BG, LS, and AB were responsible for drafting the manuscript. All authors assisted with manuscript revision. This study was supported by funding from the National Institutes of Health—National Institute on Aging RO1 AG awarded to BG.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The authors would like to thank the contributions of our study participants and acknowledge the valuable expertise and assistance of the imaging, recruitment, clinic, calorimetry, laboratory, and nutrition staff at TRI, AdventHealth Research Institute. Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, Di Francesco V, et al.

Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes. doi: PubMed Abstract CrossRef Full Text Google Scholar. Villareal DT, Apovian CM, Kushner RF, Klein S. Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, the obesity society.

Obes Res. Boulé NG, Weisnagel SJ, Lakka TA, Tremblay A, Bergman RN, Rankinen T, et al. Effects of exercise training on glucose homeostasis: the HERITAGE family study. Diabetes Care. Álvarez C, Ramírez-Campillo R, Ramírez-Vélez R, Izquierdo M.

Prevalence of non-responders for glucose control markers after 10 weeks of high-intensity interval training in adult women with higher and lower insulin resistance.

Front Physiol. Solomon TP, Malin SK, Karstoft K, Haus JM, Kirwan JP. The influence of hyperglycemia on the therapeutic effect of exercise on glycemic control in patients with type 2 diabetes mellitus. JAMA Intern Med.

Hecksteden A, Kraushaar J, Scharhag-Rosenberger F, Theisen D, Senn S, Meyer T. Individual response to exercise training-a statistical perspective.

J Appl Physiol. Atkinson G, Batterham AM. True and false interindividual differences in the physiological response to an intervention. Exp Physiol. Malin SK, Haus JM, Solomon TP, Blaszczak A, Kashyap SR, Kirwan JP. Insulin sensitivity and metabolic flexibility following exercise training among different obese insulin-resistant phenotypes.

Am J Physiol Endocrinol Metab. O'Donoghue G, Kennedy A, Andersen GS, Carr B, Cleary S, Durkan E, et al. Phenotypic responses to a lifestyle intervention do not account for inter-individual variability in glucose tolerance for individuals at high risk of type 2 diabetes.

Distefano G, Standley RA, Zhang X, Carnero EA, Yi F, Cornnell HH, et al. Physical activity unveils the relationship between mitochondrial energetics, muscle quality, and physical function in older adults. J Cachexia Sarcopenia Muscle. Pruchnic R, Katsiaras A, He J, Kelley DE, Winters C, Goodpaster BH.

Exercise training increases intramyocellular lipid and oxidative capacity in older adults. Coen P, Hames K, Leachman E, DeLany J, Ritov V, Menshikova E, et al.

Reduced skeletal muscle oxidative capacity and elevated ceramide but not diacylglycerol content in severe obesity. CrossRef Full Text Google Scholar. Malin SK, Kirwan JP. Fasting hyperglycaemia blunts the reversal of impaired glucose tolerance after exercise training in obese older adults. Diab Obes Metab.

Janssen I, Fortier A, Hudson R, Ross R. Effects of an energy-restrictive diet with or without exercise on abdominal fat, intermuscular fat, and metabolic risk factors in obese women. Ross R, Dagnone D, Jones PJ, Smith H, Paddags A, Hudson R, et al.

Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men: a randomized, controlled trial. Ann Intern Med. Williams PT, Krauss RM, Vranizan KM, Wood P. Changes in lipoprotein subfractions during diet-induced and exercise-induced weight loss in moderately overweight men.

Christiansen T, Paulsen SK, Bruun JM, Pedersen SB, Richelsen B. Exercise training versus diet-induced weight-loss on metabolic risk factors and inflammatory markers in obese subjects: a week randomized intervention study.

Stefan N, Staiger H, Wagner R, Machann J, Schick F, Häring HU, et al. A high-risk phenotype associates with reduced improvement in glycaemia during a lifestyle intervention in prediabetes. Thamer C, Machann J, Stefan N, Haap M, Schäfer S, Brenner S, et al.

High visceral fat mass and high liver fat are associated with resistance to lifestyle intervention. Malin SK, Niemi N, Solomon TP, Haus JM, Kelly KR, Filion J, et al. Exercise training with weight loss and either a high-or low-glycemic index diet reduces metabolic syndrome severity in older adults.

