Higher Eggs Consumption Predicts Very Slightly Higher Daily Step Count for Population

Higher Eggs Consumption Predicts Very Slightly Higher Daily Step Count for Population

Contents

Variables

A
Eggs 89
A
Steps 331

Categories

A
Foods 13415
A
Physical Activity 1719

Actions

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Join Study
A
Your Data

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 16.3% average decrease in Daily Step Count following above average Eggs Consumption.

Abstract

Daily Step Count was generally 0% higher than average after a total of 4 serving of Eggs over the previous 14 days.

Aggregated data from 1 study participants suggests with a HIGH degree of confidence (p=0.0345, 95% CI -497.384 to 497.416) that Eggs has a very weakly positive predictive relationship (R=0.0161) with Daily Step Count.

The highest quartile of Daily Step Count measurements were observed following an average 0.408 serving Eggs per day.

The lowest quartile of Daily Step Count measurements were observed following an average 0.423 serving of Eggs per day.

After an onset delay of 30 minutes, Daily Step Count is typically 1% higher than average over the 14 days following around 0.423 serving of Eggs Eggs.

Keywords: Eggs, Daily Step Count, 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 931 paired observations from 1 participants revealed a modest improvement in Daily Step Count following above-average Eggs exposure.

+6.8%
Change from Baseline
Modest effect on Daily Step Count
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.016
Correlation (r)
p = 0.992
Significance
z = 0.14
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Eggs:

  • Daily Step Count increased by 6.8% on average
  • Temporal analysis supports Eggs 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 Eggs values associated with high and low Daily Step Count 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

Eggs Distribution

Daily Step Count Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Eggs Consumption
Effect Variable Name Daily Step Count
Sinn Predictive Coefficient 0.00076605881288758
Confidence Level HIGH
Confidence Interval 497.39987812992
Forward Pearson Predictive Coefficient 0.0161
Critical T Value 1.646
Total Eggs Consumption Over Previous 14 days Before ABOVE Average Daily Step Count 0.408 serving
Total Eggs Consumption Over Previous 14 days Before BELOW Average Daily Step Count 0.423 serving
Duration of Action 14 days
Effect Size very weakly positive
Number of Paired Measurements 931
Optimal Pearson Product -0.00042332865033172
P Value 0.034501742789063
Statistical Significance 0.9918
Strength of Relationship 497.39987812992
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Eggs Info

Property Value
Variable Name Eggs (serving)
Aggregation Method SUM
Analysis Performed At 2020-09-15
Duration of Action 14 days
Filling Value 0
Kurtosis 8.183262044688
Maximum Allowed Value 40 serving
Mean 0.56182424242424 serving
Median 0 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 89
Number of Measurements 931
Number of Measurements (including those generated by tagged, joined, or child variables) 931
Public true
Onset Delay 30 minutes
Standard Deviation 1.0649077791847
Unit Serving
User Variables 4
Variable Category Foods
Variable ID 5545271
Variance 1.2807474379871

Steps Info

Property Value
Variable Name Daily Step Count
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 16.639977980211
Mean 6466.3629645193 count
Median 6036.923245614 count
Minimum Allowed Value 1 count
Number of Aggregate Predictors 131
Number of Aggregate Outcomes 200
Number of Measurements 88028
Number of Measurements (including those generated by tagged, joined, or child variables) 10365
Public true
Onset Delay 0 seconds
Standard Deviation 3084.0373174008
Unit Count
User Variables 280
UPC 734010049130
Variable Category Physical Activity
Variable ID 1451
Variance 12961277.144652

Introduction

Background

Eggs (Foods) and Daily Step Count (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

Do Eggs affect Daily Step Count?

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 Eggs for maximizing Daily Step Count?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Eggs and Daily Step Count. Additionally, we attempt to determine the Eggs (serving) values most likely to produce optimal Daily Step Count 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 6.8% improvement in Daily Step Count following above-average Eggs 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 Daily Step Count is statistically significant at a 95% confidence interval. The p-value of 0.9918 indicates there is less than a 99.18% probability that this result occurred by chance.

After treatment, a 16.3% decrease (669 count) from the mean baseline 9910 count was observed. The relative standard deviation at baseline was 47.9%. The observed change was 0.14 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: 2.213
Critical t-value: 1.646

Since t = 2.21 > 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 Eggs might influence Daily Step Count.

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 Eggs 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 Eggs
  • 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 Eggs was associated with a 6.8% improvement in Daily Step Count—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 6.8% 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 Eggs may influence Daily Step Count 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 Eggs would produce an observable change in Daily Step Count.
  • Duration of Action: It was assumed that Eggs could produce an observable change in Daily Step Count for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Eggs values and subsequent Daily Step Count 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

Eggs 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.

Daily Step Count 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 Eggs (serving) Affect Daily Step Count?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5545271-effect-1451-population-study
BibTeX 
@misc{sinn_cause_5545271_effect_1451_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Eggs (serving) Affect Daily Step Count?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5545271-effect-1451-population-study},
  note = {Accessed: January 6, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Eggs (serving) Affect Daily Step Count?." The Journal of Citizen Science. Accessed January 6, 2026. https://studies.crowdsourcingcures.org/study/cause-5545271-effect-1451-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Eggs (serving) Affect Daily Step Count?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5545271-effect-1451-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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