Higher Vegetable And Cheese Omelet Consumption Predicts Moderately Higher Body Weight for Population

Higher Vegetable And Cheese Omelet Consumption Predicts Moderately Higher Body Weight for Population

Contents

Variables

A
Vegetable and Cheese Omelet 25
A
Body Weight 1140

Categories

A
Foods 13415
A
Physique 41

Tags

High Confidence
Moderate Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 5.7% average increase in Body Weight following above average Vegetable And Cheese Omelet Consumption.

Abstract

<h5>Not Enough Shared Data</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Vegetable And Cheese Omelet on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a> <h5>Solution: Create a Study</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Vegetable And Cheese Omelet on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a>

Body Weight was generally 3% higher than average after a total of 1.5 serving of Vegetable And Cheese Omelet over the previous 7 days.

Aggregated data from 2 study participants suggests with a HIGH degree of confidence (p=0.001, 95% CI -1.014 to 2.106) that Vegetable And Cheese Omelet has a moderately positive predictive relationship (R=0.546) with Body Weight.

The highest quartile of Body Weight measurements were observed following an average 1.11 serving Vegetable And Cheese Omelet per day.

The lowest quartile of Body Weight measurements were observed following an average 0.219 serving of Vegetable And Cheese Omelet per day.

After an onset delay of 30 minutes, Body Weight is typically 1% lower than average over the 7 days following around 0.219 serving of Vegetable And Cheese Omelet Vegetable And Cheese Omelet.

Keywords: Vegetable And Cheese Omelet, Body Weight, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 2 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 186 paired observations from 2 participants revealed a minimal reduction in Body Weight following above-average Vegetable And Cheese Omelet exposure.

0.0%
Change from Baseline
Minimal effect on Body Weight
0.14
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.546
Correlation (r)
p = 0.181
Significance
z = 0.00
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Vegetable And Cheese Omelet:

  • Body Weight decreased by 0.0% on average
  • Temporal analysis supports Vegetable And Cheese Omelet 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 2 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 2 participants and 186 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

1.5 serving
Value Predicting Higher Body Weight
Average Vegetable And Cheese Omelet when Body Weight exceeded its mean
0.0 serving
Value Predicting Lower Body Weight
Average Vegetable And Cheese Omelet when Body Weight was below its mean

What This Suggests

Body Weight tended to be highest when Vegetable And Cheese Omelet was around 1.5 serving.

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

Vegetable And Cheese Omelet Distribution

Body Weight Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Vegetable And Cheese Omelet Consumption
Effect Variable Name Body Weight
Sinn Predictive Coefficient 0.14012112821646
Confidence Level HIGH
Confidence Interval 1.560318773885
Forward Pearson Predictive Coefficient 0.546
Critical T Value 1.646
Total Vegetable And Cheese Omelet Consumption Over Previous 7 days Before ABOVE Average Body Weight 1.11 serving
Total Vegetable And Cheese Omelet Consumption Over Previous 7 days Before BELOW Average Body Weight 0.219 serving
Duration of Action 7 days
Effect Size moderately positive
Number of Paired Measurements 186
Optimal Pearson Product 0.52265483449796
P Value 0.001
Statistical Significance 0.1807
Strength of Relationship 1.560318773885
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Vegetable and Cheese Omelet Info

Property Value
Variable Name Vegetable And Cheese Omelet
Aggregation Method SUM
Analysis Performed At 2020-09-15
Duration of Action 14 days
Filling Value 0
Kurtosis 10.951561754155
Maximum Allowed Value 40 serving
Mean 0.538674 serving
Median 0 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 25
Number of Measurements 28
Number of Measurements (including those generated by tagged, joined, or child variables) 14
Public true
Onset Delay 30 minutes
Standard Deviation 0.26788396194189
Unit Serving
User Variables 2
UPC 818609010814
Variable Category Foods
Variable ID 1621
Variance 0.071761817065685

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

Vegetable And Cheese Omelet (Foods) 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 Vegetable And Cheese Omelet 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 Vegetable And Cheese Omelet for maximizing Body Weight?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Vegetable And Cheese Omelet and Body Weight. Additionally, we attempt to determine the Vegetable And Cheese Omelet 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 0.0% reduction in Body Weight following above-average Vegetable And Cheese Omelet exposure. The Predictor Impact Score (PIS) of 0.14 indicates weak 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 0.1807 indicates there is less than a 18.07% probability that this result occurred by chance.

T-Test Details
Observed t-value: 6.635
Critical t-value: 1.646

Since t = 6.64 > 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.

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 Vegetable And Cheese Omelet 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

The accuracy of this study may be limited by the fact that <h5>Not Enough Shared Data</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Vegetable And Cheese Omelet on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a> <h5>Solution: Create a Study</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Vegetable And Cheese Omelet on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a> . A greater amount of data and more variance in the data would help to resolve this issue.

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 Vegetable And Cheese Omelet 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 Vegetable And Cheese Omelet
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 2 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 Vegetable And Cheese Omelet was associated with a 0.0% reduction in Body Weight—a minimal effect. The Predictor Impact Score of 0.14 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.14 and a 0.0% effect size, this relationship shows weak evidence. Additional observational data is recommended before investing in experimental validation. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Vegetable And Cheese Omelet may influence Body Weight 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 2 participants. Thus, the study design is equivalent to the aggregation of 2 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 Vegetable And Cheese Omelet would produce an observable change in Body Weight.
  • Duration of Action: It was assumed that Vegetable And Cheese Omelet 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 Vegetable And Cheese Omelet 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

Vegetable And Cheese Omelet data was primarily collected using MyFitnessPal. Lose weight with MyFitnessPal, the fastest and easiest-to-use calorie counter for iPhone and iPad. With the largest food database of any iOS calorie counter (over 3,000,000 foods), and amazingly fast food and exercise entry.

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 Vegetable And Cheese Omelet Affect Body Weight?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1621-effect-1486-population-study
BibTeX 
@misc{sinn_cause_1621_effect_1486_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Vegetable And Cheese Omelet Affect Body Weight?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1621-effect-1486-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Vegetable And Cheese Omelet Affect Body Weight?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1621-effect-1486-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Vegetable And Cheese Omelet Affect Body Weight?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1621-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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