Ann Nutr Metab. Böhm A, Weigert C, Staiger H, Häring HU. Exercise and diabetes: relevance and causes for response variability. Solomon TP. Sources of inter-individual variability in the therapeutic response of blood glucose control to exercise in type 2 diabetes: going beyond exercise dose.

Stephens NA, Brouwers B, Eroshkin AM, Yi F, Cornnell HH, Meyer C, et al. Exercise response variations in skeletal muscle PCr recovery rate and insulin sensitivity relate to muscle epigenomic profiles in individuals with type 2 diabetes. Bouchard C, Rankinen T.

Individual differences in response to regular physical activity. Med Sci Sports Exerc. Yudkin JS, Stehouwer C, Emeis J, Coppack S. C-reactive protein in healthy subjects: associations with obesity, insulin resistance, and endothelial dysfunction: a potential role for cytokines originating from adipose tissue?

Arterioscler Thromb Vasc Biol. Dessein PH, Woodiwiss AJ, Norton GR, Tsang L, Solomon A. Independent associations of total and high molecular weight adiponectin with cardiometabolic risk and surrogate markers of enhanced early atherogenesis in black and white patients with rheumatoid arthritis: a cross-sectional study.

Arthritis Res Ther. Puhkala J, Kukkonen-Harjula K, Mansikkamäki K, Aittasalo M, Hublin C, Kärmeniemi P, et al. Lifestyle counseling to reduce body weight and cardiometabolic risk factors among truck and bus drivers—a randomized controlled trial.

Scand J Work Environ Health. Ramírez-Vélez R, Correa-Bautista JE, Lobelo F, Izquierdo M, Alonso-Martínez A, Rodríguez-Rodríguez F, et al. High muscular fitness has a powerful protective cardiometabolic effect in adults: influence of weight status.

BMC Public Health. O'Brien PC. Procedures for comparing samples with multiple endpoints. Boule NG, Kenny GP, Larose J, Khandwala F, Kuzik N, Sigal RJ. Does metformin modify the effect on glycaemic control of aerobic exercise, resistance exercise or both? Konopka AR, Laurin JL, Schoenberg HM, Reid JJ, Castor WM, Wolff CA, et al.

This means opting for whole fruits and vegetables instead of refined carbohydrates such as processed bread and sugar, which tend to have a high glycemic index that triggers glucose spikes. It also means understanding the principles behind an insulin-resistance-friendly plate , which Ali McGowan, MS, LD, RDN, has outlined in four simple steps:.

Exercise — particularly a combination of resistance training and aerobic exercise — has been linked to improved insulin sensitivity. A review of 11 studies found a connection between increased physical activity levels and lower chances of developing insulin resistance [11].

Another study found that insulin sensitivity increased for at least 16 hours in both healthy subjects and those with type 2 diabetes after exercise [12].

The five best exercises to do for insulin resistance and weight loss are walking, squats, swimming, burpees, and yoga. Current exercise guidelines suggest that adults should do:. Cortisol , the stress hormone, is critical to survival — but chronic stress can interfere with the role that insulin plays in your body.

When your cortisol levels are consistently high, your blood sugar levels also stay elevated. High cortisol and stress can also lead to weight gain by changing the way you perceive food , leading to cravings for high-calorie, sugary snacks.

Here are some other tips to manage stress :. Consistently poor sleep — both sleep deprivation and an irregular sleep schedule — can take a significant toll on your insulin sensitivity. One study found that not getting enough sleep for just one night induced insulin resistance in healthy subjects [15].

Fortunately, catching up on rest can reverse the effects of poor sleep on insulin resistance. To improve your sleep hygiene , try the following tips:.

Incorporating these tips into your routine can help you stabilize your glucose levels and improve your insulin sensitivity, with or without a continuous glucose monitor CGM. The only way you can understand where you fall on the metabolic health spectrum is by using a CGM.

Paired with an app like Veri, a CGM can give you personalized insights about your glucose trends and variability, allowing you to understand how your body responds to your diet and lifestyle. In other words, you can see how food, exercise, sleep, and stress affect your real-time glucose levels , and their relationship to your mood and energy levels.

They consumed a diet of calories per day for the first 16 weeks, followed by an increase to 1, calories per day for the remainder of the supervised diet and exercise program. Exercise consisted of three sessions per week for the first 28 weeks and two sessions per week for the next 20 weeks.

