Higher Coffee Consumption Predicts Very Slightly Lower Stress for Population

Higher Coffee Consumption Predicts Very Slightly Lower Stress for Population

Contents

Variables

A
Coffee 326
A
Stress 1265

Categories

A
Foods 13415
A
Emotions 2028

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 4.9% average decrease in Stress following above average Coffee Consumption.

Abstract

Stress was generally 3.77619% lower than average after 11.4 serving of Coffee per 14 days.

Aggregated data from 21 study participants suggests with a HIGH degree of confidence (p=0.213, 95% CI -0.526 to 0.414) that Coffee has a very weakly negative predictive relationship (R=-0.0559) with Stress.

The highest quartile of Stress measurements were observed following an average 12.8 serving Coffee per day.

The lowest quartile of Stress measurements were observed following an average 14 serving of Coffee per day.

After an onset delay of 30 minutes, Stress is typically 6% lower than average over the 14 days following around 14 serving of Coffee Coffee.

Keywords: Coffee, Stress, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 21 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 1,318 paired observations from 21 participants revealed a minimal reduction in Stress following above-average Coffee exposure.

-3.8%
Change from Baseline
Minimal effect on Stress
0.93
Predictor Impact Score
Strong evidence for causal relationship

Supporting Statistics

High
Confidence
-0.056
Correlation (r)
p = 0.202
Significance
z = 0.67
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Coffee:

  • Stress decreased by 3.8% on average
  • The Predictor Impact Score of 0.93 suggests this relationship warrants high priority for experimental validation
  • Temporal analysis supports Coffee 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 21 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 values associated with high and low Stress 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 Distribution

Stress Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Coffee Consumption
Effect Variable Name Stress
Sinn Predictive Coefficient 0.92659825743906
Confidence Level HIGH
Confidence Interval 0.46997947653002
Forward Pearson Predictive Coefficient -0.0559
Critical T Value 1.7441904761905
Total Coffee Consumption Over Previous 14 days Before ABOVE Average Stress 12.8 serving
Total Coffee Consumption Over Previous 14 days Before BELOW Average Stress 14 serving
Duration of Action 14 days
Effect Size very weakly negative
Number of Paired Measurements 1318
Optimal Pearson Product 0.025653538434122
P Value 0.21272097940978
Statistical Significance 0.2019
Strength of Relationship 0.46997947653002
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 21

Coffee Info

Property Value
Variable Name Coffee
Aggregation Method SUM
Analysis Performed At 2021-07-18
Duration of Action 14 days
Filling Value 0
Kurtosis 12.33836841945
Maximum Allowed Value 40 serving
Mean 0.64430984666667 serving
Median 0.55333333333333 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 326
Number of Measurements 4720
Number of Measurements (including those generated by tagged, joined, or child variables) 4174
Public true
Onset Delay 30 minutes
Standard Deviation 0.40051808662978
Unit Serving
User Variables 559
UPC 813957023530
Variable Category Foods
Variable ID 84985
Variance 0.44569240881787

Stress Info

Property Value
Variable Name Stress
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8809357655084
Maximum Allowed Value 5 out of 5
Mean 3.1520495432579 out of 5
Median 3.1470732142857 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1106
Number of Aggregate Outcomes 159
Number of Measurements 4008
Number of Measurements (including those generated by tagged, joined, or child variables) 3438
Public true
Onset Delay 0 seconds
Standard Deviation 0.37961607684605
Unit 1 to 5 Rating
User Variables 484
UPC 637769766238
Variable Category Emotions
Variable ID 1923
Variance 0.3902865870594

Introduction

Background

Coffee (Foods) and Stress (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 Coffee affect Stress?

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 for maximizing Stress?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Coffee and Stress. Additionally, we attempt to determine the Coffee values most likely to produce optimal Stress 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 3.8% reduction in Stress following above-average Coffee exposure. The Predictor Impact Score (PIS) of 0.93 indicates strong evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Stress is not statistically significant at a 95% confidence interval. This suggests that the Coffee value may not have a significant influence on the Stress value, or that more data is needed to detect an effect.

After treatment, a 4.9% decrease (-0.133 out of 5) from the mean baseline 3.03 out of 5 was observed. The relative standard deviation at baseline was 29.8524%. The observed change was 0.668685 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: 1.658
Critical t-value: 1.744

Since t = 1.66 < 1.74, 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.

Based on community responses so far, 1 person feels that there is a plausible mechanism of action and 0 feel that any relationship observed between Coffee and Stress is coincidental.

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

Conclusion

📊 Preliminary Findings: With 21 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 was associated with a 3.8% reduction in Stress—a minimal effect. The Predictor Impact Score of 0.93 indicates this relationship is high priority for experimental validation.

Bottom Line: Based on a PIS of 0.93 and a 3.8% effect size, this relationship shows strong evidence and should be prioritized for experimental validation through randomized controlled trials. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Coffee may influence Stress in real-world conditions. The combination of effect size, sample size, and temporal evidence supports this as a meaningful relationship worth investigating further.

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Methods

Study Design

This study is based on data donated by 21 participants. Thus, the study design is equivalent to the aggregation of 21 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 would produce an observable change in Stress.
  • Duration of Action: It was assumed that Coffee could produce an observable change in Stress for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Coffee values and subsequent Stress 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 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.

Stress 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 Affect Stress?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-84985-effect-1923-population-study
BibTeX 
@misc{sinn_cause_84985_effect_1923_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Coffee Affect Stress?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-84985-effect-1923-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Coffee Affect Stress?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-84985-effect-1923-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Coffee Affect Stress?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-84985-effect-1923-population-study [Accessed January 10, 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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