Higher Precipitation Predicts Very Slightly Lower Blood Pressure (Systolic - Top Number) for Population

Higher Precipitation Predicts Very Slightly Lower Blood Pressure (Systolic - Top Number) for Population

Contents

Variables

A
Precipitation 428
A
Blood Pressure 897

Categories

A
Environment 564
A
Vital Signs 110

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.3% average decrease in Blood Pressure (Systolic - Top Number) following above average Precipitation.

Abstract

Blood Pressure (Systolic - Top Number) was generally 0% higher than average after an average of 3.3 millimeters of Precipitation over the previous 7 days.

Aggregated data from 11 study participants suggests with a HIGH degree of confidence (p=0.282, 95% CI -2.931 to 2.834) that Precipitation has a very weakly negative predictive relationship (R=-0.0487) with Blood Pressure.

The highest quartile of Blood Pressure measurements were observed following an average 1.3 millimeters Precipitation.

The lowest quartile of Blood Pressure measurements were observed following an average 1.36 millimeters of Precipitation.

After an onset delay of 0 seconds, Blood Pressure is typically 1% lower than average over the 7 days following around 1.36 millimeters of Precipitation Precipitation.

Keywords: Precipitation, Blood Pressure, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 11 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 2,181 paired observations from 11 participants revealed a minimal reduction in Blood Pressure following above-average Precipitation exposure.

-1.1%
Change from Baseline
Minimal effect on Blood Pressure
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.049
Correlation (r)
p = 0.570
Significance
z = 0.34
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Precipitation:

  • Blood Pressure decreased by 1.1% on average
  • Temporal analysis supports Precipitation as the predictor (not the outcome)

Interpreting the Predictor Impact Score

The Predictor Impact Score (PIS) integrates multiple Bradford Hill causal criteria into a single metric. Use this guide to interpret the score:

PIS Range Interpretation Recommended Action
≥ 0.5 Strong evidence High priority for RCT validation
0.3 - 0.5 Moderate evidence Consider for experimental investigation
0.1 - 0.3 Weak evidence Monitor for additional data
< 0.1 Insufficient evidence Low priority; may be noise

Note: PIS is a prioritization heuristic, not proof of causation. High scores indicate relationships worth investigating, not confirmed causal effects. With only 11 participants, these scores are preliminary and will become more reliable as additional data is collected.

Optimal Daily Values

No clear dose-response relationship detected. The Precipitation values associated with high and low Blood Pressure are too similar to provide meaningful dosing guidance. This may indicate a threshold effect (any amount works equally well), no effect, or insufficient data variance. With more participants, a clearer pattern may emerge.

Population Correlation

Precipitation Distribution

Blood Pressure Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Precipitation
Effect Variable Name Blood Pressure (Systolic - Top Number)
Sinn Predictive Coefficient 0.016244589475792
Confidence Level HIGH
Confidence Interval 2.8824126129381
Forward Pearson Predictive Coefficient -0.0487
Critical T Value 1.6981818181818
Average Precipitation Over Previous 7 days Before ABOVE Average Blood Pressure ( Systolic - Top Number) 1.3 millimeters
Average Precipitation Over Previous 7 days Before BELOW Average Blood Pressure ( Systolic - Top Number) 1.36 millimeters
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 2181
Optimal Pearson Product 0.05099475870148
P Value 0.28176107283091
Statistical Significance 0.5702
Strength of Relationship 2.8824126129381
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 11

Precipitation Info

Property Value
Variable Name Precipitation
Aggregation Method MEAN
Analysis Performed At 2020-09-11
Duration of Action 7 days
Kurtosis 64.239888813382
Mean 1.2347003153194 millimeters
Median 0.060031061746988 millimeters
Minimum Allowed Value 0 millimeters
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 428
Number of Measurements 396520
Number of Measurements (including those generated by tagged, joined, or child variables) 63521
Public true
Onset Delay 0 seconds
Standard Deviation 3.6917755248808
Unit Millimeters
User Variables 698
UPC 721866373106
Variable Category Environment
Variable ID 5954746
Variance 21.734974233023

Blood Pressure Info

Property Value
Variable Name Blood Pressure (Systolic - Top Number)
Aggregation Method MEAN
Analysis Performed At 2020-09-15
Duration of Action 7 days
Kurtosis 9.6485959987812
Maximum Allowed Value 100000 millimeters merc
Mean 4546.9564108281 millimeters merc
Median 4547.0725028058 millimeters merc
Minimum Allowed Value 1 millimeters merc
Number of Aggregate Predictors 780
Number of Aggregate Outcomes 117
Number of Measurements 8517
Number of Measurements (including those generated by tagged, joined, or child variables) 5176
Public true
Onset Delay 0 seconds
Standard Deviation 31.78039339699
Unit Millimeters Merc
User Variables 61
UPC 647679244474
Variable Category Vital Signs
Variable ID 1874
Variance 23939.264480794

Introduction

Background

Precipitation (Environment) and Blood Pressure (Vital Signs) are both important factors in understanding human health and well-being. This study investigates the relationship between these two variables using real-world observational data.

Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical, expensive, or unethical for studying many health relationships. Aggregated N-of-1 observational studies offer a complementary approach that leverages within-subject comparisons across large populations to identify meaningful patterns.

Research Question

Does Precipitation affect Blood Pressure?

Additionally, we seek to determine:

  1. What is the direction and magnitude of any effect?
  2. How confident can we be in this relationship based on the available data?
  3. What are the optimal levels of Precipitation for maximizing Blood Pressure?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Precipitation and Blood Pressure. Additionally, we attempt to determine the Precipitation values most likely to produce optimal Blood Pressure values.

Study Overview

This is a population-level observational study using aggregated N-of-1 methodology. By aggregating individual N-of-1 experiments, we can identify population-level patterns while accounting for the substantial individual variation that exists in most health relationships. Effect sizes are reported as percent change from baseline, enabling intuitive interpretation and comparison across different measures.

Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects

Discussion

Interpretation of Findings

Participants experienced a 1.1% reduction in Blood Pressure following above-average Precipitation exposure. The Predictor Impact Score (PIS) of 0.02 indicates insufficient evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Blood Pressure is not statistically significant at a 95% confidence interval. This suggests that the Precipitation value may not have a significant influence on the Blood Pressure value, or that more data is needed to detect an effect.

After treatment, a 0.3% decrease (-1.34 millimeters merc) from the mean baseline 125 millimeters merc was observed. The relative standard deviation at baseline was 4.56364%. The observed change was 0.34484 times the standard deviation.

A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

T-Test Details
Observed t-value: 0.962
Critical t-value: 1.698

Since t = 0.96 < 1.70, we cannot reject the null hypothesis.

Biological Plausibility

A plausible bio-chemical mechanism between predictor and outcome is critical for interpreting observational findings. This is where human judgment excels beyond statistical analysis.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Precipitation might influence Blood Pressure.

Bradford Hill Criteria Assessment

The Bradford Hill criteria provide a framework for assessing causality in observational studies. Our methodology operationalizes six of the nine criteria through the Predictor Impact Score (PIS):

Criterion How Addressed Metric
Strength Effect size magnitude Percent change from baseline (Δ%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (φ), onset delay (δ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (φgradient)
Plausibility Biological mechanism assessment Community votes on mechanism plausibility
Specificity Category appropriateness Interest factor (finterest)

Predictor Impact Score (PIS)

The PIS integrates multiple Bradford Hill criteria into a composite metric quantifying how reliably a predictor affects an outcome. Higher scores indicate stronger evidence:

Population-Level PIS:

$$\text{PIS}_{\text{agg}} = |r_{\text{forward}}| \cdot w \cdot \phi_{\text{users}} \cdot \phi_{\text{pairs}} \cdot \phi_{\text{change}} \cdot \phi_{\text{gradient}}$$

Where φ-factors are saturation functions approaching 1 as evidence accumulates:

  • φusers = 1 - e-N/10 (user saturation)
  • φpairs = 1 - e-n/nsig (pair saturation)
  • φchange = 1 - e-Δspread/Δsig (effect spread saturation)
  • w = weighted average of plausibility votes

Temporality Assessment

We assess evidence for correct causal direction using the temporality factor:

$$\phi_{\text{temporal}} = \frac{|r_{\text{forward}}|}{|r_{\text{forward}}| + |r_{\text{reverse}}|}$$

Values approaching 1 indicate the predictor precedes the outcome (supporting causation); values near 0.5 suggest ambiguous directionality; values near 0 suggest reverse causation or confounding by indication.

Limitations

As with any observational study, correlation does not prove causation. Key limitations include:

  • Unmeasured confounders: Variables not tracked may influence results
  • Self-selection bias: Health trackers may differ from the general population
  • Measurement error: Self-reported data may contain recall bias
  • Confounding by indication: Sicker individuals may use more treatments

However, within-subject comparison and temporal precedence analysis partially mitigate these limitations. If the relationship is merely coincidental, as participants independently modify their Precipitation values, the observed strength will decline over time. Spurious correlations naturally dissipate as more data is collected.

Future Directions

Future research should examine:

  • Subgroup analyses to identify individual differences in response
  • Potential confounders and mediators of the observed relationship
  • Optimal dosing and timing for Precipitation
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 11 participants, these results are based on limited data. Effect sizes and confidence will improve as more participants contribute data. Consider these findings directional rather than definitive.

Above-average Precipitation was associated with a 1.1% reduction in Blood Pressure—a minimal effect. The Predictor Impact Score of 0.02 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.02 and a 1.1% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Precipitation may influence Blood Pressure in real-world conditions. While preliminary, these results may inform future research directions. As more participants contribute data, the reliability and precision of these findings will improve substantially.