Exercise was unsupervised during the remainder of the follow-up period. Twenty-two subjects were also evaluated approximately one year after the study week To assess the effects of weight loss and exercise on insulin sensitivity, oral glucose tolerance tests were performed at baseline and at weeks 16, 24, 44 and Subjects in all three groups lost weight during the first 16 weeks.

At week 16, the mean weight loss was In the 22 subjects who returned for a final visit at week 96, weight had increased from the 44th week to the 96th week, resulting in a mean net weight loss of 9. At week 44, these subjects demonstrated a mean From weeks 44 to 96, during the unsupervised period, 14 of the 22 subjects 64 percent gained more than 5 kg 11 lb.

No significant differences were observed among the women in the three diet and exercise groups at week Assessment of glucose tolerance during the study period revealed that fasting glucose levels and glucose levels obtained after a g glucose load did not differ among the groups throughout the study.

The mean fasting insulin level and the mean insulin level in response to oral glucose decreased significantly from baseline to the 44th week, after weight loss had been achieved.

All participants provided informed consent prior to participation and the protocols used in the original investigation and this secondary analysis were approved by both University of Pittsburgh Research Ethics Board and Institutional Review Board of AdventHealth.

Participants randomized to the HED group received bi-weekly in-person general health education group sessions for the 6-month study duration, including informational seminars on medication and type 2 diabetes management.

Each session lasted ~1 h. To eliminate the confounding effects of acute caloric restriction on insulin sensitivity, the dietitian aimed to keep participant weights stable during the last 2 weeks of intervention.

Participants completed a progressive 6-month exercise training program, 4—5 days per week, 45 min per session min per week consisting of mostly walking both outside and on an indoor treadmill and the option to include stationary cycling, elliptical and rowing machines. All indoor exercise was supervised by a trained monitor; aerobic exercise performed outdoors was not supervised.

Beginning at week 8, participants also performed 2, non-consecutive resistance exercise sessions per week, 30 min per session, focused on major muscle groups using resistance exercise machines. The resistance exercises were performed at the highest weight the participant could achieve for the given number of reps 10 — 12 with proper form.

When the participant reached 3 × 12 reps, we increased the weight and reduced the reps. Blood pressure and heart rate were measured for participant safety prior to each exercise session, in addition to weekly body weight. Weight and height were measured pre- and post- intervention, and BMI was calculated.

Waist circumference was measured using the Gulick II tape measure directly on the skin. Fat mass and fat-free mass were determined by dual-energy X-ray absorptiometry DXA using a GE Lunar GE Healthcare, UK. Additionally, abdominal and thigh adipose tissue AT and muscle volume was measured by MRI at baseline and following treatment on a 3 Tesla magnet Philips Acheiva at AH TRI.

The MRI scan was performed at the mid-point of the femur to quantify thigh muscle cross-sectional area, subcutaneous, and intermuscular AT IMAT. For abdominal AT images, high resolution axial images were taken of the entire abdomen to quantify abdominal subcutaneous and visceral AT volume.

Resultant images were analyzed using Analyze A VO 2max graded exercise test was performed by an exercise physiologist on the cycle ergometer using open circuit indirect calorimetry. Following a standardized warm-up, participants exercised at a moderate intensity with the workload resistance increased gradually until they reached volitional fatigue.

In vivo muscle mitochondrial function ATPmax was calculated using the PCR recovery time constant τ and the PCr level in oxygenated muscle at rest in the vastus lateralis using phosphorus 31 P magnetic resonance spectroscopy on the 3-T magnet as previously described Insulin sensitivity was measured using the hyperinsulinemic-euglycemic clamp.

Participants arrived at the research facility prior to the clamp procedure, consumed a standard American meal, and stayed overnight in the metabolic ward.

After an overnight fast, an intravenous catheter was placed in the antecubital vein for subsequent insulin and glucose infusions and for stable isotope infusions to measure insulin sensitivity.

A primed constant infusion of [6,H2] ran throughout the clamp procedure. An additional catheter was placed in the heated hand vein in the contralateral arm to attain arterialized blood samples for blood glucose determination and for [6,H2] glucose enrichment during the insulin and glucose infusions.

After a 2. A 2 ml blood sample was collected at 0, 30, 60, , , and min as well as every 10 min during the last 30 min of the clamp for GCMS determination of [6,H2] glucose enrichment. Insulin and FFA samples were also drawn at multiple time points throughout the clamp.

