Identifying Potential Causes of High Blood Pressure from 6 months of Self-Tracking: No Large or Actionable Effects

This is an update on my experiments to determine the cause and methods to reduce my elevated blood pressure. In this post, I take a look at the correlations between blood pressure and my other self-tracking metrics.

I didn’t find any large or actionable effects, but I’m concerned that the statistical analysis I did was too simplistic or otherwise not correct.

If anyone is interested in taking a look at the data, let me know. All the raw data is provided below, but I’m happy to do additional data processing/cleaning if it would be helpful.

Summary

  • Background:
    • I’ve been measuring blood pressure, sleep, weight, hemoglobin, and cholesterol for the past 6 months.
    • This provides a (hopefully) rich dataset for identifying environmental or lifestyle factors that influence my blood pressure.
    • Notably, I observed that my blood pressure seems elevated on days after after I’ve had low blood sugar the night before, indicating a possible effect (no statistical or other rigorous analysis done)
  • Approach:
    • 6 months of self tracking data was aggregated and cleaned.
    • Pearson R and p-value were calculated for 26 metrics that seemed most likely to influence blood pressure compared with systolic pressure, diastolic pressure, and pulse.
  • Results & Conclusions:
    • No metric had a large & statistically significant correlation with either systolic or diastolic
    • Sleep had the largest correlation with systolic pressure:
      • Effect Size: -1.1 mmHg/h asleep
      • R2 = 0.05
      • p-value = 0.03
    • There was a statistically significant correlation between cholesterol and both systolic & diastolic pressure, but it was in an implausible direction (higher cholesterol showed lower blood pressure), so is likely due to a common cause.
    • Pulse showed a strong correlation with fasting blood glucose. My suspicion is that this is related to the dawn phenomenon (liver dumping glucose into the blood in the morning to provide energy) as the time from waking up to doing my BP measurements may be correlated to both measures.
      • Effect Size: 0.08 bpm/(mg/dL glucose)
      • R2 = 0.14
      • p-value = 0.0005
    • Pulse also showed a strong correlation with body weight, though this is likely due to increased aerobic exercise during the same time period.
  • Next Steps:
    • Given the small effect sizes and lack of statistical significance, unless I screwed up the analysis, I don’t see any reason to follow up on these results.
    • Instead, I’ll take a look after 3-6 months and see if additional data surfaces anything useful.
    • Inspiratory muscle training:
      • u/OrganicTransistor suggested trying strengthening my respiratory muscles based on the results in this paper.
      • The results look promising, so I’m going to give the protocol in the paper a try.
      • This study will take six weeks. I’ve currently completed 8 days and will do an interim analysis every two weeks.
    • Decrease Sodium/Potassium ratio
      • Sodium/Potassium ratio has been shown to strongly correlate with blood pressure and incidence of heart disease.
      • Many years ago, my dad has high blood pressure that lowered significantly when he reduced his sodium intake.
      • I’m going test substituting a large fraction of my added sodium intake with potassium. Experimental details and pre-registration to follow in a separate post.

Details

Purpose

  • To determine if any of the metrics I track correlate with blood pressure.

Background

For additional background, see previous post.

  • I’ve been measuring blood pressure, sleep, weight, hemoglobin, and cholesterol for the past 6 months.
  • This provides a (hopefully) rich dataset for identifying environmental or lifestyle factors that influence my blood pressure.
  • Notably, I observed that my blood pressure seems elevated on days after after I’ve had low blood sugar the night before, indicating a possible effect (no statistical or other rigorous analysis done)

Results & Discussion

Systolic & Diastolic Pressure

  • The only statistically significant effects were:
    • Total cholesterol (systolic and diastolic)
    • LDL (systolic and diastolic)
    • # Wake ups (systolic only)
    • Time to Last Wake Up (manual recording of time asleep, systolic only)
  • Pearson R is negative for the two cholesterol correlations, which is biologically implausible (there’s no reason high cholesterol would reduce blood pressure). Since I only measure cholesterol once every two weeks, there’s not much data there, so it’s likely a spurious correlation.
  • For sleep, the correlation is likely real (p=0.03, 95%CI does not overlap zero), but the effect size is to small to be useful:
    • R2 = 0.05
    • -1.1 mmHg/h of sleep (i.e. I’d need to sleep an additional 5h to reduce BP by 5 mmHg, which is impossible even if the effect stayed linear)
  • This gives further evidence to the desirability of keeping my sleep under control, but does not provide a way to meaningfully reduce my blood pressure (I already sleep 6-6.5h/night, so there’s not enough room for improvement).


