Higher Proline Intake Predicts Slightly Higher Guiltiness for Population

Higher Proline Intake Predicts Slightly Higher Guiltiness for Population

Contents

Variables

A
Proline 87
A
Guiltiness 2319

Categories

A
Nutrients 313
A
Emotions 2028

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Your Data

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Low Confidence
Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 20.4% average increase in Guiltiness following above average Proline Intake.

Abstract

Guiltiness was generally 37.36% higher than average after 0.0235 grams of Proline per 7 days.

Aggregated data from 5 study participants suggests with a LOW degree of confidence (p=0.165, 95% CI -0.508 to 1.056) that Proline has a weakly positive predictive relationship (R=0.274) with Guiltiness.

The highest quartile of Guiltiness measurements were observed following an average 0.0069 grams Proline per day.

The lowest quartile of Guiltiness measurements were observed following an average 0.00171 grams of Proline per day.

After an onset delay of 30 minutes, Guiltiness is typically 18% lower than average over the 7 days following around 0.00171 grams of Proline Proline.

Keywords: Proline, Guiltiness, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 5 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 77 paired observations from 5 participants revealed a substantial improvement in Guiltiness following above-average Proline exposure.

+37.4%
Change from Baseline
Substantial effect on Guiltiness
0.11
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

High
Confidence
0.274
Correlation (r)
p = 0.002
Significance
z = 0.91
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Proline:

  • Guiltiness increased by 37.4% on average
  • Temporal analysis supports Proline as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.002)

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 5 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 5 participants and 77 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.0 g
Value Predicting Higher Guiltiness
Average Proline when Guiltiness exceeded its mean
0.0 g
Value Predicting Lower Guiltiness
Average Proline when Guiltiness was below its mean

What This Suggests

Guiltiness tended to be highest when Proline was around 0.0 g.

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

Proline Distribution

Guiltiness Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Proline Intake
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.10784994838669
Confidence Level LOW
Confidence Interval 0.78230234353816
Forward Pearson Predictive Coefficient 0.2741
Critical T Value 1.8374
Total Proline Intake Over Previous 7 days Before ABOVE Average Guiltiness 0.0069 grams
Total Proline Intake Over Previous 7 days Before BELOW Average Guiltiness 0.00171 grams
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 77
Optimal Pearson Product 0.15475206362339
P Value 0.16498520181496
Statistical Significance 0.0023
Strength of Relationship 0.78230234353816
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 5

Proline Info

Property Value
Variable Name Proline
Aggregation Method SUM
Analysis Performed At 2022-10-12
Duration of Action 7 days
Filling Value 0
Kurtosis 28.182360473473
Mean 0.0031693552307692 grams
Median 0.0027965384615385 grams
Minimum Allowed Value 0 grams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 87
Number of Measurements 0
Number of Measurements (including those generated by tagged, joined, or child variables) 781
Public true
Onset Delay 30 minutes
Standard Deviation 0.0010525518230707
Unit Grams
User Variables 60
UPC 804879540311
Variable Category Nutrients
Variable ID 5401831
Variance 5.9575010153082E-6

Guiltiness Info

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2022-09-29
Duration of Action 24 hours
Kurtosis 2.2615389805518
Maximum Allowed Value 5 out of 5
Mean 2.365934400949 out of 5
Median 2.2960569395018 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 2056
Number of Aggregate Outcomes 263
Number of Measurements 31621
Number of Measurements (including those generated by tagged, joined, or child variables) 31621
Public true
Onset Delay 0 seconds
Standard Deviation 0.5743531754414
Unit 1 to 5 Rating
User Variables 1787
UPC 0
Variable Category Emotions
Variable ID 1335
Variance 0.72121328198661

Introduction

Background

Proline (Nutrients) and Guiltiness (Emotions) 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 Proline affect Guiltiness?

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 Proline for maximizing Guiltiness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Proline and Guiltiness. Additionally, we attempt to determine the Proline values most likely to produce optimal Guiltiness 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 37.4% improvement in Guiltiness following above-average Proline exposure. The Predictor Impact Score (PIS) of 0.11 indicates weak evidence for a causal relationship. This finding is statistically significant (p = 0.002).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Guiltiness is statistically significant at a 95% confidence interval. The p-value of 0.0023 indicates there is less than a 0.23% probability that this result occurred by chance.

After treatment, a 20.4% increase (0.635 out of 5) from the mean baseline 2.38 out of 5 was observed. The relative standard deviation at baseline was 35.7%. The observed change was 0.905615 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: 1.931
Critical t-value: 1.837

Since t = 1.93 > 1.84, 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 Proline might influence Guiltiness.

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 Proline 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 Proline
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 5 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 Proline was associated with a 37.4% improvement in Guiltiness—a substantial effect. The Predictor Impact Score of 0.11 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.11 and a 37.4% 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 Proline may influence Guiltiness in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

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Methods

Study Design

This study is based on data donated by 5 participants. Thus, the study design is equivalent to the aggregation of 5 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 Proline would produce an observable change in Guiltiness.
  • Duration of Action: It was assumed that Proline could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Proline values and subsequent Guiltiness 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

Proline 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.

Guiltiness 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.

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 Proline Affect Guiltiness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5401831-effect-1335-population-study
BibTeX 
@misc{sinn_cause_5401831_effect_1335_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Proline Affect Guiltiness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5401831-effect-1335-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Proline Affect Guiltiness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-5401831-effect-1335-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Proline Affect Guiltiness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5401831-effect-1335-population-study [Accessed January 9, 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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