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Methods

Study Design

This study is based on data donated by 11 participants. Thus, the study design is equivalent to the aggregation of 11 separate n=1 observational natural experiments.

This within-subject design is powerful because it controls for all stable individual characteristics (genetics, baseline health status, socioeconomic factors) that might otherwise confound the relationship between variables.

Data Analysis

Temporal Assumptions

The analysis incorporates temporal assumptions about the relationship between variables:

  • Onset Delay: It was assumed that 0 seconds would pass before a change in Precipitation would produce an observable change in Blood Pressure.
  • Duration of Action: It was assumed that Precipitation could produce an observable change in Blood Pressure for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Precipitation values and subsequent Blood Pressure values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:

Individual Correlation:

$$r_i = \frac{\sum(x_{ij} - \bar{x}_i)(y_{ij} - \bar{y}_i)}{\sqrt{\sum(x_{ij} - \bar{x}_i)^2 \sum(y_{ij} - \bar{y}_i)^2}}$$

Fisher's Z-Transformation:

$$z_i = \frac{1}{2} \ln\left(\frac{1 + r_i}{1 - r_i}\right)$$

Aggregated Correlation:

$$\bar{r} = \tanh(\bar{z}) \quad \text{where} \quad \bar{z} = \frac{1}{N}\sum_{i=1}^{N} z_i$$

Effect Size Calculation

Effect sizes are reported as percent change from baseline. For each participant, we compare the outcome following above-average predictor values to the overall baseline outcome:

$$\Delta\%_{\text{baseline}} = \frac{\bar{O}_{\text{follow-up}} - \bar{O}_{\text{baseline}}}{\bar{O}_{\text{baseline}}} \times 100$$

Effect Magnitude (Z-Score)

To assess effect magnitude relative to natural variability, we calculate the z-score:

$$z = \frac{|\Delta\%_{\text{baseline}}|}{\text{RSD}_{\text{baseline}}}$$

where RSDbaseline is the relative standard deviation of outcome during baseline period

A z-score > 2 indicates statistical significance (p < 0.05), meaning the observed change exceeds typical baseline fluctuation and is unlikely due to random variation.

Statistical Significance

Correlation significance is assessed using a two-tailed t-test:

$$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$$

We reject the null hypothesis (ρ = 0) at α = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

Precipitation data was primarily collected using Weather. Automatically import temperature, humidity, and ultraviolet light exposure.

Blood Pressure data was primarily collected using Withings. Withings creates smart products and apps to take care of yourself and your loved ones in a new and easy way. Discover the Withings Pulse, Wi-Fi Body Scale, and Blood Pressure Monitor.

Data Quality

Data quality measures were applied to ensure reliable results:

  • Minimum Data Requirement: Only participants with sufficient paired observations were included in the analysis.
  • Outlier Handling: Extreme values were winsorized to reduce the influence of measurement errors.
  • Missing Data: Days with missing values were handled using appropriate filling strategies based on the variable type.
  • Test User Exclusion: Test accounts and invalid users were excluded from all analyses.

Principal Investigator

Program & Methods

Mike P. Sinn
Sinn, M. P.

Designed and implemented data collection, aggregation, causal inference pipeline, and automated study generation framework. Developed the Predictor Impact Score methodology operationalizing Bradford Hill criteria for ranking causal relationships in observational data. When he tells people this at parties, they usually say they have to go check on their car.

Individual study outputs are automated, reproducible, and open to external audit. (Which I would seriously recommend.)

Contact

Cite This Study

APA Format
Sinn, M. P. (2026). Causal Analysis: Does Precipitation Affect Blood Pressure (Systolic - Top Number)?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954746-effect-1874-population-study
BibTeX 
@misc{sinn_cause_5954746_effect_1874_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Precipitation Affect Blood Pressure (Systolic - Top Number)?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954746-effect-1874-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Precipitation Affect Blood Pressure (Systolic - Top Number)?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5954746-effect-1874-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Precipitation Affect Blood Pressure (Systolic - Top Number)?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954746-effect-1874-population-study [Accessed January 10, 2026].

Study Type: Aggregated N-of-1 Observational Mega-Study
Evidence Level: Level II (Real-World Evidence)
Methodology: Bradford Hill Criteria with Predictor Impact Score (PIS)

References

This framework was originally developed in 2013 based on the Bradford Hill criteria. Subsequent literature has independently validated similar approaches to causal inference from observational data:

  1. Hill, A.B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. [Bradford Hill criteria]
  2. Lillie, E.O., et al. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173. [N-of-1 methodology]
  3. Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
  4. Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
  5. FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
  6. Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
  7. Platt, R., et al. (2018). The FDA Sentinel Initiative—an evolving national resource . New England Journal of Medicine, 379(22), 2091-2093.

This information is for research and educational purposes only, not medical advice. Consult a healthcare provider before making health decisions. Terms of Service

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