Higher Niacin Intake Predicts Moderately Higher Activeness for Population

Higher Niacin Intake Predicts Moderately Higher Activeness for Population

Contents

Variables

A
Niacin 41
A
Activeness 1326

Categories

A
Nutrients 313
A
Emotions 2028

Actions

A
Join Study
A
Your Data

Tags

High Confidence
Moderate Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 46.4% average increase in Activeness following above average Niacin Intake.

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 Niacin (%RDA) on Activeness. <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 Niacin (%RDA) on Activeness. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" class="create-study-button"> Create a Study </a>

Activeness was generally 43% higher than average after an average of 3% recommended daily allowance of Niacin over the previous 7 days.

Aggregated data from 2 study participants suggests with a HIGH degree of confidence (p=0.001, 95% CI 0.125 to 0.915) that Niacin has a moderately positive predictive relationship (R=0.52) with Activeness.

The highest quartile of Activeness measurements were observed following an average 0.63% recommended daily allowance Niacin.

The lowest quartile of Activeness measurements were observed following an average 0% recommended daily allowance of Niacin.

After an onset delay of 0 seconds, Activeness is typically 4% lower than average over the 7 days following around 0% recommended daily allowance of Niacin Niacin.

Keywords: Niacin, Activeness, 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 13 paired observations from 2 participants revealed a minimal reduction in Activeness following above-average Niacin exposure.

0.0%
Change from Baseline
Minimal effect on Activeness
0.09
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.520
Correlation (r)
p = 0.008
Significance
z = 0.00
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Niacin:

  • Activeness decreased by 0.0% on average
  • Temporal analysis supports Niacin as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.008)

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

3.0 %RDA
Value Predicting Higher Activeness
Average Niacin when Activeness exceeded its mean
0.0 %RDA
Value Predicting Lower Activeness
Average Niacin when Activeness was below its mean

What This Suggests

Activeness tended to be highest when Niacin was around 3.0 %RDA.

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

Niacin Distribution

Activeness Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Niacin Intake
Effect Variable Name Activeness
Sinn Predictive Coefficient 0.094260004942013
Confidence Level HIGH
Confidence Interval 0.39488101451577
Forward Pearson Predictive Coefficient 0.52
Critical T Value 1.771
Average Niacin Intake Over Previous 7 days Before ABOVE Average Activeness 0.63% recommended daily allowance
Average Niacin Intake Over Previous 7 days Before BELOW Average Activeness 0% recommended daily allowance
Duration of Action 7 days
Effect Size moderately positive
Number of Paired Measurements 13
Optimal Pearson Product 0.37497733264825
P Value 0.001
Statistical Significance 0.0083
Strength of Relationship 0.39488101451577
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Niacin Info

Property Value
Variable Name Niacin (%RDA)
Aggregation Method MEAN
Analysis Performed At 2020-10-09
Duration of Action 7 days
Kurtosis 10.623282498909
Mean 22.610888888889% recommended daily allowance
Median 22% recommended daily allowance
Minimum Allowed Value 0% recommended daily allowance
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 41
Number of Measurements 210
Number of Measurements (including those generated by tagged, joined, or child variables) 120
Public true
Onset Delay 0 seconds
Standard Deviation 17.741516468352
Unit % Recommended Daily Allowance
User Variables 3
UPC 753950002425
Variable Category Nutrients
Variable ID 5556571
Variance 321.55247150632

Activeness Info

Property Value
Variable Name Activeness
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8468106022057
Maximum Allowed Value 5 out of 5
Mean 2.3430371584699 out of 5
Median 2.3108746584699 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1200
Number of Aggregate Outcomes 126
Number of Measurements 30704
Number of Measurements (including those generated by tagged, joined, or child variables) 30582
Public true
Onset Delay 0 seconds
Standard Deviation 0.52428579928588
Unit 1 to 5 Rating
User Variables 1510
UPC 0
Variable Category Emotions
Variable ID 1252
Variance 0.56911364809229

Introduction

Background

Niacin (Nutrients) and Activeness (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 Niacin affect Activeness?

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 Niacin for maximizing Activeness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Niacin and Activeness. Additionally, we attempt to determine the Niacin (%RDA) values most likely to produce optimal Activeness 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 Activeness following above-average Niacin exposure. The Predictor Impact Score (PIS) of 0.09 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.008).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Activeness is statistically significant at a 95% confidence interval. The p-value of 0.0083 indicates there is less than a 0.83% probability that this result occurred by chance.

T-Test Details
Observed t-value: 7.288
Critical t-value: 1.771

Since t = 7.29 > 1.77, 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 Niacin might influence Activeness.

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 Niacin (%RDA) on Activeness. <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 Niacin (%RDA) on Activeness. <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 Niacin 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 Niacin
  • 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 Niacin was associated with a 0.0% reduction in Activeness—a minimal effect. The Predictor Impact Score of 0.09 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.09 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 Niacin may influence Activeness in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

Help End Unnecessary Suffering

Current clinical trials are 82x more expensive than necessary and take 17 years to bring treatments to market. Pragmatic trials integrated into standard healthcare could reduce costs from $41,000 to $500 per participant and compress timelines to just 2 years. Learn how redirecting just 1% of global military spending could accelerate cures for the 2 billion people suffering from treatable diseases.

Make Suffering Optional

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 0 seconds would pass before a change in Niacin would produce an observable change in Activeness.
  • Duration of Action: It was assumed that Niacin could produce an observable change in Activeness for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Niacin values and subsequent Activeness 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

Niacin data was primarily collected using MyNetDiary. MyNetDiary is an online and mobile food diary with calorie counter and online community. MyNetDiary provides instant and easy food entry, searching while you type. Enter foods 2-3 times faster than with any other food diary.

Activeness 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 Niacin (%RDA) Affect Activeness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5556571-effect-1252-population-study
BibTeX 
@misc{sinn_cause_5556571_effect_1252_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Niacin (%RDA) Affect Activeness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5556571-effect-1252-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Niacin (%RDA) Affect Activeness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-5556571-effect-1252-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Niacin (%RDA) Affect Activeness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5556571-effect-1252-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

LinkedIn Reddit