Higher Cholesterol Intake Predicts Very Slightly Higher Resilience for Population

Higher Cholesterol Intake Predicts Very Slightly Higher Resilience for Population

Contents

Variables

A
Cholesterol 113
A
Resilience 1180

Categories

A
Nutrients 313
A
Emotions 2028

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 3.1% average increase in Resilience following above average Cholesterol Intake.

Abstract

Resilience was generally 7% higher than average after a total of 213 milligrams of Cholesterol over the previous 7 days.

Aggregated data from 13 study participants suggests with a HIGH degree of confidence (p=0.219, 95% CI -0.371 to 0.504) that Cholesterol has a very weakly positive predictive relationship (R=0.0667) with Resilience.

The highest quartile of Resilience measurements were observed following an average 288 milligrams Cholesterol per day.

The lowest quartile of Resilience measurements were observed following an average 269 milligrams of Cholesterol per day.

After an onset delay of 0 seconds, Resilience is typically 6% lower than average over the 7 days following around 269 milligrams of Cholesterol Cholesterol.

Keywords: Cholesterol, Resilience, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 13 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 726 paired observations from 13 participants revealed a minimal improvement in Resilience following above-average Cholesterol exposure.

+1.9%
Change from Baseline
Minimal effect on Resilience
0.05
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.067
Correlation (r)
p = 0.221
Significance
z = 0.53
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Cholesterol:

  • Resilience increased by 1.9% on average
  • Temporal analysis supports Cholesterol 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 13 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 Cholesterol values associated with high and low Resilience 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

Cholesterol Distribution

Resilience Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Cholesterol Intake
Effect Variable Name Resilience
Sinn Predictive Coefficient 0.048522126924412
Confidence Level HIGH
Confidence Interval 0.4373287014357
Forward Pearson Predictive Coefficient 0.0667
Critical T Value 1.7506923076923
Total Cholesterol Intake Over Previous 7 days Before ABOVE Average Resilience 288 milligrams
Total Cholesterol Intake Over Previous 7 days Before BELOW Average Resilience 269 milligrams
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 726
Optimal Pearson Product 0.041012563745495
P Value 0.21940160893968
Statistical Significance 0.2207
Strength of Relationship 0.4373287014357
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 13

Cholesterol Info

Property Value
Variable Name Cholesterol
Aggregation Method SUM
Analysis Performed At 2026-01-07
Duration of Action 7 days
Filling Value 0
Kurtosis 46.856620931119
Mean 154.80732746896 milligrams
Median 8.7532432432432 milligrams
Minimum Allowed Value 0 milligrams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 113
Number of Measurements 20557
Number of Measurements (including those generated by tagged, joined, or child variables) 17286
Public true
Onset Delay 0 seconds
Standard Deviation 77.849773097845
Unit Milligrams
User Variables 188
UPC 031604013028
Variable Category Nutrients
Variable ID 1290
Variance 10440.744648417

Resilience Info

Property Value
Variable Name Resilience
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.9334519243983
Maximum Allowed Value 5 out of 5
Mean 2.5113766833812 out of 5
Median 2.4890897325572 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1070
Number of Aggregate Outcomes 110
Number of Measurements 25345
Number of Measurements (including those generated by tagged, joined, or child variables) 24386
Public true
Onset Delay 0 seconds
Standard Deviation 0.4900057068033
Unit 1 to 5 Rating
User Variables 1335
UPC 0
Variable Category Emotions
Variable ID 1436
Variance 0.52229282248446

Introduction

Background

Cholesterol (Nutrients) and Resilience (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 Cholesterol affect Resilience?

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 Cholesterol for maximizing Resilience?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Cholesterol and Resilience. Additionally, we attempt to determine the Cholesterol values most likely to produce optimal Resilience 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 1.9% improvement in Resilience following above-average Cholesterol exposure. The Predictor Impact Score (PIS) of 0.05 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 Resilience is not statistically significant at a 95% confidence interval. This suggests that the Cholesterol value may not have a significant influence on the Resilience value, or that more data is needed to detect an effect.

After treatment, a 3.1% increase (0.00938 out of 5) from the mean baseline 2.5 out of 5 was observed. The relative standard deviation at baseline was 22.9385%. The observed change was 0.525299 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: 1.351
Critical t-value: 1.751

Since t = 1.35 < 1.75, 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 Cholesterol might influence Resilience.

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

Conclusion

📊 Preliminary Findings: With 13 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 Cholesterol was associated with a 1.9% improvement in Resilience—a minimal 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 1.9% 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 Cholesterol may influence Resilience 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 13 participants. Thus, the study design is equivalent to the aggregation of 13 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 0 seconds would pass before a change in Cholesterol would produce an observable change in Resilience.
  • Duration of Action: It was assumed that Cholesterol could produce an observable change in Resilience for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Cholesterol values and subsequent Resilience 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

Cholesterol data was primarily collected using MyFitnessPal. Lose weight with MyFitnessPal, the fastest and easiest-to-use calorie counter for iPhone and iPad. With the largest food database of any iOS calorie counter (over 3,000,000 foods), and amazingly fast food and exercise entry.

Resilience 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 Cholesterol Affect Resilience?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1290-effect-1436-population-study
BibTeX 
@misc{sinn_cause_1290_effect_1436_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Cholesterol Affect Resilience?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1290-effect-1436-population-study},
  note = {Accessed: January 7, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Cholesterol Affect Resilience?." The Journal of Citizen Science. Accessed January 7, 2026. https://studies.crowdsourcingcures.org/study/cause-1290-effect-1436-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Cholesterol Affect Resilience?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1290-effect-1436-population-study [Accessed January 7, 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