我们都度过了圣诞节(Christmas)假期，并且正在考虑如何弥补两周生活方式的突然变化。大家似乎都在担心假期长胖的问题，所以我决定先量化一下圣诞节和新年(Christmas and New Year)。在从关注我的活动、睡眠和食物(sleep and food)中得到数据后，我得出了一些非常有趣的结论。在本文中，我将与您分享2014 年 12 月(December 2014)的数据，并与今年其余时间进行比较。令人惊讶的是，在查看实际数据时，与一年中的其他时间相比，假期实际上并没有那么重要。这是我学到的：
我在跟踪什么？
因为我知道我将在12 月(December)搬到新公寓，所以我选择将自己限制在几种对我来说已经很容易跟踪的指标：
-
与食物相关的指标(Food-related metrics)：我保留了一份详细的食物日志(food log)，重点关注主要的常量营养素——碳水化合物、蛋白质、纤维和脂肪——除了卡路里
-
睡眠相关指标(Sleep-related metrics)：睡眠质量(sleep quality)（由我的BodyMedia Link 臂章(BodyMedia Link armband)测量）、睡眠持续时间和(duration and time)入睡时间
-
(Activity-related metrics)我的BodyMedia Link 臂章(BodyMedia Link armband)报告的与活动相关的指标（步数、燃烧的卡路里）
-
(Weight measurement)早晚测量体重
请注意，跟踪所有内容时都会出错。从重量（秤可能有误差）到估计食物数量和BodyMedia Link 传感器(BodyMedia Link sensor)的测量值。但是，这仅意味着在某些情况下数据没有正确关联，正如您将在本案例研究(case study)中看到的那样。尽管有所有错误，仍然可以得出有趣的结论。
初步结论
我的主要结论是，12 月(December)的生活方式与我在今年余下时间的生活方式基本相同，假期加剧了一点（10-15%）。因此，如果你体重增加，那是因为你一直在保持今年余下的不健康生活方式，并且只会放大它：多吃一点，多睡一点，少动一点。但是我们大多数人只是在这段时间里才倾向于关注自己的体重，因此产生了问题出在假期的错误印象。
在此期间我跟踪了自己的体重，尽管实际上我吃的东西没有限制，但我在假期期间只增加了 1 公斤（2 磅），现在正在慢慢减少。乍一看这令人难以置信——假期只比一年中的其他时间差 10% 吗？看数据，确实如此。
这是2014 年最后 9 个月的汇总数据(aggregated data for the last 9 months of 2014)。请注意(Notice)，尽管我燃烧的卡路里低于摄入的卡路里，但体重仍在缓慢下降。那是因为我最近更积极地高估了食物（将不得不停止这种情况）。我也开始多喝水，少吃钠，使我体内(body decrease)的水总量减少。
数据的微小变化有时反映了生活的巨大变化。现在，让我们浏览一下前面提到的各个指标类别（食物、睡眠、活动、体重），以了解它们在整个假期期间(holiday season)的变化情况。
我认为整个十二月(December)都是假期(holiday season)。我选择了整个月，因为通常上半年比较忙，以便安排下半年。所以整个月都是我们日常生活的中断。
食物
当我看到数据时，我的第一反应是：“还不错！”。我12 月份(December)的平均卡路里摄入量约为每天 3700 大卡，而过去 3 个月（9 月至 11 月）(September-November)每天(day average)的平均热量为3330 大卡。有趣(Interestingly)的是，增加了 11 % increase！当然，从绝对数字来看，这是非常重要的。
如果你想知道为什么我摄入了这么多卡路里，那是因为我也燃烧了很多（由BodyMedia Link测量）。我目前的体重目标是在接下来的 3 个月内每月减掉大约 1 公斤（2 磅），所以我会更加注意。在过去的几个月里，我比平时更积极地高估了我的食物摄入量，所以实际数字一开始并没有那么高。
撇开卡路里不谈，其他食物指标看起来相当不错。自 2014 年5 月(May 2014)以来，我的脂肪摄入量一直存在问题，因为我的脂肪摄入量一直在卡路里摄入量(calorie intake)的 40% 到 45% 之间。我知道，这很糟糕，从长远来看会导致心脏病风险增加。(heart disease risk)这是因为以下变化：
- 我练习间歇性禁食(practice intermittent fasting)（只吃早餐和午餐(breakfast and lunch)）；这意味着我正在吃热量更高的食物。那些往往含有更多的脂肪。
- 我在 2014 年6 月(June 2014)的某个时候放弃了肉类（鱼肉除外）；原因是我不相信你能找到可靠、稳定的健康肉类来源。这进一步使我的脂肪摄入复杂化，因为我正在努力寻找一种低脂肪的方法来将足够的蛋白质带入我的饮食中。它们是组织生长(tissue growth)所必需的，我目前正在从蔬菜和奶酪中摄取它们。
- 我开始每天吃一个鳄梨（好吧，在过去的 9 个月里几乎 - 大约 200 个）。它含有相当多的不饱和脂肪。尽管它被认为可以降低心脏病风险(lower heart disease risk)，但总体指标仍然存在很大问题。
但好消息是，12 月份(December)的脂肪摄入量并没有比平时差。自 2014 年11 月(November 2014)以来，我一直密切关注的另一件事是钠摄入量。你看，我几个月来一直在吃咸的食物而没有太注意。过多的钠会导致心血管疾病的风险增加：
