Kuonal Lakhapatiloved the fast-paced life that came with working at logistics startup LogiNext. But working with a rapidly growing company meant sleepless nights, stress, and demanding deadlines, which meant unhealthy food habits and no time for exercise. It was no surprise that Kuonal put on extra weight.

In a bid to get fit, Kuonal enlisted his wife and nutrition expert, Aayushi Lakhapatis help. At the time, she was toying with the idea of using meal replacement products as a way to become more healthy and strong.

Aayushi decided to make some nutritious food samples for her husband to try, and within 28 days, Kuonal lost 10 kg.

That is when the both of them realised they had stumbled upon a revolutionary idea that could help people lose weight by simply eating nutritious food. They founded a meal replacement products company, 23BMI, in Mumbai.

The founding team of 23BMI

23BMI primarily retails food products that replace the main meals of the day. These products are customisable/can be modified according to a persons specific dietary requirements.

A postgraduate in internal business from Kingston University, London, and a certificate holder in food and nutrition from Mumbai University, Aayushi is responsible for new product developments and client relationships at 23BMI. She also comes up with new, innovative solutions for her company's rapidly increasing client base.

Apart from making meal replacement products, 23BMI has a community of nutritionists on its platform who curate different lines of meal replacement products and engage with clients on the company's online platform.

One of the main challenges the company faced while setting up was developing the product from identifying trustworthy manufacturing partners and conducting human trials, to obtaining necessary certifications before the launch.

The startup also found that not everyone is open to trying new consumable health products, unless they are recommended.

"We had to choose the right marketing channel to reach out to a larger audience to overcome the challenge of scaling up faster in a B2C environment. Fortunately, we were lucky to identify the right partners who helped us speed up the product development phase, and launch the product, Kuonal, says.

The company instated several feedback channels to keep perfecting the product line, and established a robust, 24/7 communication network between health coaches and clients to support continuous supervision.

23BMI creates all its 100 percent organic products in-house.

The company outsources manufacturing, production, and order-taking to distributors across the country. Aayushi and Kounal design and prepare new meal replacement products themselves.

When regularly consumed, 23BMIs meal replacement smoothies coax the human body into a dietary ketosis state, which is where the body starts to burn stored fat to produce energy.

On an average, the entire process can help a person lose up to eight kilograms a month, and is done under the careful guidance of the companys health coaches.

23BMIs weight loss plan has a few variations depending on the goal weight, and the number of days one wants to consume the meal replacement products for. The weight management package costs between Rs 15,000 and Rs 45,000, depending on the number of meals and the duration of the programme.

The wellness industry is expected to hit Rs 1.5 lakh crore by the end of 2020, and grow at a CAGR of 12 percent over the next five years, according to estimates by FICCI and EY.

Those statistics are encouraging for a bootstrapped company like 23BMI, which has seen nearly 20 percent growth since inception in 2018, on a monthly basis. It clocked revenue of Rs 75 lakh in the current financial year, after investing close to Rs 20 lakh, initially.

The companys main competitors include startups like Prameya Health, AyurUniverse, and few others who use supplements in their health management programmes.

How has the coronavirus outbreak disrupted your life? And how are you dealing with it? Write to us or send us a video with subject line 'Coronavirus Disruption' to editorial@yourstory.com

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Health startup 23BMI can help you lose weight, stay healthy without hitting the gym - YourStory

From navigating to a new place to picking out new music, algorithms have laid the foundation for large parts of modern life. Similarly, artificial Intelligence is booming because it automates and backs so many products and applications. Recently, I addressed some analytical applications for TensorFlow. In this article, Im going to lay out a higher-level view of Googles TensorFlow deep learning framework, with the ultimate goal of helping you to understand and build deep learning algorithms from scratch.

Over the past couple of decades, deep learning has evolved rapidly, leading to massive disruption in a range of industries and organizations. The term was coined in 1943 when Warren McCulloch and Walter Pitts created a computer model based on neural networks of a human brain, creating the first artificial neural networks (or ANNs). Deep learning now denotes a branch of machine learning that deploys data-centric algorithms in real-time.

Backpropagation is a popular algorithm that has had a huge impact in the field of deep learning. It allows ANNs to learn by themselves based on the errors they generate while learning. To further enhance the scope of an ANN, architectures like Convolutional Neural Networks, Recurrent Neural Networks, and Generative Networks have come into the picture. Before we delve into them, lets first understand the basic components of a neural network.

Neurons and Artificial Neural Networks

An artificial neural network is a representational framework that extracts features from the data its given. The basic computational unit of an ANN is the neuron. Neurons are connected using artificial layers through which the information passes. As the information flows through these layers, the neural network identifies patterns between the data. This type of processing makes ANNs useful for several applications, such as for prediction and classification.

Now lets take a look at the basic structure of an ANN. It consists of three layers: the input layer, the output layer, which is always fixed or constant, and the hidden layer. Inputs initially pass through an input layer. This layer always accepts a constant set of dimensions. For instance, if we wanted to train a classifier that differentiates between dogs and cats, the inputs (in this case, images) should be of the same size. The input then passes through the hidden layers and the network updates the weights and recognizes the patterns. In the final step, we classify the data at the output layer.

Weights and Biases

Every neuron inside a neural network is associated with parameters, weight and bias. The weight is an integer that controls the signals between any two neurons. If the output is desirable, meaning that the output is in proximity to the one that we expected it to produce, then the weights are ideal. If the same network is generating an erroneous output thats far away from the actual one, then the network alters the weights to improve the subsequent results.