Pulse

  • Unsurprisingly, pulse strongly correlated with pulse and heart rate variability measured by my Apple Watch. Nice to see, but not actionable.
  • Strong correlation with fasting blood glucose with a large effect size.
    • Effect Size: 0.08 bpm/(mg/dL glucose)
    • R2 = 0.14
    • p-value = 0.0005
  • My suspicion is that this is related to the dawn phenomenon (liver dumping glucose into the blood in the morning to provide energy) as the time from waking up to doing my BP measurements may be correlated to both measures. I already work to keep my fasting BG in the normal range for a non-diabetic, so there’s nothing actionable here.
  • There’s also a strong correlation between pulse and body weight, though this is likely due to increased aerobic exercise during the same time period.

Conclusions & Next Experiments

Based on the analysis above, none of the observed correlations meet my target for practical significance (ability to reduce BP by 5 mmHg under practical conditions) and p-value thresholds.

I’m concerned, however, that the statistical analysis I did was too simplistic or otherwise not correct. In particular:

  • Would a mixed-effect model or other more sophisticated technique surface effects that I can’t detect?
  • Are there interaction effects that, if accounted for, would provide better predictive value?
  • Are there other metrics that I missed (e.g. different time lags)?

If anyone is interested in taking a look at the data, let me know. All the raw data is provided below, but I’m happy to do additional data processing/cleaning if it would be helpful.

Absent someone finding an effect I missed, I don’t see any reason to follow up on these results. Instead, I’ll take a look after 3-6 months and see if additional data surfaces anything useful.

In the meantime, I’ll focus on testing additional interventions. Specifically:

  • Inspiratory muscle training:
    • u/OrganicTransistor suggested trying strengthening my respiratory muscles based on the results in this paper.
    • The results look promising, so I’m going to give the protocol in the paper a try.
    • This study will take six weeks. I’ve currently completed 8 days and will do an interim analysis every two weeks.
  • Decrease Sodium/Potassium ratio
    • Sodium/Potassium ratio has been shown to strongly correlate with blood pressure and incidence of heart disease.
    • Many years ago, my dad has high blood pressure that lowered significantly when he reduced his sodium intake.
    • I’m going test substituting a large fraction of my added sodium intake with potassium. Experimental details and pre-registration to follow in a separate post.


– QD

Methods

Pre-registration

Here.

Differences from original pre-registration:

  • Did not analyze correlations with exercise
    • Reason: insufficient data
  • Instead of a mixed-effect model, I just calculated Pearson R and p-value for each correlation.
    • Reason: Since there were no effects of a practical/ actionable magnitude, I didn’t spend the effort to figure out how to implement the mixed effect model.


Blinding

This experiment was not blinded


Procedure


Data Visualization

Data was visualized using Tableau.

Data

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Reducing Blood Pressure using Deep Breathing: No Observable Effect with Additional Data

Last week, I reported my first attempt at reducing blood pressure using a deep breathing protocol. I saw a drop in blood pressure with deep breathing, but due to the high variance in the measurements, I couldn’t tell if this was a real effect or just due to chance.

