiology, AIS Nutrition, and UCRISE for their assistance with testing sessions. References 1. ten Haaf T, van Staveren S, Oudenhoven E, Piacentini MF, Meeusen R, Roelands B, et al. 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Wu L, Van Kaer L. Contribution of lipid-reactive natural killer T cells to obesity-associated inflammation and insulin resistance. Download references. The research is supported by National High-Level Hospital Clinical Research Funding PUMCH-B , and the Beijing Municipal Science and Technology Commission Z Department of Clinical Nutrition, Chinese Academy of Medical Sciences - Peking Union Medical College, Peking Union Medical College Hospital, No. You can also search for this author in PubMed Google Scholar. Con-ceptualization, WYL and WC. Correspondence to Wei Chen. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and permissions. Li, W. Weight cycling based on altered immune microenvironment as a result of metaflammation. Nutr Metab Lond 20 , 13 Download citation. Received : 13 September Accepted : 07 February Published : 22 February Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Download ePub. Abstract As a result of the obesity epidemic, more people are concerned about losing weight; however, weight regain is common, leading to repeated weight loss and weight cycling. Introduction Worldwide, the prevalence of overweight and obesity is unstoppable [ 1 ]. Table 1 Systemic, tissue and cellular molecular correlates of WC Full size table. Systemic effects on WC: metaflammation of the endocrine system, circulatory system, and intestinal barrier In the event of weight regain WR in the WC, i. Endocrine system and WC Uncertainty surrounds the relationship between WC and metabolic diseases, which may affect systemic inflammation [ 22 ]. Circulatory systems and WC According to studies, the number of weight cycles is not only positively correlated with systolic blood pressure and fasting blood glucose levels. Intestine barrier and WC WR is associated with increased appetite and high intake of a high-fat diet, which causes damage to the gut barrier, including inhibition of mucus production, weakened tight junctions, changes in intestinal villus structures, and intestinal inflammation [ 31 ]. Full size image. Tissue changes in WC: local metaflammation of adipose and muscle tissue During the weight loss phase of WC, significant muscle loss and an inflammatory state are evident [ 56 ]. Possible causes of WC: metaflammation in the microenvironment centered on immune cells WC is frequently associated with the enduring effects of previous obesity, which may be a persistent or even partial amplification of adipose tissue's immune and inflammatory status. Expression of WC inflammation in immune cell phenotype A hyperactive immune response in adipose tissue may result in metabolic abnormalities during WC [ 83 ]. Conclusion and future directions Although significant progress has been made in recent years in the fields of weight loss, treatment of obesity, and obesity-related complications as a result of the development of new drugs and treatment modalities, research on the prevention of WR to prevent the formation of WC is still in its infancy and developing phase. 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