Higher Folic Acid Intake Predicts Moderately Higher Excitability for Population

Higher Folic Acid Intake Predicts Moderately Higher Excitability for Population

Contents

Variables

A
Folic Acid 174
A
Excitability 1174

Categories

A
Foods 13415
A
Emotions 2028

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High Confidence
Moderate Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 82.5% average increase in Excitability following above average Folic Acid Intake.

Abstract

Excitability was generally 58% higher than average after an average of 47.6 micrograms of Folic Acid over the previous 14 days.

Aggregated data from 2 study participants suggests with a HIGH degree of confidence (p=0.001, 95% CI 0.156 to 0.692) that Folic Acid has a moderately positive predictive relationship (R=0.424) with Excitability.

The highest quartile of Excitability measurements were observed following an average 7.14 micrograms Folic Acid.

The lowest quartile of Excitability measurements were observed following an average 0 micrograms of Folic Acid.

After an onset delay of 30 minutes, Excitability is typically 2% lower than average over the 14 days following around 0 micrograms of Folic Acid Folic Acid.

Keywords: Folic Acid, Excitability, 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 26 paired observations from 2 participants revealed a substantial improvement in Excitability following above-average Folic Acid exposure.

+85.2%
Change from Baseline
Substantial effect on Excitability
0.08
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.424
Correlation (r)
p = 0.013
Significance
z = 2.34
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Folic Acid:

  • Excitability increased by 85.2% on average
  • Temporal analysis supports Folic Acid as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.013)

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 26 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

47.6 mcg
Value Predicting Higher Excitability
Average Folic Acid when Excitability exceeded its mean
0.0 mcg
Value Predicting Lower Excitability
Average Folic Acid when Excitability was below its mean

What This Suggests

Excitability tended to be highest when Folic Acid was around 47.6 mcg.

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

Folic Acid Distribution

Excitability Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Folic Acid Intake
Effect Variable Name Excitability
Sinn Predictive Coefficient 0.076858159787359
Confidence Level HIGH
Confidence Interval 0.26848
Forward Pearson Predictive Coefficient 0.424
Critical T Value 1.706
Average Folic Acid Intake Over Previous 14 days Before ABOVE Average Excitability 7.14 micrograms
Average Folic Acid Intake Over Previous 14 days Before BELOW Average Excitability 0 micrograms
Duration of Action 14 days
Effect Size moderately positive
Number of Paired Measurements 26
Optimal Pearson Product 0.21619842737633
P Value 0.001
Statistical Significance 0.0129
Strength of Relationship 0.26848
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Folic Acid Info

Property Value
Variable Name Folic Acid
Aggregation Method MEAN
Analysis Performed At 2022-11-18
Duration of Action 14 days
Filling Value 0
Kurtosis 70.231523020368
Mean 3.4636100210526 micrograms
Median 0.014736842105263 micrograms
Minimum Allowed Value 0 micrograms
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 174
Number of Measurements 88
Number of Measurements (including those generated by tagged, joined, or child variables) 2007
Public true
Onset Delay 30 minutes
Standard Deviation 6.7989880914051
Unit Micrograms
User Variables 80
UPC 760488372210
Variable Category Foods
Variable ID 1323
Variance 1383.9819994913

Excitability Info

Property Value
Variable Name Excitability
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8586419214637
Maximum Allowed Value 5 out of 5
Mean 2.5200098915473 out of 5
Median 2.4911161917098 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1051
Number of Aggregate Outcomes 123
Number of Measurements 25877
Number of Measurements (including those generated by tagged, joined, or child variables) 25768
Public true
Onset Delay 0 seconds
Standard Deviation 0.56590124642268
Unit 1 to 5 Rating
User Variables 1587
UPC 0
Variable Category Emotions
Variable ID 1308
Variance 0.64891496461637

Introduction

Background

Folic Acid (Foods) and Excitability (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 Folic Acid affect Excitability?

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 Folic Acid for maximizing Excitability?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Folic Acid and Excitability. Additionally, we attempt to determine the Folic Acid values most likely to produce optimal Excitability 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 85.2% improvement in Excitability following above-average Folic Acid exposure. The Predictor Impact Score (PIS) of 0.08 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.013).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Excitability is statistically significant at a 95% confidence interval. The p-value of 0.0129 indicates there is less than a 1.29% probability that this result occurred by chance.

After treatment, a 82.5% increase (1.84 out of 5) from the mean baseline 2.16 out of 5 was observed. The relative standard deviation at baseline was 36.4%. The observed change was 2.3384 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: 11.692
Critical t-value: 1.706

Since t = 11.69 > 1.71, 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 Folic Acid might influence Excitability.

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 Folic Acid 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 Folic Acid
  • 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 Folic Acid was associated with a 85.2% improvement in Excitability—a substantial effect. The Predictor Impact Score of 0.08 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.08 and a 85.2% 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 Folic Acid may influence Excitability 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 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 Folic Acid would produce an observable change in Excitability.
  • Duration of Action: It was assumed that Folic Acid could produce an observable change in Excitability for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Folic Acid values and subsequent Excitability 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

Folic Acid data was primarily collected using General Spreadsheet. Import from a spreadsheet containing a Variable Name, Value, Measurement Event Time, and Abbreviated Unit Name field. Here is an <a href="http://bit.ly/2jz7CNl" target="_blank">example spreadsheet</a> with allowed column names, units and time format.

Excitability 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 Folic Acid Affect Excitability?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1323-effect-1308-population-study
BibTeX 
@misc{sinn_cause_1323_effect_1308_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Folic Acid Affect Excitability?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1323-effect-1308-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Folic Acid Affect Excitability?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-1323-effect-1308-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Folic Acid Affect Excitability?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1323-effect-1308-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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