Based on my repeatability study, I’ve repeated the experiment, this time measuring my blood pressure 5 times for each observation. Here’s the result:

Summary

  • 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.
    • My own tests suggested a possible effect: first, second.
  • Approach:
    • Blood pressure and pulse were measured each morning before and after the following protocols:
      • 8s inhale, 8s exhale, 5 min.
      • Normal activity, 5 min.
    • For each measurement, I took 5 readings and averaged the results.
    • Protocols were alternated by day for 10 days (5 days each protocol).
    • Average and 95% confidence intervals were compared for each metric & protocol.
  • Results & Conclusions:
    • With additional, lower variance measurements, I did not observe a meaningful drop in blood pressure or pulse. For all metrics, the difference between deep breathing and normal activity overlapped zero effect and was lower than my target for “clinical” significance.
    • While the variance is still too large to rule out a clinically significant effect size, it’s sufficiently unlikely that I’m not going to continue testing the short term effect of deep breathing.
  • Next Steps:
    • Retrospective analysis of self tracking data
      • I’ve finished the analysis and just need to write it up for posting.
      • There were no effects that were practically meaningful and statistically significant, but a few things were worth keeping an eye on.
    • Inspiratory muscle training:
      • On my last post u/OrganicTransistor suggested trying strengthening my respiratory muscles based on the results in this paper.
      • I’m going to replicate their protocol as best I can (pre-registration to follow in another post).
      • This study will take six weeks, but I will do an interim analysis every two weeks.

Details

Purpose

  • To determine the effect of deep breathing protocols on short-term blood pressure.

Background

For additional background, see previous post.

  • 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.
  • My own tests suggested a possible effect: first, second.

Results & Discussion

First, let’s take a look at the change in blood pressure for each protocol (deep breathing & normal activity). As shown in both the table and graphs above, on average:

  • Systolic pressure dropped for both deep breathing and normal activity.
    • In both cases, the magnitude was modest, 2.0 & 1.5 mmHg for deep breathing and normal activity, respectively.
    • Since I took these measurements ~1h after waking up, this drop is presumably related to my morning routine in some way (e.g. dissipation of the initial stress from waking up, relaxing during morning computer work, etc.)
  • Diastolic pressure was nearly unchanged with deep breathing (0.1 mmHg drop), but showed a modest drop for normal activity (1.2 mmHg)
  • Pulse increased during deep breathing (1.3 bpm) and stayed the same during normal activity (0.1 bpm increase).
  • Since I took these measurements ~1h after waking up, these effects, if real, are presumably related to my morning routine in some way (e.g. dissipation of the initial stress from waking up, relaxing during morning computer work, etc.)
  • Several of these effects are different than my previous observations. Notably:
    • I saw a drop in systolic and diastolic blood pressure in the normal activity condition vs. no change or increase previously.
    • I saw an increase in pulse in the normal activity condition vs. a decrease previously.
    • In no case was the difference outside of what would be expected due to the high variance in the previous experiments. As such, the differences are likely due to chance.
    • Given the much lower variance in the current experiment (5 measurements per condition vs. 1) I have a lot more confidence in the current conclusions.

Looking at the difference between means (deep breathing – normal activity) for each metric, I see a decrease of only 0.5 mmHg for systolic pressure, an increase of 1.1 mmHg for diastolic pressure, and an increase of 1.4 bpm for pulse. In all cases, the 95% CI for the difference of means overlaps zero.

Since the measured effects are below my target for “clinical” significance and have a low probability of reaching the target with larger a sample size, it looks like deep breathing doesn’t meaningfully lower my blood pressure.

As mentioned in the background section, there are numerous published studies showing moderate effect sizes (3-5 mmHg) and statistically significant blood pressure drop during deep breathing for large patient populations. While my experiments indicate that this doesn’t work for me, it doesn’t mean the literature is mistaken. Some hypotheses:

  • Most literature experiments were done in a clinical environment during the day. Due to the environment, the patients might have been more stressed, which can cause an increase in blood pressure and be mitigated by the deep breathing.
  • My baseline stress may be lower than average and therefore methods to reduce stress (e.g. deep breathing) have a reduced effect on me.
  • I breath more deeply during normal activity than average.
  • Other natural person to person variation
    • This is obviously a catch-all, but in the published studies, it was not the case that every patient showed a drop in blood pressure, just that there was a drop on average.

