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  1. #13
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    After reading this I was curious as to what calories I burnt during my average working day. Just as a rough guess for past 3 days I have kept my phone in my pocket while working and my steps have been roughly 7000 steps per day. On top of that I would have the calories burnt at each job as I am an engineer. I think I need to eat a lot more when it’s time to bulk

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    Senior Member Lean_dude27's Avatar
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    Quote Originally Posted by MrRippedZilla View Post
    This topic was inspired by a combination of this thread from POB and the generic "add/drop 500 cals" advice I see across the boards. We're going to dig into data for bulking followed by cutting and, hopefully, give some more practical recommendations to help us do things a bit more accurately.

    Bulking


    The caloric surplus you calculate vs the caloric surplus actually in play can be vastly different predominantly due to the wild inter-individual differences in NEAT (non-exercise activity thermogenesis). NEAT refers to unconscious physical activity such as fidgeting, maintaining posture, etc - stuff you don't really think about doing but are actually doing.

    To truly understand how large the inter-individual differences can be when it comes to NEAT, we'll be focused on this classic study.
    - They took non-obese, 25-36yr old adults and fed them a 1,000 calorie surplus for 8 weeks. No training or exercise protocol needed.
    - Body comp was measured via DXA & energy expenditure (EE) via doubly labeled water. Both gold standard methods.
    - The results of this surplus:
    Attachment 6338
    Most would've predicted that a 1000cal/day surplus should lead to a weight gain of 2lbs per week. So, for a total of 8 weeks, we should expect a total weight gain of 16lbs. Right?
    Well, in this carefully controlled study, only 4.7kg (10.34lbs) was gained on average. That means most would've over predicted the weight gain by 54.7%. That's a big miscalculation.

    Worth noting, of that 1,000cal surplus, only 432cals were stored & 521cals burnt off. Two-thirds of the latter through NEAT - the key difference maker.
    The range of NEAT increase was a huge variable (100-700cals/day) and explains why we have some folks who are "hard gainers". If you're eating 1,000 cals more but also, unconsciously , burning off 692 of those cals then...that "calculated" surplus isn't really what you thought it was.

    Summary & recommendations

    So, how much of a caloric surplus is needed for bulking? Answer: it depends.
    For some folks, a 200-300cal increase will be plenty. For others, you may need to go 700-800. And for a select few, such as the person in the study who only gained 1.4kg (3.1lbs), 1000+ might be required.
    Trial & error is necessary. I'd advice starting on the lower-mid range (depending on how susceptible yo are to fat gain) and adjusting from there based on results.

    How much weight gain to aim for? Here you go:

    - For natural beginners I honestly wouldn't focus too much on how much to gain as long as you're gaining. Focus on progression in the gym and dieting fundamentals (protein intake, etc) and leave the rest for later down the road.
    - For non-newbie naturals an average gain of 2-3lbs/month is PLENTY. For women in the same category, go 1.5-2.5lbs/month.

    - For enhanced lifters you can easily justify going to 5-8lbs/month for guys, 4-5lbs/month for women. Obviously it will depend on dosages, how comfortable you are with stretch marks, etc, etc but even at this rate I don't see fat gain being a real issue.


    Cutting

    This section will build off some of the stuff from this thread with regards to the metabolic adaptations that occur when dieting that make the "calculated" weight loss inaccurate. In other words, you lose less than you expected with NEAT playing a big part again. So, how much less and why?

    Does metabolic compensation explain the majority of less-than-expected weight loss in obese adults during a short-term severe diet and exercise intervention? (PM for full paper)

    OBJECTIVE:
    We investigated to what extent changes in metabolic rate and composition of weight loss explained the less-than-expected weight loss in obese men and women during a diet-plus-exercise intervention.
    DESIGN:
    In all, 16 obese men and women (41 9 years; body mass index (BMI) 39 6 kg m(-2)) were investigated in energy balance before, after and twice during a 12-week very-low-energy diet(565-650 kcal per day) plus exercise (aerobic plus resistance training) intervention. The relative energy deficit (EDef) from baseline requirements was severe (74%-87%). Body composition was measured by deuterium dilution and dual energy X-ray absorptiometry, and resting metabolic rate (RMR) was measured by indirect calorimetry. Fat mass (FM) and fat-free mass (FFM) were converted into energy equivalents using constants 9.45 kcal per g FM and 1.13 kcal per g FFM. Predicted weight loss was calculated from the EDef using the '7700 kcal kg(-1) rule'.
    RESULTS:
    Changes in weight (-18.6 5.0 kg), FM (-15.5 4.3 kg) and FFM (-3.1 1.9 kg) did not differ between genders. Measured weight loss was on average 67% of the predicted value, but ranged from 39% to 94%. Relative EDef was correlated with the decrease in RMR (R=0.70, P<0.01), and the decrease in RMR correlated with the difference between actual and expected weight loss (R=0.51, P<0.01). Changes in metabolic rate explained on average 67% of the less-than-expected weight loss, and variability in the proportion of weight lost as FM accounted for a further 5%. On average, after adjustment for changes in metabolic rate and body composition of weight lost, actual weight loss reached 90% of the predicted values.
    CONCLUSION:
    Although weight loss was 33% lower than predicted at baseline from standard energy equivalents, the majority of this differential was explained by physiological variables. Although lower-than-expected weight loss is often attributed to incomplete adherence to prescribed interventions, the influence of baseline calculation errors and metabolic downregulation should not be discounted.


