Higher Outdoor Humidity Predicts Very Slightly Lower Resilience for Population

Higher Outdoor Humidity Predicts Very Slightly Lower Resilience for Population

Contents

Variables

A
Outdoor Humidity 493
A
Resilience 1180

Categories

A
Environment 564
A
Emotions 2028

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 3.4% average decrease in Resilience following above average Outdoor Humidity.

Abstract

Resilience was generally 4% higher than average after an average of 66.3 percent of Outdoor Humidity over the previous 7 days.

Aggregated data from 67 study participants suggests with a HIGH degree of confidence (p=0.216, 95% CI -0.455 to 0.266) that Outdoor Humidity has a very weakly negative predictive relationship (R=-0.0947) with Resilience.

The highest quartile of Resilience measurements were observed following an average 65.8 percent Outdoor Humidity.

The lowest quartile of Resilience measurements were observed following an average 67.8 percent of Outdoor Humidity.

After an onset delay of 0 seconds, Resilience is typically 4% lower than average over the 7 days following around 67.8 percent of Outdoor Humidity Outdoor Humidity.

Keywords: Outdoor Humidity, Resilience, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 67 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 3,874 paired observations from 67 participants revealed a minimal reduction in Resilience following above-average Outdoor Humidity exposure.

-1.2%
Change from Baseline
Minimal effect on Resilience
1.09
Predictor Impact Score
Strong evidence for causal relationship

Supporting Statistics

High
Confidence
-0.095
Correlation (r)
p = 0.308
Significance
z = 0.46
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Outdoor Humidity:

  • Resilience decreased by 1.2% on average
  • The Predictor Impact Score of 1.09 suggests this relationship warrants high priority for experimental validation
  • Temporal analysis supports Outdoor Humidity 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

No clear dose-response relationship detected. The Outdoor Humidity values associated with high and low Resilience 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.

Population Correlation

Outdoor Humidity Distribution

Resilience Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Outdoor Humidity
Effect Variable Name Resilience
Sinn Predictive Coefficient 1.0933525219189
Confidence Level HIGH
Confidence Interval 0.36036998149079
Forward Pearson Predictive Coefficient -0.0947
Critical T Value 1.7125373134328
Average Outdoor Humidity Over Previous 7 days Before ABOVE Average Resilience 65.8 percent
Average Outdoor Humidity Over Previous 7 days Before BELOW Average Resilience 67.8 percent
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 3874
Optimal Pearson Product 0.11041097236732
P Value 0.2158955207846
Statistical Significance 0.3082
Strength of Relationship 0.36036998149079
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 67

Outdoor Humidity Info

Property Value
Variable Name Outdoor Humidity
Aggregation Method MEAN
Analysis Performed At 2020-09-11
Duration of Action 7 days
Kurtosis 2.6283326449295
Maximum Allowed Value 100 percent
Mean 68.41210747054 percent
Median 68.800418743769 percent
Minimum Allowed Value 0 percent
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 493
Number of Measurements 406399
Number of Measurements (including those generated by tagged, joined, or child variables) 114789
Public true
Onset Delay 0 seconds
Standard Deviation 11.29569799371
Unit Percent
User Variables 1037
UPC 0
Variable Category Environment
Variable ID 5954744
Variance 192.13369708306

Resilience Info

Property Value
Variable Name Resilience
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.9334519243983
Maximum Allowed Value 5 out of 5
Mean 2.5113766833812 out of 5
Median 2.4890897325572 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1070
Number of Aggregate Outcomes 110
Number of Measurements 25345
Number of Measurements (including those generated by tagged, joined, or child variables) 24386
Public true
Onset Delay 0 seconds
Standard Deviation 0.4900057068033
Unit 1 to 5 Rating
User Variables 1335
UPC 0
Variable Category Emotions
Variable ID 1436
Variance 0.52229282248446

Introduction

Background

Outdoor Humidity (Environment) and Resilience (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 Outdoor Humidity affect Resilience?

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 Outdoor Humidity for maximizing Resilience?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Outdoor Humidity and Resilience. Additionally, we attempt to determine the Outdoor Humidity values most likely to produce optimal Resilience 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 1.2% reduction in Resilience following above-average Outdoor Humidity exposure. The Predictor Impact Score (PIS) of 1.09 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 Resilience is not statistically significant at a 95% confidence interval. This suggests that the Outdoor Humidity value may not have a significant influence on the Resilience value, or that more data is needed to detect an effect.

After treatment, a 3.4% decrease (-0.0634 out of 5) from the mean baseline 2.7 out of 5 was observed. The relative standard deviation at baseline was 23.7179%. The observed change was 0.458995 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: 1.364
Critical t-value: 1.713

Since t = 1.36 < 1.71, 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.

Based on community responses so far, 2 people feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Outdoor Humidity and Resilience 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:

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

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 Outdoor Humidity 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 Outdoor Humidity
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

Above-average Outdoor Humidity was associated with a 1.2% reduction in Resilience—a minimal effect. The Predictor Impact Score of 1.09 indicates this relationship is high priority for experimental validation.

Bottom Line: Based on a PIS of 1.09 and a 1.2% effect size, this relationship shows strong evidence and should be prioritized for experimental validation through randomized controlled trials.

These findings contribute to our understanding of how Outdoor Humidity may influence Resilience 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 67 participants. Thus, the study design is equivalent to the aggregation of 67 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 Outdoor Humidity would produce an observable change in Resilience.
  • Duration of Action: It was assumed that Outdoor Humidity could produce an observable change in Resilience for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Outdoor Humidity values and subsequent Resilience 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

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

Resilience 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 Outdoor Humidity Affect Resilience?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1436-population-study
BibTeX 
@misc{sinn_cause_5954744_effect_1436_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Outdoor Humidity Affect Resilience?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1436-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Outdoor Humidity Affect Resilience?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1436-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Outdoor Humidity Affect Resilience?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954744-effect-1436-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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