Higher Coffee, Tea & Cocoa Consumption Predicts Very Slightly Higher Nausea Severity for Population

Higher Coffee, Tea & Cocoa Consumption Predicts Very Slightly Higher Nausea Severity for Population

Contents

Variables

A
Coffee, Tea & Cocoa 302
A
Nausea Severity 1123

Categories

A
Foods 13415
A
Symptoms 13336

Actions

A
Join Study
A
Your Data

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 1% average increase in Nausea Severity following above average Coffee, Tea & Cocoa Consumption.

Abstract

Nausea Severity was generally 14.1667% higher than average after 9.17 serving of Coffee, Tea & Cocoa per 14 days.

Aggregated data from 6 study participants suggests with a HIGH degree of confidence (p=0.141, 95% CI -0.299 to 0.366) that Coffee, Tea & Cocoa has a very weakly positive predictive relationship (R=0.0334) with Nausea Severity.

The highest quartile of Nausea Severity measurements were observed following an average 11.8 serving Coffee, Tea & Cocoa per day.

The lowest quartile of Nausea Severity measurements were observed following an average 10.6 serving of Coffee, Tea & Cocoa per day.

After an onset delay of 30 minutes, Nausea Severity is typically 8% lower than average over the 14 days following around 10.6 serving of Coffee, Tea & Cocoa Coffee, Tea & Cocoa.

Keywords: Coffee, Tea & Cocoa, Nausea Severity, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 6 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 1,400 paired observations from 6 participants revealed a modest improvement in Nausea Severity following above-average Coffee, Tea & Cocoa exposure.

+14.2%
Change from Baseline
Modest effect on Nausea Severity
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.033
Correlation (r)
p = 0.489
Significance
z = 0.50
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Coffee, Tea & Cocoa:

  • Nausea Severity increased by 14.2% on average
  • Temporal analysis supports Coffee, Tea & Cocoa 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 6 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 Coffee, Tea & Cocoa values associated with high and low Nausea Severity 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

Coffee, Tea & Cocoa Distribution

Nausea Severity Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Coffee, Tea & Cocoa Consumption
Effect Variable Name Nausea Severity
Sinn Predictive Coefficient 0.015069690967201
Confidence Level HIGH
Confidence Interval 0.33236641170762
Forward Pearson Predictive Coefficient 0.0334
Critical T Value 1.4276666666667
Total Coffee, Tea & Cocoa Consumption Over Previous 14 days Before ABOVE Average Nausea Severity 11.8 serving
Total Coffee, Tea & Cocoa Consumption Over Previous 14 days Before BELOW Average Nausea Severity 10.6 serving
Duration of Action 14 days
Effect Size very weakly positive
Number of Paired Measurements 1400
Optimal Pearson Product 0.038936940909034
P Value 0.14075066873676
Statistical Significance 0.4886
Strength of Relationship 0.33236641170762
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 6

Coffee, Tea & Cocoa Info

Property Value
Variable Name Coffee, Tea & Cocoa
Aggregation Method SUM
Analysis Performed At 2021-08-29
Duration of Action 14 days
Filling Value 0
Kurtosis 9.9613414986317
Maximum Allowed Value 40 serving
Mean 0.88797236070381 serving
Median 0.82478005865103 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 302
Number of Measurements 230
Number of Measurements (including those generated by tagged, joined, or child variables) 4383
Public true
Onset Delay 30 minutes
Standard Deviation 0.28073774671715
Unit Serving
User Variables 712
UPC 813957023530
Variable Category Foods
Variable ID 5978301
Variance 0.3407215857309

Nausea Severity Info

Property Value
Variable Name Nausea Severity
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 4.1188868699701
Maximum Allowed Value 5 out of 5
Mean 2.6177214523651 out of 5
Median 2.6064291139241 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 965
Number of Aggregate Outcomes 158
Number of Measurements 3928
Number of Measurements (including those generated by tagged, joined, or child variables) 3677
Public true
Onset Delay 0 seconds
Standard Deviation 0.40159824701785
Unit 1 to 5 Rating
User Variables 109
UPC 810758020393
Variable Category Symptoms
Variable ID 87494
Variance 0.43208649359802

Introduction

Background

Coffee, Tea & Cocoa (Foods) and Nausea Severity (Symptoms) 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 Coffee, Tea & Cocoa affect Nausea Severity?

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 Coffee, Tea & Cocoa for maximizing Nausea Severity?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Coffee, Tea & Cocoa and Nausea Severity. Additionally, we attempt to determine the Coffee, Tea & Cocoa values most likely to produce optimal Nausea Severity 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 14.2% improvement in Nausea Severity following above-average Coffee, Tea & Cocoa 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 Nausea Severity is statistically significant at a 95% confidence interval. The p-value of 0.4886 indicates there is less than a 48.86% probability that this result occurred by chance.

After treatment, a 1% increase (0.117 out of 5) from the mean baseline 1.39 out of 5 was observed. The relative standard deviation at baseline was 52.8333%. The observed change was 0.503955 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: 1.441
Critical t-value: 1.428

Since t = 1.44 > 1.43, 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 Coffee, Tea & Cocoa might influence Nausea Severity.

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 Coffee, Tea & Cocoa 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 Coffee, Tea & Cocoa
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 6 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 Coffee, Tea & Cocoa was associated with a 14.2% improvement in Nausea Severity—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 14.2% 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 Coffee, Tea & Cocoa may influence Nausea Severity 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.

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 6 participants. Thus, the study design is equivalent to the aggregation of 6 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 Coffee, Tea & Cocoa would produce an observable change in Nausea Severity.
  • Duration of Action: It was assumed that Coffee, Tea & Cocoa could produce an observable change in Nausea Severity for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Coffee, Tea & Cocoa values and subsequent Nausea Severity 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

Coffee, Tea & Cocoa 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.

Nausea Severity 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 Coffee, Tea & Cocoa Affect Nausea Severity?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5978301-effect-87494-population-study
BibTeX 
@misc{sinn_cause_5978301_effect_87494_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Coffee, Tea & Cocoa Affect Nausea Severity?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5978301-effect-87494-population-study},
  note = {Accessed: January 8, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Coffee, Tea & Cocoa Affect Nausea Severity?." The Journal of Citizen Science. Accessed January 8, 2026. https://studies.crowdsourcingcures.org/study/cause-5978301-effect-87494-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Coffee, Tea & Cocoa Affect Nausea Severity?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5978301-effect-87494-population-study [Accessed January 8, 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