- Wasa富含纤维的脆面包，以帮助我保持高纤维摄入量。但我从来没有考虑过检查盐。
- 橄榄油椒盐脆饼(Oil Pretzels)是为了通过引入一种健康的零食（或者我是这么认为的）来停止我的卡路里减肥。
我在2014 年 12 月(December 2014)从我的饮食中消除了这两种食物，你可以看到，即使我在某些日子大吃大喝（我的个人记录是圣诞节那天(Christmas day)吃了 7000 大卡），月平均钠(sodium average)含量要好得多。
由于我和我的妻子都不再吃肉，除了鱼，我们必须非常小心我们的蛋白质摄入量。让我们高兴(Happily)的是，我们的妈妈在我们假期回家时注意到了这一点，我们参加的大餐都富含鱼肉。所以在12 月(December)，我的蛋白质平均摄入量为 13.26%，这是我戒掉其他肉类后的最高值。
对我来说，记录一场盛宴让我感觉更放松，更享受它。这有点违反直觉，但原因是我知道我现在可以评估我的过剩并将其与今年剩余时间发生的情况进行比较。
最后，即使节日食品大多是熟食且缺乏纤维，我仍然设法在12 月(December)平均每天拉出 45 克纤维。尽管我停止吃瓦萨脆饼(Wasa Crispbread)。
睡觉
我密切关注的两个睡眠指标是：睡眠分数(Sleep Score)（0-100，越高越好）和入睡时间(Time to Fall Asleep)。我通过在睡觉时佩戴BodyMedia Link来测量两者。睡眠分数(Sleep Score)是您睡觉的时间（根据传感器）与在床上花费的时间之间的比率。一晚的数据可能如下所示：
虽然很容易理解我为什么使用睡眠评分(sleep score)，但入睡时间对(Time to Fall Asleep)我来说很重要，因为我是一个过度活跃的人，难以入睡。为了改善这一点，我采用了许多不同的技巧：睡前不戴眼镜从我的Kindle阅读（让我的眼睛疲倦），空腹睡觉，这样我晚上的能量更少（间歇性禁食），避免完全是咖啡。随着时间的推移，它已被证明是衡量我做得如何的一个很好的衡量标准。
2014 年 12 月，我的睡眠评分(Sleep Score)平均值恢复到 86%，这是我自初夏以来从未达到的出色表现，当时我每天在新鲜空气中步行大量英里，更加放松。尽管在寻找新公寓并将所有东西搬进去时有压力（我们用它们装满了一辆慷慨的面包车），但得分还是不错的。
入睡(Time to Fall Asleep)时间处于历史最低点。基本上(Basically)，只要我有时间上床，我就会立即入睡。我根本没有精力从我的Kindle阅读，这是一个好兆头，但它让我想起了我珍贵的书籍并改变了我的阅读习惯(reading routine)。
睡眠时间与过去 2 个月（ (Slept)10 月和 11 月(October and November)）大致相同- 370 分钟。这明显高于9 月份(September)（有 357 个）。9月(September)开始做Programmer Fitness的工作，当时还不知道如何安排时间。这导致了很多深夜，在月底，我决定多睡一会儿，安排更规律的作息时间。
我的睡眠分数(sleep score)通常是 85%，这意味着我在床上花费了 7 个小时，并且睡了其中的 6 个。这是包括周末和节假日的平均值。如果这听起来像一个低值，请知道我试图增加它，但不相信结果。
活动
在开发Programmer Fitness时，我注意到一个普通人可以控制的唯一有意义的燃烧卡路里量与我们走的步数成正比。下面是来自实际客户的两张图表来证明这一点。第一个是将燃烧的卡路里分解为基础代谢率(Basal Metabolic Rate)、中等活动和高强度活动(Moderate Activity and Intense Activity)（加起来就是燃烧卡路里的总数）。
第二个是适度活动(Moderate Activity)与步数的可怕关联。
这让我比你想象的更关心我的步数。令人惊讶的是：我在12 月(December)的步数并不是 2014 年最低的。可以很简单地解释：
- 这个月我们不得不搬到新公寓。这意味着在选择我们搬进去的那一套之前要先看 15 到 20 套公寓。这需要大量的开车和步行。
- 新公寓比旧公寓大很多。我的妻子开玩笑说我们将在新的步骤中获得更多的步骤。
- 实际上移动我们的东西也需要大量的移动。
这就是我设法达到建议的每天 10k 限制(day limit)的方法，尽管12 月(December)很忙，外面气温很低。好消息！
在燃烧的卡路里方面，与前几个月相比略有下降。我们的Qwan Ki Do训练暂停了 2 周，我没有时间自己弥补。事情在一月份(January)恢复正常！
重量
我故意把体重留在最后有两个原因：第一，因为我没有在假期量化自己来控制体重。我也过于激进地高估了摄入量。我心里早就知道了，但看到数据说服我改变我的行为。
我的总摄入量高于燃烧的总卡路里，但我仍然减轻了一点体重。
这有两个原因：
- 我停止吃富含钠的Wasa Crispbread和椒盐脆饼。(Wasa Crispbread)所以我开始排除更多的水，这些水一直留在我的身体里。
- 激进的高估。从长远来看，即使是额外的 10% 也很重要。
从现在开始，我将尝试更加注意我的日志记录。
今年十二月我学到了什么
我很高兴我做到了。我从没想过数据会如此丰富。我的主要结论是，尽管我们在12 月(December)的行为差异很大，但我们吃的东西、睡眠方式和活动量的总体差异很小。当然，在大局中。