Conclusions & Next Experiments

It looks like deep breathing doesn’t meaningfully lower my blood pressure. The measured effects are below my target for “clinical” significance and have a low probability of reaching the target with larger a sample size.

Given that I’m not going to continue testing the short term effect of deep breathing on blood pressure. For my next experiments, I’m going to look at the following:

  • Retrospective analysis of self tracking data
    • I’ve finished the analysis and just need to write it up for posting. There were no effects that were practically meaningful and statistically significant, but a few things were worth keeping an eye on.
  • Inspiratory muscle training:
    • On my last post u/OrganicTransistor suggested trying strengthening my respiratory muscles based on the results in this paper.I’m going to replicate their protocol as best I can (pre-registration to follow in another post).
    • This study will take six weeks, but I will do an interim analysis every two weeks.
  • Increasing my Potassium:Sodium ratio
    • Still figuring out how to test this in a rigorous way. Will pre-register as soon as I work it out.


– QD

Methods

Pre-registration

Here & here.

Differences from original pre-registration:

  • Instead of using students t-test, I compared 95% confidence intervals between conditions (mathematically equivalent for a threshold of p = 0.05)


Blinding

This experiment was not blinded


Procedure

  • Each morning at ~6am, I measured my blood pressure before and after the following protocols:
    • 8s inhale, 8s exhale, 5 min.
    • Normal activity, 5 min.
  • Breath timing was controlled using the iBreath app.
  • Blood pressure measurements were performed using an Omron Evolve blood pressure meter.
    • For each measurement, I placed the meter on my left arm, ~4 cm above my elbow. Measurements were taken seated, with my feet on the ground and arms resting on a flat surface at a comfortable height (same every time).
    • 5 measurements were taking with no pause in-between measurements (other than to write down the result) and the average of the 5 measurements was used.


Data Visualization

Data was visualized using Tableau.

Data

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Reducing Blood Pressure using Deep Breathing: No Statistically Significant Effect Observed in First Attempt (Data too noisy)

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For my studies to determine ways to reduce my blood pressure, the first intervention I’m testing is deep breathing protocols. Here I report my initial results:

Summary

  • Background:
  • Approach:
    • Blood pressure and pulse were measured each morning before and after the following protocols:
      • 8s inhale, 8s exhale, 5 min.
      • Normal activity, 5 min.
      • 8s inhale, 8s exhale, 15 min.
      • Normal activity, 15 min.
    • Each protocol/time combination was measured 5 times.
    • Average and 95% confidence intervals were compared for each metric & protocol.
  • Results & Conclusions:
    • For each time condition, a blood pressure drop was observed on average during deep breathing, while an increase was observed during normal activity. The opposite effect was observed for pulse (increased during deep breathing).
    • Due to the high variance in the measurements, the 95% confidence interval for the difference overlaps zero, so the results are not statistically significant and could easily be due to chance.
  • Next Steps:
    • I will repeat the experiments, but measure blood pressure 5 times for each observation, increasing measurement precision.
    • For these experiments, I will test only 5 min. deep breathing and normal activity, but run 10 trials of each, with an interim analysis at 5 trials each.

Details

Purpose

  • To determine the effect of deep breathing protocols on short-term blood pressure.

Background

For additional background, see previous post.

Results & Discussion

Caveat

All of these experiments were done before I tested the repeatability of my blood pressure meter and I only took one measurement per observation (i.e. one measurement before and one after each period). This was a big mistake on my part, as the variance between measurements was way to high and no results are statistically significant (i.e. could easily be due to chance).

Given this, please take all discussion/conclusions presented here as only suggestive for further experiments. I will be repeating this work with 5 measurements per observation.

Blood Pressure & Pulse Change during the Interventions

First, let’s take a look at the change in blood pressure during each session. As shown in both the table and graphs above, on average:

  • Systolic pressure dropped in both the 5 & 15 min. deep breathing conditions, while it increased during normal activity.
  • Diastolic pressure dropped in the 5 min. deep breathing condition, increased during 15 min., and increased in both times for normal activity
  • Pulse increased during 5 min. deep breathing, dropped during 15 min., and dropped in both times for normal activity

As discussed above, the 95% confidence interval overlaps zero for all of these measurements, so the results could easily be due to chance. However, they are consistent with my initial exploratory measurements.

