Higher Average Daily Outdoor Temperature Predicts Slightly Lower Rheumetoid Arthritis for Population

Higher Average Daily Outdoor Temperature Predicts Slightly Lower Rheumetoid Arthritis for Population

Contents

Variables

A
Average Daily Outdoor Temperature 107
A
Rheumetoid Arthritis 858

Categories

A
Environment 564
A
Symptoms 13336

Tags

High Confidence
Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 17.7% average decrease in Rheumetoid Arthritis following above average Average Daily Outdoor Temperature.

Abstract

Rheumetoid Arthritis was generally 21.6% lower than average after 64 degrees fahrenheit of Average Daily Outdoor Temperature per 7 days.

Aggregated data from 1 study participants suggests with a HIGH degree of confidence (p=0.001, 95% CI -0.358 to -0.054) that Average Daily Outdoor Temperature has a weakly negative predictive relationship (R=-0.206) with Rheumetoid Arthritis.

The highest quartile of Rheumetoid Arthritis measurements were observed following an average 53.4 degrees fahrenheit Average Daily Outdoor Temperature.

The lowest quartile of Rheumetoid Arthritis measurements were observed following an average 63.5 degrees fahrenheit of Average Daily Outdoor Temperature.

After an onset delay of 0 seconds, Rheumetoid Arthritis is typically 4% lower than average over the 7 days following around 63.5 degrees fahrenheit of Average Daily Outdoor Temperature Average Daily Outdoor Temperature.

Keywords: Average Daily Outdoor Temperature, Rheumetoid Arthritis, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 1 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 422 paired observations from 1 participants revealed a moderate reduction in Rheumetoid Arthritis following above-average Average Daily Outdoor Temperature exposure.

-21.6%
Change from Baseline
Moderate effect on Rheumetoid Arthritis
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.206
Correlation (r)
p = 1.000
Significance
z = 0.57
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Average Daily Outdoor Temperature:

  • Rheumetoid Arthritis decreased by 21.6% on average
  • Temporal analysis supports Average Daily Outdoor Temperature 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 1 participants, these scores are preliminary and will become more reliable as additional data is collected.

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 1 participants and 422 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

52.8 F
Value Predicting Higher Rheumetoid Arthritis
Average Average Daily Outdoor Temperature when Rheumetoid Arthritis exceeded its mean
64.0 F
Value Predicting Lower Rheumetoid Arthritis
Average Average Daily Outdoor Temperature when Rheumetoid Arthritis was below its mean

What This Suggests

Rheumetoid Arthritis tended to be lowest (best) when Average Daily Outdoor Temperature was around 64.0 F.

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

Average Daily Outdoor Temperature Distribution

Rheumetoid Arthritis Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Average Daily Outdoor Temperature
Effect Variable Name Rheumetoid Arthritis
Sinn Predictive Coefficient 0.019593975487996
Confidence Level HIGH
Confidence Interval 0.15184517200806
Forward Pearson Predictive Coefficient -0.2059
Critical T Value 1.646
Average Average Daily Outdoor Temperature Over Previous 7 days Before ABOVE Average Rheumetoid Arthritis 53.4 degrees fahrenheit
Average Average Daily Outdoor Temperature Over Previous 7 days Before BELOW Average Rheumetoid Arthritis 63.5 degrees fahrenheit
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 422
Optimal Pearson Product 0.11429495657387
P Value 0.001
Statistical Significance 1
Strength of Relationship 0.15184517200806
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Average Daily Outdoor Temperature Info

Property Value
Variable Name Average Daily Outdoor Temperature
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 2.6039819469393
Maximum Allowed Value 134 degrees fahrenheit
Mean 63.749346093146 degrees fahrenheit
Median 64.281163531761 degrees fahrenheit
Minimum Allowed Value -87 degrees fahrenheit
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 107
Number of Measurements 95016
Number of Measurements (including those generated by tagged, joined, or child variables) 22144
Public true
Onset Delay 0 seconds
Standard Deviation 7.7762207716368
Unit Degrees Fahrenheit
User Variables 658
Variable Category Environment
Variable ID 6038776
Variance 118.83499021982

Rheumetoid Arthritis Info

Property Value
Variable Name Rheumetoid Arthritis
Aggregation Method MEAN
Analysis Performed At 2022-11-18
Duration of Action 24 hours
Kurtosis 3.1183583180922
Maximum Allowed Value 5 out of 5
Mean 2.0919 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 563
Number of Aggregate Outcomes 295
Number of Measurements 780
Number of Measurements (including those generated by tagged, joined, or child variables) 780
Public true
Onset Delay 0 seconds
Standard Deviation 1.0432592877799
Unit 1 to 5 Rating
User Variables 1
Variable Category Symptoms
Variable ID 6054464
Variance 1.088389941539

Introduction

Background

Average Daily Outdoor Temperature (Environment) and Rheumetoid Arthritis (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 Average Daily Outdoor Temperature affect Rheumetoid Arthritis?

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 Average Daily Outdoor Temperature for maximizing Rheumetoid Arthritis?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Average Daily Outdoor Temperature and Rheumetoid Arthritis. Additionally, we attempt to determine the Average Daily Outdoor Temperature values most likely to produce optimal Rheumetoid Arthritis 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 21.6% reduction in Rheumetoid Arthritis following above-average Average Daily Outdoor Temperature 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 Rheumetoid Arthritis is statistically significant at a 95% confidence interval. The p-value of 1.0000 indicates there is less than a 100.00% probability that this result occurred by chance.

After treatment, a 17.7% decrease (-0.524 out of 5) from the mean baseline 2.43 out of 5 was observed. The relative standard deviation at baseline was 37.6%. The observed change was 0.57 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: 4.815
Critical t-value: 1.646

Since t = 4.81 > 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.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Average Daily Outdoor Temperature might influence Rheumetoid Arthritis.

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 Average Daily Outdoor Temperature 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 Average Daily Outdoor Temperature
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 1 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 Average Daily Outdoor Temperature was associated with a 21.6% reduction in Rheumetoid Arthritis—a moderate 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 21.6% 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 Average Daily Outdoor Temperature may influence Rheumetoid Arthritis 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.

Help End Unnecessary Suffering

Current clinical trials are 82x more expensive than necessary and take 17 years to bring treatments to market. Pragmatic trials integrated into standard healthcare could reduce costs from $41,000 to $500 per participant and compress timelines to just 2 years. Learn how redirecting just 1% of global military spending could accelerate cures for the 2 billion people suffering from treatable diseases.

Make Suffering Optional

Methods

Study Design

This study is based on data donated by 1 participants. Thus, the study design is equivalent to the aggregation of 1 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 Average Daily Outdoor Temperature would produce an observable change in Rheumetoid Arthritis.
  • Duration of Action: It was assumed that Average Daily Outdoor Temperature could produce an observable change in Rheumetoid Arthritis for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Average Daily Outdoor Temperature values and subsequent Rheumetoid Arthritis 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

Average Daily Outdoor Temperature 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.

Rheumetoid Arthritis 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 Average Daily Outdoor Temperature Affect Rheumetoid Arthritis?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6054464-population-study
BibTeX 
@misc{sinn_cause_6038776_effect_6054464_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Average Daily Outdoor Temperature Affect Rheumetoid Arthritis?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6054464-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Average Daily Outdoor Temperature Affect Rheumetoid Arthritis?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6054464-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Average Daily Outdoor Temperature Affect Rheumetoid Arthritis?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6054464-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

LinkedIn Reddit