Higher Interest Predicts Moderately Higher Overall Mood for Population

Higher Interest Predicts Moderately Higher Overall Mood for Population

Contents

Variables

A
Interest 1116
A
Overall Mood 7137

Categories

A
Emotions 2028
A
Emotions 2028

Tags

High Confidence
Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 15.9% average increase in Overall Mood following above average Interest.

Abstract

Overall Mood was generally 8% higher than average after an average of 2.99 out of 5 of Interest over the previous 24 hours.

Aggregated data from 353 study participants suggests with a HIGH degree of confidence (p=0.192, 95% CI -0.334 to 0.986) that Interest has a moderately positive predictive relationship (R=0.326) with Overall Mood.

The highest quartile of Overall Mood measurements were observed following an average 2.94 out of 5 Interest.

The lowest quartile of Overall Mood measurements were observed following an average 2.51 out of 5 of Interest.

After an onset delay of 0 seconds, Overall Mood is typically 7% lower than average over the 24 hours following around 2.51 out of 5 of Interest Interest.

Keywords: Interest, Overall Mood, N-of-1 trials, real-world evidence, causal inference, observational study

High Confidence: With 353 participants, these findings have strong statistical power.

Results

Primary Findings

Analysis of 6,625 paired observations from 353 participants revealed a modest improvement in Overall Mood following above-average Interest exposure.

+14.2%
Change from Baseline
Modest effect on Overall Mood
0.11
Predictor Impact Score
Weak evidence for causal relationship

Supporting Statistics

High
Confidence
0.326
Correlation (r)
p = 0.091
Significance
z = 0.93
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Interest:

  • Overall Mood increased by 14.2% on average
  • Temporal analysis supports Interest 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.

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.

3.0 /5
Value Predicting Higher Overall Mood
Average Interest when Overall Mood exceeded its mean
2.3 /5
Value Predicting Lower Overall Mood
Average Interest when Overall Mood was below its mean

What This Suggests

Overall Mood tended to be highest when Interest was around 3.0 /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

Interest Distribution

Overall Mood Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Interest
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.11257000267506
Confidence Level HIGH
Confidence Interval 0.65998294811766
Forward Pearson Predictive Coefficient 0.3257
Critical T Value 1.7886685552408
Average Interest Over Previous 24 hours Before ABOVE Average Overall Mood 2.94 out of 5
Average Interest Over Previous 24 hours Before BELOW Average Overall Mood 2.51 out of 5
Duration of Action 24 hours
Effect Size moderately positive
Number of Paired Measurements 6625
Optimal Pearson Product 0.2616746787805
P Value 0.19174273007708
Statistical Significance 0.0912
Strength of Relationship 0.65998294811766
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 353

Interest Info

Property Value
Variable Name Interest
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 1.7272696492406
Maximum Allowed Value 5 out of 5
Mean 2.6249221665872 out of 5
Median 2.6086223891273 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1004
Number of Aggregate Outcomes 112
Number of Measurements 22487
Number of Measurements (including those generated by tagged, joined, or child variables) 22416
Public true
Onset Delay 0 seconds
Standard Deviation 0.52915160502345
Unit 1 to 5 Rating
User Variables 1438
UPC 0
Variable Category Emotions
Variable ID 1356
Variance 0.57705976921701

Overall Mood Info

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2020-09-12
Duration of Action 24 hours
Kurtosis 3.3832907631011
Maximum Allowed Value 5 out of 5
Mean 3.1202433341482 out of 5
Median 3.1415600073553 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 6425
Number of Aggregate Outcomes 712
Number of Measurements 617070
Number of Measurements (including those generated by tagged, joined, or child variables) 561596
Public true
Onset Delay 0 seconds
Standard Deviation 0.38176118810538
Unit 1 to 5 Rating
User Variables 9142
UPC 767674073845
Variable Category Emotions
Variable ID 1398
Variance 0.30220747449488

Introduction

Background

Interest (Emotions) and Overall Mood (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 Interest affect Overall Mood?

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 Interest for maximizing Overall Mood?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Interest and Overall Mood. Additionally, we attempt to determine the Interest values most likely to produce optimal Overall Mood 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 14.2% improvement in Overall Mood following above-average Interest exposure. The Predictor Impact Score (PIS) of 0.11 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 Overall Mood is not statistically significant at a 95% confidence interval. This suggests that the Interest value may not have a significant influence on the Overall Mood value, or that more data is needed to detect an effect.

After treatment, a 15.9% increase (0.329 out of 5) from the mean baseline 3 out of 5 was observed. The relative standard deviation at baseline was 21.9547%. The observed change was 0.929969 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: 1.631
Critical t-value: 1.789

Since t = 1.63 < 1.79, 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.

Based on community responses so far, 16 people feel that there is a plausible mechanism of action and 4 feel that any relationship observed between Interest and Overall Mood is coincidental.

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

Conclusion

Above-average Interest was associated with a 14.2% improvement in Overall Mood—a modest effect. The Predictor Impact Score of 0.11 indicates this relationship is warranting continued monitoring.

Bottom Line: Based on a PIS of 0.11 and a 14.2% effect size, this relationship shows weak evidence. Additional observational data is recommended before investing in experimental validation.

These findings contribute to our understanding of how Interest may influence Overall Mood in real-world conditions. While preliminary, these results may inform future research directions.

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Methods

Study Design

This study is based on data donated by 353 participants. Thus, the study design is equivalent to the aggregation of 353 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 Interest would produce an observable change in Overall Mood.
  • Duration of Action: It was assumed that Interest could produce an observable change in Overall Mood for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Interest values and subsequent Overall Mood 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

Interest 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.

Overall Mood 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 Interest Affect Overall Mood?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1356-effect-1398-population-study
BibTeX 
@misc{sinn_cause_1356_effect_1398_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Interest Affect Overall Mood?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1356-effect-1398-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Interest Affect Overall Mood?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1356-effect-1398-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Interest Affect Overall Mood?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1356-effect-1398-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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