Higher Glucose (dextrose) Intake Predicts Moderately Higher Guiltiness for Population

Higher Glucose (dextrose) Intake Predicts Moderately Higher Guiltiness for Population

Contents

Variables

A
Glucose (Dextrose) 48
A
Guiltiness 2319

Categories

A
Nutrients 313
A
Emotions 2028

Actions

A
Join Study
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Your Data

Tags

High Confidence
Moderate Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 41.7% average increase in Guiltiness following above average Glucose (dextrose) Intake.

Abstract

Guiltiness was generally 55.6% higher than average after 0.0144 grams of Glucose per 7 days.

Aggregated data from 1 study participants suggests with a HIGH degree of confidence (p=0.001, 95% CI 0.038 to 1.046) that Glucose has a moderately positive predictive relationship (R=0.542) with Guiltiness.

The highest quartile of Guiltiness measurements were observed following an average 0.01 grams Glucose per day.

The lowest quartile of Guiltiness measurements were observed following an average 0 grams of Glucose per day.

After an onset delay of 30 minutes, Guiltiness is typically 50% lower than average over the 7 days following around 0 grams of Glucose Glucose.

Keywords: Glucose, Guiltiness, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 1 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 5 paired observations from 1 participants revealed a substantial improvement in Guiltiness following above-average Glucose exposure.

+55.6%
Change from Baseline
Substantial effect on Guiltiness
0.05
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.542
Correlation (r)
p = 0.001
Significance
z = 1.18
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Glucose:

  • Guiltiness increased by 55.6% on average
  • Temporal analysis supports Glucose as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.001)

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 1 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 1 participants and 5 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.0 g
Value Predicting Higher Guiltiness
Average Glucose when Guiltiness exceeded its mean
0.0 g
Value Predicting Lower Guiltiness
Average Glucose when Guiltiness was below its mean

What This Suggests

Guiltiness tended to be highest when Glucose was around 0.0 g.

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

Glucose Distribution

Guiltiness Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Glucose (dextrose) Intake
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.051578119016116
Confidence Level HIGH
Confidence Interval 0.50375
Forward Pearson Predictive Coefficient 0.542
Critical T Value 2.015
Total Glucose (dextrose) Intake Over Previous 7 days Before ABOVE Average Guiltiness 0.01 grams
Total Glucose (dextrose) Intake Over Previous 7 days Before BELOW Average Guiltiness 0 grams
Duration of Action 7 days
Effect Size moderately positive
Number of Paired Measurements 5
Optimal Pearson Product 0.83139004791055
P Value 0.001
Statistical Significance 0.001
Strength of Relationship 0.50375
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Glucose (Dextrose) Info

Property Value
Variable Name Glucose (dextrose)
Aggregation Method SUM
Analysis Performed At 2022-08-10
Duration of Action 7 days
Filling Value 0
Kurtosis 148.5280869698
Mean 0.001286051 grams
Median 0.0012333333333333 grams
Minimum Allowed Value 0 grams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 48
Number of Measurements 0
Number of Measurements (including those generated by tagged, joined, or child variables) 22
Public true
Onset Delay 30 minutes
Standard Deviation 0.00022223316358509
Unit Grams
User Variables 18
Variable Category Nutrients
Variable ID 5401241
Variance 2.5465154399033E-7

Guiltiness Info

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2022-09-29
Duration of Action 24 hours
Kurtosis 2.2615389805518
Maximum Allowed Value 5 out of 5
Mean 2.365934400949 out of 5
Median 2.2960569395018 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 2056
Number of Aggregate Outcomes 263
Number of Measurements 31621
Number of Measurements (including those generated by tagged, joined, or child variables) 31621
Public true
Onset Delay 0 seconds
Standard Deviation 0.5743531754414
Unit 1 to 5 Rating
User Variables 1787
UPC 0
Variable Category Emotions
Variable ID 1335
Variance 0.72121328198661

Introduction

Background

Glucose (Nutrients) and Guiltiness (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 Glucose affect Guiltiness?

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 Glucose for maximizing Guiltiness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Glucose and Guiltiness. Additionally, we attempt to determine the Glucose (dextrose) values most likely to produce optimal Guiltiness 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 55.6% improvement in Guiltiness following above-average Glucose exposure. The Predictor Impact Score (PIS) of 0.05 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.001).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Guiltiness is statistically significant at a 95% confidence interval. The p-value of 0.0010 indicates there is less than a 0.10% probability that this result occurred by chance.

After treatment, a 41.7% increase (0.833 out of 5) from the mean baseline 1.5 out of 5 was observed. The relative standard deviation at baseline was 47.1%. The observed change was 1.1785 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: 5.000
Critical t-value: 2.015

Since t = 5.00 > 2.02, 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 Glucose might influence Guiltiness.

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

Conclusion

📊 Preliminary Findings: With 1 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 Glucose was associated with a 55.6% improvement in Guiltiness—a substantial effect. The Predictor Impact Score of 0.05 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.05 and a 55.6% 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 Glucose may influence Guiltiness in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

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Methods

Study Design

This study is based on data donated by 1 participants. Thus, the study design is equivalent to the aggregation of 1 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 Glucose would produce an observable change in Guiltiness.
  • Duration of Action: It was assumed that Glucose could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Glucose values and subsequent Guiltiness 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

Glucose 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.

Guiltiness 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 Glucose (dextrose) Affect Guiltiness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5401241-effect-1335-population-study
BibTeX 
@misc{sinn_cause_5401241_effect_1335_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Glucose (dextrose) Affect Guiltiness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5401241-effect-1335-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Glucose (dextrose) Affect Guiltiness?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-5401241-effect-1335-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Glucose (dextrose) Affect Guiltiness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5401241-effect-1335-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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