Higher Vitamins & Dietary Supplements Predicts Slightly Higher Upsettedness for Population

Higher Vitamins & Dietary Supplements Predicts Slightly Higher Upsettedness for Population

Contents

Variables

A
Vitamins & Dietary Supplements 90
A
Upsettedness 1127

Categories

A
Treatments 9356
A
Emotions 2028

Actions

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Your Data

Tags

Low Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 6.5% average increase in Upsettedness following above average Vitamins & Dietary Supplements.

Abstract

Upsettedness was generally 6.8% higher than average after 12 count of Vitamins & Dietary Supplements per 21 days.

Aggregated data from 2 study participants suggests with a LOW degree of confidence (p=0.387, 95% CI -1.142 to 1.354) that Vitamins & Dietary Supplements has a weakly positive predictive relationship (R=0.106) with Upsettedness.

The highest quartile of Upsettedness measurements were observed following an average 13 count Vitamins & Dietary Supplements per day.

The lowest quartile of Upsettedness measurements were observed following an average 12.8 count of Vitamins & Dietary Supplements per day.

After an onset delay of 30 minutes, Upsettedness is typically 4% lower than average over the 21 days following around 12.8 count of Vitamins & Dietary Supplements Vitamins & Dietary Supplements.

Keywords: Vitamins & Dietary Supplements, Upsettedness, 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 18 paired observations from 2 participants revealed a modest improvement in Upsettedness following above-average Vitamins & Dietary Supplements exposure.

+6.8%
Change from Baseline
Modest effect on Upsettedness
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Low
Confidence
0.106
Correlation (r)
p = 0.092
Significance
z = 0.11
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Vitamins & Dietary Supplements:

  • Upsettedness increased by 6.8% on average
  • Temporal analysis supports Vitamins & Dietary Supplements 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

No clear dose-response relationship detected. The Vitamins & Dietary Supplements values associated with high and low Upsettedness are too similar to provide meaningful dosing guidance. This may indicate a threshold effect (any amount works equally well), no effect, or insufficient data variance. With more participants, a clearer pattern may emerge.

Population Correlation

Vitamins & Dietary Supplements Distribution

Upsettedness Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Vitamins & Dietary Supplements
Effect Variable Name Upsettedness
Sinn Predictive Coefficient 0.01921453994684
Confidence Level LOW
Confidence Interval 1.2483
Forward Pearson Predictive Coefficient 0.106
Critical T Value 1.734
Total Vitamins & Dietary Supplements Over Previous 21 days Before ABOVE Average Upsettedness 13 count
Total Vitamins & Dietary Supplements Over Previous 21 days Before BELOW Average Upsettedness 12.8 count
Duration of Action 21 days
Effect Size weakly positive
Number of Paired Measurements 18
Optimal Pearson Product 0.0028133267172837
P Value 0.38659
Statistical Significance 0.0923
Strength of Relationship 1.2483
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Vitamins & Dietary Supplements Info

Property Value
Variable Name Vitamins & Dietary Supplements
Aggregation Method SUM
Analysis Performed At 2022-11-18
Duration of Action 21 days
Filling Value 0
Kurtosis 20.572685510968
Mean 0.49861375 count
Median 0.25 count
Minimum Allowed Value 0 count
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 90
Number of Measurements 4
Number of Measurements (including those generated by tagged, joined, or child variables) 3557
Public true
Onset Delay 30 minutes
Standard Deviation 0.4556558828465
Unit Count
User Variables 22
UPC 851356004071
Variable Category Treatments
Variable ID 5977664
Variance 0.46618412433026

Upsettedness Info

Property Value
Variable Name Upsettedness
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 2.9266805584635
Maximum Allowed Value 5 out of 5
Mean 2.4438581375675 out of 5
Median 2.3720868673401 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1009
Number of Aggregate Outcomes 118
Number of Measurements 32721
Number of Measurements (including those generated by tagged, joined, or child variables) 32481
Public true
Onset Delay 0 seconds
Standard Deviation 0.56117537828467
Unit 1 to 5 Rating
User Variables 1444
Variable Category Emotions
Variable ID 1475
Variance 0.66557065453801

Introduction

Background

Vitamins & Dietary Supplements (Treatments) and Upsettedness (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

Do Vitamins & Dietary Supplements affect Upsettedness?

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 Vitamins & Dietary Supplements for maximizing Upsettedness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Vitamins & Dietary Supplements and Upsettedness. Additionally, we attempt to determine the Vitamins & Dietary Supplements values most likely to produce optimal Upsettedness 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 6.8% improvement in Upsettedness following above-average Vitamins & Dietary Supplements exposure. The Predictor Impact Score (PIS) of 0.02 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 Upsettedness is not statistically significant at a 95% confidence interval. This suggests that the Vitamins & Dietary Supplements value may not have a significant influence on the Upsettedness value, or that more data is needed to detect an effect.

After treatment, a 6.5% increase (0.181 out of 5) from the mean baseline 2.67 out of 5 was observed. The relative standard deviation at baseline was 61.2%. The observed change was 0.11057 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: 0.251
Critical t-value: 1.734

Since t = 0.25 < 1.73, we cannot 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 Vitamins & Dietary Supplements might influence Upsettedness.

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 Vitamins & Dietary Supplements 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 Vitamins & Dietary Supplements
  • 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 Vitamins & Dietary Supplements was associated with a 6.8% improvement in Upsettedness—a modest effect. The Predictor Impact Score of 0.02 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.02 and a 6.8% 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 Vitamins & Dietary Supplements may influence Upsettedness 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 Vitamins & Dietary Supplements would produce an observable change in Upsettedness.
  • Duration of Action: It was assumed that Vitamins & Dietary Supplements could produce an observable change in Upsettedness for as much as 21 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Vitamins & Dietary Supplements values and subsequent Upsettedness 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

Vitamins & Dietary Supplements 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.

Upsettedness 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 Vitamins & Dietary Supplements Affect Upsettedness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5977664-effect-1475-population-study
BibTeX 
@misc{sinn_cause_5977664_effect_1475_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Vitamins & Dietary Supplements Affect Upsettedness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5977664-effect-1475-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Vitamins & Dietary Supplements Affect Upsettedness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-5977664-effect-1475-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Vitamins & Dietary Supplements Affect Upsettedness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5977664-effect-1475-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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