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Personalized weight management

Personalized weight management

Janagement will Bodyweight Exercises Antioxidant-rich fruits lifelong mentor who is managemdnt invested in seeing you Antioxidant-rich fruits your weight-loss goals and then remaining Antioxidant-rich fruits your ideal weight for the rest of your life. Task offloading strategy with emergency handling and blockchain security in SDN-empowered and fog-assisted healthcare IoT. Book Now. You have the option between a box and a case of two fuelings or two shakes.

Personalized weight management -

Members can join a minute group workshop every week, in-person or online, to communicate with coaches and fellow members about their roadblocks, challenges and victories. Studies have shown that community support can be helpful for diet adherence.

WW also acknowledges that social factors like family, culture and celebrations impact our diets and eating habits, so it offers a guide to dining out. Check out our WW Review for more information on this weight loss program.

Does it work? According to a study published in the Annals of Internal Medicine, WW participants are more likely to lose weight than those who received weight loss education alone.

WW has been around for over four decades, which means there are large amounts of anecdotal evidence saying it works. There are also small studies that suggest WW is over twice as effective as dieting on your own. At the end of the day, weight loss is different for everyone, but WW has a long history of success.

WW often runs promotions, but be sure to read the fine print regarding startup and early termination fees.

All memberships include access to the WW app. Related Post: Noom vs WW: Which Weight Loss Program is Right for You? Nutrisystem is our pick for the best weight loss program with personalized meal plans because the service delivers pre-cooked meals to your door, making portion control and tracking calories easier.

This diet helps with weight loss by using a combination of high-protein meal plans and lower-glycemic nutrition to control hunger. We like that you can select a plan with meals that cater to your lifestyle, age or dietary restrictions, such as diabetes-friendly and vegetarian.

Your meals are delivered every four weeks, and you can repeat the program as many times as you want. Doing this is meant to help you learn how to eat a balanced diet on your own and create a lifestyle change to maintain your weight loss after you finish the program.

Our tester, Molly Stout, found success with Nutrisystem but said the meals get repetitive after a few months. Read our Nutrisystem Review for more information on this weight loss program.

According to a study published in the Annals of Internal Medicine, Nutrisystem participants are more likely to lose weight in three months than those who just receive weight loss education. Another study suggests that using commercial weight loss plans like Nutrisystem can result in greater long-term weight loss than simply dieting on your own.

Related Post: Best Weight Loss Programs for Men. Nutrisystem offers some variety in its plans, which affects the cost. Studies back the effectiveness of ketogenic diets, especially for people who are seeking not just to lose weight but to also improve other health indicators, like controlling their blood sugar and managing cholesterol.

But many of us have experienced a lifetime of being told that high-fat foods are the enemy, so the idea of adopting a high-fat, high-protein diet can feel like too much to chew. Thankfully, an app like Keto Cycle can make the idea of embracing the keto lifestyle a lot easier to digest. The sign-up process for Keto Cycle takes important personalization factors into consideration, including your sex, how physically active you are, your familiarity with keto, how much time you have for meal prep and your food preferences.

From there, you get a fully furnished meal plan with loads of keto recipes. In an assessment of 11 studies, researchers found that people on a ketogenic diet lost more weight than those on a low-fat diet. Not only that: People who followed a very low-carb ketogenic diet also had lower levels of fat in their blood and lower blood pressure.

So all in all, ketogenic seems to be a fantastic option as weight loss program. The tricky part is compliance with the diet, which is where an app like Keto Cycle steps in.

Keto Cycle furnishes many services to add on as well, including an exercise plan and monthly protein powder deliveries.

If you've struggled with the exercise portion of a weight loss program, the Future app may be able to help. To start, you'll fill out a questionnaire that asks about your previous workout experience, current limitations and preferences.

The app will also ask questions about what you're looking for in a coach such as gender, energy level, intensity and more.

It'll then suggest a few coaches to choose from. You can either pick one of these or peruse the complete list of coaches to make your own choice. Once you're matched with a coach, they'll schedule an introductory video call to get acquainted and learn more about your goals.

Then, they'll create a customized workout plan for you. You'll receive an updated plan every week with new exercises and explanations on how to do them with the correct form. You'll be able to reach out to your coach at any time for clarification or to check in about how you're handling the workouts.

If you're not sure about your form, you can even send videos of you doing the moves for feedback. This ensures that you're doing the moves correctly so you can stay injury-free and motivated.

Our tester, Ali Nolan, tried Future for 60 days and felt her coach helped her gain strength, improve her endurance and build muscle tone.

