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Copyright © The Economist Newspaper Limited 2019. All rights reserved. Death of the calorie For more than a century we’ve counted on calories to tell us what will make us fat. It’s time to bury the world’s most misleading measure Mar 16th 2019 This piece was originally published in the April/May 2019 issue of 1843, our sister magazine of ideas, lifestyle and culture THE FIRST time that Salvador Camacho thought he was going to die he was sitting in his father’s Chrysler sedan with a friend listening to music. The 22-year-old engineering student was parked near his home in the central Mexican city of Toluca and in the fading evening light he didn’t notice two tattooed men approach. Tori Amos’s hit, “Bliss”, had just started playing when the gang members pointed guns at the young men. ADVERTISING inRead invented by Teads Get our daily newsletter Upgrade your inbox and get our Daily Dispatch and Editor's Picks. Sign up now So began a 24-hour ordeal. Strong willed and solidly built, Camacho was singled out as the more stubborn of the pair. He was blindfolded and beaten. One robber eventually threw him to the ground, put a gun to the back of his head and told him it was time to die. He passed out, waking in a field with his hands tied behind his back, almost naked. Camacho survived but, traumatised, he sank into depression. Soon he was drinking heavily and binge eating. His weight ballooned from a trim 70kg to 103kg. That led to his second near-death experience, eight years later, in 2007. He remembers waking up and blinking at bright lights: he was being wheeled on a stretcher into a hospital emergency ward, with an attack of severe arrhythmia, or irregular heart beat. “A cardiologist told me that if I didn’t lose weight and get my health under control I would be dead in five years,” he says. That second crisis forced Camacho belatedly to deal with the trauma of the first. To help with what he now understands was post-traumatic stress disorder, he started having counselling and taking antidepressants and anti-anxiety drugs. To address his physical health, he tried to lose weight. This effort propelled him to the centre of one of the most fraught scientific debates of our age: the calorie wars, a fierce disagreement about diet and weight control. Today, more than a decade after his cardiologist’s stark warning, Camacho lives in the Swiss city of Basel. He is relaxed and confident, except when two topics come up. When he recounts his kidnapping his gaze drops, his smile vanishes and he becomes noticeably quieter, although he says his panic attacks have virtually disappeared. The other touchy topic is weight control, which causes him to shake his head in anger at what he and millions of other dieters have gone through. “It’s just ridiculous,” he says with exasperation and a touch of venom. “People are living with real pain and guilt and all they get is advice that is confused or just plain wrong.” The guidance that Camacho’s doctors gave him, along with a string of nutritionists and his own online research, was unanimous. It would be familiar to the millions of people who have ever tried to diet. “Everybody tells you that to lose weight you have to eat less and move more,” he says, “and the way to do that is to count your calories.” At his heaviest, Camacho’s body-mass index—the ratio of his height to his weight—reached 35.6, well above the 30 mark that doctors define as clinically obese. Most government guidelines indicated that, as a man, he needed 2,500 calories a day to maintain his weight (the target for women is 2,000). Nutritionists told Camacho that if he ate fewer than 2,000 calories a day, a weekly “deficit” of 3,500 would mean that he would lose 0.5kg a week. With a desk job as a planning engineer in a Mexican hospital, he knew it would take real discipline to trim his pudgy frame. But as his kidnappers had quickly realised, he is an unusually determined character. He began getting up before dawn each day to run 10km. He also started accounting for every morsel of food he consumed. “I filled in Excel spreadsheets every night, every week and every month listing everything I ate. It became a real obsession for me,” says Camacho. Out went the Burger King Whoppers, fried tacos packed with pork and cheese, and tortas (Mexican sandwiches filled with meat, refried beans, avocado and peppers). Out too went his usual steady flow of beer and wine. In came carefully measured low-fat cheese and turkey sandwiches, salads, canned peach juice, Gatorade and Coke Zero, with three Special-K low-calorie diet bars a day. “I was always tired and hungry and I would get really moody and distracted,” he says. “I was thinking about food all the time.” He was constantly told that if he got the maths right—consuming fewer calories than he burned each day—the results would soon show. “I really did everything you are supposed to do,” he insists with the tone of a schoolboy who completed his homework yet still failed a big test. He bought a battery of exercise monitoring devices to measure how many calories he was expending on his runs. “I was told to exercise for at least 45 minutes at least four or five times a week. I actually ran for more than an hour every day.” He kept to low-fat, low-calorie food for three years. It simply didn’t work. At one point he lost about 10kg but his weight rebounded, though he still restricted his calories. Dieters the world over will be familiar with Camacho’s frustrations. Most studies show that more than 80% of people regain any lost weight in the long term. And like him, when we fail, most of us assume that we are too lazy or greedy—that we are at fault. As a general rule it is true that if you eat vastly fewer calories than you burn, you’ll get slimmer (and if you consume far more, you’ll get fatter). But the myriad faddy diets flogged to us each year belie the simplicity of the formula that Camacho was given. The calorie as a scientific measurement is not in dispute. But calculating the exact calorific content of food is far harder than the confidently precise numbers displayed on food packets suggest. Two items of food with identical calorific values may be digested in very different ways. Each body processes calories differently. Even for a single individual, the time of day that you eat matters. The more we probe, the more we realise that tallying calories will do little to help us control our weight or even maintain a healthy diet: the beguiling simplicity of counting calories in and calories out is dangerously flawed. The calorie is ubiquitous in daily life. It takes top billing on the information label of most packaged food and drinks. Ever more restaurants list the number of calories in each dish on their menus. Counting the calories we expend has become just as standard. Gym equipment, fitness devices around our wrists, even our phones tell us how many