Higher Breakfast Intake Predicts Slightly Higher Overall Mood for Population

Higher Breakfast Intake Predicts Slightly Higher Overall Mood for Population

Contents

Variables

A
Breakfast 54
A
Overall Mood 7137

Categories

A
Foods 13415
A
Emotions 2028

Tags

Low Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 9.3% average decrease in Overall Mood following above average Breakfast Intake.

Abstract

Overall Mood was generally 3% higher than average after a total of Yes of Breakfast over the previous 12 days.

Aggregated data from 4 study participants suggests with a LOW degree of confidence (p=0.323, 95% CI -0.351 to 0.609) that Breakfast has a weakly positive predictive relationship (R=0.129) with Overall Mood.

The highest quartile of Overall Mood measurements were observed following an average Yes Breakfast per day.

The lowest quartile of Overall Mood measurements were observed following an average Yes of Breakfast per day.

After an onset delay of 30 minutes, Overall Mood is typically 3% lower than average over the 12 days following around Yes of Breakfast Breakfast.

Keywords: Breakfast, Overall Mood, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 4 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 61 paired observations from 4 participants revealed a modest reduction in Overall Mood following above-average Breakfast exposure.

-5.6%
Change from Baseline
Modest effect on Overall Mood
0.37
Predictor Impact Score
Moderate evidence for causal relationship

Supporting Statistics

Low
Confidence
0.129
Correlation (r)
p = 0.258
Significance
z = 0.29
Effect Magnitude
φ = 0.45
Temporality

What This Means

When participants had above-average Breakfast:

  • Overall Mood decreased by 5.6% on average
  • The Predictor Impact Score of 0.37 suggests this relationship warrants consideration for further investigation

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 4 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 4 participants and 61 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

1.0 yes/no
Value Predicting Higher Overall Mood
Average Breakfast when Overall Mood exceeded its mean
6.0 yes/no
Value Predicting Lower Overall Mood
Average Breakfast when Overall Mood was below its mean

What This Suggests

Overall Mood tended to be highest when Breakfast was around 1.0 yes/no.

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

Breakfast Distribution

Overall Mood Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Breakfast Intake
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.37230757144602
Confidence Level LOW
Confidence Interval 0.47996
Forward Pearson Predictive Coefficient 0.1293
Critical T Value 1.7193
Total Breakfast Intake Over Previous 12 days Before ABOVE Average Overall Mood Yes
Total Breakfast Intake Over Previous 12 days Before BELOW Average Overall Mood Yes
Duration of Action 12 days
Effect Size weakly positive
Number of Paired Measurements 61
Optimal Pearson Product 0.042724216168242
P Value 0.32311
Statistical Significance 0.2583
Strength of Relationship 0.47996
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 4

Breakfast Info

Property Value
Variable Name Breakfast
Aggregation Method SUM
Analysis Performed At 2020-10-09
Duration of Action 14 days
Filling Value 0
Kurtosis 7.2092410711077
Mean Yes
Median Yes
Minimum Allowed Value No
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 54
Number of Measurements 169
Number of Measurements (including those generated by tagged, joined, or child variables) 169
Public true
Onset Delay 30 minutes
Standard Deviation 0.22804227061048
Unit Yes/No
User Variables 8
UPC 602401725447
Variable Category Foods
Variable ID 5953160
Variance 0.086861831412493

Overall Mood Info

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2020-09-12
Duration of Action 24 hours
Kurtosis 3.3832907631011
Maximum Allowed Value 5 out of 5
Mean 3.1202433341482 out of 5
Median 3.1415600073553 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 6425
Number of Aggregate Outcomes 712
Number of Measurements 617070
Number of Measurements (including those generated by tagged, joined, or child variables) 561596
Public true
Onset Delay 0 seconds
Standard Deviation 0.38176118810538
Unit 1 to 5 Rating
User Variables 9142
UPC 767674073845
Variable Category Emotions
Variable ID 1398
Variance 0.30220747449488

Introduction

Background

Breakfast (Foods) and Overall Mood (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 Breakfast affect Overall Mood?

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 Breakfast for maximizing Overall Mood?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Breakfast and Overall Mood. Additionally, we attempt to determine the Breakfast values most likely to produce optimal Overall Mood 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 5.6% reduction in Overall Mood following above-average Breakfast exposure. The Predictor Impact Score (PIS) of 0.37 indicates moderate evidence for a causal relationship.

Statistical Significance

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

After treatment, a 9.3% decrease (-0.188 out of 5) from the mean baseline 3.58 out of 5 was observed. The relative standard deviation at baseline was 18.225%. The observed change was 0.29057 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: 0.585
Critical t-value: 1.719

Since t = 0.58 < 1.72, 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 Breakfast and Overall Mood 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 Breakfast 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 Breakfast
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 4 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 Breakfast was associated with a 5.6% reduction in Overall Mood—a modest effect. The Predictor Impact Score of 0.37 indicates this relationship is worthy of further investigation.

Bottom Line: Based on a PIS of 0.37 and a 5.6% effect size, this relationship shows moderate evidence and merits consideration for further investigation in controlled studies. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Breakfast may influence Overall Mood 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 4 participants. Thus, the study design is equivalent to the aggregation of 4 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 Breakfast would produce an observable change in Overall Mood.
  • Duration of Action: It was assumed that Breakfast could produce an observable change in Overall Mood for as much as 12 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Breakfast values and subsequent Overall Mood 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

Breakfast 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.

Overall Mood 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 Breakfast Affect Overall Mood?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5953160-effect-1398-population-study
BibTeX 
@misc{sinn_cause_5953160_effect_1398_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Breakfast Affect Overall Mood?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5953160-effect-1398-population-study},
  note = {Accessed: January 14, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Breakfast Affect Overall Mood?." The Journal of Citizen Science. Accessed January 14, 2026. https://studies.crowdsourcingcures.org/study/cause-5953160-effect-1398-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Breakfast Affect Overall Mood?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5953160-effect-1398-population-study [Accessed January 14, 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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