Higher Wind Speed Predicts Very Slightly Lower Upper Back Pain for Population

Higher Wind Speed Predicts Very Slightly Lower Upper Back Pain for Population

Contents

Variables

A
Wind Speed 146
A
Upper Back Pain 50

Categories

A
Environment 564
A
Symptoms 13336

Tags

Medium Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 3.3% average decrease in Upper Back Pain following above average Wind Speed.

Abstract

Upper Back Pain was generally 6.3% lower than average after 8.05 miles per hour of Wind Speed per 7 days.

Aggregated data from 1 study participants suggests with a MEDIUM degree of confidence (p=0.0608, 95% CI -0.088 to 0.071) that Wind Speed has a very weakly negative predictive relationship (R=-0.0085) with Upper Back Pain.

The highest quartile of Upper Back Pain measurements were observed following an average 8.19 miles per hour Wind Speed per day.

The lowest quartile of Upper Back Pain measurements were observed following an average 8 miles per hour of Wind Speed per day.

After an onset delay of 0 seconds, Upper Back Pain is typically 0% lower than average over the 7 days following around 8 miles per hour of Wind Speed Wind Speed.

Keywords: Wind Speed, Upper Back Pain, 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 472 paired observations from 1 participants revealed a modest reduction in Upper Back Pain following above-average Wind Speed exposure.

-6.3%
Change from Baseline
Modest effect on Upper Back Pain
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.009
Correlation (r)
p = 1.000
Significance
z = 0.17
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Wind Speed:

  • Upper Back Pain decreased by 6.3% on average
  • Temporal analysis supports Wind Speed 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

No clear dose-response relationship detected. The Wind Speed values associated with high and low Upper Back Pain 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

Wind Speed Distribution

Upper Back Pain Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Wind Speed
Effect Variable Name Upper Back Pain
Sinn Predictive Coefficient 0.00080888199065337
Confidence Level MEDIUM
Confidence Interval 0.079753769319036
Forward Pearson Predictive Coefficient -0.0085
Critical T Value 1.646
Total Wind Speed Over Previous 7 days Before ABOVE Average Upper Back Pain 8.19 miles per hour
Total Wind Speed Over Previous 7 days Before BELOW Average Upper Back Pain 8 miles per hour
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 472
Optimal Pearson Product -0.00034477697853493
P Value 0.060767614223095
Statistical Significance 1
Strength of Relationship 0.079753769319036
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Wind Speed Info

Property Value
Variable Name Wind Speed
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 6.3988813978043
Mean 7.1613504352846 miles per hour
Median 6.6608885542169 miles per hour
Minimum Allowed Value 0 miles per hour
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 146
Number of Measurements 95052
Number of Measurements (including those generated by tagged, joined, or child variables) 15334
Public true
Onset Delay 0 seconds
Standard Deviation 2.5999585544253
Unit Miles per Hour
User Variables 666
Variable Category Environment
Variable ID 6038789
Variance 12.985072605195

Upper Back Pain Info

Property Value
Variable Name Upper Back Pain
Aggregation Method MEAN
Analysis Performed At 2021-06-18
Duration of Action 24 hours
Kurtosis 2.864916296742
Maximum Allowed Value 5 out of 5
Mean 2.77534 out of 5
Median 2.5 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 29
Number of Aggregate Outcomes 21
Number of Measurements 837
Number of Measurements (including those generated by tagged, joined, or child variables) 837
Public true
Onset Delay 0 seconds
Standard Deviation 1.012158907872
Unit 1 to 5 Rating
User Variables 9
UPC 0
Variable Category Symptoms
Variable ID 90723
Variance 1.5663497462558

Introduction

Background

Wind Speed (Environment) and Upper Back Pain (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 Wind Speed affect Upper Back Pain?

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 Wind Speed for maximizing Upper Back Pain?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Wind Speed and Upper Back Pain. Additionally, we attempt to determine the Wind Speed values most likely to produce optimal Upper Back Pain 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 6.3% reduction in Upper Back Pain following above-average Wind Speed 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 Upper Back Pain 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 3.3% decrease (-0.0956 out of 5) from the mean baseline 1.52 out of 5 was observed. The relative standard deviation at baseline was 37.7%. The observed change was 0.17 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: 1.940
Critical t-value: 1.646

Since t = 1.94 > 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 Wind Speed might influence Upper Back Pain.

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 Wind Speed 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 Wind Speed
  • 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 Wind Speed was associated with a 6.3% reduction in Upper Back Pain—a modest 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 6.3% 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 Wind Speed may influence Upper Back Pain 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 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 Wind Speed would produce an observable change in Upper Back Pain.
  • Duration of Action: It was assumed that Wind Speed could produce an observable change in Upper Back Pain for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Wind Speed values and subsequent Upper Back Pain 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

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

Upper Back Pain 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 Wind Speed Affect Upper Back Pain?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-6038789-effect-90723-population-study
BibTeX 
@misc{sinn_cause_6038789_effect_90723_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Wind Speed Affect Upper Back Pain?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-6038789-effect-90723-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Wind Speed Affect Upper Back Pain?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-6038789-effect-90723-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Wind Speed Affect Upper Back Pain?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-6038789-effect-90723-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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