calories we have supposedly burned in a single exercise session or over the course of a day. It wasn’t always thus. For centuries, scientists assumed that it was the mass of food consumed that was significant. In the late 16th century an Italian physician named Santorio Sanctorius invented a “weighing chair”, dangling from a giant scale, in which he sat at regular intervals to weigh himself, everything he ate and drank, and all the faeces and urine he produced. Despite 30 years of compulsive chair dangling, Sanctorius answered few of his own questions about the impact that his consumption had on his body. Only later did the focus shift to the energy different foodstuffs contained. In the 18th century Antoine Lavoisier, a French aristocrat, worked out that burning a candle required a gas from the air—which he named oxygen—to fuel the flame and release heat and other gases. He applied the same principle to food, concluding that it fuels the body like a slow-burning fire. He built a calorimeter, a device big enough to hold a guinea pig, and measured the heat the creature generated to estimate how much energy it was producing. Unfortunately the French revolution—specifically the guillotine—cut short his thinking on the subject. But he had started something. Other scientists later constructed “bomb calorimeters” in which they burned food to measure the heat—and thus the potential energy—released from it. The calorie—which comes from “calor”, the Latin for “heat”—was originally used to measure the efficiency of steam engines: one calorie is the energy required to heat 1kg of water by one degree Celsius. Only in the 1860s did German scientists begin using it to calculate the energy in food. It was an American agricultural chemist, Wilbur Atwater, who popularised the idea that it could be used to measure both the energy contained in food and the energy the body expended on things like muscular work, tissue repair and powering the organs. In 1887, after a trip to Germany, he wrote a series of wildly popular articles in Century, an American magazine, suggesting that “food is to the body what fuel is to the fire.” He introduced the public to the notion of “macronutrients”—carbohydrates, protein and fat—so called because the body needs a lot of them. For different people, food can take between 8 and 80 hours to travel from dinner plate to toilet bowl Today many of us want to monitor our calorie consumption in order to lose or maintain our weight. Atwater, the son of a Methodist minister, was motivated by the opposite concern: at a time when malnutrition was widespread, he sought to help poor people find the most costeffective items to fill themselves up. To see how much energy different macronutrients provided to the body, he fed samples of an “average” American diet of that era—which he believed to be heavy in molasses cookies, barley meal and chicken gizzards—to a group of male students in a basement at Wesleyan University in Middletown, Connecticut. For up to 12 days at a time a volunteer would eat, sleep and lift weights while sealed inside a sixfoot-high chamber measuring four feet wide by seven feet deep. The energy in each meal was calculated by burning identical foods in a bomb calorimeter. The walls were filled with water, and changes in its temperature allowed Atwater to calculate how much energy the students’ bodies were generating. His team collected the students’ faeces and burned that too, to see how much energy had been left in the body in the digestion process. This was pioneering stuff for the 1890s. Atwater eventually concluded that a gram of either carbohydrate or protein made an average of four calories of energy available to the body, and a gram of fat offered an average of 8.9 calories, a figure later rounded up to nine calories for convenience. We now know far more about the workings of the human body: Atwater was right that some of a meal’s potential energy was excreted, but had no idea that some was also used to digest the meal itself, and that the body expends different amounts of energy depending on the food. Yet more than a century after igniting the faeces of Wesleyan students, the numbers Atwater calculated for each macronutrient remain the standard for measuring the calories in any given food stuff. Those experiments were the basis of Salvador Camacho’s daily calorific arithmetic. Atwater transformed the way the public thought about food, with his simple belief that “a calorie is a calorie”. He counselled the poor against eating too many leafy green vegetables because they weren’t sufficiently dense in energy. By his account, it made no difference whether calories came from chocolate or spinach: if the body absorbed more energy than it used, then it would store the excess as body fat, causing you to put on weight. That idea captured the public imagination. In 1918 the first book was published in America based on the notion that a healthy diet was no more complicated than the simple addition and subtraction of calories. “You may eat just what you like—candy, pie, cake, fat meat, butter, cream but count your calories!” wrote Lulu Hunt Peters in “Diet and Health”. “Now that you know you can have the things you like, proceed to make your menus containing very little of them.” The book sold millions. By the 1930s the calorie had become entrenched in both the public mind and government policy. Its exclusive focus on the energy content of food, rather than its vitamin content, say, went virtually unchallenged. Rising incomes and greater female participation in the workforce meant that by the 1960s people were eating out more often or buying prepared food, so they wanted more information about what they were consuming. Nutritional information on foodstuffs was widespread but haphazard; many items carried outlandish claims about their health benefits. Labelling became standardised and mandatory in America only in 1990. The emphasis and use of this information shifted too. By the late 1960s, obesity was becoming a pressing health concern as people became more sedentary and started eating highly processed foods and lots of sugar. As the number of people who needed to lose weight grew, changing diets became the focus of attention. So began the war on fat, in which Atwater’s calorie calculations were an unwitting ally. Because counting calories was seen as an objective arbiter of the health qualities of a foodstuff, it seemed logical that the most calorie-laden part of any food item—fat—must be bad for you. By this measure, dishes low in calories, but rich in sugar and carbohydrates, seemed healthier. People were increasingly willing to blame fat for many of the health ills of modern life, helped along by the sugar lobby: in 2016, a researcher at the University of California uncovered documents from 1967 showing that sugar companies secretly funded studies at Harvard University designed to blame fat for the growing obesity