Useful Links: Part 1 | Part 2
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In the third and final instalment of this multi-part series on bodyweight data and dieting, I’m going to tackle the following topic: How do you make adjustments to a muscle gain phase based on your bodyweight?

Before we dive into this subject, I want to point out that the most important component in a muscle gain phase is your training, not your diet.
 
Your diet can only help you gain muscle if you’re giving your body the necessary stimulus to grow muscle, a stimulus that only training can offer.
 
If you eat in a caloric surplus, but you don’t train, you can eat all the protein you want… but the only mass you’ll be putting on, sadly, will be fat.
 
So, before you read the rest of this article, make sure you have a solid training program, and that you’re consistent with it.

In Part 1 of this article series, I explained:
 

1. Why your bodyweight is a useful tool in a fat loss or muscle gain phase
2. How to track and collect bodyweight data

 
In this second part, I’ll explain how to interpret and utilise your bodyweight data from the beginning to the end of a fat loss phase in order to diet and maintain the results successfully.
 
As stated in the previous article, the only data that matters in the context of muscle gain and fat loss is your bodyweight average per week and/or per month, which is what I’ll be referring to in the rest of this blog post.

​To achieve any goal, you need to collect and analyse data.
 
Without it, you wouldn’t know
 

• If you’re making progress
• The rate at which you’re progressing
• What is working
• What is not working

 
In other words, without data, you can’t make the right adjustments to your plan.
 
You could still accomplish your goal, but you’d probably run into one of these two scenarios:
 

1. You can’t maintain the achieved results because you made excessively drastic decisions that you can’t sustain in the long term
2. You take an unnecessarily long time to reach your target because your decisions were too conservative

 
However, having data isn’t enough to secure effective and efficient results. Inconsistent and unreliable data, or data that you’re interpreting incorrectly, can be just as damaging as the absence of it.
 
For example, with fitness goals such as fat loss and muscle gain, a common mistake is to focus too much on day-to-day fluctuations on the scale, which are normal, and too little on your average weight increases or reductions over a longer period of time, which is what matters most for these fitness endeavours.
 
This multi-part article series will provide you with the knowledge to assess your bodyweight data in order to achieve fat loss or muscle gain.
 
In Part 1, we’ll delve into:
 

• Why you need to weigh yourself
• How to weigh yourself to get consistent and reliable data
• How to record and assess this data

 
In the rest of the series, we’ll cover how to adjust your diet and training protocol in response to changes in bodyweight.

Reflecting on the past two years of coaching people online and in person, I created a list of the advice that my clients have found most helpful in the pursuit of their goals, but that seems to be underrated in an online fitness space rife with detox teas and abs-blasting workouts.
 
This article breaks down these tips into the following categories:
 

1. Nutrition
2. Training
3. Habits
4. Mindset

​Training and dieting plateaus are normal to an extent, but they can cripple your results if they are too frequent.
 
Being able to assess your training in an objective manner and to identify why you’ve plateaued, is going to be essential to chart a path out of your current funk and to create a preventative strategy for the future.
 
This article breaks down the following common reasons for a plateau and the troubleshooting approach to each one:

1. The structure of your current program
2. Too much variety
3. Not enough variety

 
Start with the reason that seems most likely, apply the suggestions for that alone, then wait at least four to six weeks before re-evaluating whether you’re still “stuck”.

Why not try all of these solutions at once?
 
Though it might seem slower to produce results, this trial-and-error approach is the most effective in the long run. Altering too many variables at once will make it challenging, if not impossible, to figure out which change was the most helpful.
 
Now, let’s tackle the first reason: