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 Non-Gmo Lecithin Powder on Walk Or Run Distance. <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 Non-Gmo Lecithin Powder on Walk Or Run Distance. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a>
Walk Or Run Distance was generally 10% higher than average after a total of 0 serving of Non-Gmo Lecithin Powder over the previous 7 days.
Aggregated data from 2 study participants suggests with a HIGH degree of confidence (p=0.00355, 95% CI -585.05 to 584.301) that Non-Gmo Lecithin Powder has a moderately negative predictive relationship (R=-0.375) with Walk Or Run Distance.
The highest quartile of Walk Or Run Distance measurements were observed following an average 0.845 serving Non-Gmo Lecithin Powder per day.
The lowest quartile of Walk Or Run Distance measurements were observed following an average 2.07 serving of Non-Gmo Lecithin Powder per day.
After an onset delay of 30 minutes, Walk Or Run Distance is typically 14% lower than average over the 7 days following around 2.07 serving of Non-Gmo Lecithin Powder Non-Gmo Lecithin Powder.
Keywords: Non-Gmo Lecithin Powder, Walk Or Run Distance, 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 1,078 paired observations from 2 participants revealed a minimal reduction in Walk Or Run Distance following above-average Non-Gmo Lecithin Powder exposure.
Supporting Statistics
What This Means
When participants had above-average Non-Gmo Lecithin Powder:
- Walk Or Run Distance decreased by 0.0% on average
- Temporal analysis supports Non-Gmo Lecithin Powder 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 1,078 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.
What This Suggests
Walk Or Run Distance tended to be lowest (best) when Non-Gmo Lecithin Powder was around 5.0 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
Non-Gmo Lecithin Powder Distribution
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Walk Or Run Distance Distribution
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Statistical Summary
Relationship Statistics
| Property | Value |
|---|---|
| Cause Variable Name | Non-Gmo Lecithin Powder Consumption |
| Effect Variable Name | Walk Or Run Distance |
| Sinn Predictive Coefficient | 0.03394266706959 |
| Confidence Level | HIGH |
| Confidence Interval | 584.6759331326 |
| Forward Pearson Predictive Coefficient | -0.3745 |
| Critical T Value | 1.646 |
| Total Non- Gmo Lecithin Powder Consumption Over Previous 7 days Before ABOVE Average Walk Or Run Distance | 0.845 serving |
| Total Non- Gmo Lecithin Powder Consumption Over Previous 7 days Before BELOW Average Walk Or Run Distance | 2.07 serving |
| Duration of Action | 7 days |
| Effect Size | moderately negative |
| Number of Paired Measurements | 1078 |
| Optimal Pearson Product | 0.13802346622067 |
| P Value | 0.00355 |
| Statistical Significance | 0.9248 |
| Strength of Relationship | 584.6759331326 |
| Study Type | population |
| Analysis Performed At | 2026-01-04 |
| Number of Participants | 2 |
Non-Gmo Lecithin Powder Info
| Property | Value |
|---|---|
| Variable Name | Non-Gmo Lecithin Powder |
| Aggregation Method | SUM |
| Analysis Performed At | 2020-09-17 |
| Duration of Action | 14 days |
| Filling Value | 0 |
| Kurtosis | 1.6312891019082 |
| Maximum Allowed Value | 40 serving |
| Mean | 1.455 serving |
| Median | 2.25 serving |
| Minimum Allowed Value | 0 serving |
| Number of Aggregate Predictors | 0 |
| Number of Aggregate Outcomes | 51 |
| Number of Measurements | 913 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 913 |
| Public | true |
| Onset Delay | 30 minutes |
| Standard Deviation | 1.2498439575112 |
| Unit | Serving |
| User Variables | 2 |
| UPC | 819543011653 |
| Variable Category | Foods |
| Variable ID | 54071 |
| Variance | 1.5621211722499 |
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
Non-Gmo Lecithin Powder (Foods) 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 Non-Gmo Lecithin Powder affect Walk Or Run Distance?
Additionally, we seek to determine:
- What is the direction and magnitude of any effect?
- How confident can we be in this relationship based on the available data?
- What are the optimal levels of Non-Gmo Lecithin Powder for maximizing Walk Or Run Distance?
Study Objective
The objective of this study is to determine the nature of the relationship (if any) between Non-Gmo Lecithin Powder and Walk Or Run Distance. Additionally, we attempt to determine the Non-Gmo Lecithin Powder 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 0.0% reduction in Walk Or Run Distance following above-average Non-Gmo Lecithin Powder exposure. The Predictor Impact Score (PIS) of 0.03 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 statistically significant at a 95% confidence interval. The p-value of 0.9248 indicates there is less than a 92.48% probability that this result occurred by chance.
T-Test Details
Since t = 4.27 > 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 Non-Gmo Lecithin Powder 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:
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:
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 Non-Gmo Lecithin Powder on Walk Or Run Distance. <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 Non-Gmo Lecithin Powder on Walk Or Run Distance. <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 Non-Gmo Lecithin Powder 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 Non-Gmo Lecithin Powder
- 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 Non-Gmo Lecithin Powder was associated with a 0.0% reduction in Walk Or Run Distance—a minimal effect. The Predictor Impact Score of 0.03 indicates this relationship is requiring additional data before conclusions.
Bottom Line: Based on a PIS of 0.03 and a 0.0% 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 Non-Gmo Lecithin Powder 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 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 Non-Gmo Lecithin Powder would produce an observable change in Walk Or Run Distance.
- Duration of Action: It was assumed that Non-Gmo Lecithin Powder could produce an observable change in Walk Or Run Distance for as much as 7 days after the stimulus event.
Statistical Methods
For each participant, we calculated the Pearson correlation coefficient between Non-Gmo Lecithin Powder 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:
Fisher's Z-Transformation:
Aggregated Correlation:
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:
Effect Magnitude (Z-Score)
To assess effect magnitude relative to natural variability, we calculate the z-score:
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:
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
Non-Gmo Lecithin Powder 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.
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
Cite This Study
@misc{sinn_cause_54071_effect_1304_population_study_2026,
author = {Sinn, Mike P.},
title = {Causal Analysis: Does Non-Gmo Lecithin Powder Affect Walk Or Run Distance?},
year = {2026},
publisher = {The Journal of Citizen Science},
url = {https://studies.crowdsourcingcures.org/study/cause-54071-effect-1304-population-study},
note = {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:
- 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]
- 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]
- Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
- Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
- FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
- Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
- 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