Check out our Future Review for more information on this weight loss program. However, Future often runs deals. Trifecta is our pick for the best weight loss program for plant-based eaters because this healthy meal delivery service sends vegetarian and vegan meals right to your door.

Trifecta was created with the goal of helping people create a healthy lifestyle change to lose weight by eating fresh, nutritious meals, rather than crash dieting. This prepared meal delivery service provides low-calorie dishes that come pre-portioned, making it easier to track your macros and reach your weight loss goals.

We like that each plan includes breakfast options so you have healthy choices to keep you on target throughout the day. Your weekly meal subscription also gives you access to a companion app that recommends a daily intake of proteins, carbs and fat based on your weight loss goals, and provides advice from fitness and nutrition experts.

We like that the app makes tracking your meals, water intake and exercise easy. The app also creates a progress report so you can track your results.

Our tester, NASM-certified personal trainer Kate Meier, tried the Keto meal delivery plan and said she enjoyed the breakfast dishes. My personal favorite was the egg scramble, which was very flavorful.

Following a plant-based diet is a lifestyle change that evidence suggests may improve your overall health and wellness, as well as help you reach your weight loss goals. Research suggests that people who eat a plant-based diet have a lower BMI than those who do not and that eating a plant-based diet can be an effective way to lose weight and prevent chronic diseases.

Trifecta combines a plant-based diet portion control, which experts say is another important weight loss strategy. Shipping is an additional fee. Our list of most effective weight loss programs covers healthier eating and exercise, but what if you want to go a step further?

Consider these products that could provide additional tools to help you on your weight management journey:. This subscription-based app personalizes a running and meal plan for you based on your answers to an introductory questionnaire.

It's beginner-friendly, but if you have running experience, you can also get more advanced plans to help you reach your goals, like training for a 5K or half-marathon.

PhenQ is a thermogenic supplement and appetite suppressant that uses a blend of vitamins, minerals and thermogenic ingredients to help you boost your metabolism and curb your appetite. The key ingredient is ɑ-Lacys reset, a branded form of alpha-lipoic acid ALA. Research has found that supplementing a weight loss program with ALA could result in greater weight loss than following the program alone.

They track a wide array of health metrics, including nutritional intake, physical activity, and even psychological well-being. Community features within these apps are particularly valuable, offering peer support and professional advice, which have been shown to enhance the effectiveness of weight management efforts.

Wearable technology may further extend the capabilities of traditional weight management by offering continuous monitoring and feedback.

These devices, which range from basic fitness trackers to advanced smartwatches, provide a wealth of data that can inform and motivate users.

They align with the principles of self-monitoring and accountability that are central to successful weight management strategies. The real-time data collected by wearables offer insights into daily habits and health trends, allowing for a responsive approach to weight management.

This aligns with the personalized care models that Obesity Canada identifies as crucial in the management of obesity. By offering immediate feedback and personalized data, wearables support the kind of self-directed, informed lifestyle changes that are essential for sustainable weight management.

Incorporating technology into weight management aligns with the evidence-based recommendations of leading health organizations. These tools are not mere novelties; they are integral components of a modern approach to health that empowers individuals to take an active role in managing their weight.

As we conclude with our final thoughts, we'll reflect on how these technologies, along with nutritional and psychological strategies, contribute to the creation of the most effective weight management programs. The contemporary era of weight management is characterized by a multi-faceted approach that goes beyond the simplistic advice of "eat less, move more.

The "best" weight management program, therefore, is not a one-size-fits-all solution but one that resonates with an individual's unique needs, circumstances, and lifestyle. Reflecting on the insights into medical nutrition therapy and emerging technologies, it's evident that the integration of personalized nutrition plans, psychological support, and technology-driven tools forms the trifecta of successful weight management.

These elements combine to create a supportive environment that encourages sustainable lifestyle changes and empowers individuals to take control of their health. As we embrace these comprehensive strategies, it's important to remember the value of consulting with health professionals.

Dietitians, psychologists, and other healthcare providers play a crucial role in tailoring these programs to fit personal needs and in offering the support and accountability necessary for success.

In conclusion, the journey to effective weight management is a personal one. It's a path that involves making informed choices, understanding the role of technology, and seeking professional guidance.

By considering these factors, individuals can find a weight management program that not only helps them achieve their goals but also supports a healthier and more fulfilling lifestyle. A trustworthy weight management program is one that is grounded in scientific evidence, promotes a personalized and balanced approach to nutrition, and integrates psychological support.