你长胖不是因为圣诞(Christmas)假期，而是因为你现有的生活方式。正如我所提到的，我们倾向于在这段时间更密切地关注我们的体重，这就是我们注意到这一年发生的事情的时候。
在量化方面(quantifying front)，我会更加保守地高估食物。乍一看，9 个月似乎是很多数据，但我很快意识到您需要更多数据。
我希望看到的一个指标是饮水量(water intake)。我最近开始记录它，但我还不是很擅长。我注意到，在宴会上，我们同时吃得更多，喝得更少。有一种理论认为，大多数时候我们的饥饿感实际上是口渴(fact thirst)，但我们无法正确区分它们。
我想通过祝你量化新年(Quantified New Year)快乐来结束这篇文章！愿(May)你所有的指标都能在 2015 年迎接挑战。
Andrei Ismail 是一位经验丰富的软件工程师，拥有创业经验和人工智能博士学位。他在 6 个月内减掉了 50 磅，目前正在为极客制定健身教练计划，可在WEIGHT LOSS FOR ENGINEERS获得。(Andrei Ismail is an experienced software engineer with startup experience under his belt and a PhD in Artificial Intelligence. He has lost 50 pounds in 6 months and is currently working on a fitness coaching program for geeks, available at WEIGHT LOSS FOR ENGINEERS.)
Christmas Doesn't Make You Fat, The Rest Of The Year Does
We've all been through the Christmas hоlidayѕ, and are thinking of ways to compensate for thе sudden change in lifestyle for 2 weeks. Everyone seems to be worried about the weight put on during the holidays, so I decided to have the first quantified Chrіstmas and Nеw Year. After getting the data from paying attention to my activity, sleep аnd food, I have drawn some very interesting conclusions. In this article, I will share with you the data from Decеmber 2014, compared to the rest of the уear. The surprising insіght is thаt the holidays are, in fact, not that significant compared to thе rest of the year when looking at the actual data. Here's what I have learned:
What Was I Tracking?
Because I knew I was moving to a new apartment in December, I chose to restrict myself to several types of metrics that were already easy to track for me:
-
Food-related metrics: I keep a detailed food log, that focuses on the main macronutrients - carbs, proteins, fibers, and fat - in addition to calories
-
Sleep-related metrics: sleep quality (as measured by my BodyMedia Link armband), sleep duration and time to fall asleep
-
Activity-related metrics, as reported by my BodyMedia Link armband (number of steps, calories burned)
-
Weight measurement during the morning and evening
Note that there is an error in tracking everything. From the weight (the scale might have an error), to estimating the food quantities and the measurement of the BodyMedia Link sensor. However, that only implies that the data is not correlated correctly in some situations, as you will see in this case study. Despite all errors, interesting conclusions can still be drawn.