epidemic. That the dietary “fat” found in olive oil, bacon and butter is branded with the same word as the unwanted flesh around our middles made it all the easier to demonise. A US Senate committee report in 1977 recommended a low-fat, lowcholesterol diet for all, and other governments followed suit. The food industry responded with enthusiasm, removing fat, the most caloriedense of macronutrients, from food items and replacing it with sugar, starch and salt. As a bonus, the thousands of new cheap and tasty “lowcal” and “low-fat” products which Camacho used to diet tended to have longer shelf lives and higher profit margins. But this didn’t lead to the expected improvements in public health. Instead, it coincided almost exactly with the most dramatic rise in obesity in human history. Between 1975 and 2016 obesity almost tripled worldwide, according to the World Health Organisation (WHO): nearly 40% of over-18s—some 1.9bn adults—are now overweight. That contributed to a rapid rise in cardiovascular diseases (mainly heart disease and stroke) which became the leading cause of death worldwide. Rates of type-2 diabetes, which is often linked to lifestyle and diet, have more than doubled since 1980. It wasn’t only wealthy countries that saw such trends. In Mexico, middle-class urban families such as Camacho’s got fatter too. As a child Camacho was fit and loved playing football. But at the age of ten, in 1988, he was one of many young Mexicans who started stacking on weight as increasing trade with America saw cheap sweets and fizzy drinks flood the shops, a process known as the “Coca-colonisation” of Mexico. “There were suddenly all these flavours you had never tasted, with chocolates, candies and Dr Pepper,” Camacho remembers: “Overnight I got fat.” When his uncles teased him about his bulging waistline, he cut back on sweets and stayed in good shape until his kidnapping 12 years later. Other Mexicans just kept bulking up. In 2013 Mexico overtook America as the most obese country in the world. Labels on food may understate their calories by up to 20% To combat this trend, governments worldwide have enshrined caloriecounting in policy. The WHO attributes the “fundamental cause” of obesity worldwide to “an energy imbalance between calories consumed and calories expended”. Governments the world over persist in offering the same advice: count and cut calories. This has infiltrated ever more areas of life. In 2018 the American government ordered food chains and vending machines to provide calorie details on their menus, to help consumers make “informed and healthful decisions”. Australia and Britain are headed in similar directions. Government bodies advise dieters to record their meals in a calorie journal to lose weight. The experimental efforts of a 19th-century scientist stand barely changed— and are barely questioned. Millions of dieters give up when their calorie-counting is unsuccessful. Camacho was more stubborn than most. He took photos of his meals to record his intake more accurately, and would log into his calorie spreadsheets from his phone. He thought about every morsel he ate. And he bought a proliferation of gadgets to track his calorie output. But he still didn’t lose much weight. One problem was that his sums were based on the idea that calorie counts are accurate. Food producers give impressively specific readings: a slice of Camacho’s favourite Domino’s double pepperoni pizza is supposedly 248 calories (not 247 nor 249). Yet the number of calories listed on food packets and menus are routinely wrong. Susan Roberts, a nutritionist at Tufts University in Boston, has found that labels on American packaged foods miss their true calorie counts by an average of 18%. American government regulations allow such labels to understate calories by up to 20% (to ensure that consumers are not short-changed in terms of how much nutrition they receive). The information on some processed frozen foods misstates their calorific content by as much as 70%. That isn’t the only problem. Calorie counts are based on how much heat a foodstuff gives off when it burns in an oven. But the human body is far more complex than an oven. When food is burned in a laboratory it surrenders its calories within seconds. By contrast, the real-life journey from dinner plate to toilet bowl takes on average about a day, but can range from eight to 80 hours depending on the person. A calorie of carbohydrate and a calorie of protein both have the same amount of stored energy, so they perform identically in an oven. But put those calories into real bodies and they behave quite differently. And we are still learning new insights: American researchers discovered last year that, for more than a century, we’ve been exaggerating by about 20% the number of calories we absorb from almonds. The process of storing fat—the “weight” many people seek to lose—is influenced by dozens of other factors. Apart from calories, our genes, the trillions of bacteria that live in our gut, food preparation and sleep affect how we process food. Academic discussions of food and nutrition are littered with references to huge bodies of research that still need to be conducted. “No other field of science or medicine sees such a lack of rigorous studies,” says Tim Spector, a professor of genetic epidemiology at Kings College in London. “We can create synthetic DNA and clone animals but we still know incredibly little about the stuff that keeps us alive.” What we do know, however, suggests that counting calories is very crude and often misleading. Think of a burger, the kind of food that Camacho eschewed during his early efforts to lose weight. Take a bite and the saliva in your mouth starts to break it down, a process that continues when you swallow, transporting the morsel towards your stomach and beyond to be churned further. The digestive process transforms the protein, carbohydrates and fat in the burger into their basic compounds so that they are tiny enough to be absorbed into the bloodstream via the small intestine to fuel and repair the trillions of cells in the body. But the basic molecules from each macronutrient play very different roles within the body. All carbohydrates break down into sugars, which are the body’s main fuel source. But the speed at which your body gets its fuel from food can be as important as the amount of fuel. Simple carbohydrates are swiftly absorbed into the bloodstream, providing a fast shot of energy: the body absorbs the sugar from a can of fizzy drink at a rate of 30 calories a minute, compared with two calories a minute from complex carbohydrates such as potatoes or rice. That matters, because a sudden hit of sugar prompts the rapid release of insulin, a hormone that carries the sugar out of the bloodstream and into the body’s cells. Problems arise when there is too much sugar in the blood. The liver can store some of the excess, but any that remains