As previously covered I've tracked my diet and various health metrics consistently over the last two years, with some sporadic data from before then as well, I export all of the Cronometer data to my Excel sheet, as well as the various Fitbit and qualitative lifestyle factors that I track. As mentioned before I'm not particularly worried about the absolute value of some of the Fitbit markers but rather their trend or correlation with other things, based on my experience for myself the Fitbit HRV for example has been quite a reliable marker of my overall recovery and wellbeing. From here each day is organized as a row of data with the various columns dedicated to each nutrient or health metric. Here are all the correlations as I normally view them right now. A lot of data and information! Don't jump to any conclusions yet! The top row shows how many data points are in that column, followed by which metric is in the column, and then I have my correlation values which are calculated based on that metrics correlation to the health metric that I'm interested in such as RHR or HRV, followed by the name of the metric again and then the maximum, average, and minimum values of that metric. So for example; the first column tracks calorie consumption, and here shows a negative correlation with the next day HRV with an RSQ (R^2) value of 0.059, a magnitude of 29%, and a significance of 1.7%. The magnitude is calculated from the slope of the correlation, multiplied by the maximum variation/difference of the metric in question (Calories), and converted to a percentage of the maximum variation of the metric I'm analyzing (HRV), and the significance is the magnitude multiplied by the RSQ. Each row is color coded using the automatic excel cell coloring feature with the top 10% and bottom 10% being red or green, and values close to zero being clear. (slope cells not colored as I usually keep it hidden). Essentially this shows that my calorie consumption has a maximum explanatory power of 29% for my next day HRV, however, the correlation is quite weak with the low RSQ of only 0.059 leading to a "significance" of 1.7%. Now I do realize this is not necessarily the statisical approach used, however it works for my purposes and allows me to quickly see which nutrients or lifestyle factors might need a further look. So on that note; how does HRV and calorie, and carbohydrate consumption correlate? Using my data from cronometer (which excludes a few of the data points from older data before I used cronometer) we can see the RSQ increases slightly. There does appear to be a correlation between total calorie consumption and next day HRV from looking at the graph, and in my experience my highest HRV days are almost always while I'm eating at maintenance or in a slight deficit. However recently I've started to question the strength of this correlation, as I've tested finishing eating my food earlier in the day, and this seems to significantly help, oftentimes I would've in the past eaten my last large meal before bed around 10-11 pm and sleep around midnight, however since moving my last meal-time to finish between 7 or 9 pm, I've been able to eat a higher amount without having such a reduction in HRV.. This has only been tried recently, and I'm currently in a weight reduction phase so it will be some time before I can test my theory out properly. For reference I usually have breakfast around 10-11 am. Lets move to a stronger metric, and one that I find interesting as well, Net carbohydrates-from my cronometer tracking. Net carbs have a stronger correlation even than the total calories consumed, which is interesting given that my total calorie consumption is highly correlated to my carbohydrate consumption, as when I eat more it is almost always in the form of carbohydrates. This might indicate that carbohydrates seem to have a more negative effect on my HRV than the other macros. Interestingly fructose and glucose seem to have no correlation whatsoever with HRV, and it is mostly driven by starch and added sugars in the form of sucrose. This could be due to the endotoxin effect when eating significant amounts of carbohydrates leading to gut flora growth and subsequent death. A commonly known strong factor that suppresses HRV is alcohol consumption, and this is verified in my data where alcohol consumption has a magnitude of 43%, and a significance of 3.2%. Now despite the RSQ of 0.07, I do believe the effect of alcohol on HRV is very significant. Clearly, there is a large spread for the days where I do not consume alcohol due to things such as activity and being sick (the strongest factor in poor HRV), leading to the low RSQ. However, as alcohol is increased HRV is always seen to significantly reduce. For now, I will leave this post as is, please let me know in the comments, or message me directly if there is anything I should cover or statistical analysis you would like to see using my data. In the future I will cover some specific metrics, please consider this post an introduction into the correlation and data tracking that I do. Thanks, Andrew

