Higher Apples Intake Predicts Slightly Lower Distress for Population

Higher Apples Intake Predicts Slightly Lower Distress for Population

Contents

Variables

A
Apples 90
A
Distress 1349

Categories

A
Foods 13415
A
Emotions 2028

Actions

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Join Study
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Your Data

Tags

Medium Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 3.6% average decrease in Distress following above average Apples Intake.

Abstract

Distress was generally 2.5% higher than average after 4.5 count of Apples per 14 days.

Aggregated data from 2 study participants suggests with a MEDIUM degree of confidence (p=0.144, 95% CI -0.371 to -0.003) that Apples has a weakly negative predictive relationship (R=-0.187) with Distress.

The highest quartile of Distress measurements were observed following an average 6.49 count Apples per day.

The lowest quartile of Distress measurements were observed following an average 6.57 count of Apples per day.

After an onset delay of 30 minutes, Distress is typically 6% lower than average over the 14 days following around 6.57 count of Apples Apples.

Keywords: Apples, Distress, 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 120 paired observations from 2 participants revealed a minimal improvement in Distress following above-average Apples exposure.

+2.5%
Change from Baseline
Minimal effect on Distress
0.03
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.187
Correlation (r)
p = 0.498
Significance
z = 0.35
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Apples:

  • Distress increased by 2.5% on average
  • Temporal analysis supports Apples 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

No clear dose-response relationship detected. The Apples values associated with high and low Distress 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

Apples Distribution

Distress Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Apples Intake
Effect Variable Name Distress
Sinn Predictive Coefficient 0.033861095878223
Confidence Level MEDIUM
Confidence Interval 0.18387
Forward Pearson Predictive Coefficient -0.1868
Critical T Value 1.7085
Total Apples Intake Over Previous 14 days Before ABOVE Average Distress 6.49 count
Total Apples Intake Over Previous 14 days Before BELOW Average Distress 6.57 count
Duration of Action 14 days
Effect Size weakly negative
Number of Paired Measurements 120
Optimal Pearson Product 0.076612094576721
P Value 0.14412
Statistical Significance 0.4976
Strength of Relationship 0.18387
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Apples Info

Property Value
Variable Name Apples
Aggregation Method SUM
Analysis Performed At 2022-08-10
Duration of Action 14 days
Filling Value 0
Kurtosis 12.601242881758
Mean 0.67458839473684 count
Median 0.65131578947368 count
Minimum Allowed Value 0 count
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 90
Number of Measurements 35
Number of Measurements (including those generated by tagged, joined, or child variables) 815
Public true
Onset Delay 30 minutes
Standard Deviation 0.27326210771195
Unit Count
User Variables 78
Variable Category Foods
Variable ID 96324
Variance 0.50252463991593

Distress Info

Property Value
Variable Name Distress
Aggregation Method MEAN
Analysis Performed At 2020-09-17
Duration of Action 24 hours
Kurtosis 2.8522483497534
Maximum Allowed Value 5 out of 5
Mean 2.4777342286981 out of 5
Median 2.4229733332357 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1209
Number of Aggregate Outcomes 140
Number of Measurements 33096
Number of Measurements (including those generated by tagged, joined, or child variables) 32968
Public true
Onset Delay 0 seconds
Standard Deviation 0.53719668856315
Unit 1 to 5 Rating
User Variables 1486
UPC 647297398818
Variable Category Emotions
Variable ID 1305
Variance 0.6312526863217

Introduction

Background

Apples (Foods) and Distress (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

Do Apples affect Distress?

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 Apples for maximizing Distress?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Apples and Distress. Additionally, we attempt to determine the Apples values most likely to produce optimal Distress 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 2.5% improvement in Distress following above-average Apples exposure. The Predictor Impact Score (PIS) of 0.03 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 Distress is not statistically significant at a 95% confidence interval. This suggests that the Apples value may not have a significant influence on the Distress value, or that more data is needed to detect an effect.

After treatment, a 3.6% decrease (-0.0298 out of 5) from the mean baseline 1.73 out of 5 was observed. The relative standard deviation at baseline was 28.65%. The observed change was 0.35198 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: 1.538
Critical t-value: 1.709

Since t = 1.54 < 1.71, 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 Apples might influence Distress.

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 Apples 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 Apples
  • 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 Apples was associated with a 2.5% improvement in Distress—a minimal effect. The Predictor Impact Score of 0.03 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.03 and a 2.5% 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 Apples may influence Distress 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.

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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 30 minutes would pass before a change in Apples would produce an observable change in Distress.
  • Duration of Action: It was assumed that Apples could produce an observable change in Distress for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Apples values and subsequent Distress 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

Apples 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.

Distress 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 Apples Affect Distress?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-96324-effect-1305-population-study
BibTeX 
@misc{sinn_cause_96324_effect_1305_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Apples Affect Distress?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-96324-effect-1305-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Apples Affect Distress?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-96324-effect-1305-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Apples Affect Distress?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-96324-effect-1305-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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