Bias, the other parameter, is the algorithms tendency to consistently learn the wrong thing by not taking into account all the information in the data. For the model to be accurate, bias needs to be low. If there are inconsistencies in the dataset, like missing values, fewer data tuples, or erroneous input data, the bias would be high and the predicted values could be wrong.

Working of a Neural Network

Before we get started with TensorFlow, lets examine how a neural network produces an output with weights, biases, and input by taking a look at the first neural network, called Perceptron, which dates back to 1958. The Perceptron network is a simple binary classifier. Understanding how this works will allow us to comprehend the workings of a modern neuron.

The Perceptron network is a supervised machine learning technique that uses a binary classifier function by mapping a vector of binary variables to a single binary output. It works as follows:

Multiply the inputs (x1, x2, x3) of the network to their corresponding weights (w1, w2, w3).

Add the multiplied weights and inputs together. This is called the weighted sum, denoted by, x1*w1 + x2*w2 +x3*w3

Apply the activation function. Determine whether the weighted sum is greater than a threshold (say, 0.5), if yes, assign 1 as the output, otherwise assign 0. This is a simple step function.

Of course, Perceptron is a simple neural network that doesnt wholly consider all the concepts necessary for an end-to-end neural network. Therefore, lets go over all the phases that a neural network has to go through to build a sophisticated ANN.

Input

A neural network has to be defined with the number of input dimensions, output features, and hidden units. All these metrics fall in a common basket called hyperparameters. Hyperparameters are numeric values that determine and define the neural network structure.

Weights and biases are set randomly for all neurons in the hidden layers.

Feed Forward

The data is sent into the input and hidden layers, where the weights get updated for every iteration. This creates a function that maps the input with the output data. Mathematically, it is defined asy=f(x), where y is the output, x is the input, and f is the activation function.

For every forward pass (when the data travels from the input to the output layer), the loss is calculated (actual value minus predicted value). The loss is again sent back (backpropagation) and the network is retrained using a loss function.

Output error

The loss is gradually reduced using gradient descent and loss function.

The gradient descent can be calculated with respect to any weight and bias.

Backpropagation

We backpropagate the error that traverses through each and every layer using the backpropagation algorithm.

Output

By minimizing the loss, the network re-updates the weights for every iteration (One Forward Pass plus One Backward Pass) and increases its accuracy.

As we havent yet talked about what an activation function is, Ill expand that a bit in the next section.

Activation Functions

An activation function is a core component of any neural network. It learns a non-linear, complex functional mapping between the input and the response variables or output. Its main purpose is to convert an input signal of a node in an ANN to an output signal. That output signal is the input to the subsequent layer in the stack. There are several types of activation functions available that could be used for different use cases. You can find a list comprising the most popular activation functions along with their respective mathematical formulae here.

Now that we understand what a feed forward pass looks like, lets also explore the backward propagation of errors.

Loss Function and Backpropagation

During training of a neural network, there are too many unknowns to be deciphered. As a result, calculating the ideal weights for all the nodes in a neural network is difficult. Therefore, we use an optimization function through which we could navigate the space of possible ideal weights to make good predictions with a trained neural network.

We use a gradient descent optimization algorithm wherein the weights are updated using the backpropagation of error. The term gradient in gradient descent refers to an error gradient, where the model with a given set of weights is used to make predictions and the error for those predictions is calculated. The gradient descent optimization algorithm is used to calculate the partial derivatives of the loss function (errors) with respect to any weight w and bias b. In practice, this means that the error vectors would be calculated commencing from the final layer, and then moving towards the input layer by updating the weights and biases, i.e., backpropagation. This is based on differentiations of the respective error terms along each layer. To make our lives easier, however, these loss functions and backpropagation algorithms are readily available in neural network frameworks such as TensorFlow and PyTorch.

Moreover, a hyperparameter called learning rate controls the rate of adjustment of weights of a network with respect to the gradient descent. The lower the learning rate, the slower we travel down the slope (to reach the optimum, or so-called ideal case) while calculating the loss.

TensorFlow is a powerful neural network framework that can be used to deploy high-level machine learning models into production. It was open-sourced by Google in 2015. Since then, its popularity has increased, making it a common choice for building deep learning models. On October 1st, a new, stable version got released, called TensorFlow 2.0, with a few major changes:

Eager Execution by Default - Instead of creating tf.session(), we can directly execute the code as usual Python code. In TensorFlow 1.x, we had to create a TensorFlow graph before computing any operation. In TensorFlow 2.0, however, we can build neural networks on the fly.

Keras Included - Keras is a high-level neural network built on top of TensorFlow. It is now integrated into TensorFlow 2.0 and we can directly import Keras as tf.keras, and thereby define our neural network.

TF Datasets - A lot of new datasets have been added to work and play with in a new module called tf.data.

1.0 Support: All the existing TensorFlow 1.x code can be executed using TensorFlow 2.0; we need not modify any of our previous code.

Major Documentation and API cleanup changes have also been introduced.

The TensorFlow library was built based on computational graphs a runtime for executing such computational graphs. Now, lets perform a simple operation in TensorFlow.

Here, we declared two variables a and b. We calculated the product of those two variables using a multiplication operation in Python (*) and stored the result in a variable called prod. Next, we calculated the sum of a and b and stored them in a variable named sum. Lastly, we declared the result variable that would divide the product by the sum and then would print it.

This explanation is just a Pythonic way of understanding the operation. In TensorFlow, each operation is considered as a computational graph. This is a more abstract way of describing a computer program and its computations. It helps in understanding the primitive operations and the order in which they are executed. In this case, we first multiply a and b, and only when this expression is evaluated, we take their sum. Later, we take prod and sum, and divide them to output the result.

