Higher Barometric Pressure Predicts Very Slightly Higher Distress for Population

Higher Barometric Pressure Predicts Very Slightly Higher Distress for Population

Contents

Variables

A
Barometric Pressure 488
A
Distress 1349

Categories

A
Environment 564
A
Emotions 2028

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 1.8% average increase in Distress following above average Barometric Pressure.

Abstract

Distress was generally 8.64302% higher than average after 101000 pascal of Barometric Pressure per 7 days.

Aggregated data from 86 study participants suggests with a HIGH degree of confidence (p=0.172, 95% CI -0.374 to 0.409) that Barometric Pressure has a very weakly positive predictive relationship (R=0.0177) with Distress.

The highest quartile of Distress measurements were observed following an average 72300 pascal Barometric Pressure.

The lowest quartile of Distress measurements were observed following an average 102000 pascal of Barometric Pressure.

After an onset delay of 42 seconds, Distress is typically 7% lower than average over the 7 days following around 102000 pascal of Barometric Pressure Barometric Pressure.

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

High Confidence: With 86 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 4,129 paired observations from 86 participants revealed a modest improvement in Distress following above-average Barometric Pressure exposure.

+8.6%
Change from Baseline
Modest effect on Distress
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.018
Correlation (r)
p = 0.266
Significance
z = 0.93
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Barometric Pressure:

  • Distress increased by 8.6% on average
  • Temporal analysis supports Barometric Pressure 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.

Optimal Daily Values (Precision Dosing)

Based on the observed relationship, we can estimate the predictor values associated with the best and worst outcomes. These values enable personalized dosing recommendations.

ℹ️ Moderate Confidence: Based on 86 participants. Values are reasonably reliable but may refine with additional data.

100,293.6 Pa
Value Predicting Higher Distress
Average Barometric Pressure when Distress exceeded its mean
101,104.8 Pa
Value Predicting Lower Distress
Average Barometric Pressure when Distress was below its mean

What This Suggests

Distress tended to be highest when Barometric Pressure was around 100,293.6 Pa.

Important: These values reflect correlations, not guaranteed causal effects. Individual responses may vary. Use as a starting point for personal experimentation, not as a definitive prescription. Consult healthcare providers before making treatment decisions.

Population Correlation

Barometric Pressure Distribution

Distress Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Barometric Pressure
Effect Variable Name Distress
Sinn Predictive Coefficient 0.01769674106226
Confidence Level HIGH
Confidence Interval 0.39146983454281
Forward Pearson Predictive Coefficient 0.0177
Critical T Value 1.716988372093
Average Barometric Pressure Over Previous 7 days Before ABOVE Average Distress 72300 pascal
Average Barometric Pressure Over Previous 7 days Before BELOW Average Distress 102000 pascal
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 4129
Optimal Pearson Product 0.11871398960958
P Value 0.17240919263239
Statistical Significance 0.266
Strength of Relationship 0.39146983454281
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 86

Barometric Pressure Info

Property Value
Variable Name Barometric Pressure
Aggregation Method MEAN
Analysis Performed At 2021-02-28
Duration of Action 7 days
Kurtosis 5.6618049086104
Maximum Allowed Value 1113250 pascal
Mean 101628.07699443 pascal
Median 101662.40909091 pascal
Minimum Allowed Value 10132 pascal
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 488
Number of Measurements 2807
Number of Measurements (including those generated by tagged, joined, or child variables) 2807
Public true
Onset Delay 0 seconds
Standard Deviation 555.26877913763
Unit Pascal
User Variables 1082
UPC 794628323701
Variable Category Environment
Variable ID 96380
Variance 547086.06358889

Distress Info

Property Value
Variable Name Distress
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 2.8522483497534
Maximum Allowed Value 5 out of 5
Mean 2.4777342286981 out of 5
Median 2.4229733332357 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1209
Number of Aggregate Outcomes 140
Number of Measurements 33096
Number of Measurements (including those generated by tagged, joined, or child variables) 32968
Public true
Onset Delay 0 seconds
Standard Deviation 0.53719668856315
Unit 1 to 5 Rating
User Variables 1486
UPC 647297398818
Variable Category Emotions
Variable ID 1305
Variance 0.6312526863217

Introduction

Background

Barometric Pressure (Environment) and Distress (Emotions) 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 Barometric Pressure affect Distress?

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 Barometric Pressure for maximizing Distress?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Barometric Pressure and Distress. Additionally, we attempt to determine the Barometric Pressure values most likely to produce optimal Distress 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 8.6% improvement in Distress following above-average Barometric Pressure 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 Distress is statistically significant at a 95% confidence interval. The p-value of 0.2660 indicates there is less than a 26.60% probability that this result occurred by chance.

After treatment, a 1.8% increase (0.104 out of 5) from the mean baseline 2.38 out of 5 was observed. The relative standard deviation at baseline was 27.8988%. The observed change was 0.931841 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: 2.097
Critical t-value: 1.717

Since t = 2.10 > 1.72, we 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 Barometric Pressure might influence Distress.

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 Barometric Pressure 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 Barometric Pressure
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

Above-average Barometric Pressure was associated with a 8.6% improvement in Distress—a modest 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 8.6% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available.

These findings contribute to our understanding of how Barometric Pressure may influence Distress in real-world conditions. While preliminary, these results may inform future research directions.

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Methods

Study Design

This study is based on data donated by 86 participants. Thus, the study design is equivalent to the aggregation of 86 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 42 seconds would pass before a change in Barometric Pressure would produce an observable change in Distress.
  • Duration of Action: It was assumed that Barometric Pressure could produce an observable change in Distress for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Barometric Pressure values and subsequent Distress 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

Barometric Pressure data was primarily collected using General Spreadsheet. Import from a spreadsheet containing a Variable Name, Value, Measurement Event Time, and Abbreviated Unit Name field. Here is an <a href="http://bit.ly/2jz7CNl" target="_blank">example spreadsheet</a> with allowed column names, units and time format.

Distress data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

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 Barometric Pressure Affect Distress?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-96380-effect-1305-population-study
BibTeX 
@misc{sinn_cause_96380_effect_1305_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Barometric Pressure Affect Distress?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-96380-effect-1305-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Barometric Pressure Affect Distress?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-96380-effect-1305-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Barometric Pressure Affect Distress?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-96380-effect-1305-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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