Higher Guiltiness Predicts Very Slightly Lower Heart Rate (Pulse) for Population

Higher Guiltiness Predicts Very Slightly Lower Heart Rate (Pulse) for Population

Contents

Variables

A
Guiltiness 2319
A
Heart Rate (Pulse) 1784

Categories

A
Emotions 2028
A
Vital Signs 110

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 1.8% average decrease in Heart Rate (Pulse) following above average Guiltiness.

Abstract

Heart Rate (Pulse) was generally 4% higher than average after an average of 2.39 out of 5 of Guiltiness over the previous 24 hours.

Aggregated data from 3 study participants suggests with a MEDIUM degree of confidence (p=0.203, 95% CI -13.315 to 13.222) that Guiltiness has a very weakly negative predictive relationship (R=-0.0467) with Heart Rate.

The highest quartile of Heart Rate measurements were observed following an average 2.38 out of 5 Guiltiness.

The lowest quartile of Heart Rate measurements were observed following an average 2.37 out of 5 of Guiltiness.

After an onset delay of 0 seconds, Heart Rate is typically 6% lower than average over the 24 hours following around 2.37 out of 5 of Guiltiness Guiltiness.

Keywords: Guiltiness, Heart Rate, 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 335 paired observations from 3 participants revealed a modest improvement in Heart Rate following above-average Guiltiness exposure.

+8.7%
Change from Baseline
Modest effect on Heart Rate
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.047
Correlation (r)
p = 0.511
Significance
z = 0.45
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Guiltiness:

  • Heart Rate increased by 8.7% on average
  • Temporal analysis supports Guiltiness 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 Guiltiness values associated with high and low Heart Rate 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

Guiltiness Distribution

Heart Rate Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Guiltiness
Effect Variable Name Heart Rate (Pulse)
Sinn Predictive Coefficient 0.00060518946459519
Confidence Level MEDIUM
Confidence Interval 13.268230810645
Forward Pearson Predictive Coefficient -0.0467
Critical T Value 1.7256666666667
Average Guiltiness Over Previous 24 hours Before ABOVE Average Heart Rate ( Pulse) 2.38 out of 5
Average Guiltiness Over Previous 24 hours Before BELOW Average Heart Rate ( Pulse) 2.37 out of 5
Duration of Action 24 hours
Effect Size very weakly negative
Number of Paired Measurements 335
Optimal Pearson Product 0.050269185951224
P Value 0.20297762542397
Statistical Significance 0.5113
Strength of Relationship 13.268230810645
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 3

Guiltiness Info

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2022-09-29
Duration of Action 24 hours
Kurtosis 2.2615389805518
Maximum Allowed Value 5 out of 5
Mean 2.365934400949 out of 5
Median 2.2960569395018 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 2056
Number of Aggregate Outcomes 263
Number of Measurements 31621
Number of Measurements (including those generated by tagged, joined, or child variables) 31621
Public true
Onset Delay 0 seconds
Standard Deviation 0.5743531754414
Unit 1 to 5 Rating
User Variables 1787
UPC 0
Variable Category Emotions
Variable ID 1335
Variance 0.72121328198661

Heart Rate (Pulse) Info

Property Value
Variable Name Heart Rate (Pulse)
Aggregation Method MEAN
Analysis Performed At 2020-09-15
Duration of Action 7 days
Kurtosis 3.9578716925837
Maximum Allowed Value 300 beats per minute
Mean 87.826883616319 beats per minute
Median 86.968273025911 beats per minute
Minimum Allowed Value 20 beats per minute
Number of Aggregate Predictors 1612
Number of Aggregate Outcomes 172
Number of Measurements 44806
Number of Measurements (including those generated by tagged, joined, or child variables) 25333
Public true
Onset Delay 0 seconds
Standard Deviation 12.471536279914
Unit Beats per Minute
User Variables 225
UPC 851697006178
Variable Category Vital Signs
Variable ID 1342
Variance 335.93870785439

Introduction

Background

Guiltiness (Emotions) and Heart Rate (Vital Signs) 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 Guiltiness affect Heart Rate?

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 Guiltiness for maximizing Heart Rate?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Guiltiness and Heart Rate. Additionally, we attempt to determine the Guiltiness values most likely to produce optimal Heart Rate 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 8.7% improvement in Heart Rate following above-average Guiltiness exposure. The Predictor Impact Score (PIS) of 0.00 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 Heart Rate is not statistically significant at a 95% confidence interval. This suggests that the Guiltiness value may not have a significant influence on the Heart Rate value, or that more data is needed to detect an effect.

After treatment, a 1.8% decrease (7.83 beats per minute) from the mean baseline 90.7 beats per minute was observed. The relative standard deviation at baseline was 20.0667%. The observed change was 0.450902 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: 1.204
Critical t-value: 1.726

Since t = 1.20 < 1.73, 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 Guiltiness might influence Heart Rate.

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 Guiltiness 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 Guiltiness
  • 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 Guiltiness was associated with a 8.7% improvement in Heart Rate—a modest 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 8.7% 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 Guiltiness may influence Heart Rate 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 Guiltiness would produce an observable change in Heart Rate.
  • Duration of Action: It was assumed that Guiltiness could produce an observable change in Heart Rate for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Guiltiness values and subsequent Heart Rate 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

Guiltiness 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.

Heart Rate data was primarily collected using Withings. Withings creates smart products and apps to take care of yourself and your loved ones in a new and easy way. Discover the Withings Pulse, Wi-Fi Body Scale, and Blood Pressure Monitor.

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 Guiltiness Affect Heart Rate (Pulse)?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1335-effect-1342-population-study
BibTeX 
@misc{sinn_cause_1335_effect_1342_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Guiltiness Affect Heart Rate (Pulse)?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1335-effect-1342-population-study},
  note = {Accessed: January 9, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Guiltiness Affect Heart Rate (Pulse)?." The Journal of Citizen Science. Accessed January 9, 2026. https://studies.crowdsourcingcures.org/study/cause-1335-effect-1342-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Guiltiness Affect Heart Rate (Pulse)?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1335-effect-1342-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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