TensorFlow Basics

To get started with TensorFlow, we should be aware of a few essentials related to computational graphs. Lets discuss them in brief:

Variables and Placeholders: TensorFlow uses the usual variables, which can be updated at any point of time, except that these need to be initialized before the graph is executed. Placeholders, on the other hand, are used to feed data into the graph from outside. Unlike variables, they dont need to be initialized.Consider a Regression equation, y = mx+c, where x and y are placeholders, and m and c are variables.

Constants and Operations: Constants are the numbers that cannot be updated. Operations represent nodes in the graph that perform computations on data.

Graph is the backbone that connects all the variables, placeholders, constants, and operators.

Prior to installing TensorFlow 2.0, its essential that you have Python on your machine. Lets look at its installation procedure.

Python for Windows

You can download it here.

Click on the Latest Python 3 release - Python x.x.x. Select the option that suits your system (32-bit - Windows x86 executable installer, or 64-bit - Windows x86-64 executable installer). After downloading the installer, follow the instructions that are displayed on the setup wizard. Make sure to add Python to your PATH using environment variables.

Python for OSX

You can download it here.

Click on the Latest Python 3 release - Python x.x.x. Select macOS 64-bit installer,and run the file.

Python on OSX can also be installed using Homebrew (package manager).

To do so, type the following commands:

Python for Debian/Ubuntu

Invoke the following commands:

This installs the latest version of Python and pip in your system.

Python for Fedora

Invoke the following commands:

This installs the latest version of Python and pip in your system.

After youve got Python, its time to install TensorFlow in your workspace.

To fetch the latest version, pip3 needs to be updated. To do so, type the command

Now, install TensorFlow 2.0.

This automatically installs the latest version of TensorFlow onto your system. The same command is also applicable to update the older version of TensorFlow.

The argument tensorflow in the above command could be any of these:

tensorflow Latest stable release (2.x) for CPU-only.

tensorflow-gpu Latest stable release with GPU support (Ubuntu and Windows).

tf-nightly Preview build (unstable). Ubuntu and Windows include GPU support.

tensorflow==1.15 The final version of TensorFlow 1.x.

To verify your install, execute the code:

Now that you have TensorFlow on your local machine, Jupyter notebooks are a handy tool for setting up the coding space. Execute the following command to install Jupyter on your system:

Now that everything is set up, lets explore the basic fundamentals of TensorFlow.

Tensors have previously been used largely in math and physics. In math, a tensor is an algebraic object that obeys certain transformation rules. It defines a mapping between objects and is similar to a matrix, although a tensor has no specific limit to its possible number of indices. In physics, a tensor has the same definition as in math, and is used to formulate and solve problems in areas like fluid mechanics and elasticity.

Although tensors were not deeply used in computer science, after the machine learning and deep learning boom, they have become heavily involved in solving data crunching problems.

Scalars

The simplest tensor is a scalar, which is a single number and is denoted as a rank-0 tensor or a 0th order tensor. A scalar has magnitude but no direction.

Vectors

A vector is an array of numbers and is denoted as a rank-1 tensor or a 1st order tensor. Vectors can be represented as either column vectors or row vectors.

A vector has both magnitude and direction. Each value in the vector gives the coordinate along a different axis, thus establishing direction. It can be depicted as an arrow; the length of the arrow represents the magnitude, and the orientation represents the direction.

Matrices

A matrix is a 2D array of numbers where each element is identified by a set of two numbers, row and column. A matrix is denoted as a rank-2 tensor or a 2nd order tensor. In simple terms, a matrix is a table of numbers.

Tensors

A tensor is a multi-dimensional array with any number of indices. Imagine a 3D array of numbers, where the data is arranged as a cube: thats a tensor. When its an nD array of numbers, that's a tensor as well. Tensors are usually used to represent complex data. When the data has many dimensions (>=3), a tensor is helpful in organizing it neatly. After initializing, a tensor of any number of dimensions can be processed to generate the desired outcomes.

TensorFlow represents tensors with ease using simple functionalities defined by the framework. Further, the mathematical operations that are usually carried out with numbers are implemented using the functions defined by TensorFlow.

Firstly, lets import TensorFlow into our workspace. To do so, invoke the following command:

This enables us to use the variable tf thereafter.

Now, lets take a quick overview of the basic operations and math, and you can simultaneously execute the code in the Jupyter playground for a better understanding of the concepts.

tf.Tensor

The primary object in TensorFlow that you play with is tf.Tensor. This is a tensor object that is associated with a value. It has two properties bound to it: data type and shape. The data type defines the type and size of data that will be consumed by a tensor. Possible types include float32, int32, string, et cetera. Shape defines the number of dimensions.

tf.Variable()

The variable constructor requires an argument which could be a tensor of any shape and type. After creating the instance, this variable is added to the TensorFlow graph and can be modified using any of the assign methods. It is declared as follows:

Output:

tf.constant()

The tensor is populated with a value, dtype, and, optionally, a shape. This value remains constant and cannot be modified further.

The rest is here:
A Lightning-Fast Introduction to Deep Learning and TensorFlow 2.0 - Built In

A PRE-PANDEMIC survey warned that 13million British adults are now classed as obese at a cost to the NHS of around 1billion a year.

Given that a UK report suggests two thirds of those who have fallen seriously ill with Covid-19 were either obese or seriously overweight, one wonders how many of those 13million are using these weeks of lockdown to make some life-saving changes that will not only benefit them but our over-stretched hospitals.

