I’ve started paying more attention to my breathing in the past few weeks and have noticed that when I go for a walk in the mornings or a run in the evening, I develop a runny nose that goes away shortly after I go back inside. It’s not terrible, but is annoying and prevents me from breathing comfortably through my nose.
From a quick search, my symptoms match closely with exercise induced rhinitis (list of articles). Numerous studies have found that exercise induced rhinitis is usually caused by allergies. I have never had nasal allergies, but it’s possible I’ve developed them or that they’ve always been mild enough that I haven’t noticed.
I’d like to determine whether my symptoms are, in fact, being caused by allergies and, if so, if there’s any simple interventions I can do to mitigate them.
Here’s my plan:
Step 1: Test if the symptoms are caused by just being outside or only during exercise
Go outside to the same location where I exercise and wait for 30 min. (same length as walks/runs).
Record whether I develop a runny nose and its severity.
Step 2 Test if the symptoms are ameliorated by allergy medication
Take fast-acting allergy medication or a placebo 1 hour before exercising.
Record whether I develop a running nose and its severity.
This experiment will be blinded by placing the pills inside of opaque gel caps and have another person randomize the treatment days for me.
Run the experiment for 10 weekdays & 4 weekend days (exercise locations differ)
If no effect is seen, repeat this experiment with long-acting (24h) allergy medication, but randomize by week instead of by day.
Questions
Does this approach seem reasonable? Any other measurements/tests I should try?
Does anyone else have this problem? If so, any recommendations for interventions to try?
Phase 2: Testing Deep Breathing to Lower Blood Pressure
Approach (details below)
Measure blood pressure and pulse before & after the most well studied protocols as well as normal breathing.
If any protocols show significant reduction in blood pressure, optimize the protocol and design/execute an experiment to test the long term effect.
Analysis
Student’s t-test will be used to test if the blood pressure change for any of the protocols is different from that of normal breathing.
Questions:
Phase 1
Any other metrics I should be looking at?
Does this analytical approach seem reasonable? Are there different statistical approaches I should be taking (details below)?
Phase 2
Has anyone tried this? If so, what breathing protocols have worked for you?
Any suggestions for other interventions to try?
Any comments or critiques of the experimental design or analysis?
Anything else I should be measuring while doing this?
It would significantly improve these studies to have a larger number of participants. If you’re interested in collaborating on this or other scientifically rigorous self-experiments with blood pressure, low-carb foods, supplements, or other health interventions, please let me know in the comments or via the contact form on the right.
Details
Purpose
To identify environmental or controllable factors that have a significant impact on my blood pressure.
To quantify the effect of known interventions for reducing blood pressure.
To find a set of interventions that enable me to reduce my blood pressure below 120/80 mmHg.
Background
Figure 1. Weekly average of blood pressure as measured by Omron home blood pressure monitors.
Figure 2. Cumulative incidence of cardiovascular disease vs. time for different blood pressure groups from a study of the South Korean nationwide health screening database (6.4M participants).
Table 1. Rate of cardiovascular disease for different blood pressure groups from a study of the South Korean nationwide health screening database (6.4M participants).
Given this, I’d like to see if I can reduce my blood pressure and reduce the strain on my heart and circulatory system.
There are numerous medications that lower blood pressure, but all risk of side effects. Before I pursue that route, I’d like to better understand the cause of my elevated blood pressure and see if any diet or lifestyle interventions can ameliorate it.
As mentioned above, I’ve been measuring my blood pressure for the past 4 months, along with blood glucose, sleep, weight, and exercise. This provides a (hopefully) rich dataset for identifying environmental or lifestyle factors that influence my blood pressure. Notably, I’ve noticed that my blood pressure is elevated on days after I’ve had low blood sugar the night before, indicating a possible effect (no statistical analysis done).
Type of exercise the previous day (aerobic vs. strength training) and frequency of aerobic exercise
Manually recorded
Analysis
A mixed effect model will be used to calculate the effect size, standard error, and p-value for the correlation between each metric and systolic and diastolic blood pressure
Effects will be of significant magnitude if a reduction of 5 mmHg can be achieved via a practical variation in the correlating metric.
Given the large number of metrics being looked at, I will use p-value thresholds of:
0.02 for planning testing interventions
0.05 for follow up experiments to confirm the correlation
0.1 for further monitoring/assessment as I get more data
Questions
Any other metrics I should be looking at?