Hepatic insulin sensitivity was assessed as the suppression of endogenous glucose production EGP during steady state using the glucose enrichment data. Lipid profiles total cholesterol, HDL, LDL, VLDL, and triglycerides and HbA1C were measured by a fasting blood draw and analyzed in the clinical chemistry laboratory at AH TRI using standard assays.

During fasting conditions and following 30—45 min after the start of the glucose clamp, a percutaneous muscle biopsy of the vastus lateralis was performed using previously published methods A biopsy sample was taken 10—15 cm above the knee under local anesthesia with a 5-mm Bergstrom needle and suction.

A portion of the tissue was prepared for immunohistochemistry. Histochemical analyses were performed on serial sections using established methods in our laboratory Briefly, muscle was placed vertically in mounting medium on cork and frozen in isopentane cooled with liquid nitrogen.

Biopsy samples were sectioned 10 um using a cryotome and fixed prior to staining. Sections were incubated in a primary antibody cocktail at 4°C overnight [BA-F8 type I; IgG2b; ; 6H1 type IIX; IgM; ; and SC type IIA; IgG1; ]. All antibodies were obtained from the University of Iowa Hybridoma Bank.

Subsequently, slides were incubated in secondary antibody cocktail consisting of DyLight IgG2b; , Alexa Fluor IgM; , and Alexa Fluor IgG1; AlexaFluor conjugated wheat germ agglutinin WGA was used to stain glycoconjugate N-acetylglucosamine and N-acetylneuraminic acid residues.

Digital images 4X magnification of one section per skeletal muscle biopsy were captured using a Nikon eclipse Ti microscope Nikon Technologies, California and image analysis was performed using NIS elements software 4. One-way ANOVAs were performed to evaluate baseline differences between groups.

In cases where the assumption of normality assessed using the Shapiro Wilk test was not met, baseline comparisons between groups for these specific variables were performed using the non-parametric Kruskal Wallis test.

When a significant difference for the overall model was detected, a Tukey's post-hoc test for multiple comparisons was performed.

Statistical analysis was completed using GraphPad Prism version 8. Participant baseline characteristics and ranges of percent change following intervention are summarized in Table 1.

Table 1. Figure 1. Figure 2. Recent focus on the application of personalized lifestyle-based medicine in the last decade has stimulated an exponential increase in observations related to response heterogeneity.

In the present study, our primary findings indicate that the addition of exercise to energy restriction-induced weight loss improves the proportion of High Responders for glycemic control and cardiometabolic risk compared to weight loss alone and a time-matched control group.

Our findings have novel implications for enhancing our understanding of the impact of lifestyle interventions on the variability of important clinical variables in older obese adults that may support future efforts to tailor lifestyle interventions to the individual and optimize treatment outcomes.

To our knowledge no prior studies have assessed the independent contributions of weight loss with or without exercise to the response heterogeneity in insulin sensitivity and cardiometabolic risk, particularly in the older obese population. Additionally, in prior analyses that examine variability, studies have typically been small, and the majority lack a control group, precluding the ability to assess intervention-independent effects on response 4 , 5 , The current trial includes a time-matched control group that allows assessment of intervention responses beyond both technical error and day-to-day biological fluctuations 6 , 7.

Using this approach, we observed that exercise combined with energy intake restriction-induced weight loss is a superior approach for improving the proportion of individuals who achieve a favorable response for both insulin sensitivity and cardiometabolic risk compared to weight loss alone or no intervention.

While others have suggested a similar mean group response to exercise vs. diet-induced weight loss in men for several clinical outcomes 14 — 17 , our findings suggest that more individuals will achieve a greater response magnitude to intervention with the combination of diet-induced weight loss and exercise compared to diet alone.

Taken together, our novel findings reinforce and provide support for the inclusion of regular exercise in addition to dietary recommendations to improve the likelihood that an individual responds favorably to treatment.

We completed a comprehensive assessment of relationships between baseline traits and response for glycemic control and cardiometabolic risk, including clinical laboratory outcomes, MRI-derived body composition, aerobic fitness, muscle and hepatic insulin sensitivity, and immunohistochemical analysis of fiber type and capillary density.

Specifically, in both intervention groups, higher baseline triglycerides and VLDL-cholesterol were associated with greater improvement in cardiometabolic risk while higher plasma insulin and HOMA-IR were associated with increased insulin sensitivity.