is stashed as fat. So consuming large quantities of sugar is the fastest way to create body fat. And, once the insulin has done its work, blood-sugar levels slump, which tends to leave you hungry, as well as plumper. Getting fat is a consequence of civilisation. Our ancestors would have enjoyed a heavy hit of sugar perhaps four times a year, when a new season produced fresh fruit. Many now enjoy that kind of sugar kick every day. The average person in the developed world consumes 20 times as much sugar as people did even during Atwater’s time. But it is a different story when you eat complex carbohydrates such as cereals. These are strung together from simple carbohydrates, so they also break down into sugar, but because they do so more slowly, your blood-sugar levels remain steadier. The fruit juices that Camacho was encouraged to drink contained fewer calories than one of his wholegrain buns but the bread delivered less of a sugar hit and left him feeling satiated for longer. You absorb fewer calories eating toast that has been left to go cold Other macronutrients have different functions. Protein, the dominant component of meat, fish and dairy products, acts as the main building block for bone, skin, hair and other body tissues. In the absence of sufficient quantities of carbohydrates it can also serve as fuel for the body. But since it is broken down more slowly than carbohydrates, protein is less likely to be converted to body fat. Fat is a different matter again. It should leave you feeling fuller for longer, because your body splits it into tiny fatty acids more slowly than it processes carbohydrates or protein. We all need fat to make hormones and to protect our nerves (a bit like plastic coating protects an electric wire). Over millennia, fat has also been a crucial way for humans to store energy, allowing us to survive periods of famine. Nowadays, even without the risk of starvation, our bodies are programmed to store excess fuel in case we run out of food. No wonder a single measure—the energy content—can’t capture such complexity. Our fixation with counting calories assumes both that all calories are equal and that all bodies respond to calories in identical ways: Camacho was told that, since he was a man, he needed 2,500 calories a day to maintain his weight. Yet a growing body of research shows that when different people consume the same meal, the impact on each person’s blood sugar and fat formation will vary according to their genes, lifestyles and unique mix of gut bacteria. Research published this year showed that a certain set of genes is found more often in overweight people than in skinny ones, suggesting that some people have to work harder than others to stay thin (a fact that many of us already felt intuitively to be true). Differences in gut microbiomes can alter how people process food. A study of 800 Israelis in 2015 found that the rise in their blood-sugar levels varied by a factor of four in response to identical food. Some people’s intestines are 50% longer than others: those with shorter ones absorb fewer calories, which means that they excrete more of the energy in food, putting on less weight. The response of your own body may also change depending on when you eat it. Lose weight and your body will try to regain it, slowing down your metabolism and even reducing the energy you spend on fidgeting and twitching your muscles. Even your eating and sleeping schedules can be important. Going without a full night’s sleep may spur your body to create more fatty tissue, which casts a grim light on Camacho’s years of early-morning exertion. You may put on more weight eating small amounts over 12-15 hours than eating the same food in three distinct meals over a shorter period. There’s a further weakness in the calorie-counting system: the amount of energy we absorb from food depends on how we prepare it. Chopping and grinding food essentially does part of the work of digestion, making more calories available to your body by ripping apart cell walls before you eat it. That effect is magnified when you add heat: cooking increases the proportion of food digested in the stomach and small intestine, from 50% to 95%. The digestible calories in beef rises by 15% on cooking, and in sweet potato some 40% (the exact change depends on whether it is boiled, roasted or microwaved). So significant is this impact that Richard Wrangham, a primatologist at Harvard University, reckons that cooking was necessary for human evolution. It enabled the neurological expansion that created Homo sapiens: powering the brain consumes about a fifth of a person’s metabolic energy each day (cooking also means we didn’t need to spend all day chewing, unlike chimps). The difficulty in counting accurately doesn’t stop there. The calorie load of carbohydrate-heavy items such as rice, pasta, bread and potatoes can be slashed simply by cooking, chilling and reheating them. As starch molecules cool they form new structures that are harder to digest. You absorb fewer calories eating toast that has been left to go cold, or leftover spaghetti, than if they were freshly made. Scientists in Sri Lanka discovered in 2015 that they could more than halve the calories potentially absorbed from rice by adding coconut oil during cooking and then cooling the rice. This made the starch less digestible so the body may take on fewer calories (they have yet to test on human beings the precise effects of rice cooked in this way). That’s a bad thing if you’re malnourished, but a boon if you’re trying to lose weight. Different parts of a vegetable or fruit may be absorbed differently too: older leaves are tougher, for example. The starchy interior of sweetcorn kernels is easily digested but the cellulose husk is impossible to break down and passes through the body untouched. Just think about that moment when you look into the toilet bowl after eating sweetcorn. As with so many dieters, Camacho’s efforts to accurately track his calories “in” were doomed. But so too were his attempts to track his calories “out”. The message from many public authorities and food producers, especially fast-food companies that sponsor sports events, is that even the unhealthiest foods will not make you fat if you do your part by taking plenty of exercise. Exercise does, of course, have clear health benefits. But unless you’re a professional athlete, it plays a smaller part in weight control than most people believe. As much as 75% of the average person’s daily energy expenditure comes not through exercise but from ordinary daily activities and from keeping your body functioning by digesting food, powering organs and maintaining a regular body temperature. Even drinking iced water— which delivers no energy—forces the body to burn calories to maintain its preferred temperature, making it the only known case of consuming something with “negative” calories. A popular expression in English