Higher Cloud Cover Amount Predicts Slightly Higher Fear for Population

Higher Cloud Cover Amount Predicts Slightly Higher Fear for Population

Contents

Variables

A
Cloud Cover Amount 231
A
Fear 1115

Categories

A
Environment 564
A
Emotions 2028

Actions

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Your Data

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High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 4% average increase in Fear following above average Cloud Cover Amount.

Abstract

Fear was generally 0.410714% higher than average after 50.5 percent of Cloud Cover Amount per 7 days.

Aggregated data from 56 study participants suggests with a HIGH degree of confidence (p=0.209, 95% CI -0.251 to 0.46) that Cloud Cover Amount has a weakly positive predictive relationship (R=0.104) with Fear.

The highest quartile of Fear measurements were observed following an average 50.6 percent Cloud Cover Amount.

The lowest quartile of Fear measurements were observed following an average 43.4 percent of Cloud Cover Amount.

After an onset delay of 0 seconds, Fear is typically 5% lower than average over the 7 days following around 43.4 percent of Cloud Cover Amount Cloud Cover Amount.

Keywords: Cloud Cover Amount, Fear, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 56 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 2,473 paired observations from 56 participants revealed a minimal improvement in Fear following above-average Cloud Cover Amount exposure.

+0.4%
Change from Baseline
Minimal effect on Fear
0.10
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

High
Confidence
0.104
Correlation (r)
p = 0.250
Significance
z = 0.52
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Cloud Cover Amount:

  • Fear increased by 0.4% on average
  • Temporal analysis supports Cloud Cover Amount 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 56 participants. Values are reasonably reliable but may refine with additional data.

50.0 %
Value Predicting Higher Fear
Average Cloud Cover Amount when Fear exceeded its mean
50.5 %
Value Predicting Lower Fear
Average Cloud Cover Amount when Fear was below its mean

What This Suggests

Fear tended to be highest when Cloud Cover Amount was around 50.0 %.

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

Cloud Cover Amount Distribution

Fear Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Cloud Cover Amount
Effect Variable Name Fear
Sinn Predictive Coefficient 0.10381468068857
Confidence Level HIGH
Confidence Interval 0.35566727930851
Forward Pearson Predictive Coefficient 0.1042
Critical T Value 1.7183035714286
Average Cloud Cover Amount Over Previous 7 days Before ABOVE Average Fear 50.6 percent
Average Cloud Cover Amount Over Previous 7 days Before BELOW Average Fear 43.4 percent
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 2473
Optimal Pearson Product 0.064187084715343
P Value 0.20904792394179
Statistical Significance 0.2496
Strength of Relationship 0.35566727930851
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 56

Cloud Cover Amount Info

Property Value
Variable Name Cloud Cover Amount
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 2.0115150048528
Maximum Allowed Value 100 percent
Mean 44.949018299081 percent
Median 40.628521126761 percent
Minimum Allowed Value 0 percent
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 231
Number of Measurements 171797
Number of Measurements (including those generated by tagged, joined, or child variables) 1437
Public true
Onset Delay 0 seconds
Standard Deviation 35.25604949636
Unit Percent
User Variables 284
UPC 0
Variable Category Environment
Variable ID 5954747
Variance 1291.6193754063

Fear Info

Property Value
Variable Name Fear
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 3.4116931250559
Maximum Allowed Value 5 out of 5
Mean 2.2840202686203 out of 5
Median 2.2359612572647 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1002
Number of Aggregate Outcomes 113
Number of Measurements 22861
Number of Measurements (including those generated by tagged, joined, or child variables) 22709
Public true
Onset Delay 0 seconds
Standard Deviation 0.47483589524388
Unit 1 to 5 Rating
User Variables 1670
UPC 0
Variable Category Emotions
Variable ID 1313
Variance 0.56609015151219

Introduction

Background

Cloud Cover Amount (Environment) and Fear (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 Cloud Cover Amount affect Fear?

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 Cloud Cover Amount for maximizing Fear?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Cloud Cover Amount and Fear. Additionally, we attempt to determine the Cloud Cover Amount values most likely to produce optimal Fear 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 0.4% improvement in Fear following above-average Cloud Cover Amount exposure. The Predictor Impact Score (PIS) of 0.10 indicates weak evidence for a causal relationship.

Statistical Significance

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

After treatment, a 4% increase (-0.00903 out of 5) from the mean baseline 2.12 out of 5 was observed. The relative standard deviation at baseline was 26.3661%. The observed change was 0.517197 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: 1.283
Critical t-value: 1.718

Since t = 1.28 < 1.72, 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 Cloud Cover Amount might influence Fear.

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 Cloud Cover Amount 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 Cloud Cover Amount
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

Above-average Cloud Cover Amount was associated with a 0.4% improvement in Fear—a minimal effect. The Predictor Impact Score of 0.10 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.10 and a 0.4% effect size, this relationship shows weak evidence. Additional observational data is recommended before investing in experimental validation.

These findings contribute to our understanding of how Cloud Cover Amount may influence Fear 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 56 participants. Thus, the study design is equivalent to the aggregation of 56 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 Cloud Cover Amount would produce an observable change in Fear.
  • Duration of Action: It was assumed that Cloud Cover Amount could produce an observable change in Fear for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Cloud Cover Amount values and subsequent Fear 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

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

Fear 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 Cloud Cover Amount Affect Fear?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1313-population-study
BibTeX 
@misc{sinn_cause_5954747_effect_1313_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Cloud Cover Amount Affect Fear?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1313-population-study},
  note = {Accessed: January 6, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Cloud Cover Amount Affect Fear?." The Journal of Citizen Science. Accessed January 6, 2026. https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1313-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Cloud Cover Amount Affect Fear?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954747-effect-1313-population-study [Accessed January 6, 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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