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After all, fast food restaurants have been closed and we all had much more time on our hands, so what better opportunity to rid ourselves of poor eating habits and get fit?

Better still, the likes of Joe Wicks and Mr Motivator are giving free, online fitness classes. All you need is a TV and the inclination.

But while many will have used this virtual house arrest to shake off some unhealthy old habits (smoking, sleeping tablets, etc), the reaction to Adeles weight loss suggests that, for some, her transformation is a betrayal of everything fat activists hold dear.

Of the 200,000 largely positive comments about the star losing an incredible seven stone, some sniped that she looked unhealthy (she doesnt) and questioned how she lost it the inference being that she had succumbed to having a gastric band.

But as Pete Geracimo, her former personal trainer, points out: It was never about getting super-skinny. It was about getting her healthy.

Not least, one suspects, for the benefit of her seven-year-old son Angelo. He adds: She embraced better eating habits and committed to her fitness and is sweating! I could not be prouder or happier for her.

Hear hear. Yet alarmingly, research out earlier this year found that millions of women hid their efforts to slim for fear of being mocked or labelled anti-feminist.

Saints preserve us. The truth, of course, is that if someone is deeply insecure about their own weight, they either pooh-pooh or, worse, lash out at the efforts of those who manage to transform their lifestyle via willpower and hard slog.

Far easier to do that than take a look at our own bad habits and attempt to change them. True, there are plenty of people who look overweight but are physically fit.

And there are plenty of skinny malinkies who cant run for a bus without needing oxygen. At the end of the day, being happy within yourself is not about how you look, but how you feel.

And if you feel that you are unfit thanks to lousy eating habits and lack of exercise, then you should be able to do something about it without being taken to task by those who dont have the same self-discipline.

Particularly as last weeks research from the University of Liverpool found that obesity increased the risk of dying from coronavirus by 37 per cent. You cant argue with that.

BOREDOM at Moore Towers has reached such a crisis point that, on Sunday, I linked my iPhone to our portable speaker, put the songs on Shuffle and said we had to endure enjoy whatever track came up.

At first there were plenty of goodies from Floyd, Bowie, Prefab Sprout and Talk Talk, but then Twinkle Twinkle Little Star popped up, followed by You Spin Me Round by...Alvin And The Chipmunks.

For the record, our youngest is now 16. It seems its not just the kitchen cupboards that need a clear-out.

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BRINGING 66million people out of lockdown was never going to be easy.

But Boriss much-trumpeted grand statement was a damp squib thats left us as confused as Adam and Eve on Mothers Day.

In a nutshell, four-year-olds will be able go to school with lots of kids theyre not related to but not see other kids in their family, we can go to work with colleagues but not have a chat with them in their garden, we can do unlimited exercise which we could have done anyway, if we go abroad we have to self-isolate for a fortnight...unless its France, and you can drive to other destinations but not too far, whatever that means.

As for stay alert, is that the same as Police 5 presenter Shaw Taylors old mantra of: Keep em peeled?

Except we could actually see someone coming at us with a crowbar.

A virus that can fit 48squillion times on to the head of a pin might prove harder to spot.

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GIVEN that the use of face masks is being actively encouraged but not the professional ones needed by key workers, a number of DIY versions are being suggested.

Some involve an element of seamstress skills, but for those who want a quick and effective mask they can drum up with the minimum effort, this is my favourite.Find a bandana or any other square of material and fold it in half, then half again.

Loop a childs hair tie (a rubber band will work but not as well) over each end until it resembles a boiled sweet, then fold over the ends to meet in the middle (see pics, above).

Then simply place the wrong side against your face, loop the hair ties around your ears, and voila.

Youre welcome.

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FASHION designer Freya Edmondson daughter of Ab Fab star Jennifer Saunders and actor Ade Edmondson has posted this photo of herself doing a sumptuous, heart-opening backbend, while writing on a notepad.

Freya, who teaches free online yoga courses, is just 29 and, therefore, wonderfully flexible.

As a former enthusiastic gymnast, it reminds me of my youth, when I would watch TV while doing the box splits with my legs stretched out at right angles to my body.

With my 58th birthday looming this Sunday, if I attempted it now it would take four paramedics and a system of pulleys to get me on my feet again.

BEWARE

THE wife of former Manchester United star Angel di Maria has branded the city a s***hole and says she feared that the women there would kill her.

They will now.

ACADEMICS may lord it over the rest of us when it comes to quadratic equations, subatomic phenomena or the three components of the epidemiological triangle, but when it comes to common sense they are often found lacking.

Quite how Professor Lockdown (aka Neil Ferguson) thought he could preach to the rest of us about social distancing while inviting his mistress to his house is anyones guess, but he did, and now hes rightly had to resign for hypocrisy if nothing else.

He says: I accept I made an error...I thought I was immune.

From Covid-19...or criticism?

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THE latest series of Killing Eve may have had a lukewarm reception from critics, but central character Villanelles fashion choices have been attracting a lot of attention.

One frock cost 1,500, another 1,400, and her red leather catsuit would set you back an eye-watering 1,942.

I know these are desperate times for all businesses, but since when have fashionistas been clamouring to get their clothes on the back of a psychotic murderer?

It gives a whole new meaning to dressed to kill.

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HAIRDRESSERS are to stay closed until July 4 at the earliest.

