Studies are typically relatively short (weeks to months). However, we often want to know what happens with longer-term adherence to interventions such as a training protocol or a diet.
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Therefore, data from studies needs to be extrapolated; make assumptions what would happen if the interventions would be continued.
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Extrapolation requires assumptions. Essentially, we’re trying to make an educated guess what would happen. This guess should be based on the available data and logic.
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For example, if a study observes 5 kg fat loss after 12 weeks on a diet, that does not necessarily mean you can expect an additional 5 kg fat loss in the next 12 weeks.
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It is important to realize that extrapolation is heavily influenced by personal bias.
For example, if you are a proponent of a certain training style or diet, it is more likely that your extrapolation is (too) optimistic.
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It has been shown that a ketogenic diet decreases exercise performance, while a high carbohydrate diet increases exercise performance during a 3-week training program.
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A common critique is that this study gives a misleading picture, because the ketogenic diet is claimed to require a longer adaptation phase. Once the athlete is fully keto-adapted, the superiority of the ketogenic diet would become apparent (scenario 4).
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But scenario 4 is a drastic change compared to the observed data, requires several assumptions, and therefore the burden of proof would be on those would claim this scenario is most likely.
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An assumption of this scenario is that keto-adaptation requires >3 weeks, but there is no convincing supporting evidence.
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The second assumption of this scenario is that further keto-adaptation would drastically improve exercise performance. However, it could also be argued that further keto-adaptation would only further decreases exercise efficiency and performance (scenario 1).
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It is up to each individual to extrapolate how they see fit. The more an extrapolation diverges from the observed data, the more assumptions are involved. If no strong evidence for these can be provided, it may be a sign that the extrapolation is mostly wishful thinking.
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The ketogenic diet restricts carbohydrates with the purpose of lowering insulin levels. Insulin is a hormone that is involved in fat metabolism. Therefore, some suggest that a ketogenic diet is very effective for fat loss.
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This study investigated whether a ketogenic diet increases fat loss compared to a high-carbohydrate diet in overweight and obese men.
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Our new paper:
Pre-sleep Protein Ingestion Increases Mitochondrial Protein Synthesis Rates During Overnight Recovery from Endurance Exercise: A Randomized Controlled Trial
Prostate cancer is one of the most common types of cancer. An effective treatment is androgen deprivation therapy (ADT), which lowers testosterone levels to castration levels. However, such low testosterone levels tend to result in a loss of muscle mass and strength.
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Our study investigated the effect of resistance training with or without protein supplementation on muscle mass and strength in prostate cancer patients undergoing ADT.
Exercise and protein ingestion are the main factors that stimulate muscle protein synthesis (the main process driving muscle adaptations such as growth).
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It has been reported that athletes are more likely to consume excessive amounts of alcohol, especially as part of binge-drinking practices in team sports. Can these practices affect muscle protein synthesis?
Muscle growth occurs when muscle protein synthesis exceeds muscle protein breakdown. Resistance exercise stimulates both muscle protein synthesis and breakdown. Ingestion of protein further increases muscle protein synthesis and is needed to achieve positive protein net balance.
In young adults, 20 g of high-quality protein seems optimal to stimulate post-exercise muscle protein synthesis. However, is this also true for older adults?