Looking at the difference between means for each time condition, I see a drop of ~2.5 mmHg for systolic pressure, ~2 mmHg for diastolic, and an increase of ~2 bpm for pulse for deep breathing vs. normal activity. Again, 95% CI overlaps zero for all conditions, but the effect size is on the edge of worthwhile (I had pre-registered that I would follow up on effect sizes >3 mmHg).

Conclusions & Next Experiments

Given the high observed variance, I am going to repeat these experiments with my new measurement protocol (5 measurements/observation). I have already started these experiments and should have them completed in ~2 weeks. In the meantime, I will finish up analyzing my historical data and report that out next week.


– QD

Methods

Pre-registration

Here.

Differences from original pre-registration:

  • Instead of using students t-test, I compared 95% confidence intervals between conditions (mathematically equivalent for a threshold of p = 0.05)


Blinding

This experiment was not blinded


Procedure

  • Each morning at ~6am, I measured my blood pressure before and after the following protocols:
    • 8s inhale, 8s exhale, 5 min.
    • Normal activity, 5 min.
    • 8s inhale, 8s exhale, 15 min.
    • Normal activity, 15 min.
  • Breath timing was controlled using the iBreath app.
  • Blood pressure measurements were performed using an Omron Evolve blood pressure meter.
    • For each measurement, I placed the meter on my left arm, ~4 cm above my elbow. Measurements were taken seated, with my feet on the ground and arms resting on a flat surface at a comfortable height (same every time).


Data Visualization

Data was visualized using Tableau.

Data

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Weekly Update & Health Statistics: 12/12/21

This week, I posted the first experiments from my attempt to reduce my blood pressure. I started by measuring the repeatability (within-instrument variation) and reproducibility (between instrument variation) of my Omron Evolve blood pressure monitor. Unfortunately, the standard deviation was high compared to my target reduction (~3 vs. 10 mmHg), meaning that I’ll need to measure multiple times per observation in order to get sufficient precision.

Lesson for future experiments: I should have measured the repeatability & reproducibility before starting any other experiments. I finished up the deep breathing study before I got these results, and from a quick look at the data, the error bars are too large to draw a conclusion and I’ll need to repeat it. Lesson learned, always run a power calculation first…

Experiments this week:

  • Whole foods: none (traveling)
  • Blood pressure:
    • Completed the deep breathing study, started repeat with 5 measurements/observation
    • Analysis of repeatability testing

Next week:

  • Food effect:
    • continued testing of whole foods
  • Blood pressure:
    • Post initial deep breathing study & continue the repeat.
    • Analysis of historical data.


– QD

Active & Planned Experiments

Let me know in the comments if there’s any other experiments you’d like to see.


– QD

Observations & Data

Continue reading “Weekly Update & Health Statistics: 12/12/21” →

Reducing Blood Pressure without Medication Phase 0: Measurement Repeatability & Reproducibility

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For my studies to determine interventions to reduce my blood pressure, the main measurement device I’ll be using will be an Omron Evolve blood pressure monitor. In order to understand the measurements I make, I’m going to need to know the repeatability and reproducibility of the device as well as any systematic biases in the measurements. Notably, some people see blood pressure readings drop with repeat measurements (see comment from Gary Wolf here) and I need to know the magnitude of the effect for any paired sample testing over short time periods.

To measure the repeatability and reproducibility of my Omron Evolve blood pressure meters, I tested (details in below):

  • Repeatability: 19 sets of 5 measurements on the same meter
  • Reproducibility: 56 paired measurements on two different meters (one immediately following the other)

Here’s what I found.