Consistent with our findings are those from a week diet and exercise intervention in individuals aged 18—75 years who were at risk for type 2 diabetes 9 , wherein High Responders for glucose AUC assessed by 2-h OGTT had higher baseline weight, visceral AT, fasting glucose, 2-h OGTT glucose, and triglycerides and lower HDL-cholesterol compared to those who experienced an adverse response or attenuated response to the intervention.

However, our findings also contradict many others who observed blunted responses to exercise interventions associated with metabolically unhealthy outcome levels at baseline 4 , 5 , 18 — Several factors may explain the discrepant findings, including differences in sample demographics and disease diagnosis, duration of disease, medication use, dissimilar outcome variables, correlation vs.

categorical response analysis, intervention characteristics, etc. Thus, further investigation is warranted to evaluate whether response heterogeneity and predictors of response differ across population subtypes and lifestyle modifications to move closer to personalized lifestyle medicine that optimizes changes in clinical outcomes based on individual characteristics.

Numerous mechanisms have been highlighted as potential contributors to an individual's response to lifestyle intervention 21 , Prior work from our group demonstrated that skeletal muscle DNA methylation and RNA expression patterns reflective of elevations in antioxidant defense, insulin signaling, and mitochondrial metabolism were present in Non-Responders based on changes in PCR recovery rate i.

These molecular characteristics of Non-Responders correlated with higher baseline insulin sensitivity and muscle mitochondrial function in vivo Taken together, these mechanistic findings support the interpretation of our observations that indicate a higher metabolic burden and less healthy skeletal muscle phenotype allows for a greater window of opportunity for improvement.

Thus, factors across a range of molecular and metabolic outcomes genetics, epigenetics, metabolism, physiology, etc. likely play a role in an individual's response to intervention and should be further exploited in future studies Given growing interest in the study of individual responses and its implications for personalized exercise and diet prescription, it is important to consider the clinical relevance and interpretation of our findings.

This notion is complicated by the range of important health outcomes under interrogation that do not necessarily change in concert. The use of Z-scores to reflect the concurrent change in a collection of predefined outcomes is not a novel concept 25 — However, we extend this application to the study of interindividual variability.

Compared to the interventions described above that focus on a singular outcome, the use of Z-scores appears to reduce the proportion of individuals who respond poorly or do not respond to intervention Thus, in this field of response heterogeneity, it may be helpful to consolidate related outcomes to provide an integrative assessment of physiological responses and improve clinical applications and inferences.

There are limitations in our study that should be considered. This is particularly true for measures of skeletal muscle fiber type and MRI-derived AT. While these are simple associations and do not imply causation, our findings do prompt future work with appropriately powered trials to combine data from molecular, metabolic, physiological and clinical measures to assess predictors of response to weight loss with and without exercise.

Our participants reflected a range in diabetes status, from no diabetes to frank type 2 diabetes and thus, differed in medication use. Recent interest in the interaction effects of exercise and medication use on response across a range of outcomes has revealed inconsistent findings.

Contrary to these findings, in both older adults 31 and those with prediabetes 32 , the increase in whole-body insulin sensitivity following 12 weeks of aerobic exercise training was attenuated in those taking metformin concurrently. Similar discrepancies are seen with the interaction between statin use and exercise, where evidence from obese elderly males suggests no impact of statins on the beneficial effects of 12 weeks of exercise 33 , whereas the addition of statins blunted the increase in cardiorespiratory fitness and citrate synthase activity in overweight or obese adults Taken together, factors associated with medication use e.

Additionally, we do not have adherence and compliance records for all participants; both may impact response variability. Future work carefully accounting for energy balance is warranted in order to definitively make these conclusions.

In conclusion, the addition of exercise to energy restriction-induced weight loss improves the number of older obese adults who achieve improvement in insulin sensitivity and cardiometabolic risk.

Additionally, individuals with poorer metabolic status at baseline are more likely to experience greater improvements in clinical outcomes with these lifestyle interventions. Our data contributes novel findings with regards to individual response variation to lifestyle interventions, moving us closer to identifying predictors of response and tailoring lifestyle-based treatments to the individual.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Institutional Review Board of AdventHealth. BG concepted and designed the primary trial and assisted LS and AB in conceptualizing this secondary analysis.

RS and EC coordinated the primary trial and organized all data collection. FY provided statistical support for the manuscript. AB completed statistical analysis and data interpretation.