As mentioned in my previous Blog post, I've been tracking my diet since Mid-2021 until now, along with other health metrics. In this post I will cover: My average diet since June 2021-May 11, 2023 My weight changes and corresponding diet changes. Some interesting graphs related to these topics and discussion of these. First thing to note; I've kept the fully dedicated excel sheet for weight tracking separate from the full sheet that contains all of my diet and health metric tracking, simply for ease of use, and as well because the one that contains the full dataset has only one row for each day, while the weight tracking can have multiple rows for each day for when I weigh myself multiple times in a day. I show the full sheet in the next blog post. Here is a screenshot from the weight/calorie tracking sheet being discussed today. Each row has the corresponding date, and when multiple weights are used for the same day, the calories and macros are only written on the first row. Next, I have the HRV, which is honestly not really relevant to this sheet ("Diet") and is truly covered in the full "Correlation" sheet. Then I have my weight listed, Each day I would try to weigh myself in the morning, and if not possible would weigh myself another time of day. Days with multiple weights usually have a morning weight and an evening weight, and for my purposes distinguishing between the two is not important. The next column that has data that I have to enter is the % bodyfat, which is simply taken from the Renpho bodyfat % scale. Its important to note that the scale shows some pretty large differences based on my weight, showing that it doesn't deal with water weight, or food weight super accurately. Lastly, I have a Note column. The dark green columns are automatically calculated columns. The calculated weight column is based on the previous day's calculated weight, my estimated metabolic rate, which is simply how many calories I've burned on average per day, and that day's calorie intake. I've used the estimate of 3500 calories per lb of body fat to calculate the weight changes. This doesn't deal with lean body mass changes or water weight, however, it's nice to show the effect of diet calories on my weight easily. In order to calculate my metabolic rate or calorie needs per day, I choose a section of my actual weights from the scale that is at least two weeks long and use the slope of my weight to calculate the average calorie deficit or surplus. I do not use any calorie calculator or online estimator. Use of your own weight changes over a sufficiently long period of time assuming no changes to your diet is IMO the most accurate way to estimate your caloric needs. This need can also change significantly over time due to lifestyle, diet, and other factors. I've had times when my maintenance calories were only 2300 Kcal/day, and currently, it's up to 3300 kcal/day. The other dark green columns; Deviation is the difference in measured weight to calculated weight, Lean Body Mass is measured weight *(100-% bodyfat), and perceived leanness is something I came up with which is Lean Body Mass divided by % bodyfat. This is my favorite graph using this data, and it also shows the cells I use to calculate the maintenance calories. Using the intercept and slope functions, and the X-values as the date, Y-values as my actual body weight, this yields the change in weight per day in lbs, and using the estimate of 3500 calories/lb of bodyfat, this gives the average surplus or deficit. Then using my average caloric intake for the same selected time period, these can be summed to give the maintenance calories. You may also notice the standard deviation in weight, this is calculated using the Excel STDEV() function on the previously mentioned Deviation column. We can see that it's 1.8 lbs, and 2 std dev is approx 3.6 lbs, which means it's completely reasonable to have a weight fluctuation from -3 lbs to +3lbs from the actual weight within a very short time frame. This highlights the importance of using an adequate number of days' average weight when calculating your current weight or your caloric needs. Otherwise, it is very easy to be led to believe you are gaining or losing weight at a completely unreasonable pace. These large changes in weight can be easily due to hydration, carbs (leading to glycogen storage loss or filling), lack of bowel movements, salt intake, creatine etc. Finally, the current weight is taken using the Y=mx+b formula with the Intercept and slope, as well as the current date, or alternatively the calculated weight, these two values being equal if everything is set up properly. Currently, I'm cutting weight for an arm wrestling competition on May 13 where I have to be <154 lbs for the weight class, therefore I've been in a pretty severe caloric deficit, as well I'm cutting out salt and carbs for the last two days, and finally going to dehydrate to reach the goal weight. I may go into a weight-cutting post eventually, but I'll just leave it at that for now. From the start of the chart you can see I had been losing weight slowly, this being after a period of about 2 years of lack of significant weight training or cardio-based exercise, throughout which I had maintained my weight, but progressively lost muscle and gained fat. At the time I choose a small calorie deficit in an attempt to keep it easy to maintain. It did work, however looking back it did have an effect on my metabolism which became significantly reduced. During that period of time cutting weight, I bike rode a lot and was doing basic bodyweight strength training, which kept my maintenance calories at approx 2950/day. This period ended at the same time that I had severely injured myself, (shattering my right scapula), resulting in a 5 day hospital stay, 3 stainless steel plates and 16 screws, and a partially collapsed lung later I was now in recovery mode, so I essentially became sedentary. This resulted in my maintenance calories reducing all the way down to approx 2350/day. Likely due to becoming sedentary, as well as possible metabolic changes from the period of weight loss. Also note