Does this analysis seem reasonable? Are there different statistical approaches I should be taking?
Phase 2: Testing Deep Breathing to Lower Blood Pressure
Background
Numerous studies, reviews, and meta-analyses have shown deep breathing to lower blood pressure in both the short and long-term (example 1, example 2).
Effect sizes are moderate (3-5 mmHg) and statistically significant for large patient populations (>10,000 patients in some studies).
Numerous breathing protocols have been tested, with varying results.
Approach
Measure blood pressure and pulse before & after the most well studied protocols as well as normal breathing.
For each protocol, measure at least three times. If the protocol shows a reduction in blood pressure, measure an additional 5 times to confirm.
Conduct measurements 1/day in the mornings.
If any protocols show significant reduction in blood pressure, optimize the protocol and design/execute an experiment to test the long term effect.
Measurement
Blood pressure and pulse will be measured with an Omron Evolve.
Analysis
Student’s t-test will be used to test if the blood pressure change for any of the protocols is different from that of normal breathing.
Questions
Has anyone tried this? If so, what breathing protocols have worked for you?
Any suggestions for other interventions to try?
Any comments or critiques of the experimental design or analysis?
Anything else I should be measuring while doing this?
About a week ago a reader, /u/genetastic, reached out about collaborating on experiments to determine the effect of vinegar on blood glucose after meal consumption.
Like most of you, I had heard all the nigh-magical, pseudoscience claims about using apple cider vinegar to treat diabetes. However, when you dig into the literature, there’s a sizable number of peer-reviewed studies, including several decent meta-analyses, showing that consumption of vinegar with a meal can reduce the blood glucose impact in both diabetic and non-diabetic subjects (see background below for details). There’s also a lot of open questions, including:
Is the effect large enough to matter for practical meals?
What types of meals does vinegar affect?
What is the best protocol to get a large effect without unpleasant side effects?
What’s the underlying mechanism?
Is the effect specific to vinegar or do other acids work?
/u/genetastic, a third collaborator /u/kabong, and I decided to answer these questions with community self-experiment.
Below, I give more details on the background literature and pre-register our protocol and analyses.
It would significantly improve the study to have a larger number of participants. If you’re interested in collaborating on this or other scientifically rigorous self-experiments with low-carb foods, supplements, or other health interventions, please let me know in the comments or via the contact form on the right.
Details
Purpose
To replicate (or fail to replicate) the existing literature and quantify the effect of vinegar on blood glucose level after consumption of complex carbohydrates.
To better understand the underlying mechanism by determining how this effect varies with person/metabolic status, dose, source of calories, and type of acid.
Figure 1. Forest plot showing individual and pooled random effect standard mean difference (95% CI) of trials testing the effect of vinegar on glucose area under the curve. Test of overall effect: z = 2.42, p = 0.01.Figure 2. Forest plot showing individual and pooled random effect standard mean difference (95% CI) of trials testing the effect of vinegar on insulin area under the curve. Test of overall effect: z = 3.73, p < 0.001.
All together this is decent evidence for the acid as inhibitor of alpha-amylase as mechanism hypothesis
One of the biggest challenges in the vinegar/acid literature is that all of the experiments were done with different meals, protocols, and doses, making it difficult to integrate data from multiple studies. To address this issue and answer some of the open questions about this effect, /u/genetastic, /u/kabong, and I decided to do a series of community self-experiments.
While we each have different motivations and interests, overall, the questions we’re looking to answer are:
Is the effect large enough to matter for practical meals?
What types of meals does vinegar affect?
What is the best protocol to get a large effect without unpleasant side effects?
What’s the underlying mechanism?
Is the effect specific to vinegar or do other acids work?
To answer these questions, we will be conducting experiments using the protocol below.
Methods
Materials
Meals:
white bread (starch)
dried dates (simple sugars)
tortilla with beans, salsa, & avocado (starch, fat, and protein)
Vinegar:
Apple cider or white vinegar
As large a quantity as comfortable, not to exceed 30g
Diluted in as little water as tolerable
Blinding
Vinegar supplementation will not be blinded
However, the protocol was established in advance and adhered to without modification once experiments started.
Procedure
Each participant is using a slightly different procedure
Summary: I’m trying to sleep longer, but am waking up too early in the morning. I’d like to test some interventions to sleep longer (including melatonin) and am looking for advice.
