Higher Suicidality Predicts Moderately Higher Stress for Population

Higher Suicidality Predicts Moderately Higher Stress for Population

Contents

Variables

A
Suicidality 451
A
Stress 1265

Categories

A
Symptoms 13336
A
Emotions 2028

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Low Confidence
Moderate Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 14.8% average decrease in Stress following above average Suicidality.

Abstract

Stress was generally 11.333% lower than average after 4.09 out of 5 of Suicidality per 5 days.

Aggregated data from 3 study participants suggests with a LOW degree of confidence (p=0.0817, 95% CI 0.139 to 0.98) that Suicidality has a moderately positive predictive relationship (R=0.559) with Stress.

The highest quartile of Stress measurements were observed following an average 3.43 out of 5 Suicidality.

The lowest quartile of Stress measurements were observed following an average 3.55 out of 5 of Suicidality.

After an onset delay of 0 seconds, Stress is typically 7% lower than average over the 5 days following around 3.55 out of 5 of Suicidality Suicidality.

Keywords: Suicidality, Stress, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 3 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 28 paired observations from 3 participants revealed a modest reduction in Stress following above-average Suicidality exposure.

-11.3%
Change from Baseline
Modest effect on Stress
0.14
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

Low
Confidence
0.559
Correlation (r)
p = 0.177
Significance
z = 1.52
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Suicidality:

  • Stress decreased by 11.3% on average
  • Temporal analysis supports Suicidality 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 3 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 Suicidality values associated with high and low Stress 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

Suicidality Distribution

Stress Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Suicidality
Effect Variable Name Stress
Sinn Predictive Coefficient 0.14496037058161
Confidence Level LOW
Confidence Interval 0.42053
Forward Pearson Predictive Coefficient 0.5593
Critical T Value 1.825
Average Suicidality Over Previous 5 days Before ABOVE Average Stress 3.43 out of 5
Average Suicidality Over Previous 5 days Before BELOW Average Stress 3.55 out of 5
Duration of Action 5 days
Effect Size moderately positive
Number of Paired Measurements 28
Optimal Pearson Product 0.58670842685806
P Value 0.081687
Statistical Significance 0.1773
Strength of Relationship 0.42053
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 3

Suicidality Info

Property Value
Variable Name Suicidality
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 2.3445222150486
Maximum Allowed Value 5 out of 5
Mean 2.5248428571429 out of 5
Median 2.5202904761905 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 323
Number of Aggregate Outcomes 128
Number of Measurements 886
Number of Measurements (including those generated by tagged, joined, or child variables) 831
Public true
Onset Delay 0 seconds
Standard Deviation 0.37766901722564
Unit 1 to 5 Rating
User Variables 170
UPC 0
Variable Category Symptoms
Variable ID 87709
Variance 0.43734514362597

Stress Info

Property Value
Variable Name Stress
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8809357655084
Maximum Allowed Value 5 out of 5
Mean 3.1520495432579 out of 5
Median 3.1470732142857 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1106
Number of Aggregate Outcomes 159
Number of Measurements 4008
Number of Measurements (including those generated by tagged, joined, or child variables) 3438
Public true
Onset Delay 0 seconds
Standard Deviation 0.37961607684605
Unit 1 to 5 Rating
User Variables 484
UPC 637769766238
Variable Category Emotions
Variable ID 1923
Variance 0.3902865870594

Introduction

Background

Suicidality (Symptoms) and Stress (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 Suicidality affect Stress?

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 Suicidality for maximizing Stress?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Suicidality and Stress. Additionally, we attempt to determine the Suicidality values most likely to produce optimal Stress 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 11.3% reduction in Stress following above-average Suicidality exposure. The Predictor Impact Score (PIS) of 0.14 indicates weak evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Stress is statistically significant at a 95% confidence interval. The p-value of 0.1773 indicates there is less than a 17.73% probability that this result occurred by chance.

After treatment, a 14.8% decrease (-0.683 out of 5) from the mean baseline 4.06 out of 5 was observed. The relative standard deviation at baseline was 13.267%. The observed change was 1.5176 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: 4.582
Critical t-value: 1.825

Since t = 4.58 > 1.83, 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 Suicidality might influence Stress.

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

Conclusion

📊 Preliminary Findings: With 3 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 Suicidality was associated with a 11.3% reduction in Stress—a modest effect. The Predictor Impact Score of 0.14 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.14 and a 11.3% effect size, this relationship shows weak evidence. Additional observational data is recommended before investing in experimental validation. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Suicidality may influence Stress 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 3 participants. Thus, the study design is equivalent to the aggregation of 3 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 Suicidality would produce an observable change in Stress.
  • Duration of Action: It was assumed that Suicidality could produce an observable change in Stress for as much as 5 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Suicidality values and subsequent Stress 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

Suicidality 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.

Stress 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 Suicidality Affect Stress?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-87709-effect-1923-population-study
BibTeX 
@misc{sinn_cause_87709_effect_1923_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Suicidality Affect Stress?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-87709-effect-1923-population-study},
  note = {Accessed: January 7, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Suicidality Affect Stress?." The Journal of Citizen Science. Accessed January 7, 2026. https://studies.crowdsourcingcures.org/study/cause-87709-effect-1923-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Suicidality Affect Stress?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-87709-effect-1923-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

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