that prior to this time I had never attempted to lose weight in my life, and was essentially always either trying to maintain my weight or gain more weight for muscle-building purposes. As a child, my parents cooked all the meals and eating out was extremely rare, however, we did use canola oil (common) or olive oil (rarely), and most of our meals were such things as stir-fried, chicken with rice and veggies, pork-chops, fruit, and my breakfast was almost always cereal, lunch being some kind of sandwich maybe with peanut butter and jam etc. IMO a healthy diet compared to average but not perfect looking back. When I moved out for University (2015), for first year I tried gaining as much weight as possible by eating as much cafeteria food as possible while choosing relatively clean options, I successfully gained approx 10 lbs that year without gaining much fat. From second year onward I cooked almost all of my own food, and at this point I cut out seed oils, I used butter or olive oil only for cooking. When I did eat out I would have something like Subway. All of this to say; although my diet was pretty good, I probably did have stored Linoleic acid/PUFA, which when released during the prolonged diet phase in 2021 could have contributed to my reduced metabolic rate in addition to the effect of becoming sedentary. This is shown in the above graph; orange shows the estimated maintenance calories, and blue shows the individual days of calorie intake throughout the same time period as the weight graph, as well as moving averages of my calorie intake. After the injury, I ate naturally in terms of quantity, however, I did focus on protein, avoiding any seed oils ( now cutting out even olive oil ), dairy, meat, and fruit, with the vegetables tomatoes, peppers, occasional potatoes, and carrots. Once my injury healed enough and my physio was mostly done I started weight training again which coincided with gyms opening up again in Ontario in early 2022, My goal being to return to my prior best strength in all metrics, and I started the sport of arm-wrestling. (previously my best shape was at the end of 2019/beginning of 2020 before covid, when my squat/bench/deadlift was 395/250/455 lbs each, and I was able to boulder climb V6, and started to be able to Lead climb 5.11.) I ate as much as I wanted to, while trying to keep what I consider a healthy diet, my weight increased slowly as I gained muscle, and from then on I've been gaining muscle mass/maintaining muscle mass, with the occasional very short weight cut for competition purposes, which can be seen in the weight graph. The weight graph visualizes both my actual weight, my calculated weight, and two moving averages of my actual weight. The high agreement between the shorter-term moving average and the calculated weight is enough evidence for me that this method of tracking and calculating my weight is sufficiently accurate. Next, I will briefly show my average diet macros and micros from Cronometer over this two-year period. Please let me know if you have any specific questions about the weight tracking or anything else! For the macros in Cronometer I set them based initially on what I naturally wanted to eat after tracking them for some time, and since then have made small adjustments; my protein goal is 27.%, carbs, 33.8%, and fat 38.8%. I set the BMR based on the maintenance calories and set the activity level to zero to avoid any weird results from the activity tracking from the Fitbit. I chose the highlighted nutrients based on the ones that I considered most relevant based on my research as well as ones that may be lacking in my diet or that I wanted to maximize. Most of the nutrient targets I left at the RDA with the exception of; starch, sodium, zinc, cholesterol, monounsaturated fat, polyunsaturated fat, (Omega 3, Omega 6), saturated fat, and trans fats, as well as setting a goal for the following amino acids; alanine, arginine, aspartic acid, glycine. These last ones were chosen based on the results of my correlation sheet which I will get into in future posts. Some basic reasons, which may be individually discussed in future posts. Starch: Based on my correlation sheet Sodium: Increased from the RDA because of physical activity, as well as based on some research etc. Zinc: Increased the max threshold because of the occasional intake of high amounts of copper from Liver or spirulina, as I attempt to keep these balanced. Cholesterol: set to 300mg as this is the amount that has the lowest all-cause mortality when not adjusted for other factors, I can find the study upon request. Set my upper limit to 1500 mg. Monounsaturated and Saturated fat, set a minimum goal of 20g MUFA, and 30g SAT, which is way below my usual intake, but I just wanted the nice green bars to show up haha PUFA set to 8g as an achievable goal to aim for daily considering i have a relatively high fat diet. -Omega 3 set to 2.5 g, I do believe omega-3 has some benefits, and it does appear to have anti-inflammatory effects, so I increased it to help achieve a more balanced Omega-3:Omega-6 ratio that would be more historically accurate. -Omega 6 set to 5 g, It is very difficult to get below this amount even avoiding all seed oils, even beef contains some. -Trans fats- just increased it from zero slightly because my diet is high in dairy products, and they contain some potentially beneficial trans fats. For now I think this post is long enough, I can go into more detail on any point here or things such as which foods contribute the most to certain nutrients etc in future posts! Just let me know. Thanks for reading!