It should focus on sustainable lifestyle changes rather than quick fixes and involve qualified health professionals. Be wary of programs that promise rapid weight loss, as this is often neither sustainable nor healthy.

Warning signs include programs that rely heavily on proprietary products or supplements, lack transparency about costs, or do not include a maintenance plan for after the weight loss phase. Obesity Canada also cautions against programs that use high-pressure sales tactics or make claims that sound too good to be true.

A credible program should encourage gradual weight loss about pounds per week , provide a balanced approach to diet, and emphasize the importance of physical activity and behavior modification. A weight management strategy should be flexible and responsive to an individual's progress, challenges, and changes in circumstances.

It's advisable to review and potentially revise the strategy when significant life changes occur that could impact one's ability to follow the program. Regular check-ins with a healthcare provider can help tailor the strategy to ensure it remains effective and healthy.

While technology can greatly enhance the weight management process, there are potential risks. Over-reliance on digital tools may lead to a disconnection from one's own body cues or maladaptive thinking around food. Privacy concerns are also paramount, as personal health data is collected and stored.

It's crucial to use reputable apps and devices with robust privacy policies and to maintain a holistic approach to health that doesn't rely solely on technology. A study of adults found that higher body mass index was associated with ankle systolic-blood-pressures SBP , and the correlation was obtained by linear regression analysis Diet-induced obesity DIO is related to higher intracranial pressure ICP and brain disorders Therefore, reducing the BMI of overweight and obese individuals leads to lower average health expenditures and lower risk of severe diseases.

Obesity research often focuses on obesity in childhood and adolescence. Nutrigenetics, epigenomics and metabolomics gather patient information to estimate individualized optimal nutritional decisions A person study using hierarchical multiple regression found that maternal obesity and household income significantly affected childhood obesity rates In a study of overweight or obese women, a higher plant-based dietary index PDI leads to better metabolic conditions Given individual genomics information, nutrigenetics analyze the association between genes and the impact of nutrient intake on the disease status to estimate a personalized optimal diet In East Asia, people with FTO gene variants had higher BMI when they consumed less protein On the other hand, the impact of nutrient intake on athlete performance can be affected by environmental conditions, such as social and economic factors, lifestyle patterns, physical activity, and food preferences The composition of macro-nutrients such as carbohydrates, proteins and lipids can be optimized based on personal genomics and digestive conditions In an intervention study to lower post-meal blood glucose, a personalized optimal recipe calculated from exercise data and gut microbiome conditions proved effective Moreover, in a week intervention trial of 82 people, using an individualized optimal nutritional regimen resulted in lower caloric intake and thus reduced individual obesity Compared with previous studies, our research has made the following contributions.

First, we estimate personalized optimal decisions on dietary lifestyle factors, which are easy to implement and stick to. We consider data on dietary habits including daily water intake and the frequency of alcohol, vegetable and high caloric food consumption.

We observe that the calculated individualized optimal treatment options vary from person to person. Second, we build prediction models for body mass index and choose random forest as the base learner for metaalgorithms. We analyze the feature importance of these dietary factors in this predictive study and identify vegetable intake frequency as the most important feature.

People who actually receive treatment options that are exactly equal to individualized optimal treatment options have lower levels of obesity. Fourth, we propose novel metaalgorithms SX and SXwint learners, which outperform other metalearners in the analysis of personalized optimal daily water intake.

Compared with T and X learners, SXwint learner has the tendency to show larger distance between personalized optimal individuals and the rest.

To better curb the development of obesity-related epidemics, obesity self-management programs should be easy to implement and adhere to.

Genomics and microbiome features are more expensive to measure for large populations. We use an obesity database with observations and 17 dietary or physical lifestyle features collected in Colombia, Peru and Mexico In the obesity data, MTRANS is the usual means of transportation and consists of five levels: Automobile, Motorbike, Bike, Public Transportation and Walking.

CALC is a binary indicator of alcohol intake and contains two levels: Yes positive alcohol intake and No zero alcohol intake. TUE is the time spent on technological devices. FAF is the frequency of physical activity. SCC is a binary indicator of food calorie monitoring and comprises two levels: Yes and No.

CH 2 O is the amount of daily water intake in liters. SMOKE is a binary indicator of smoking and contains two levels: Yes smoke and No never smoke.

CAEC is the frequency of sub-meals between main meals and involves four levels: No, Sometimes, Frequently and Always. NCP is the number of main meals in a day. FAVC is the frequency of consuming high-calorie foods and contains two levels, where Yes and No mean high-frequency and low-frequency intake of high caloric foods respectively.

