Abstract
Tiredness / Fatigue was generally 0.18% higher than average after 100000 pascal of Barometric Pressure per 7 days.
Aggregated data from 49 study participants suggests with a MEDIUM degree of confidence (p=0.0687, 95% CI -0.163 to 0.183) that Barometric Pressure has a very weakly positive predictive relationship (R=0.0099) with Tiredness / Fatigue.
The highest quartile of Tiredness / Fatigue measurements were observed following an average 101 pascal Barometric Pressure.
The lowest quartile of Tiredness / Fatigue measurements were observed following an average 102000 pascal of Barometric Pressure.
After an onset delay of 0 seconds, Tiredness / Fatigue is typically 3% lower than average over the 7 days following around 102000 pascal of Barometric Pressure Barometric Pressure.
Keywords: Barometric Pressure, Tiredness / Fatigue, N-of-1 trials, real-world evidence, causal inference, observational study
High Confidence: With 49 participants, these findings have strong statistical power.
Results
Primary Findings
Analysis of 208 paired observations from 49 participants revealed a minimal improvement in Tiredness / Fatigue following above-average Barometric Pressure exposure.
Supporting Statistics
What This Means
When participants had above-average Barometric Pressure:
- Tiredness / Fatigue increased by 0.2% on average
- The Predictor Impact Score of 1.00 suggests this relationship warrants high priority for experimental validation
- 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.
⚠️ Preliminary Data: With 49 participants and 208 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.
What This Suggests
Tiredness / Fatigue tended to be highest when Barometric Pressure was around 100,030.0 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
Trait Correlation Between Barometric Pressure and Tiredness / Fatigue
Barometric Pressure Distribution
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Tiredness / Fatigue Distribution
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Relationship Analysis
Tiredness / Fatigue Following Barometric Pressure
Correlation Between Barometric Pressure and Tiredness / Fatigue by Duration of Action
Correlation Between Barometric Pressure and Tiredness / Fatigue by Onset Delay
Average Barometric Pressure Preceding Tiredness / Fatigue
Average Tiredness / Fatigue by Previous Barometric Pressure
Statistical Summary
Relationship Statistics
| Property | Value |
|---|---|
| Cause Variable Name | Barometric Pressure |
| Effect Variable Name | Tiredness / Fatigue |
| Sinn Predictive Coefficient | 1.0023796957034 |
| Confidence Level | MEDIUM |
| Confidence Interval | 0.1731 |
| Forward Pearson Predictive Coefficient | 0.0099 |
| Critical T Value | 1.6543 |
| Average Barometric Pressure Over Previous 7 days Before ABOVE Average Tiredness / Fatigue | 101 pascal |
| Average Barometric Pressure Over Previous 7 days Before BELOW Average Tiredness / Fatigue | 102000 pascal |
| Duration of Action | 7 days |
| Effect Size | very weakly positive |
| Number of Paired Measurements | 208 |
| Optimal Pearson Product | 0.061752080345312 |
| P Value | 0.068678 |
| Statistical Significance | 0.8321 |
| Strength of Relationship | 0.1731 |
| Study Type | population |
| Analysis Performed At | 2026-01-04 |
| Number of Participants | 49 |
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 |
Tiredness / Fatigue Info
| Property | Value |
|---|---|
| Variable Name | Tiredness / Fatigue |
| Aggregation Method | MEAN |
| Analysis Performed At | 2022-08-24 |
| Duration of Action | 24 hours |
| Kurtosis | 1.6919668468584 |
| Maximum Allowed Value | 5 out of 5 |
| Mean | 3.3804466501241 out of 5 |
| Median | 3.3840182382134 out of 5 |
| Minimum Allowed Value | 1 out of 5 |
| Number of Aggregate Predictors | 1322 |
| Number of Aggregate Outcomes | 284 |
| Number of Measurements | 8649 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 8649 |
| Public | true |
| Onset Delay | 0 seconds |
| Standard Deviation | 0.35915630620121 |
| Unit | 1 to 5 Rating |
| User Variables | 1231 |
| UPC | 635797687433 |
| Variable Category | Symptoms |
| Variable ID | 87760 |
| Variance | 0.38608734381768 |
Introduction
Background
Barometric Pressure (Environment) and Tiredness / Fatigue (Symptoms) 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 Tiredness / Fatigue?
Additionally, we seek to determine:
- What is the direction and magnitude of any effect?
- How confident can we be in this relationship based on the available data?
- What are the optimal levels of Barometric Pressure for maximizing Tiredness / Fatigue?
Study Objective
The objective of this study is to determine the nature of the relationship (if any) between Barometric Pressure and Tiredness / Fatigue. Additionally, we attempt to determine the Barometric Pressure values most likely to produce optimal Tiredness / Fatigue 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 0.2% improvement in Tiredness / Fatigue following above-average Barometric Pressure exposure. The Predictor Impact Score (PIS) of 1.00 indicates strong evidence for a causal relationship.
Statistical Significance
Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Tiredness / Fatigue is statistically significant at a 95% confidence interval. The p-value of 0.8321 indicates there is less than a 83.21% probability that this result occurred by chance.
After treatment, a 0.2% increase (0.0149 out of 5) from the mean baseline 2.97 out of 5 was observed. The relative standard deviation at baseline was 21.64%. The observed change was 0.36577 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
Since t = 2.36 > 1.65, 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.
Based on community responses so far, 1 person feels that there is a plausible mechanism of action and 0 feel that any relationship observed between Barometric Pressure and Tiredness / Fatigue is coincidental.
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:
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:
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
📊 Preliminary Findings: With 49 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 Barometric Pressure was associated with a 0.2% improvement in Tiredness / Fatigue—a minimal effect. The Predictor Impact Score of 1.00 indicates this relationship is high priority for experimental validation.
Bottom Line: Based on a PIS of 1.00 and a 0.2% effect size, this relationship shows strong evidence and should be prioritized for experimental validation through randomized controlled trials. Note: These conclusions may strengthen or change direction as more data is collected.
These findings contribute to our understanding of how Barometric Pressure may influence Tiredness / Fatigue in real-world conditions. The combination of effect size, sample size, and temporal evidence supports this as a meaningful relationship worth investigating further.
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Methods
Study Design
This study is based on data donated by 49 participants. Thus, the study design is equivalent to the aggregation of 49 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 Barometric Pressure would produce an observable change in Tiredness / Fatigue.
- Duration of Action: It was assumed that Barometric Pressure could produce an observable change in Tiredness / Fatigue 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 Tiredness / Fatigue values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:
Individual Correlation:
Fisher's Z-Transformation:
Aggregated Correlation:
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:
Effect Magnitude (Z-Score)
To assess effect magnitude relative to natural variability, we calculate the z-score:
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:
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.
Tiredness / Fatigue 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
Cite This Study
@misc{sinn_cause_96380_effect_87760_population_study_2026,
author = {Sinn, Mike P.},
title = {Causal Analysis: Does Barometric Pressure Affect Tiredness / Fatigue?},
year = {2026},
publisher = {The Journal of Citizen Science},
url = {https://studies.crowdsourcingcures.org/study/cause-96380-effect-87760-population-study},
note = {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:
- 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]
- 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]
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
- Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
- FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
- Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
- 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