Summary

  • Experiments:
    • Repeatability: 19 sets of 5 measurements on the same meter
    • Reproducibility: 56 paired measurements on two different meters (one immediately following the other)
  • Results:
    • Within meter standard deviation was ~3 mmHg, which is high compared to my target reduction of 10 mmHg.
    • I see a drop in blood pressure with repeat readings, but it’s relatively small (~0.5-1 mmHg/measurement over 5 measurements), and safe to ignore.
    • There’s no detectable difference between my two meters. Since the older one has been used for ~4 months, that indicates that there’s likely no change in the meter over time.
  • Conclusions:
    • Given the high variance vs. my target change in blood pressure, going forward I will take sets of 5 measurements for every observation.
    • This gives an estimated 95% CI of 2.6 mmHg systolic. Still higher than I’d like, but it should allow me to identify reasonable effect sizes (I’ll, of course, need to do power calculations for each planned experiment).

Details

Purpose

  • To determine the repeatability & reproducibility of blood pressure measurements using my Omron Evolve blood pressure meters.
  • To quantify the drop in blood pressure with repeat measurements at the same sitting.

Background

See previous post.

Results & Discussion

Within-meter Repeatability

First, let’s take a look at the within meter precision. The pooled standard deviation over 19 sets of 5 measurements was 2.5-3.5 mmHg (95% CI) for systolic and a bit lower for diastolic. This means that for a single-point measurement, I’d have a 95% confidence interval of ~6 mmHg, larger than most effect sizes seen for BP interventions and half the reduction I need to get to normal blood pressure.

To quantify the drop in blood pressure with repeat measurement, I looked at both the initial drop (1st – 2nd measurement) and the slope over all 5 measurements. I observed a drop for systolic and diastolic pressure in both cases. Only the diastolic slope was statistically significant (95% CI does not overlap 0), but given that I see an effect for all four metrics and of consistent magnitude, the drop is likely real. That said, the drop is only ~0.5-1 mmHg/measurement, small enough to safely ignore for most experiments I plan to do.


Between-meter Reproducibility

Next, let’s look at the variation between meters. For this experiment, I used an older meter that I’ve been using daily for ~4 months and compared it to a newer meter of the same make/model that I bought when I mistakenly thought I had lost the original.

For the 56 paired reproducibility measurements, I alternated which meter I used first, giving me another data set to test for a drop in reading with repeat measurements. In this case, I saw a drop with diastolic pressure, 1.4 mmHg [0.4, 2.4 95% CI], but not systolic pressure, -0.3 [-1.4, 0.8 95% CI]. However, the confidence intervals are consistent with the previous measurements, again indicating the effect is likely real.

Comparing the two meters, there’s no measurable difference. Average difference is <0.3 mmHg with 95% confidence intervals comfortably overlapping zero. Since the older one has been used for ~4 months, that also indicates that there’s likely no change in the meter over time.

Conclusions & Next Experiments

Given the high observed variance, going forward I will start measuring sets of 5 repeat measurements for each observation. This gives an estimated 95% CI of 2.6 mmHg systolic. Still higher than I’d like, but it should allow me to identify reasonable effect sizes (I’ll, of course, need to do power calculations for each planned experiment).

Unfortunately, I’ve already finished my initial testing of deep breathing protocols using only single-point measurements. I’ll go ahead and analyze that data, but if the results are inconclusive, I will repeat the experiment with this new protocol.


– QD

Methods

Pre-registration

This experiment was not pre-registered.


Blinding

This experiment was not blinded


Procedure

  • General:
    • Blood pressure measurements we performed using an Omron Evolve blood pressure meter.
    • For each measurement, I placed the meter on my left arm, ~4 cm above my elbow. Measurements were taken seated, with my feet on the ground and arms resting on a flat surface at a comfortable height (same every time).
  • Repeatability
    • For 8 days, whenever I measured my blood pressure, I would repeat the measurement 5 times, with no breaks in between measurements.
  • Reproducibility
    • For 14 days, whenever I measured my blood pressure, I would repeat the measurement twice, once with each of two meters.


Data Visualization

Data was visualized using Tableau.

Data

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