tells us not to “compare apples and oranges” and assume them to be the same: yet calories put pizzas and oranges, or apples and ice cream, on the same scale, and deems them equal. After three years of dedicated calorie-counting Camacho changed tack. While recovering from running the 2010 marathon in San Diego he took up Crossfit training, an exercise regime that includes high-intensity training and weightlifting. There he met people using a very different method to control their weight. Like him, they exercised regularly. But rather than limiting their calories, they ate natural foods, what Camacho calls “stuff from a real plant, not an industrial plant”. Fed up with feeling like a hungry failure, he decided to give it a go. He ditched his heavily processed low-calorie products and focused on the quality of his food rather than quantity. He stopped feeling ravenous all the time. “It sounds simple but I decided to listen to my body and eat whenever I was hungry but only when I was hungry, and to eat real food, not food ‘products’,” he says. He went back to items that he’d long banned himself from eating. He had his first rasher of bacon in three years and enjoyed cheese, whole-fat milk and steaks. He immediately felt less hungry and happier. More surprising, he quickly began to lose his extra fat. “I was sleeping so much better and within a couple of months I stopped the depression and anxiety medication,” he says. “I went from always feeling guilty and angry and afraid to feeling in control of myself and actually proud of my own body. Suddenly I could enjoy eating and drinking again.” The weight stayed off and in 2012 he moved to Heidelberg in Germany, a world away from the hectic streets of Mexico, to study for a masters degree in public health. “The idea hit me that I could combine my own experience with academic work to try to help other people overcome these various barriers that I had found.” After his masters he embarked on a doctorate on how to tackle obesity in Mexico. Today he is married to a German scholar, Erica Gunther, who has studied food systems around the world. Their diet includes things he used to shun, such as egg yolks, olive oil and nuts. Two days a week the couple stick to vegetarian meals but otherwise he devours steak, kidneys, liver and some of his favourite Mexican dishes—barbacoa (lamb), carnitas (pork) and tacos with grilled meat. His wife enjoys making a traditional Mexican sweet pastry called pan de muerto (bread of death). “Before I would have run an extra two hours to compensate for eating that but now I don’t care, I just make sure it is a treat, not an everyday thing.” Having spent years trying to forgo alcohol, he has a glass or two of wine several times a week, and goes for a beer with friends from his gym. The body absorbs 30 calories a minute from fizzy drinks, compared with 2 calories a minute from potatoes Sweating through three or four workouts a week, he is as well-muscled as a professional rugby player. A stable 80kg, he has very little body fat, though he is still considered overweight by the body-mass-index charts, which rate many beefed-up professional athletes as too heavy. The only relapse of anxiety he suffers nowadays happens when he hears Tori Amos singing “Bliss”—the song playing when he was kidnapped— which he says “is a real pity because it’s a great song”. Today Camacho could be described as a calorie dissident, one of a small but growing number of academics and scientists who say that the persistence of calorie-counting compounds the obesity epidemic, rather than remedying it. Counting calories has disrupted our ability to eat the right amount of food, he says, and has steered us towards poor choices. In 2017 he wrote an academic paper that was one of the most savage attacks on the calorie system published in a peer-reviewed journal. “I’m actually embarrassed at what I used to believe,” he says. “I was doing everything I could to follow the official advice but it was totally wrong and I feel stupid for never even questioning it.” Given the vast evidence that calorie-counting is imprecise at best, and contributes to rising obesity at worst, why has it persisted? The simplicity of calorie-counting explains its appeal. Metrics that tell consumers the extent to which foods have been processed, or whether they will suppress hunger, are harder to understand. Faced with the calorie juggernaut, none has gained wide acceptance. The scientific and health establishment knows that the current system is flawed. A senior adviser to the UN’s Food and Agriculture Organisation warned in 2002 that the Atwater “factors” of 4-4-9 at the heart of the calorie-counting system were “a gross oversimplification” and so inaccurate that they could mislead consumers into choosing unhealthy products because they understate the calories in some carbohydrates. The organisation said it would give “further consideration” to overhauling the system but 17 years later there is little momentum for change. It even rejected the idea of harmonising the many methods that are used in different countries—a label in Australia can give a different count from one in America for the same product. Officials at the WHO also acknowledge the problems of the current system, but say it is so entrenched in consumer behaviour, public policy and industry standards that it would be too expensive and disruptive to make big changes. The experiments that Atwater conducted a century ago, without calculators or computers, have never been repeated even though our understanding of how our bodies work is vastly improved. There is little funding or enthusiasm for such work. As Susan Roberts at Tufts University says, collecting and analysing faeces “is the worst research job in the world”. The calorie system, says Camacho, lets food producers off the hook: “They can say, ‘We’re not responsible for the unhealthy products we sell, we just have to list the calories and leave it to you to manage your own weight’.” Camacho and other calorie dissidents argue that sugar and highly processed carbohydrates play havoc with people’s hormonal systems. Higher insulin levels mean more energy is converted into fat tissues leaving less available to fuel the rest of the body. That in turn drives hunger and overeating. In other words the constant hunger and fatigue suffered by Camacho and other dieters may be symptoms of being overweight, rather than the cause of the problem. Yet much of the food industry defends the status quo too. To change how we assess the energy and health values of food would undermine the business model of many companies. The only major organisation to shift the emphasis beyond calories is one dedicated to helping its customers slim down: Weight Watchers. In 2001 the world’s best-known dieting firm introduced a points system that moved away from focusing exclusively on calories to also classifying foods according to their sugar