Higher Walk Or Run Distance Predicts Very Slightly Higher Body Weight for Population

Higher Walk Or Run Distance Predicts Very Slightly Higher Body Weight for Population

Contents

Variables

A
Walk or Run Distance 891
A
Body Weight 1140

Categories

A
Physical Activity 1719
A
Physique 41

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 1.9% average increase in Body Weight following above average Walk Or Run Distance.

Abstract

Body Weight was generally 4% higher than average after a total of 1310 meters of Walk Or Run Distance over the previous 7 days.

Aggregated data from 71 study participants suggests with a HIGH degree of confidence (p=0.132, 95% CI -4.143 to 4.179) that Walk Or Run Distance has a very weakly positive predictive relationship (R=0.018) with Body Weight.

The highest quartile of Body Weight measurements were observed following an average 178 meters Walk Or Run Distance per day.

The lowest quartile of Body Weight measurements were observed following an average 3870 meters of Walk Or Run Distance per day.

After an onset delay of 0 seconds, Body Weight is typically 2% lower than average over the 7 days following around 3870 meters of Walk Or Run Distance Walk Or Run Distance.

Keywords: Walk Or Run Distance, Body Weight, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 71 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 16,454 paired observations from 71 participants revealed a minimal improvement in Body Weight following above-average Walk Or Run Distance exposure.

+1.3%
Change from Baseline
Minimal effect on Body Weight
0.51
Predictor Impact Score
Strong evidence for causal relationship

Supporting Statistics

High
Confidence
0.018
Correlation (r)
p = 0.782
Significance
z = 0.70
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Walk Or Run Distance:

  • Body Weight increased by 1.3% on average
  • The Predictor Impact Score of 0.51 suggests this relationship warrants high priority for experimental validation
  • Temporal analysis supports Walk Or Run Distance 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 71 participants. Values are reasonably reliable but may refine with additional data.

1,311.2 m
Value Predicting Higher Body Weight
Average Walk Or Run Distance when Body Weight exceeded its mean
1,497.4 m
Value Predicting Lower Body Weight
Average Walk Or Run Distance when Body Weight was below its mean

What This Suggests

Body Weight tended to be highest when Walk Or Run Distance was around 1,311.2 m.

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

Walk Or Run Distance Distribution

Body Weight Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Walk Or Run Distance
Effect Variable Name Body Weight
Sinn Predictive Coefficient 0.50858002120695
Confidence Level HIGH
Confidence Interval 4.1606906003002
Forward Pearson Predictive Coefficient 0.018
Critical T Value 1.6674366197183
Total Walk Or Run Distance Over Previous 7 days Before ABOVE Average Body Weight 178 meters
Total Walk Or Run Distance Over Previous 7 days Before BELOW Average Body Weight 3870 meters
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 16454
Optimal Pearson Product 0.1066259939802
P Value 0.13223540544232
Statistical Significance 0.7821
Strength of Relationship 4.1606906003002
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 71

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

Body Weight Info

Property Value
Variable Name Body Weight
Aggregation Method MEAN
Analysis Performed At 2020-09-23
Duration of Action 7 days
Kurtosis 29.271534088526
Maximum Allowed Value 1000 pounds
Mean 168.9619340574 pounds
Median 168.27481272906 pounds
Minimum Allowed Value 0 pounds
Number of Aggregate Predictors 883
Number of Aggregate Outcomes 257
Number of Measurements 108822
Number of Measurements (including those generated by tagged, joined, or child variables) 21092
Public true
Onset Delay 0 seconds
Standard Deviation 8.7190661755282
Unit Pounds
User Variables 417
UPC 875011003902
Variable Category Physique
Variable ID 1486
Variance 594.35417755402

Introduction

Background

Walk Or Run Distance (Physical Activity) and Body Weight (Physique) 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 Walk Or Run Distance affect Body Weight?

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 Walk Or Run Distance for maximizing Body Weight?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Walk Or Run Distance and Body Weight. Additionally, we attempt to determine the Walk Or Run Distance values most likely to produce optimal Body Weight 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 1.3% improvement in Body Weight following above-average Walk Or Run Distance exposure. The Predictor Impact Score (PIS) of 0.51 indicates strong evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Body Weight is statistically significant at a 95% confidence interval. The p-value of 0.7821 indicates there is less than a 78.21% probability that this result occurred by chance.

After treatment, a 1.9% increase (2.02 pounds) from the mean baseline 168 pounds was observed. The relative standard deviation at baseline was 5.92254%. The observed change was 0.70092 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: 4.574
Critical t-value: 1.667

Since t = 4.57 > 1.67, 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.

Based on community responses so far, 1 person feels that there is a plausible mechanism of action and 0 feel that any relationship observed between Walk Or Run Distance and Body Weight is coincidental.

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 Walk Or Run Distance 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 Walk Or Run Distance
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

Above-average Walk Or Run Distance was associated with a 1.3% improvement in Body Weight—a minimal effect. The Predictor Impact Score of 0.51 indicates this relationship is high priority for experimental validation.

Bottom Line: Based on a PIS of 0.51 and a 1.3% effect size, this relationship shows strong evidence and should be prioritized for experimental validation through randomized controlled trials.

These findings contribute to our understanding of how Walk Or Run Distance may influence Body Weight in real-world conditions. The combination of effect size, sample size, and temporal evidence supports this as a meaningful relationship worth investigating further.

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Methods

Study Design

This study is based on data donated by 71 participants. Thus, the study design is equivalent to the aggregation of 71 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 Walk Or Run Distance would produce an observable change in Body Weight.
  • Duration of Action: It was assumed that Walk Or Run Distance could produce an observable change in Body Weight for as much as 7 days after the stimulus event.

Statistical Methods

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

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

Body Weight 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 Walk Or Run Distance Affect Body Weight?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1304-effect-1486-population-study
BibTeX 
@misc{sinn_cause_1304_effect_1486_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Walk Or Run Distance Affect Body Weight?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1304-effect-1486-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Walk Or Run Distance Affect Body Weight?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1304-effect-1486-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Walk Or Run Distance Affect Body Weight?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1304-effect-1486-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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