    Brief comments on the methodology

    - At 565-650cals/day, all from lean meat & veggies, we're talking about a crash diet.
    - Protein intake was too low for both sexes (0.94g/kg for men, 0.9g/kg for women) and really needed to be at least 1.5g/kg in this context.
    - Not all meals were lab provided, which makes compliance issues (not doing what you were told) likely. Especially on a crash diet where we knowing bingeing is more likely.

    - On the plus side, these folks did undertake 4 cardio sessions & 2 lifting sessions per week. Cardio started at 30 mins per session and gradually built up to 60mins. Lifting involved full body work (shoulder press, chest press, lat pull downs, leg press, bench, squats, upright rows & ab stuff) starting at 60%1RM and working up to 80%1RM across 2 sets to begin with before moving on to 3 sets per movement.

    Results & discussion


    Attachment 6337
    The main discovery was that weight loss was 33% less than predicted. Here's why:
    - Resting metabolic rate (RMR) went down by 228cals/day (11%) & diet induced thermogenesis (DIT), which was only predicted not actually measured, went down by 236cals/day. That's a total of 464cals/day explaining 60% of the less than expected weight loss.
    - Less lean mass was lost than predicted. The calculation predicted 79% fat, 21% LBM loss but LBM loss was actually only 18.8% (3-3.1kg out of a total weight loss of 16.1-16.3kg). That's another 30%, taking us to 90% of the difference between predicted & real weight loss being explained.
    - The last 10% can be explained by lack of compliance by the subjects. Happens all the time and more likely with a study design like this.

    Why am I telling you all of this? To show that predicted weight loss becomes incredibly inaccurate once you're deep into your cut.
    Some metabolic adaptations are inevitable and easily explained. RMR drops because you now weigh less = less energy needed to maintain lower body weight. DIT drops because you've been consuming less food = less energy needed for digestion. These are components of adaptive thermogenesis.



    Adaptive thermogenesis is also responsible for the wildly varied drop in energy expenditure due primarily to a drop in NEAT. This drop can be anywhere from 10-27% depending on a myriad of factors (how long you've been dieting, how you've been dieting, activity levels, bf% change, etc). Some, but not all, of this is avoidable - do not go batshit crazy with the cardio, do not have batshit crazy macros and do lift some real weight to keep your muscles.


    Summary & recommendations

    So, we now know why predicting weight loss in inaccurate. What can we do about it?
    - Give yourself 15-20% leeway when it comes to caloric intake. Be flexible. That means being prepared to drop it by that extra amount as you get deeper into dieting. I generically recommend starting with a 20% deficit to see weekly results and rolling on from there.
    - Drugs will help reduce the impact of all of this. Not completely but more than good enough.

    Dealing with women specifically:
    - You girls tend to have lower energy requirements than men and that may cause problems down the line. A typical smaller female may need 1800-2000cals to maintain. 20% deficit knocks that down to 1450-1600cals. 15-25% leeway knocks that down to an average of 1150-1300 cals. This is why women are more susceptible to eating disorders and health issues when dieting.

    - Health issues are coming once fat intake gets too low. Thyroid will absolutely crash if calories get too low. It's difficult to track progress over a short timeframe due to the menstrual cycle impact on water retention. My point - taking extreme action can get you girls into big trouble really quickly.
    - Cardio, not taken to excess, will help tremendously. Do not rely on diet alone.
    - Diet breaks are your best friend. Use them.
    - Do not be afraid to reach out to someone who knows what the **** they're doing. Prevention is so much easier than treatment in this situation.

    Amazing read, Thank you!!

  3. The Following User Says Thank You to Lean_dude27 For This Useful Post:

    MrRippedZilla (09-12-2018)

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