and saturated fat content, and their impact on appetite. Chris Stirk, the firm’s general manager in Britain, says the organisation made the change because relying on calories to lose weight is “outdated”: “Science evolves daily, monthly, yearly, let alone since the 1800s.” Many of us know instinctively that not all calories are the same. A lollipop and an apple may contain similar numbers of calories but the apple is clearly better for us. But after a lifetime of hearing about the calorie and its role in supposedly foolproof diet advice we could be forgiven for being confused about how best to eat. It’s time to lay it to rest. This piece was originally published in the April/May 2019 issue of 1843, our sister magazine of ideas, lifestyle and culture. The author is Peter Wilson. He lost 13kg in four months thanks to what he learned researching this story Mar 16th 2019 Copyright © The Economist Newspaper Limited 2019. All rights reserved. Retail Sales Bounce Up 17.7% After Record Drop As States Reopen (From NPR’s Marketplace) As more states and cities allowed restaurants and shopping centers to reopen, U.S. retail spending swung big in May, climbing 17.7%, the U.S. Commerce Department said Tuesday. Major stock indexes rose after the report was released. Spending is still down 6.1% from a year earlier because of the coronavirus pandemic. And economists warn of a long and uncertain recovery. But May's upswing follows a record historic collapse in March and April, when retail spending nose-dived as people avoided outings for food or shopping, especially for clothes and furniture. Retail sales — a measure that includes spending on gasoline, cars, food and drink — are a key part of the economy, which is sputtering back at different rates across the country after weeks of lockdowns. May's sales also got a boost from people spending their tax refunds and coronavirus financial assistance. As businesses reopen, however, several states have reported new spikes in coronavirus cases. CORONAVIRUS LIVE UPDATES Stocks Soar After Strong Retail Sales And Reported Trump Infrastructure Plan That has added to warnings that Americans' shopping habits may be changed for a long time, if not forever. The pandemic, for example, has accelerated the shift to online orders, including food and groceries. This made online retail the only category to see demand grow even during the April meltdown, when retail sales overall fell a revised 14.7% from March. Here's where people were spending in May, compared with a month earlier: ▪ Clothing and accessories stores: +188% ▪ Furniture stores: +89.7% ▪ Sports, music and other hobby stores: +88.2% ▪ Electronics stores: +50.5% ▪ Department stores: +36.9% ▪ Restaurants and bars: +29.1% ▪ Gas stations: +12.8% ▪ Online retailers: +9% ▪ Big-box stores: +6% ▪ Grocery stores: +1.3% Many of these categories are seeing dramatically lower demand compared with prepandemic times. People are still spending much less on clothing and electronics and at gas stations, restaurants and department stores. This has exacerbated the struggles of many retailers and suburban malls. Preppy fashion store J. Crew, luxury department store Neiman Marcus and mall staple J.C. Penney declared bankruptcy in May. Retail chains might permanently close up to 25,000 U.S. stores this year, the majority of them in malls, according to a report by Coresight Research. At the start of the pandemic, in March, the group had forecast 15,000 permanent store closures. CORONAVIRUS LIVE UPDATES Another 1.5 Million File For Unemployment As States Continue To Reopen Economies Though shoppers appear to have been enthusiastic to enjoy spring air and leave the homes where they've been cooped up for weeks, tens of millions remain unemployed. Companies have started to rehire, but the Federal Reserve is projecting that the unemployment rate will still be more than 9% by the end of 2020. "Is it possible the worst of the coronavirus pandemic is behind us? Maybe, but we are not out of the woods yet, and uncertainty abounds," National Retail Federation Chief Economist Jack Kleinhenz said earlier this month. "With such [sizable] disruptions, it is difficult to tally the damage or determine the future." sp Supplement Regression Analysis LEARNING OBJECTIVE 1 Describe how regression analysis is used to classify mixed costs. The high-low method is often used to estimate fixed and variable costs for a mixed-cost situation. An advantage of the high-low method is that it is easy to apply. But, how accurate and reliable is the estimated cost equation that it produces? For example, consider the example shown in Illustration 1, which indicates the cost equation line produced by the high-low method for Metro Transit Company’s maintenance costs. How well does the high-low method represent the relationship between miles driven and total cost? This line is close to, and in some cases bisects, nearly all of the data points. Therefore, in this case, the high-low method provides a cost equation that is a very good fit for this data set. It separates fixed and variable costs in an accurate and reliable way. Illustration 1 Scatter plot for Metro Transit Company While the high-low method works well for the Metro Transit data set, a weakness of this method is that it employs only two data points and ignores the rest. If those two data points are representative of the entire data set, then the high-low method provides reasonable results (as seen in Illustration 1). But, if the high and low data points are not representative of the rest of the data set, then the results are misleading. To illustrate, assume that Hillary Trucking Company has 12 months of maintenance cost data, as shown in Illustration 2. 1 2 Supplement Regression Analysis Illustration 2 Maintenance costs and mileage data for Hillary Trucking Company Month Miles Driven Total Cost Month Miles Driven Total Cost January February March April May June 20,000 40,000 35,000 50,000 30,000 43,000 $30,000 49,000 46,000 63,000 42,000 52,000 July August September October November December 15,000 28,000 60,000 55,000 19,000 65,000 $39,000 41,000 72,000 67,000 29,000 63,000 The high and low activities are 65,000 miles in December and 15,000 miles in July. The maintenance costs at these two levels are $63,000 and $39,000, respectively. The difference in maintenance costs is $24,000 ($63,000 − $39,000), and the difference in miles is 50,000 (65,000 − 15,000). Therefore, for Hillary Trucking, variable cost per unit under the high-low method is $0.48 ($24,000 ÷ 50,000). To determine fixed costs, we subtract total variable costs at the low activity level as follows. Fixed costs = $39,000 − ($0.48 × 15,000) = $31,800 Therefore, the cost equation based on the high-low method for this data produces the following formula: Maintenance costs = = Intercept + Slope $31,800 + ($0.48 × Miles driven) Illustration 3 shows a scatter plot of the data with a line representing