Comment

ALLY ROSSGemma Collins' new ITVBe lockdown show proves she's a self-obsessed tyrant

Comment

OLIVIA UTLEYItll do our spoiled young Remainers good to remain in Britain for the hols

Comment

THE SUN SAYSAs commuters head back to work Britain must not jeopardise lockdown progress

Comment

Leo McKinstryBoris's travel plans will ruin Great British escapes and hurt the economy

Comment

TREVOR KAVANAGHLockdown will turn out to be a terrible mistake & bug is holding us ransom

Comment

THE SUN SAYSCoronavirus may be with us for years - we simply must get back to work

Up to now I have been making do with the occasional fringe cut using a pair of nail scissors.

But given that my style (for want of a better word) is supposed to be choppy bob, and I now resemble Richard lll, it seems I will have to tackle the rest of it too.

Worse, The Bloke will have to do the back and, God forbid, baldly (no, thats not a typo) go where no man has gone before.

Go here to see the original:
Why cant people like Adele lose weight without trolls having a go despite it being life-saving? - The Sun

One of the only encouraging parts of postponing my May 2020 wedding due to the coronavirus outbreak has been one simple realization: Im not alone. Thanks to the internet, Ive managed to find scores of other couples who are postponing their big day because of COVID-19 too. Together weve commiserated, compared backup plans, and tried to find the silver lining over direct messages or Instagram comments. Its been something thats brought me a great deal of comfort in a time that has been filled with sadness, chaos, and disappointment. But its also brought me various versions of one recurring idea: At least theres more time to reach my goal weight.

Ironically, I spent our one-and-a-half-year-long engagement in an active, daily battle to not diet for our wedding. After a decade of yo-yo dieting and obsessive exercise habits, I was ready for a lifestyle that was healthy and, for once, actually sustainable. This meant no more crash diets, no more starving myself, no more apps that told me how I should feel about myself based on the numerical amount of calories or carbs I had consumed in a single day. I was exhausted by all of it, and I knew that if I could stay balanced and confident through the wedding-planning process without telling myself daily that I had to be smaller, then it would be a victory. I told myself that it would set the tone for the rest of my life. If I could ignore the targeted ads for sweating for the wedding programs and detox shakes and bridal bootcamps and achieving the right body for my dream dress, then I could probably ignore that stuff forever. I knew Id be happier and healthier for it.

This is all way easier said than done.

As our wedding date got closer, the weight-loss-obsessed parts of my brain started to become harder to ignore. When I tried my dress on six months before the wedding and it was too tight, I quickly spiraled to the same dark places I had been so many times before. These were the places that told me that skipping meals was okay. That going to bed just feeling just a little hungry was success. That maybe it would be just fine if I made myself throw up a time or two. I found myself visiting Reddit pages where people would write about how their latest fast was going, devouring comments about how many hours, days, weeks, it had been since people had eaten solid food.

My body was a before, but wedding dresses were only for afters.

Id lie in bed at night long after my fianc had fallen asleep and scroll through before-and-after weight-loss photos, searching for bodies with similar proportions to mine. I would note the amount of time it took them to become smaller, gauging it against how much time I had until the wedding. At the time it felt like I was looking for inspiration, but looking back I think I just wanted to confirm what all those targeted ads had already told me: My body was a before, but wedding dresses were only for afters.

Part of me was angry that I had let so much time pass without trying to lose weight, and part of me was angry that I cared at all. I had spent a year successfully pushing away all these thoughts, yet here I was againwith the same thoughts of self-hatred and shame that had existed in my brain in high school and college. The same ugly thoughts that Id had when I was a size 10 and a size 14: If I was smaller, this would be better. I had been 50 pounds lighter and telling myself the same thingthat a big family vacation would be more special if I were thinner. That my first day of college would be more thrilling if I had done more sit-ups. That a first date would go better if I had stuck with a diet. My actual, physical size had never really altered that specific thought at all.

The rest is here:
I Refuse to Let My Wedding Be a Weight-Loss Goal - Glamour

Bagsvrd, Denmark, 13 May 2020 Novo Nordisk today announced headline results from STEP 4, the first completed phase 3a trial in the STEP programme. STEP 4 is a randomised, double-blind, multicentre, placebo-controlled, withdrawal trial exploring sustained weight management with semaglutide vs placebo. The 68-week trial investigated the effect of once-weekly subcutaneous (sc) semaglutide 2.4 mg on body weight in 902 people with obesity or overweight with comorbidities. After the 20-week run-in period, the 803 people reaching the target dose of semaglutide 2.4 mg had reduced their mean body weight from 107.2 kg to 96.1 kg and were randomised to continued treatment with either once-weekly sc semaglutide 2.4 mg or placebo for 48 weeks.

The trial achieved its primary objective by demonstrating that in all people randomised1, continued treatment with sc semaglutide 2.4 mg for 48 weeks (after the run-in period) resulted in an additional mean weight loss of 7.9%, from a mean baseline body weight at randomisation of 96.1 kg, whereas people on placebo regained 6.9% of the body weight. The treatment difference was statistically significant. People who received sc semaglutide 2.4 mg for 68 weeks (run-in period plus 48 weeks) achieved a total weight loss of 17.4%.

When evaluating the effects of treatment if taken as intended2, people who continued treatment with sc semaglutide 2.4 mg achieved an additional mean weight loss of 8.8% whereas people on placebo regained 6.5%. The treatment difference was statistically significant. People who stayed on sc semaglutide 2.4 mg for 68 weeks achieved a weight loss of 18.2%.

In the trial, sc semaglutide 2.4 mg appeared to have a safe and well-tolerated profile. The most common adverse events among people treated with sc semaglutide 2.4 mg were gastrointestinal events. As seen previously with GLP-1 receptor agonists, most events were transient and mild or moderate in severity.