Preliminary Conclusions
My main conclusion is that the lifestyle in December is mostly the same lifestyle that I have during the rest of the year, exacerbated a bit (10-15%) by the holidays. So if you gain weight, it's because you are maintaining the unhealthy lifestyle from the rest of the year and only amplifying it: eating a bit more, sleeping a bit more, moving a bit less. But most of us only tend to pay attention to our weight during this period, so a false impression that the problem lies in the holidays is created.
I tracked my own weight during this period, and even though there was literally no limit on what I ate, I only gained 1 kg (2 lbs) during the holidays, which is slowly coming off now. That is unbelievable at first sight - are holidays only 10% worse than the rest of the year? Looking at the data, it is true.
Here is the aggregated data for the last 9 months of 2014. Notice how the weight is still trending down slowly, despite the fact that my calories burned are below calories eaten. That is because I have been over-estimating food more aggressively lately (will have to stop that). I also started drinking more water, while eating less sodium, making the total quantity of water in my body decrease.
Small changes in the data sometimes reflect huge changes in life. Now, let's go through the individual categories of metrics mentioned before (food, sleep, activity, weight) in order to see how they varied throughout the holiday season.
I considered the whole month of December as the holiday season. I chose the whole month because the first half is usually busier in order to make arrangements for the second half. So the whole month is a disruption from our normal routine.
Food
My first reaction when seeing the data was: "It wasn't that bad!". My average calorie intake for December was about 3700 kcal per day, compared to the 3330 kcal per day average for the past 3 months (September-November). Interestingly enough, that is an 11% increase! In absolute numbers, it's quite significant, of course.
If you're wondering why I'm eating so many calories, it's because I also burn a lot (as measured by the BodyMedia Link). My current weight goal is to lose about 1 kg (2 lbs) per month for the next 3 months, so I will pay more attention. During the last months, I've over-estimated my food intake more aggressively than usual, so the actual numbers are not that high to begin with.
Leaving calories aside, the other food metrics look pretty good. I've been having a problem with fat intake since May 2014, as my fat intake has been somewhere between 40% and 45% of the calorie intake. This is bad, and leads to increased heart disease risk on the longer term, I know. This is because of the following changes:
- I practice intermittent fasting (eating only breakfast and lunch); this means that I am eating foods that are denser in calories. Those tend to contain more fat.
- I gave up meat (except for fish meat) sometime in June 2014; the reason is because I do not trust that you can find a reliable, constant source of healthy meat. This further complicates my fat intake, as I am struggling to find a low-fat way to bring enough proteins into my diet. They are required for tissue growth, and I am currently taking them from vegetables and cheese.
- I started eating one avocado per day (well, almost - about 200 in the past 9 months). It contains quite a lot of unsaturated fat. Even though it is believed to lower heart disease risk, the overall metric is still quite problematic.
But the good thing is, fat intake during December wasn't worse than the usual. Another thing that I had been closely monitoring since November 2014 was sodium intake. You see, I had been eating salty foods for months without paying too much attention. Too much sodium leads to increased risk of cardiovascular disease:
- Wasa fiber-rich crispbread in order to help keep my fiber intake high. But I never considered checking the salt as well.
- Olive Oil Pretzels in order to halt my weight loss with its calories by introducing a healthy snack (or so I thought).
I eliminated both from my diet in December 2014, and you can see that even though I was feasting on certain days (my personal record being 7000 kcal eaten in the Christmas day), the monthly sodium average is much better.
Since my wife and I are both not eating meat anymore except fish, we have to be very careful about our protein intake. Happily for us, our moms took notice of that when we visited our families for the holidays, and the big meals we took part in were rich in fish meat. So in December, I had a 13.26% average intake from proteins, highest ever since I quit eating other types of meats.
For me, logging a feast makes me feel more relaxed and enjoy it more. This is a bit counterintuitive, but the reason is that I know I can now evaluate my excess and compare it to what happens to the rest of the year.
Finally, even though holiday food is mostly cooked and lacks fiber, I still managed to pull of a 45 grams per day of fiber average in December. Despite the fact that I stopped eating the Wasa Crispbread.