the high-low method cost equation. Note that most of the data points for Hillary Trucking are a significant distance from the line. For example, at 19,000 miles, the observed maintenance cost is $29,000, but the equation predicts $40,920 [$31,800 + ($0.48 × 19,000)]. That is a difference of $11,920 ($40,920 − $29,000). In this case, the high-low method cost equation does not provide a good representation of the relationship between miles driven and maintenance costs. To derive a more representative cost equation, the company should employ regression analysis. Illustration 3 Scatter plot for Hillary Trucking Company Supplement Regression Analysis 3 Regression analysis is a statistical approach that estimates the cost equation by employing information from all data points, not just the highest and lowest ones. While it involves mathematical analysis taught in statistics courses (which we will not address here), we can provide you with a basic understanding of how regression analysis works. Consider Illustration 3, which highlights the distance that each data point is from the high-low cost equation line. What regression analysis does is to find a cost equation that results in a cost equation line that minimizes the sum of the (squared) distances from the line to the data points. Many software packages perform regression analysis. In Illustration 4, we use the Intercept and Slope functions in Excel to estimate the regression equation for the Hillary Trucking Company data.1 Illustration 4 Excel spreadsheet for Hillary Trucking Company The resulting cost equation is: Maintenance costs = = Intercept + Slope $18,502 + ($0.81 × Miles driven) Compare this to the high-low cost equation: Maintenance costs = = Intercept + Slope $31,800 + ($0.48 × Miles driven) 1 To use the Intercept and Slope functions in Excel, enter your data in two columns in an Excel spreadsheet. The first column should be your “X” variable (miles driven, cells B2 to B13 in our example). The second column should be your “Y” variable (maintenance costs, cells C2 to C13 in our example). Next, in a separate cell, choosing from Excel’s statistical functions, enter =Intercept(C2:C13,B2:B13) and in a different cell enter =Slope(C2:C13,B2:B13). 4 Supplement Regression Analysis As Illustration 5 shows, the intercept and slope differ significantly between the regression equation (green) and the high-low equation (red).2 The regression cost equation line does not bisect the high and low data points but instead follows a path that minimizes the cumulative distance from all of the data points. By doing so, it provides a cost equation that is more representative of the relationship between miles driven and total maintenance costs than the high-low method. Illustration 5 Scatter plot and cost equation lines Why should managers care about the accuracy of the cost equation? Managers make many decisions that require that mixed costs be separated into fixed and variable components. Inaccurate classifications of these costs might cause a manager to make an inappropriate decision. For example, Hillary Trucking Company’s break-even point differs significantly depending on which of these two cost equations was used. If Hillary Trucking relies on the high-low method, it would have a distorted view of the level of sales it would need in order to break even. In addition, misrepresentation for fixed and variable costs could result in inappropriate decisions, such as whether to discontinue a product line. It would also result in inaccurate product costing under activity-based costing. While regression analysis usually provides more reliable estimates of the cost equation, it does have its limitations. First, the regression approach that we applied above assumes a linear relationship between the variables (that is, an increase or decrease in one variable results in a proportional increase or decrease in the other). If the actual relationship differs significantly from linearity, then linear regression can provide misleading results. (Nonlinear regression is addressed in advanced statistics courses.) In addition, regression estimates can be severely influenced by “outliers” – data points that differ significantly from the rest of the observations. It is therefore good practice to plot data points in a scatter graph to identify outliers and then investigate the reasons why they differ. In some cases, outliers must be adjusted for or eliminated. Finally, regression estimation is most accurate when it is based on a large number of data points. However, collecting data can be time-consuming and costly. In some cases, there simply are not enough observable data points to arrive at a reliable estimate. SUMMARY OF LEARNING OBJECTIVE 1 2 Describe how regression analysis is used to classify mixed costs. The high-low method provides a quick estimate of the cost equation for a mixed cost. However, the high-low method is based on only the highest and lowest data points. Regression analysis provides an estimate of the cost equation based on all data points. The cost equation line that results from regression analysis minimizes the sum of the (squared) distances of all of the data points from the cost equation line. Computer programs such as Excel enable easy estimation of the cost equation with regression. To plot a scatter graph in Excel, highlight the data and then click on Scatter under the Insert tab. To draw the cost equation line, click on the scatter plot, then select Layout and Trendline. In order to get the cost equation line to intercept the Y axis, under Trendline Options in the Backward field, enter the lowest value of your X variable. For example, for Hillary Trucking, we entered 15,000. Supplement Regression Analysis 5 QUESTIONS 1. James Brooks estimated the variable and fixed components of his company’s utility costs using the high-low method. He is concerned that the cost equation that resulted from the high-low method might not provide an accurate representation of his company’s utility costs. What is the inherent weakness of the high-low method? What alternative approach might Brooks use, and what are its advantages? 2. Mary Webster owns and manages a company that provides trenching services. Her clients are companies that need to lay power lines, gas lines, and fiber optic cable. Because trenching machines require considerable maintenance due to the demanding nature of the work, Mary has created a scatter plot that displays her monthly maintenance costs. If Mary were to estimate a cost equation line using regression analysis for the data in her scatter plot, what primary characteristic would that line display? 