Achieving sustained weight loss without medical therapy is known to be very challenging. STEP 4 shows that people continuing treatment with semaglutide achieved a further substantial weight loss while people switching to placebo, on the other hand, regained a significant amount of weight, said Mads Krogsgaard Thomsen, executive vice president and chief science officer of Novo Nordisk and continues, this highlights that obesity is a chronic disease requiring sustained treatment, and we look forward to sharing additional results from the ongoing STEP programme.

About obesity and sc semaglutide 2.4 mg for weight management Obesity is a chronic disease that requires long-term management. It is associated with many serious health consequences and decreased life expectancy. Obesity-related complications are numerous and include type 2 diabetes, heart disease,obstructive sleep apnoea, chronic kidney disease, non-alcoholic fatty liver disease and cancer.

Once-weekly sc semaglutide 2.4 mg is being investigated by Novo Nordisk as a treatment for adults with obesity. Semaglutide is an analogue of the human glucagon-like peptide-1 (GLP-1) hormone. It induces weight loss by reducing hunger, increasing feelings of fullness and thereby helping people eat less and reduce their calorie intake.

About the STEP clinical programmeSTEP (Semaglutide Treatment Effect in People with obesity) is a phase 3 clinical development programme with once-weekly sc semaglutide 2.4 mg in obesity. The global clinical phase 3a programme consists of 4 trials, having enrolled approximately 4,500 adults with overweight or obesity.

STEP 1 a 68-week safety and efficacy trial of sc semaglutide 2.4 mg versus placebo in 1,961 adults with obesity or overweight.

STEP 2 a 68-week safety and efficacy trial of sc semaglutide 2.4 mg versus placebo and once-weekly sc semaglutide 1.0 mg in 1,210 adults with type 2 diabetes and either obesity or overweight.

STEP 3 - a 68-week safety and efficacy trial of sc semaglutide 2.4 mg versus placebo in combination with intensive behavioural treatment in 611 adults with obesity or overweight.

STEP 4 a 68-week safety and efficacy trial of sc semaglutide 2.4 mg versus placebo in 803 adults with obesity or overweight who have reached the target dose of 2.4 mg after a 20-week run-in.

Further information

1 Based on the treatment policy estimand (primary statistical approach): treatment effect regardless of treatment adherence or initiation of other anti-obesity therapies2 Based on the trial product estimand (secondary statistical approach): treatment effect if all people adhered to treatment and did not initiate other anti-obesity therapies

Company announcement No 34 / 2020

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Semaglutide 2.4 mg demonstrates superior and sustained weight loss versus placebo and in addition a 17.4% weight loss after 68 weeks in STEP 4 trial -...

After Adele revealed a transformation on Instagram, fans began buzzing about the singer's weight loss.

For years, it's been rumored that the star follows a meal plan based on the book "The Sirtfood Diet." The regimen includes multiple phases and focuses on "cellular wellness at a genetic level," according to Aidan Goggins, one of the creators of the diet. The Duchess of Cambridge's sister Pippa Middleton and boxer Connor McGregor have also been said to use the diet.

According to Goggins, the Sirtfood diet was created as "the antidote to conventional dieting." He and his partner, Glen Matten, were both "diet skeptics" because of their history as nutritional medical scientists. Instead of focusing on weight loss, the Sirtfood diet is based on activating sirtuin genes. Sirtuins are proteins that are members of a family of enzymes. They activate and are typically utilized in response to stress and metabolism, explained Kristin Kirkpatrick, a registered dietitian.

"The weight loss is not the primary goal, but as a consequence of rejuvenating our cellular wellness at a genetic level, which essentially resets our metabolism," said Goggins. "Secondly, in contrast to other popularized diets where the focus is on cutting out foods, with Sirtfoods we can only reap the benefits through eating, and this means indulging in your favorite foods, not restriction."

Kirkpatrick, who works with the Cleveland Clinic in Ohio, said that she's had patients come to her asking about the Sirtfood diet multiple times, and while there is some science to support the effects of the diet, she said many of the studies and research are very new.

"I think the diet is harmless, but we don't have data showing that it's got long-term sustainability," she said. "The whole theory behind the diet is that certain foods are going to activate these sirtuins, which are related to proteins in the body."

Kirkpatrick noted that the Sirtfood diet operates similarly to intermittent fasting diets, which have also been shown to help with weight loss.

Goggins said that when he and Matten worked in a high-end private health club in London, they became "increasingly concerned" about "increasingly extreme diet trends" and the foods that people "villainized."

"You'd be afraid to eat," he said. "And it all became about reducing calorie intake."

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When creating the diet, they wanted to make sure that people felt free to eat some of their favorite foods while still getting important nutrients.

"It is important that we not only get plenty of these foods in our diet but also ensure our meals contain a diverse array of them as it is the synergy of their combination in meals and juices where the real benefits come from," Googins said.

The real goal of the Sirtfood diet is to pack one's meals with as many "sirtuin-activating nutrients" as possible. In lab settings, sirtuin genes have been activated, but Kirkpatrick noted that despite a pilot study by Goggins and Matten that analyzed 40 people on the diet, there haven't been any other studies in humans.

"Sirtfoods are all readily available and accessible plant foods," Goggins said. "Some of the top Sirtfoods include leafy greens like arugula, kale and parsley, strawberries, walnuts, extra virgin olive oil, dark chocolate, curry spices, green tea, red wine and coffee."

On the website for the diet, Goggins and Matten recommend trying to "Sirtify" meals where possible and adding Sirtfoods to favorite meals or substituting regular ingredients with a Sirtfood alternative.

Kirkpatrick praised the food choices the diet recommends, but said that there's nothing showing that the foods in the diet will trigger the genes as advertised.