Sleep
The two sleep metrics that I closely follow are: Sleep Score (0-100, the higher the better) and Time to Fall Asleep. I measure both by wearing my BodyMedia Link with me to bed. The Sleep Score is the ratio between the time it you were sleeping (according to the sensor), and the time spent in bed. One night's data might look like this:
While it's easy to understand why I use the sleep score, Time to Fall Asleep is important to me because I am a hyperactive person with problems falling asleep. To improve that, I employ a number of different tricks: read before bedtime from my Kindle without my glasses on (to make my eyes tired), sleep on an empty stomach so that I have less energy during the evening (intermittent fasting), avoid coffee altogether. Over time, it has proven a very good measure of how well I am doing.
During December 2014, my Sleep Score average was back up to 86%, a stellar performance I had not achieved since the beginning of summer, when I was walking daily plenty of miles per day in fresh air and relaxing more. The score was good despite the stress associated with searching for a new apartment and moving all the things into it (we filled a generous van with them).
The Time to Fall Asleep was at an all-time low. Basically, whenever I had time to hit the bed, I would fall asleep immediately. I did not have energy to read from my Kindle at all, which was a good sign but it got me thinking about my precious books and changing my reading routine.
Slept minutes were about the same as the last 2 months (October and November) - 370 minutes. This was significantly higher than September (which had 357). In September I started working on Programmer Fitness, and I did not know how to organize my time yet. This led to a lot of late nights, and at the end of the month, I decided to get a bit more sleep and a more regular schedule.
My sleep score is usually 85%, which means I spend 7 hours in bed, and sleep 6 of them. That is an average which includes weekends and holidays. If it sounds like a low value, please know that I tried to increase it, but wasn't convinced about the results.
Activity
While working on Programmer Fitness, I noticed that the only meaningful amount of burned calories an average person can control is proportional to the number of steps we make. Below are two graphs from an actual client to prove that. The first is the breakdown of burned calories into the Basal Metabolic Rate, Moderate Activity and Intense Activity (adding up to the total number of burned calories).
The second one is the scary correlation of Moderate Activity with the number of steps.
That makes me care about the number of my steps more than you'd expect. And surprise: the number of steps I made in December were not the lowest of 2014. It can be explained quite simply:
- We had to move to a new apartment this month. This meant viewing 15-20 apartments before choosing the one we moved in. That required a lot of driving and walking.
- The new apartment is substantially bigger than the older one. My wife was joking that we will get a larger number of steps in the new one.
- Actually moving our things also required a lot of movement.
This is how I managed to achieve the recommended 10k per day limit, despite the busy December with freezing temperatures outside. Good news!
In terms of burned calories, there is a slight decrease compared to the previous months. We had a 2-week pause in our Qwan Ki Do trainings, and I did not have time to compensate for that on my own. Things are back to normal in January though!
Weight
I intentionally left the weight at the end for 2 reasons: first, it's because I did not quantify myself during the holidays in order to manage my weight. I was also over-estimating the intake too aggressively. I knew it in my heart already, but seeing the data convinced me to change my behaviour.
My total intake is higher than my total calories burned, but I still lost a bit of weight.
There are 2 reasons for this:
- I stopped eating the Wasa Crispbread and the pretzels, which were rich in sodium. So I started to eliminate a bit more water, which was being held in my body.
- The aggressive over-estimation. Even an extra 10% is important on the long term.
I will try to be more mindful of my logging from now on.
What I Have Learned This December
I am so glad I did this. I never imagined the data would be so rich. My main conclusion is that even though our behaviour differs quite a lot in December, the overall differences in what we eat, how we sleep and how much we move are small. In the big picture, of course.
You don't grow fat because of the Christmas holidays, but due to your existing lifestyle. As I mentioned, we tend to pay closer attention to our weight during this time, and that is when we notice what had been happening during the year.
On the quantifying front, I will over-estimate food more conservatively. 9 months seems like a lot of data at first sight, but I quickly realised you need more.
One metric that I would have liked to see is water intake. I started to log it recently, but I'm not very good at it yet. I've noticed that when feasting, we simultaneously eat more and drink less. There is a theory that most of the time our feeling of hunger is in fact thirst, but we cannot correctly distinguish between them.
I would like to end this article by wishing you a Happy Quantified New Year! May all your metrics rise to the challenge in 2015.
Andrei Ismail is an experienced software engineer with startup experience under his belt and a PhD in Artificial Intelligence. He has lost 50 pounds in 6 months and is currently working on a fitness coaching program for geeks, available at WEIGHT LOSS FOR ENGINEERS.
