Higher Determination Predicts Very Slightly Higher Sadness for Population

Higher Determination Predicts Very Slightly Higher Sadness for Population

Contents

Variables

A
Determination 1197
A
Sadness 488

Categories

A
Emotions 2028
A
Emotions 2028

Tags

Low Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 8.7% average decrease in Sadness following above average Determination.

Abstract

Sadness was generally 26% higher than average after 1.5 out of 5 of Determination per 24 hours.

Aggregated data from 1 study participants suggests with a LOW degree of confidence (p=0.184, 95% CI -1.341 to 1.463) that Determination has a very weakly positive predictive relationship (R=0.061) with Sadness.

The highest quartile of Sadness measurements were observed following an average 2 out of 5 Determination.

The lowest quartile of Sadness measurements were observed following an average 1.67 out of 5 of Determination.

After an onset delay of 0 seconds, Sadness is typically 13% lower than average over the 24 hours following around 1.67 out of 5 of Determination Determination.

Keywords: Determination, Sadness, 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 Sadness following above-average Determination exposure.

+26.0%
Change from Baseline
Substantial effect on Sadness
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.061
Correlation (r)
p = 0.004
Significance
z = 1.84
Effect Magnitude
φ = 0.15
Temporality

What This Means

When participants had above-average Determination:

  • Sadness increased by 26.0% on average
  • Temporal analysis suggests possible reverse causation—interpret with caution
  • This relationship is statistically significant (p = 0.004)

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.

2.0 /5
Value Predicting Higher Sadness
Average Determination when Sadness exceeded its mean
1.5 /5
Value Predicting Lower Sadness
Average Determination when Sadness was below its mean

What This Suggests

Sadness tended to be highest when Determination was around 2.0 /5.

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

Determination Distribution

Sadness Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Determination
Effect Variable Name Sadness
Sinn Predictive Coefficient 0.0005804917565036
Confidence Level LOW
Confidence Interval 1.4024811743637
Forward Pearson Predictive Coefficient 0.061
Critical T Value 2.015
Average Determination Over Previous 24 hours Before ABOVE Average Sadness 2 out of 5
Average Determination Over Previous 24 hours Before BELOW Average Sadness 1.67 out of 5
Duration of Action 24 hours
Effect Size very weakly positive
Number of Paired Measurements 5
Optimal Pearson Product 0.15688625792335
P Value 0.18375206717824
Statistical Significance 0.0041
Strength of Relationship 1.4024811743637
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Determination Info

Property Value
Variable Name Determination
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8666418506987
Maximum Allowed Value 5 out of 5
Mean 2.5598820793888 out of 5
Median 2.5463832585949 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1076
Number of Aggregate Outcomes 121
Number of Measurements 20822
Number of Measurements (including those generated by tagged, joined, or child variables) 20696
Public true
Onset Delay 0 seconds
Standard Deviation 0.50596606788843
Unit 1 to 5 Rating
User Variables 1383
UPC 0
Variable Category Emotions
Variable ID 1299
Variance 0.54867203517318

Sadness Info

Property Value
Variable Name Sadness
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.8758355588753
Maximum Allowed Value 5 out of 5
Mean 3.0344322222222 out of 5
Median 2.9870366666667 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 407
Number of Aggregate Outcomes 81
Number of Measurements 1906
Number of Measurements (including those generated by tagged, joined, or child variables) 1866
Public true
Onset Delay 0 seconds
Standard Deviation 0.37863342794762
Unit 1 to 5 Rating
User Variables 113
UPC 796714612041
Variable Category Emotions
Variable ID 90183
Variance 0.39553007386659

Introduction

Background

Determination (Emotions) and Sadness (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 Determination affect Sadness?

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 Determination for maximizing Sadness?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Determination and Sadness. Additionally, we attempt to determine the Determination values most likely to produce optimal Sadness 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 26.0% improvement in Sadness following above-average Determination exposure. The Predictor Impact Score (PIS) of 0.00 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.004).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Sadness is not statistically significant at a 95% confidence interval. This suggests that the Determination value may not have a significant influence on the Sadness value, or that more data is needed to detect an effect.

After treatment, a 8.7% decrease (0.867 out of 5) from the mean baseline 3.33 out of 5 was observed. The relative standard deviation at baseline was 14.1%. The observed change was 1.83848 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: 1.245
Critical t-value: 2.015

Since t = 1.25 < 2.02, 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 Determination might influence Sadness.

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 Determination 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 Determination
  • 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 Determination was associated with a 26.0% improvement in Sadness—a substantial effect. The Predictor Impact Score of 0.00 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.00 and a 26.0% 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 Determination may influence Sadness 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 0 seconds would pass before a change in Determination would produce an observable change in Sadness.
  • Duration of Action: It was assumed that Determination could produce an observable change in Sadness for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Determination values and subsequent Sadness 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

Determination 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.

Sadness 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 Determination Affect Sadness?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1299-effect-90183-population-study
BibTeX 
@misc{sinn_cause_1299_effect_90183_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Determination Affect Sadness?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1299-effect-90183-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Determination Affect Sadness?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1299-effect-90183-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Determination Affect Sadness?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1299-effect-90183-population-study [Accessed January 10, 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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