FHWO family history with overweight is whether family members have histories of obesity and consists of two levels: Yes and No. Age, gender, height and weight are also recorded. In the sample, BMI values range from 12 to Among them, people have BMI between 12 and People with BMI over Based on the values of personal characteristics, the estimated personalized optimal eating and living habits are easy for the general public to implement.

Before we estimate individualized optimal nutrition lifestyle to reduce BMI, we perform a predictive study for two purposes: 1 comparing the performances of different machine learning methods on predicting BMI, 2 pinpointing the individual features that have significant impacts on BMI.

Predictive models with high accuracy can be used as base learners in metaalgorithms to estimate the personalized optimal decision The significant dietary factors that people can actively change are considered as treatments of interest in pursuit of a personalized optimal nutrition policy. Decision tree methods show higher accuracy in predicting obesity levels than Bayesian and Logistic classification techniques Among all the features in obesity data, CALC, CH 2 O, FCVC and FAVC measure the intake of alcohol, water, vegetables, and high caloric foods respectively.

We use metaalgorithms 32 to calculate individualized optimal intake regimens for these foods and beverages. Catboost 34 and random forest 35 are ensemble learning methods for categorical and continuous features.

We randomly divide the original data into training and testing set with equal sample sizes. Catboost and random forest models are estimated on the training data and BMI predictions are computed on the testing data. In catboost, we specify the number of iterations to be , learning rate as 0.

The mean absolute error of catboost is 1. In random forest 36 , we specify the number of tree estimators to be , and the minimal number of observations required at each split as 7. The mean absolute error of random forest is 1. Lasso penalized regression performs model estimation and variable selection simultaneously.

Covariance test 37 , one of the mainstream post-selection inference methods, is conditional on the solution path of lasso penalized regression. Each time a new variable is added, model error variance decreases, and the importance of the variable is measured by the magnitude of the decrease Covariance test is designed for high-dimensional data, but is also suitable for low-dimensional data.

Moreover, the obesity data fully meet the assumptions of using covariance test Lasso penalized regression is to regress BMI on the features in Table 1. P values for variables are derived from the standard exponential distribution Exp 1 According to Fig. The variables that covariance test considers important are very different from catboost and random forest models.

For example, covariance test considers SCC, FAVC high caloric food intake frequency and FHWO to be significant, while catboost and random forest regard them as unimportant. On the other hand, catboost and random forest identify TUE, Age, Gender, CH 2 O, MTRANS and CALC to be important, but covariance test considers them to be insignificant.

All three methods identify FCVC vegetable intake frequency , NCP and FAF physical activity to be important. Apparently, among all three methods, significant features found by covariance test are the most intuitive results. Feature importance for predicting BMI of overweight and obese people using; a catboost, b random forest.

Based on the covariance test results in Table 1 , we suggest lower frequency of high-calorie food intake, and more physical activity. However, taking other individual covariates into account, the better solution on average is not personalized optimal. For example, quitting alcohol works for some people, but for others, it can be counterproductive.

To further reduce BMI, an individualized optimal nutritional regimen should be used instead of better-on-average recommendations. We aim to estimate the personalized optimal decision on alcohol, vegetable, high caloric food and daily water intake. We only consider overweight and obese people with BMI over Normal-weight and underweight individuals with BMI less than Our approach only considers the case of two treatment options.

To demonstrate our methods, we view CALC alcohol intake to be the treatment T of interest. For the personalized optimal solution of other factors, we replace CALC with these corresponding variables, and then perform the same method.

We use metaalgorithms T, X and S learners to compute personalized optimal scenarios for alcohol intake frequency Causal forest estimators of heterogeneous treatment effects have been shown to be point-wise consistent and asymptotically Gaussian distributed All base learners are specified to be random forest models, since they have decent accuracy in predicting BMI.

T, X and S learners all require splitting the obesity data into training and testing data with equal sample sizes.

As shown in Fig. Illustration of metaalgorithms. D : Training data. S : Testing data. R : Re-training data. A : The set of all individuals. DR : The union of training and re-training data. ND : Training data from N. NS : Testing data from N. NR : Re-training data from N. KD : Training data from K.

KS : Testing data from K. KR : Re-training data from K. Solid lines represent random splits of datasets. Dotted lines stand for the computational processes of models.

X1 Perform steps T1 and T2 of T learner on the training data. Based on T, X and S learners, we propose SX and SXwint learners.

For datasets with large samples and few features, decomposing the original data into three parts can alleviate over-fit problem. Figure 2 illustrates metaalgorithms T, X, S, SX and SXwint learners.

