Higher Outdoor Humidity Predicts Very Slightly Higher Very Unproductive Score for Population

Higher Outdoor Humidity Predicts Very Slightly Higher Very Unproductive Score for Population

Contents

Variables

A
Outdoor Humidity 493
A
Very Unproductive Score 241

Categories

A
Environment 564
A
Goals 126

Tags

Medium Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 7.8% average decrease in Very Unproductive Score following above average Outdoor Humidity.

Abstract

Very Unproductive Score was generally 8% higher than average after an average of 68.8 percent of Outdoor Humidity over the previous 7 days.

Aggregated data from 6 study participants suggests with a MEDIUM degree of confidence (p=0.27, 95% CI -0.249 to 0.259) that Outdoor Humidity has a very weakly positive predictive relationship (R=0.0051) with Very Unproductive Score.

The highest quartile of Very Unproductive Score measurements were observed following an average 67.2 percent Outdoor Humidity.

The lowest quartile of Very Unproductive Score measurements were observed following an average 68.2 percent of Outdoor Humidity.

After an onset delay of 0 seconds, Very Unproductive Score is typically 11% lower than average over the 7 days following around 68.2 percent of Outdoor Humidity Outdoor Humidity.

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

Preliminary: Based on 6 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 370 paired observations from 6 participants revealed a minimal improvement in Very Unproductive Score following above-average Outdoor Humidity exposure.

+0.0%
Change from Baseline
Minimal effect on Very Unproductive Score
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.005
Correlation (r)
p = 0.469
Significance
z = 0.31
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Outdoor Humidity:

  • Very Unproductive Score increased by 0.0% on average
  • 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. With only 6 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 Outdoor Humidity values associated with high and low Very Unproductive Score 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

Outdoor Humidity Distribution

Very Unproductive Score Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Outdoor Humidity
Effect Variable Name Very Unproductive Score
Sinn Predictive Coefficient 0.0011505303694762
Confidence Level MEDIUM
Confidence Interval 0.25383133950538
Forward Pearson Predictive Coefficient 0.0051
Critical T Value 1.6728333333333
Average Outdoor Humidity Over Previous 7 days Before ABOVE Average Very Unproductive Score 67.2 percent
Average Outdoor Humidity Over Previous 7 days Before BELOW Average Very Unproductive Score 68.2 percent
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 370
Optimal Pearson Product 0.10339616904203
P Value 0.27007256560614
Statistical Significance 0.4691
Strength of Relationship 0.25383133950538
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 6

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

Very Unproductive Score Info

Property Value
Variable Name Very Unproductive Score
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Filling Value 0
Kurtosis 25.311779979718
Mean 0.4385353065176 percent
Median 0.090475598290598 percent
Number of Aggregate Predictors 190
Number of Aggregate Outcomes 51
Number of Measurements 931
Number of Measurements (including those generated by tagged, joined, or child variables) 77
Public true
Onset Delay 0 seconds
Standard Deviation 0.6020795343691
Unit Percent
User Variables 26
Variable Category Goals
Variable ID 6057116
Variance 0.6757506944964

Introduction

Background

Outdoor Humidity (Environment) and Very Unproductive Score (Goals) 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 Very Unproductive Score?

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 Very Unproductive Score?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Outdoor Humidity and Very Unproductive Score. Additionally, we attempt to determine the Outdoor Humidity values most likely to produce optimal Very Unproductive Score 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 0.0% improvement in Very Unproductive Score following above-average Outdoor Humidity exposure. The Predictor Impact Score (PIS) of 0.00 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 Very Unproductive Score is not statistically significant at a 95% confidence interval. This suggests that the Outdoor Humidity value may not have a significant influence on the Very Unproductive Score value, or that more data is needed to detect an effect.

After treatment, a 7.8% decrease (0.0242 percent) from the mean baseline 0.384 percent was observed. The relative standard deviation at baseline was 0.483333%. The observed change was 0.313776 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: 1.188
Critical t-value: 1.673

Since t = 1.19 < 1.67, 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 Outdoor Humidity might influence Very Unproductive Score.

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

📊 Preliminary Findings: With 6 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 Outdoor Humidity was associated with a 0.0% improvement in Very Unproductive Score—a minimal effect. The Predictor Impact Score of 0.00 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.00 and a 0.0% 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 Outdoor Humidity may influence Very Unproductive Score 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 6 participants. Thus, the study design is equivalent to the aggregation of 6 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 Very Unproductive Score.
  • Duration of Action: It was assumed that Outdoor Humidity could produce an observable change in Very Unproductive Score 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 Very Unproductive Score 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.

Very Unproductive Score data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.

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 Very Unproductive Score?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954744-effect-6057116-population-study
BibTeX 
@misc{sinn_cause_5954744_effect_6057116_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Outdoor Humidity Affect Very Unproductive Score?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954744-effect-6057116-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Outdoor Humidity Affect Very Unproductive Score?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5954744-effect-6057116-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Outdoor Humidity Affect Very Unproductive Score?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954744-effect-6057116-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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