Over the past 5 weeks, I’ve been making an effort to get more sleep. I’ve been able to hit an average time asleep of ~7h and, qualitatively, I’ve been feeling a lot less tired and have been able to concentrate better in the afternoons.
I’d like to see if sleeping even longer will result in further improvement. However, over the last week I’ve noticed that I’ve been waking up earlier and earlier (before my morning alarm). I stay in bed (eyes closed) until the alarm, but can’t go back to sleep.
Based on my data so far, there’s no clear correlation with time I fell asleep or total time asleep. Might be a correlation with heart rate variability, but I need more data to be sure.
I’d like to test some interventions to sleep longer. I already exercise in the evenings and for as long as I’m willing to do (~30 min. high intensity, 5-10 min. stretching), my last meal is 4h before going to bed, and my CGM does not show a consistent rise in blood sugar before waking up.
combinations of ingredients (e.g. how much does indigestible fiber, fat, or protein slow carb absorption
When I tried this before, I added ingredients to my normal meals measured the change in my normal BG trends (see Next Experiments). This proved too noisy and I couldn’t get a clean measure of the effect of even pure glucose in a reasonable number of measurements (see Next Experiments).
This time, I have a continuous glucose monitor (Freestyle Libre, post coming soon on accuracy vs. fingerstick and attempts to calibrate it) and am going to try to more carefully isolate the effects of the ingredient being tested.
This is going to be a lot of work and take many weeks, so I was hoping to get some feedback on my experimental design before I start. If you’re interested, please take a look and leave your feedback/critique in the comments.
It’d really improve the experiment to have more people participating. Let me know in the comments or by e-mail if you want to join in (see sidebar).
Proposed Experiment
Note: I put some specific questions at the end
Goals:
Determine effect of individual ingredients on the blood sugar of person with Type 2 diabetes
Determine effect of combining ingredients on same.
Develop model to predict the effect on blood sugar of meals that’s more accurate than standard carb+protein counting
Approach:
Calibrate Instruments: Over several days, measure blood sugar by both CGM (Freestyle Libre) and BGM (Freestyle Lite). Develop a calibration curve to increase accuracy of CGM data
Note: I’m already doing this and initial indication is that ~75% of the discrepancy between the two meters can be accounted for by a simple linear gain + offset error
Establish Baseline: Monitor blood sugar while skipping breakfast & lunch (both food & insulin) to identify a period of time where my blood sugar is stable for a long enough (need at least 2-4 hours).
Will collect data on at least 3 days in which I’m not exercising in the morning (M, W, F)
To reduce potential noise, need to be careful not to overeat or eat late the night before.
Measure Food Effects: For each ingredient or combination of interest, follow the same procedure as in the baseline, but at the selected time, consume a fixed, measured quantity of the ingredient and monitor blood sugar by CGM and BGM (every 30 min.) for 2 hours or until my blood sugar is stable for at least 1 h.
Initial quantity will be selected based on my previous experience of what will raise my blood sugar by ~20 mg/dL.
Based on the initial results, I will test different quantities of the ingredients until I have a dose-response curve with BG increases from 0 to 40 mg/dL or the quantity exceeds what I would reasonably consume in a sitting, whichever is smaller.
Number experiments will be at least 3 per ingredient or combination.
Initial Ingredients to Test:
Glucose tablet – baseline to which everything else will be compared
Dissolved glucose – effect of dissolving an ingredient
Whey protein – effect of protein
Casein protein – effect of protein type
Allulose – my favorite “indigestible” sweetener for baking & ice-cream
Oat-fiber – low-calorie, low-carb flour replacement I use for muffins and cookies
Inulin – used in a lot of low-carb foods
Questions
Current design tests one ingredient at a time. This is a lot simpler and lets me get results for the first ingredients sooner, but does introduce a systematic variation between ingredients (the week). My thought was to mitigate this by re-testing glucose at some frequency to measure week-to-week variation. Do you think this is sufficient or is there a better design?
I’m not planning to repeat quantities of a given ingredient multiple times, but instead vary the quantity. Since the end result of interest is change in BG as a function of quantity, I figured this would be more experimentally efficient. Are there any problems with this approach?
Since experiments will be done on M, W, F, there will be a 1-2 day washout period between ingredients. Is this sufficient or do I need to separate ingredients by week to ensure a two day washout?
Are there any other ingredients you’d like to see me test?