"We don't know if these very specific foods, if you eat just these foods and nothing else, that these things will be triggered," said Kirkpatrick.

The diet has two phases. For the first three days on the diet, one should consume "three Sirtfood green juices and one full meal rich in Sirtfoods" daily, for a total of just 1,000 calories per day.

On days four through seven, people should increase their intake to 1,500 calories a day by consuming "two green juices and two meals." According to Goggins, people lose 7 pounds in seven days during this phase, though Kirkpatrick noted that anyone eating just 1,000 calories per day would see weight-loss effects no matter what they were consuming.

The second phase lasts for two weeks and is called a "maintenance period." Intended to help people lose weight steadily, dieters can eat "three balanced Sirtfood-rich meals every day, plus one green juice." The two phases can be repeated "whenever you'd like for a fat-loss boost."

Kirkpatrick said that the phases of the diet made her unsure of its sustainability.

"The actual foods are good," said Kirkpatrick. "The first phase is very much dependent on green juices and 'No more than 1,000 calories' ... Anything with 1,000 calories and pretty severe caloric restriction is going to lead to weight loss, but the body will bounce back. The body's searching for alternative fuels. Once you get into those later phases and you start relaxing the motivation you had in weeks one and two, you're more likely to gain weight."

However, Goggins said that once the phases have ended, the diet focuses more on what you're eating instead of how much.

"While the initial one-week phase has a calorie restriction, there is no counting for the maintenance," he said. "We now eat as we always were intended, and did throughout history."

Kirkpatrick advised combining the positive parts of the diet, like its focus on green foods, and mix it with other things known to be tied to weight loss, like stress management, sleep and exercise, and things like eating in moderation and trying to adhere to a healthier eating plan overall.

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What is the Sirtfood diet? Adele's rumored diet, explained - Today.com

For just $20, the Wyze Scale measures not only weight, but also body fat, BMI, heart rate and more.

Friends, let us not be ruled by the bathroom scale. Must we define our self-worth by a number at our feet? Are we to let our days be ruined upon discovering we've gained a pound? Nay, I tell you. Nay! Cast your scales into the river and look upon them no more!

Wait a sec... scale deal! The new Wyze Scale just arrived, and it's priced at $19.99. Shipping and sales tax might bring your total closer to $30, but that's still an extremely good deal on a surprisingly good smart-scale. Many others cost considerably more.

Wyze is the company behind a growing catalog of crazy-inexpensive smart-home products, including the$20 CNET-favorite Wyze Cam, the $7 Wyze Bulb and the$100 Wyze Lock. With the Scale and forthcoming Wyze Band, the company looks to be expanding into health and wellness.

Read more: The best smart scales for 2020

Like many of its smart ilk, the Wyze Scale pairs with your phone so you can track various metrics: weight, of course, but also things like body-fat percentage, muscle mass, metabolic age and half a dozen others. It even measures heart rate. All this happens courtesy of electrical signals sent through your feet. Thankfully, it has weight-only safety modes for pregnant women and people with pacemakers.

What impressed me most about the scale is the design: It's really pretty, looking like something that could easily cost $100, and it has heft. (You can't begrudge Wyze charging a shipping fee for this.) I also like that it supports up to eight users, though that means each person in your family needs a phone, the Wyze app and a Wyze account.

It can integrate with Apple Health and Google Fit now; Fitbit and Samsung Health support are in the works.

The only real downside I can see is the IPX3 waterproof rating, meaning you shouldn't step out of the shower and directly onto it. Anything more than a mild splash could be trouble.

But for $20? When some smart scales sell for as much as $100 to $150? Heck, you'd be hard-pressed to find a "dumb" scale this cheap.

Now playing: Watch this: Health care may never be the same. Mobile can help

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CNET's Cheapskate scours the web for great deals on tech products and much more. For the latest deals and updates, follow the Cheapskateon FacebookandTwitter. Find more great buys on theCNET Deals pageand check out ourCNET Coupons pagefor the latest promo codes fromBest Buy,Walmart,Amazonandmore. Questions about the Cheapskate blog? Find the answers on our FAQ page.

The information contained in this article is for educational and informational purposes only and is not intended as health or medical advice. Always consult a physician or other qualified health provider regarding any questions you may have about a medical condition or health objectives.

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Holy weight-loss, the Wyze Scale is an amazing deal at $20 - CNET

Six of every 10 Americans are living with at least one chronic condition, including diabetes and heart disease, according to the Centers for Disease Control and Prevention. Chronic diseases can significantly impact ones ability to lead a full life and are among the leading driver of health care costs.

What if you had access to a personalized tool that empowered you to improve your health, conquer your chronic disease, and increase the health, wellness and happiness in your life? What if that assistance came at no cost?

Welcome to Omada, a health care solution offered through Blue Cross Blue Shield, and for which coverage begins July 1. Omada understands the challenges people face in their efforts to get healthy, and harnesses the power of accountability, support, tools and insights. This is designed to help people overcome the barriers that can inhibit their ability to become the healthiest versions of themselves.

Lifestyle Matters

Many chronic diseases are the result of the cumulative effect of poor decisions. These conditions are frequently influenced by what you eat or dont eat, lack of physical activity, smoking, alcohol consumption and failing to undergo regular health screenings such as colonoscopies and mammograms.

Thats where Omada comes in. Its a unique and effective solution that:

Its common for people to tackle various aspects of their health conditions as separate activities. In contrast, Omada provides a single solution that combines behavioral and data science to tackle multiple conditions. This efficiency sets participants up for success.