BG, LS, and AB were responsible for drafting the manuscript. All authors assisted with manuscript revision. This study was supported by funding from the National Institutes of Health—National Institute on Aging RO1 AG awarded to BG. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The authors would like to thank the contributions of our study participants and acknowledge the valuable expertise and assistance of the imaging, recruitment, clinic, calorimetry, laboratory, and nutrition staff at TRI, AdventHealth Research Institute.

Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, Di Francesco V, et al. Health consequences of obesity in the elderly: a review of four unresolved questions.

Int J Obes. doi: PubMed Abstract CrossRef Full Text Google Scholar. Villareal DT, Apovian CM, Kushner RF, Klein S. Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, the obesity society.

Obes Res. Boulé NG, Weisnagel SJ, Lakka TA, Tremblay A, Bergman RN, Rankinen T, et al. Effects of exercise training on glucose homeostasis: the HERITAGE family study. Diabetes Care. Álvarez C, Ramírez-Campillo R, Ramírez-Vélez R, Izquierdo M. Prevalence of non-responders for glucose control markers after 10 weeks of high-intensity interval training in adult women with higher and lower insulin resistance.

Front Physiol. Solomon TP, Malin SK, Karstoft K, Haus JM, Kirwan JP. The influence of hyperglycemia on the therapeutic effect of exercise on glycemic control in patients with type 2 diabetes mellitus. JAMA Intern Med. Hecksteden A, Kraushaar J, Scharhag-Rosenberger F, Theisen D, Senn S, Meyer T.

Individual response to exercise training-a statistical perspective. J Appl Physiol. Atkinson G, Batterham AM. True and false interindividual differences in the physiological response to an intervention.

Exp Physiol. Malin SK, Haus JM, Solomon TP, Blaszczak A, Kashyap SR, Kirwan JP. Insulin sensitivity and metabolic flexibility following exercise training among different obese insulin-resistant phenotypes. Am J Physiol Endocrinol Metab. O'Donoghue G, Kennedy A, Andersen GS, Carr B, Cleary S, Durkan E, et al.

Phenotypic responses to a lifestyle intervention do not account for inter-individual variability in glucose tolerance for individuals at high risk of type 2 diabetes.

BMI measures your height compared to your weight. Too much belly fat can increase your risk for type 2 diabetes, heart disease, and stroke. Waist circumference waist size takes belly fat into account and helps predict your risk of health problems from being overweight.

Women whose waist measures more than 35 inches and men whose waist measures more than 40 inches are at higher risk. Losing weight can reduce belly fat and lower that risk!

To measure your waist correctly, stand and place a tape measure around your middle, just above your hipbones. Measure your waist just after you breathe out. Read about these three people who were able to shed the pounds and keep them off. If you have diabetes, you may find your blood sugar levels are easier to manage and that you need less diabetes medicine after you lose weight.

Many people who lose weight notice that they have more energy and sleep better too. A healthy weight goal is one thing; dropping the pounds is quite another. If there were an easy way to lose weight and keep it off, everyone would be doing it.

With that in mind, you may need to try different things to figure out what works best for you day to day. Some people cut back on sugar and eat more protein to stay fuller longer. Others focus on filling up with extra fruits and vegetables, which leaves less room for unhealthy food.

Still others limit variety for most meals and stick with choices that they know are healthy and filling. The details will depend on what you like and what fits in best with your life. If you need ideas and support, talk to a registered dietitian or diabetes educator your doctor can give you a referral.

Physical activity can make you feel better, function better, and sleep better. Here are the basic guidelines:. If you have diabetes , physical activity can help you manage the condition along with your weight.

Being active makes you more sensitive to insulin the hormone that allows cells in your body to use blood sugar for energy. Lower insulin levels can help prevent fat storage and weight gain. Learn more about being active when you have diabetes here.

While how you lose weight will be highly personalized, these pointers have helped others reach their goal and could help you, too. Control your environment so temptation is out of the picture and healthy habits are in.

Some ideas:. Too little sleep makes dieting much harder because it increases your hunger and appetite, especially for high-calorie, high-carb foods. Too little sleep also triggers stress hormones, which tell your body to hang onto fat.

Outsmart this problem by being physically active, which has been shown to help you fall asleep faster and sleep better. And these tips are tried and true: no screens an hour before bedtime, avoid heavy meals and alcohol before bedtime, and keep your bedroom dark and cool.

Try this interactive Body Weight Planner to calculate calories and activity needed to get to your goal weight and maintain it. Writing down what you eat is the single best predictor of weight loss success. Guess how long it takes yes, studies have been done?