Higher Wearing Sunscreen Predicts Very Slightly Higher Walk Or Run Distance for Population

Higher Wearing Sunscreen Predicts Very Slightly Higher Walk Or Run Distance for Population

Contents

Variables

A
Wearing Sunscreen 17
A
Walk or Run Distance 891

Categories

A
Treatments 9356
A
Physical Activity 1719

Actions

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Your Data

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Low Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.8% average decrease in Walk Or Run Distance following above average Wearing Sunscreen.

Abstract

Walk Or Run Distance was generally 0% higher than average after a total of 3 applications of Wearing Sunscreen over the previous 21 days.

Aggregated data from 1 study participants suggests with a LOW degree of confidence (p=0.385, 95% CI -783.222 to 783.273) that Wearing Sunscreen has a very weakly positive predictive relationship (R=0.0251) with Walk Or Run Distance.

The highest quartile of Walk Or Run Distance measurements were observed following an average 0.143 applications Wearing Sunscreen per day.

The lowest quartile of Walk Or Run Distance measurements were observed following an average 0.136 applications of Wearing Sunscreen per day.

After an onset delay of 30 minutes, Walk Or Run Distance is typically 0% lower than average over the 21 days following around 0.136 applications of Wearing Sunscreen Wearing Sunscreen.

Keywords: Wearing Sunscreen, Walk Or Run Distance, 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 100 paired observations from 1 participants revealed a minimal improvement in Walk Or Run Distance following above-average Wearing Sunscreen exposure.

+2.7%
Change from Baseline
Minimal effect on Walk Or Run Distance
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Low
Confidence
0.025
Correlation (r)
p = 0.359
Significance
z = 0.05
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Wearing Sunscreen:

  • Walk Or Run Distance increased by 2.7% on average
  • Temporal analysis supports Wearing Sunscreen 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 Wearing Sunscreen values associated with high and low Walk Or Run Distance 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

Wearing Sunscreen Distribution

Walk Or Run Distance Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Wearing Sunscreen
Effect Variable Name Walk Or Run Distance
Sinn Predictive Coefficient 0.0011942904132913
Confidence Level LOW
Confidence Interval 783.24756630717
Forward Pearson Predictive Coefficient 0.0251
Critical T Value 1.66
Total Wearing Sunscreen Over Previous 21 days Before ABOVE Average Walk Or Run Distance 0.143 applications
Total Wearing Sunscreen Over Previous 21 days Before BELOW Average Walk Or Run Distance 0.136 applications
Duration of Action 21 days
Effect Size very weakly positive
Number of Paired Measurements 100
Optimal Pearson Product 0.0013105117859399
P Value 0.38522675621832
Statistical Significance 0.3585
Strength of Relationship 783.24756630717
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Wearing Sunscreen Info

Property Value
Variable Name Wearing Sunscreen
Aggregation Method SUM
Analysis Performed At 2020-09-15
Duration of Action 21 days
Filling Value 0
Kurtosis 6.5075665859564
Maximum Allowed Value 20 applications
Mean 0.11864 applications
Median 0 applications
Minimum Allowed Value 0 applications
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 17
Number of Measurements 47
Number of Measurements (including those generated by tagged, joined, or child variables) 47
Public true
Onset Delay 30 minutes
Standard Deviation 0.3247482002983
Unit Applications
User Variables 2
UPC 883836474975
Variable Category Treatments
Variable ID 1339
Variance 0.10546139359699

Walk or Run Distance Info

Property Value
Variable Name Walk Or Run Distance
Aggregation Method SUM
Analysis Performed At 2020-09-11
Duration of Action 7 days
Kurtosis 18.843598392696
Maximum Allowed Value 175000 meters
Mean 3558.0042632859 meters
Median 3158.90008036 meters
Minimum Allowed Value 1 meters
Number of Aggregate Predictors 642
Number of Aggregate Outcomes 249
Number of Measurements 123085
Number of Measurements (including those generated by tagged, joined, or child variables) 87104
Public true
Onset Delay 0 seconds
Standard Deviation 2361.166855319
Unit Meters
User Variables 378
UPC 744960759935
Variable Category Physical Activity
Variable ID 1304
Variance 9674812.3231088

Introduction

Background

Wearing Sunscreen (Treatments) and Walk Or Run Distance (Physical Activity) 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 Wearing Sunscreen affect Walk Or Run Distance?

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 Wearing Sunscreen for maximizing Walk Or Run Distance?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Wearing Sunscreen and Walk Or Run Distance. Additionally, we attempt to determine the Wearing Sunscreen values most likely to produce optimal Walk Or Run Distance 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 2.7% improvement in Walk Or Run Distance following above-average Wearing Sunscreen 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 Walk Or Run Distance is not statistically significant at a 95% confidence interval. This suggests that the Wearing Sunscreen value may not have a significant influence on the Walk Or Run Distance value, or that more data is needed to detect an effect.

After treatment, a 0.8% decrease (125 meters) from the mean baseline 4540 meters was observed. The relative standard deviation at baseline was 50.6%. The observed change was 0.0543284 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: 0.265
Critical t-value: 1.660

Since t = 0.26 < 1.66, 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 Wearing Sunscreen might influence Walk Or Run Distance.

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 Wearing Sunscreen 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 Wearing Sunscreen
  • 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 Wearing Sunscreen was associated with a 2.7% improvement in Walk Or Run Distance—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 2.7% 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 Wearing Sunscreen may influence Walk Or Run Distance 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 30 minutes would pass before a change in Wearing Sunscreen would produce an observable change in Walk Or Run Distance.
  • Duration of Action: It was assumed that Wearing Sunscreen could produce an observable change in Walk Or Run Distance for as much as 21 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Wearing Sunscreen values and subsequent Walk Or Run Distance 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

Wearing Sunscreen data was primarily collected using GitHub. GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over four million people use GitHub to build amazing things together.

Walk Or Run Distance data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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 Wearing Sunscreen Affect Walk Or Run Distance?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1339-effect-1304-population-study
BibTeX 
@misc{sinn_cause_1339_effect_1304_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Wearing Sunscreen Affect Walk Or Run Distance?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1339-effect-1304-population-study},
  note = {Accessed: January 6, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Wearing Sunscreen Affect Walk Or Run Distance?." The Journal of Citizen Science. Accessed January 6, 2026. https://studies.crowdsourcingcures.org/study/cause-1339-effect-1304-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Wearing Sunscreen Affect Walk Or Run Distance?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1339-effect-1304-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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