Higher Fat Intake Predicts Slightly Higher Back Pain for Population

Higher Fat Intake Predicts Slightly Higher Back Pain for Population

Contents

Variables

A
Fat Intake 122
A
Back Pain 1303

Categories

A
Nutrients 313
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 53.9% average increase in Back Pain following above average Fat Intake.

Abstract

Back Pain was generally 27.6% higher than average after 0 grams of Fat Intake per 7 days.

Aggregated data from 2 study participants suggests with a HIGH degree of confidence (p=0.282, 95% CI -1.137 to 1.354) that Fat Intake has a weakly positive predictive relationship (R=0.109) with Back Pain.

The highest quartile of Back Pain measurements were observed following an average 0.772 grams Fat Intake.

The lowest quartile of Back Pain measurements were observed following an average 0.277 grams of Fat Intake.

After an onset delay of 0 seconds, Back Pain is typically 0% lower than average over the 7 days following around 0.277 grams of Fat Intake Fat Intake.

Keywords: Fat Intake, Back Pain, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 2 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 652 paired observations from 2 participants revealed a substantial improvement in Back Pain following above-average Fat Intake exposure.

+27.6%
Change from Baseline
Substantial effect on Back Pain
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.109
Correlation (r)
p = 0.070
Significance
z = 0.58
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Fat Intake:

  • Back Pain increased by 27.6% on average
  • Temporal analysis supports Fat Intake 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 2 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 2 participants and 652 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

101.8 g
Value Predicting Higher Back Pain
Average Fat Intake when Back Pain exceeded its mean
0.0 g
Value Predicting Lower Back Pain
Average Fat Intake when Back Pain was below its mean

What This Suggests

Back Pain tended to be highest when Fat Intake was around 101.8 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

Fat Intake Distribution

Back Pain Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Fat Intake
Effect Variable Name Back Pain
Sinn Predictive Coefficient 0.019685839863505
Confidence Level HIGH
Confidence Interval 1.2456626773917
Forward Pearson Predictive Coefficient 0.1086
Critical T Value 1.8305
Average Fat Intake Over Previous 7 days Before ABOVE Average Back Pain 0.772 grams
Average Fat Intake Over Previous 7 days Before BELOW Average Back Pain 0.277 grams
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 652
Optimal Pearson Product 0.019733762554765
P Value 0.28187883934368
Statistical Significance 0.0703
Strength of Relationship 1.2456626773917
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Fat Intake Info

Property Value
Variable Name Fat Intake
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 7 days
Filling Value 0
Kurtosis 19.815004444051
Maximum Allowed Value 5000 grams
Mean 312.57820547945 grams
Median 190.53424657534 grams
Minimum Allowed Value 0 grams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 122
Number of Measurements 1992
Number of Measurements (including those generated by tagged, joined, or child variables) 1389
Public true
Onset Delay 0 seconds
Standard Deviation 315.94128273954
Unit Grams
User Variables 115
UPC 0
Variable Category Nutrients
Variable ID 1310
Variance 4694746.5634324

Back Pain Info

Property Value
Variable Name Back Pain
Aggregation Method MEAN
Analysis Performed At 2020-09-23
Duration of Action 24 hours
Kurtosis 2.4506730088922
Maximum Allowed Value 5 out of 5
Mean 2.9034218408496 out of 5
Median 2.8847373287671 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1136
Number of Aggregate Outcomes 167
Number of Measurements 3710
Number of Measurements (including those generated by tagged, joined, or child variables) 2918
Public true
Onset Delay 0 seconds
Standard Deviation 0.36868577891095
Unit 1 to 5 Rating
User Variables 405
UPC 711583981326
Variable Category Symptoms
Variable ID 1919
Variance 0.37862284909721

Introduction

Background

Fat Intake (Nutrients) and Back Pain (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 Fat Intake affect Back Pain?

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 Fat Intake for maximizing Back Pain?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Fat Intake and Back Pain. Additionally, we attempt to determine the Fat Intake values most likely to produce optimal Back Pain 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 27.6% improvement in Back Pain following above-average Fat Intake 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 Back Pain is not statistically significant at a 95% confidence interval. This suggests that the Fat Intake value may not have a significant influence on the Back Pain value, or that more data is needed to detect an effect.

After treatment, a 53.9% increase (0.356 out of 5) from the mean baseline 2.31 out of 5 was observed. The relative standard deviation at baseline was 37.9%. The observed change was 0.581045 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: 0.665
Critical t-value: 1.831

Since t = 0.66 < 1.83, 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 Fat Intake might influence Back Pain.

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

Conclusion

📊 Preliminary Findings: With 2 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 Fat Intake was associated with a 27.6% improvement in Back Pain—a substantial 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 27.6% 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 Fat Intake may influence Back Pain 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 2 participants. Thus, the study design is equivalent to the aggregation of 2 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 Fat Intake would produce an observable change in Back Pain.
  • Duration of Action: It was assumed that Fat Intake could produce an observable change in Back Pain for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Fat Intake values and subsequent Back Pain 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

Fat Intake 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.

Back Pain 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 Fat Intake Affect Back Pain?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1310-effect-1919-population-study
BibTeX 
@misc{sinn_cause_1310_effect_1919_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Fat Intake Affect Back Pain?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1310-effect-1919-population-study},
  note = {Accessed: January 6, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Fat Intake Affect Back Pain?." The Journal of Citizen Science. Accessed January 6, 2026. https://studies.crowdsourcingcures.org/study/cause-1310-effect-1919-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Fat Intake Affect Back Pain?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1310-effect-1919-population-study [Accessed January 6, 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