Request for Feedback: Experimental Design to Determine if I have Allergy Induced Rhinitis (Runny Nose)

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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?

Thanks in advance for your help!


– QD


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Request for Feedback: Experimental Design for Blood Pressure and Breathing Experiments

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Summary

I’m trying to identify causes and ways of reducing my elevated blood pressure and am looking for feedback on my experimental design & protocols.

Studies/Experiments:

  • Phase 1: Identify Potential Causes of Elevated Blood Pressure from Existing Self-Tracking Data
    • Approach (details below)
      • Use a mixed-effect model to look for look for significant correlations in data I’ve already collected.
      • If I find anything promising, design additional studies to confirm the relationship and test interventions.
    • Metrics to look at
      • blood glucose, sleep, exercise, weight/body, pulse, HRV
  • 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.

I’ve been measuring my blood pressure over the past 4 months and it’s consistently over the American Heart Association target of 120/80 mmHg for “Normal” blood pressure. Of more concern, I frequently measure Systolic blood pressure of >130 mmHg, which is considered Stage 1 Hypertension.

Elevated blood pressure is associated with an increased risk of cardiovascular disease (41.5/100k person years, hazard ratio 1.14 vs. normal BP, see Figure 2 and Table 1).

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).

From an American Hearth Association evaluation of methods non-medication approaches to reduce blood pressure, with the exception of aerobic exercise (which I already do), the most well evidenced methods of reducing blood pressure are meditation and deep breathing.


Proposed Experiments

Phase 1: Identify Potential Causes of Elevated Blood Pressure from Existing Self-Tracking Data

  • Data
    • Blood pressure:
      • systolic and diastolic blood pressure
      • Measured by Omron Evolve
    • Glucose:
      • Same day: fasting BG
      • Previous day: average BG, time low (70, 60, & 50), time high (120, 140, 160), & coefficient of variation
      • Previous evening (after 7p): same as previous day
      • Measured by Dexcom G6
    • Sleep:
      • Time asleep, number of wake-ups, early rising (time woke before alarm)
      • Measured manually and by Apple Watch (less reliable but more data)
    • Other heart markers:
      • pulse (sleeping, morning, and awake), heart rate variability
      • Measured by Apple Watch and Omron Evolve
    • Body:
    • Exercise:
      • 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?

Thanks in advance for your comments & feedback!


– QD


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Does Vinegar Really Lower Blood Glucose? If so, how? – Literature Survey & Pre-registration for an N=3 Community Experiment

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Summary

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.

Background

Link to list and summaries of literature reviewed

Over the past 20 years, several clinical trials have shown that consumption of vinegar with a meal can reduce the post-meal blood glucose concentration on both non-diabetic and diabetic patients. A meta-analysis of 11 high-quality studies showed a significant and systematic reduction in glucose and insulin area under the curve (see Figures 1 & 2).

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.

Based on this, I believe that vinegar has an effect. However, there’s no clear consensus on how or why vinegar lowers blood glucose. Various mechanisms have been proposed, including:

  • Delayed gastric emptying
  • Increased glucose uptake by muscles
  • Inhibition of alpha-amylase, leading to slower breakdown of starches

I’m particularly intrigued by the work of the Le Feunteun group, that argues that the effect is not due to vinegar specifically, but rather reduced pH slowing the breakdown of starch by inhibiting the enzyme alpha-amylase. Supporting this claim:

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
  • QD (u/sskaye):
    • Meals are eaten contemporaneously with vinegar or an equal amount of water at ~10:30a.
    • Blood sugar is monitored for 5h using a Dexcom G6, with calibration performed 15-30 min. before the start of each experiments.
    • Treatments are alternated daily V-/V+/W (V-: meal with no vinegar; V+: meal with vinegar; W: wash/no experiment.
  • u/genetastic:
    • Meals are eaten contemporaneously with vinegar between breakfast/lunch. CGM data is checked to make sure that BG is at baseline before a test.
    • Treatments are alternated daily with no wash period.
  • u/kabong:
    • Meals are eaten contemporaneously with vinegar.
    • Blood sugar is monitored for 3h using a Freestyle Libre, with calibration using a fingerstick meter.
    • Treatments are 3x per week, each at the end of a >24h fast.

Measurements

Analysis

  • Peak blood glucose, iAuC, and time to peak blood glucose will be calculated for each experiment
  • Student’s t-test will be used to test if the values for any of the above metrics were different with and without consumption of vinegar.
  • Additional exploratory analysis may be done based on the data, but will be noted as such

Data Processing & Visualization

iAUC will be calculated using the trapezoid method. Data will be visualized using Tableau.


Data

All data will be posted after analysis.


Results & Discussion

Results will be posted and discussed after the data is analyzed.


Conclusions & Next Experiments

Conclusions & next experiments will be posted after the data is analyzed.


– QD


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Please Critique my Experiment Design: Self-Experiment to Sleep Longer

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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.

The only thought I had was to try melatonin. It’s typically used to control when you go to sleep, but it last long enough in the bloodstream that it might impact time asleep as well.

Plan:

  • Self-blinded study using melatonin placed inside placebo capsules
  • Concentrations: 0, 0.3, 3 mg (random assignment)
  • Duration: 4 weeks

Questions:

  • Anyone else have the same problem? What has worked for you to sleep later?
  • Any other suggestions for interventions to try?
  • Any comments or critiques on the melatonin experimental design?
    • Is random assignment by day sufficient or do I need to block by a longer time period, use washout days, etc.?


– QD


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Please Critique my Experiment Design: Measuring the Effect of Low-carb Ingredients on Blood Sugar

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For my next set of experiments, I want to measure the effect of different foods on blood sugar. I’m particularly interested in the effect of:

  • low-carb flour and sugar replacements (e.g. oat-fiber, lupin flour, allulose, etc.)
  • 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:
    1. 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
    2. 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).
      • Based on previous experiments, I’ll need to wait until after lunch.
      • 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.
    3. 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?
  • Are you interested in joining the experiment?


– QD


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