Participants have averaged 4.7% weight loss in the first year and 4.2% weight loss in the second. Statistics like this are impressive because they morph into even bigger health gains. If a person can achieve a 5% to 7% loss in weight, they will reduce their chance of developing Type 2 diabetes by 58%. Focusing on lasting lifestyle changes is a key to sustaining healthyweight loss and significantly decreasing long-term health care-related expenses.

How it Works

Omada will reach out to employees, spouses and eligible dependents on the Gratiot Isabella health plan and who are at risk for developing Type 2 diabetes and heart disease.

If you qualify, you will receive a wireless scale that is already linked to your account. If applicable, you may also receive a blood pressure monitor or glucose meter. Its not just your equipment thats connected; users are linked to one-on-one guidance from an Omada health coach who will keep you on track and offer support.

This personal connection is crucial. Dr. Carolyn Bradner Jasik, chief medical officer with Omada Health, writes in her Omada blog entry that, Decades of research in behavioral science is clear building the confidence to make and sustain changes requires consistent human support from real people with whom an individual has a relationship and to whom they feel accountable. Tech solutions try to mirror this with AI-based bot coaching, automated reminders or human coaching by a pool of staff. But when it comes to delivering outcomes, there is just no substitute for an ongoing, one-on-one connection.

There are several other features that help keep you motivated and focused on your success, including weekly interactive lessons on topics such as eating, exercising, sleeping and stress management. Youll find topics on everything from meditation to medication.

Omada also provides insights into attaining health goals by utilizing real-time data and personalized coach feedback, so you can find encouragement during each step of your progress.

This is an online community of like-minded peers that provide group motivation and even a little friendly competition. Interacting with others who are facing similar challenges adds another dimension of human support from real people.

Just as chronic lifestyle-influenced diseases stem from personal choices, so does the alternative: opting to make a change and be healthier moving forward. With the Omada program, you have the power to take that first step on a journey to better health and a better you.

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Diabetes Prevention and Health Weight Loss | Just For You - Cedar Valley Daily Times

Adele's weight loss sparked online debate

Is media coverage of weight loss transformations inherently fatphobic?

In this video, Plant Based News founder Klaus Mitchell discusses recent coverage of musical icon Adele's weight loss - and rumors that she lost around 100lb on a predominantly plant-based diet.

When the article was shared on Instagram, it provoked not only fierce debate, but abuse.

"To be clear, no one is saying they love Adele more because of her weight loss - and if they do, we don't condone that view," says Mitchell in the video. "What came as a surprise to me, was when the article we published was met with criticism and demands to take it down."

He discusses some of the responses - including one that accused PBN of 'equating a woman's worth with her weight' by reporting on the information.

"My question to you is whether PBN should feel guilty for reporting on body transformations?" he asks, pointing out that the story published the fact of Adele's weight loss, without offering a moral view on that.

When this video was shared on PBN's Instagram, it garnered many responses, with one commentator saying: "Adele was one of the most famous, popular and loved artists on earth before she lost all that weight. Not sure how people can say she became more loved and got more media attention after losing weight. Her media attention has been enormous throughout her career independent of her body shape."

Another suggested: "When people start throwing the fat-shaming card on any fitness-related content. Its because they are critiquing their own roadblocks and fears."

But some felt that discussion of weight loss is inherently problematic, with one Instagram user saying: "Yes, it is a gendered issue and that's why the article is problematic, answered your own questions there really. I also hate veganism being seen as a quick diet to thinness. That's not why people should be going vegan and no one be promoting diet culture [sic]."

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Are Weight-Loss Transformation Success Stories Fat Shaming? - Plant Based News

While 2020 has been a funky and frustrating year for many, one person showing the year who's boss is Adele after revealing her extreme weight loss! After losing more than 100 lbs. Adele's new waistline has become the center of conversation for many but one source is coming forward to reveal that even though the Grammy winner has put in such hard work to shed so many pounds, she's feeling a little self-conscious from all the attention.

"Adele is finding the attention very embarrassing and still cringes when she walks into a room and all eyes are on her," the insider told the Heat. "She admits that although she may now look the best she ever has, she still gets incredibly self-conscious about her appearance, and sometimes finds it hard to believe that she looks as good as people say she does." Since her hit "Hello" bled into the ears of millions, the stunningly, talented singer has stayed mostly out of the spotlight.

It seems as though Adele was putting a positive spin on a dark time in her life after it was announced that she would be getting a divorce from estranged husband, Simon Konecki. After three years of marriage, the two found it best to go their separate ways, but little did fans know that during this time, she would lose so much weight. After sharing her progress on Instagram, her comments section was filled with nothing but praise, including from fellow celebrity friends like Chrissy Teigen, Rita Wilson and more. Even though she's been receiving so much love, the source explained she's still "shocked" when she looks at her new body.

"She says she's socked when she looks in the mirror, but she's really enjoying buying so many new clothes and is having a blast trying things on from skimpy dresses to statement red-carpet creations. It's like she's making up for lost time, and she's spent thousands of dollars." The source added, "She says that she feels anxious whenever she goes out she's never been truly comfortable with all of the attention. Her friends are really trying to work hard on persuading her that she's gorgeous and helping to build up her self-confidence, but it's going to take a while."

It was reported that Adele was receiving advice from pals like Beyonc throughout her weight loss journey. The Grammy winner has been using the same trainer as Robbie Williams' wife Ayda and says, "I don't believe she liked exercise much, but she has changed her lifestyle, and I believe 90 percent was dieting."

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Adele Reportedly Embarrassed and 'Self-Conscious' Over Recent Weight Loss - PopCulture.com