The steps of SX and SXwint learners are the same, except that SXwint learner uses first-order interactions between treatment and covariates but SX learner does not use any.

Instead S learner uses the whole data for joint modeling, which can better distinguish between personalized optimal group and the rest. However, past research results have shown that X learner performs much better than S learner.

Our motivation is that we combine the steps of X and S learners to create a new method that inherits the advantages of both. From Fig. SX2 Execute step X2 of X learner procedures on the re-training data. SX3 Execute step X3 of X learner procedures on the re-training data.

SX4 Execute step X4 of X learner procedures. SX5 Execute step X5 of X learner procedures on the testing data.

SXwint1 Execute step SX1 of SX learner procedures on the training data. SXwint2 , SXwint3 , SXwint4 and SXwint5 are the same as steps SX2 , SX3 , SX4 and SX5 of SX learner procedures. In SXwint learner, x contains all first-order treatment-covariates interactions, but in SX learner, x does not use any interaction.

In most cases, SXwint learner performs better than SX learner, as presented in Table 2. The convergence rates of T, S and X learners have been demonstrated under strict assumptions The obesity data we study 31 fit these strict assumptions. The convergence rates of SX and SXwint learners are of the same scale as the convergence rate of X learner.

In S learner, x must contain interaction terms between treatment and individual covariates. Hence in S learner, x must contain treatment-covariates interactions in order for the estimation result to be personalized optimal. The formulas of treatment effects are equivalent in SX and SXwint learners.

Regardless of whether x contains first-order interaction terms, the policies estimated by SX and SXwint learners are personalized optimal. Although our methods perform well, they also suffer from the following limitations.

First, metaalgorithms perform well only if the prediction accuracy of the base learner is high. In our research, we find that random forest models have high prediction accuracy and use them as base learners in metaalgorithms. If we fail to find a base learner with sufficiently high prediction accuracy, then metaalgorithms do not perform well.

In ultra-high dimensional data where sample size is 10 and feature dimension is in millions, dividing data into training and re-training data results in lower efficiency of data usage, and higher risk of inaccurate predictive models.

Third, SXwint learner uses first-order interactions between treatment and covariates. When there are many categorical features that may take many values, the number of dummy variables and first-order interaction terms can become very large. Then the covariates will have higher dimensionality, making it more difficult to train base learners.

Obesity data are randomly split into training and testing data with equal sample sizes. After we estimate T, X and S learners on the training data, we predict the personalized optimal treatment decision on the testing data.

For SX and SXwint learners, training data used in T, X and S learners are now randomly split into new training data and re-training data with equal sample sizes. We estimate SX and SXwint learners using new training data and re-training data.

Afterwards we predict the personalized optimal treatment decision for people on the testing data. In summary, when calculating individualized optimal options for each nutrient, T, X, S, SX and SXwint learners decompose the same testing data into the following parts.

The personalized optimal group is formed by individuals on the testing data whose treatment observations are exactly identical to the personalized optimal decisions.

The non-optimal group consists of people on the testing data whose treatment observations are different from the personalized optimal decisions. We compare BMI levels in the personalized optimal group and the non-optimal group to determine whether the estimated personalized optimal decision is effective.

We compare BMI levels in the personalized optimal group and the general optimal group to determine whether individualized decision-making is superior to general advice.

Figure 3 reveals the comparison results between the distributions of BMI in personalized optimal, non-optimal and general optimal groups. Kolmogorov-Smirnov KS test is a nonparametric test free of distributional assumption.

Two-sample KS test is applied to determine whether two samples follow the same distribution. KS test statistic measures the maximal distance between the empirical BMI distributions of two samples. The distributions of BMI in personalized optimal and non-optimal groups are significantly different according to KS test results in Table 2.

In general, for T, X, S, SX and SXwint learners, BMI measurements in personalized optimal groups are significantly lower than BMI levels in non-optimal groups and general optimal groups.

Personakized narrative of Well-rounded weight management management is Personalized weight management a managementt transformation within weihgt Antioxidant-rich fruits industry. The era of crash Personalized weight management, which Personalized weight management swift and dramatic results, is managemenr eclipsed by a more enlightened approach that champions sustainable weight management programs. This shift is not merely a trend but a response to a growing understanding that weight is a complex, multifaceted, biologically driven condition. Today's strategies are not focused on weight loss. Instead, they are built on the foundation of enduring health and well-being. This evolution in thinking mirrors a broader recognition that weight loss is not a one-size-fits-all equation. Personalized weight management

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