3. What are some of the limitations of regression analysis? BRIEF EXERCISE BE-1 Data for Stiever Corporation’s maintenance costs is shown below. July August September October November December Units Produced 18,000 32,000 36,000 22,000 40,000 38,000 Total Cost $32,000 48,000 55,000 38,000 66,100 62,000 Compute the variable- and fixed-cost elements using regression analysis. Present your solution in the form of a cost equation. (We recommend that you use the Intercept and Slope functions in Excel.) EXERCISE E-1 The controller of Standard Industries has collected the following monthly expense data for analyzing the cost behavior of electricity costs. January February March April May June July August September October November December Total Electricity Costs $2,500 3,000 3,600 4,500 3,200 4,900 4,100 3,800 5,100 4,200 3,300 6,100 Total Machine Hours 300 350 500 690 400 700 650 520 680 630 350 720 6 Supplement Regression Analysis Instructions (a) Determine the fixed- and variable-cost components using regression analysis. (We recommend the use of Excel.) (b) Prepare a scatter plot using Excel. Present the cost equation line estimated in part (a). (c) What electricity cost does the cost equation estimate for a level of activity of 500 machine hours? By what amount does this differ from March’s observed cost for 500 machine hours? 12/1/2020 Annual hotel occupancy forecast to decline 29% over the next 12 months with $75 billion in projected revenue losses | TravelDailyNews International 01  TUE, DEC (/) Daily travel & tourism news portal for the international travel trade market since 1999 Hospitality Annual hotel occupancy forecast to decline 29% over the next 12 months with $75 billion in projected revenue losses Vicky Karantzavelou (/pro le/u/vicky.karantzavelou) / 19 Aug 2020  09:29  2120 Magid HTL Forecast Tracker, analyzed in conjunction with Horwath HTL, says COVID-driven declines span both business and leisure travel intentions. The ongoing impact of the COVID-19 pandemic will lead to a 29% decline in annual hotel occupancy over the next 12 months, resulting in a projected revenue loss for the industry of about $75 Billion in room revenue alone. The estimate is according to the Magid HTL Forecast Tracker, analyzed in conjunction with Horwath HTL, the hotel, tourism, and leisureconsulting brand. The forecast shows declines being driven by business and leisure travel intentions alike, but consumer sentiment for attending a meeting or conference over the next 12 months was the most telling showing a projected decline of 22%. Additionally, while travel incidence is being impacted, the research shows that frequency intention remains stable from pre-COVID levels. This suggests that while a smaller percentage plan to travel in the future, those that do plan to travel are likely to do so at a frequency that resembles their pre-COVID behavior. The Magid HTL Forecast Tracker uses a baseline of self-reported consumer travel behavior that was collected 12 months prior to the pandemic breakout in March 2020. Travel intentions subsequently are being collected at various time intervals to assess changes in intention. Both “Reach,” representing the percentage of the population who plan at least one travel experience within the designated time frame, and 1/3 12/1/2020 Annual hotel occupancy forecast to decline 29% over the next 12 months with $75 billion in projected revenue losses | TravelDailyNews International “Frequency,” representing the anticipated number of experiences for the designated time frames, are considered in the forecast. This data is then used to determine the trajectory of the expected time to return to “normal,” as well as whether there has been a signi cant upturn or downturn in travel intention from the previous wave. “The forecast shows the continuing signi cant impact COVID is having on hotel occupancy,” says Rick Garlick, Vice President, Strategy Consultant, Magid. “Currently, the forecast suggests a 39% decline in occupancy for the next month. If the average occupancy at this time of the year (summer) is 70%, this would put current occupancy around 43%.” “Many hotel companies are creating new forecast models for the remainder of this year and next without the bene t of insight into the mindset of the traveling consumer,” said John Fareed, Chairman, Horwath HTL, America. “This data will allow hoteliers to better understand the customer's intentions and create marketing offers to consumers that will position them to survive and thrive this crisis.” The most recent wave of research, which was conducted July 29th to August 2nd, shows that 71% of consumers expect to next stay in a hotel 24 months from now. That is down from the baseline behavior of 89% who said so in March and the 74% who said the same in the beginning of June. Only 56% of consumers said they expected to next stay in a hotel a year from now, compared to 62% who said so in June and 79% who reported the same in March. 2/3 12/1/2020 Annual hotel occupancy forecast to decline 29% over the next 12 months with $75 billion in projected revenue losses | TravelDailyNews International ( According to the most recent wave, consumers anticipate staying at hotels 7.26 times per year. That frequency is actually up from the June research, where consumers reported an intention of 6.74 times per year and from the “baseline” reported behavior of 6.58 times per year. The data suggests a brighter outlook for vacation rentals, with travelers having a higher trust level than with hotels. While vacation rentals are anticipated to be down for the short term, the Magid HTL Forecast Tracker suggests their recovery will be much quicker. While vacation rentals are currently forecasted to be down 64% for the last part of the summer, they are expected to return to normal in 12 months. Airline travel intentions are paralleling hotel intentions, with the current forecast more pessimistic than in the previous wave. Within the next 12 months, air travel is expected to decline 31%, in line with the expected 29% drop in occupancy. 3/3
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Article: Death of the Calorie
When it comes to the health and fitness realm, calories are a lifeboat and a source of falsities. This
article describes the significance of calorie counting to lead a healthy lifestyle. The scientific community
began to be interested in the energy quality of food in the 18th century. Antoine Lavoisier, a French
scientist, discovered that oxygen was a necessary element for combustion and believed the same was
true of nutrition. In the end, by the onset of the 20th century, calorie counting became a key piece of
diet and health counseling. Governments around the world are using calorie counting as one of the
methods to curb obesity. On the other hand, such a method fails since it ignores the different
metabolisms of some foods in our bodies. Calories tabulated from food labels are very less reliable
sometimes. It has been established that these labels can underestimate the actual calorie content by as
much as 20%. The absorption and the behavior of calories have drastically changed with time. Although

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