Higher Unknown Activities Predicts Slightly Higher Interest for Population

Higher Unknown Activities Predicts Slightly Higher Interest for Population

Contents

Variables

A
Unknown Activities 109
A
Interest 1116

Categories

A
Activities 1637
A
Emotions 2028

Tags

Low Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 5.5% average increase in Interest following above average Unknown Activities.

Abstract

Interest was generally 2% higher than average after a total of 0 seconds of Unknown Activities over the previous 7 days.

Aggregated data from 5 study participants suggests with a LOW degree of confidence (p=0.229, 95% CI -0.318 to 0.607) that Unknown Activities has a weakly positive predictive relationship (R=0.145) with Interest.

The highest quartile of Interest measurements were observed following an average 0 seconds Unknown Activities per day.

The lowest quartile of Interest measurements were observed following an average 529 milliseconds of Unknown Activities per day.

After an onset delay of 0 seconds, Interest is typically 1% lower than average over the 7 days following around 529 milliseconds of Unknown Activities Unknown Activities.

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

Preliminary: Based on 5 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 96 paired observations from 5 participants revealed a minimal improvement in Interest following above-average Unknown Activities exposure.

+1.8%
Change from Baseline
Minimal effect on Interest
0.06
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.145
Correlation (r)
p = 0.044
Significance
z = 0.39
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Unknown Activities:

  • Interest increased by 1.8% on average
  • Temporal analysis supports Unknown Activities as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.044)

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 5 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 5 participants and 96 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.0 h
Value Predicting Higher Interest
Average Unknown Activities when Interest exceeded its mean
0.0 h
Value Predicting Lower Interest
Average Unknown Activities when Interest was below its mean

What This Suggests

Interest tended to be highest when Unknown Activities was around 0.0 h.

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

Unknown Activities Distribution

Interest Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Unknown Activities
Effect Variable Name Interest
Sinn Predictive Coefficient 0.056935015762888
Confidence Level LOW
Confidence Interval 0.46223
Forward Pearson Predictive Coefficient 0.1447
Critical T Value 1.722
Total Unknown Activities Over Previous 7 days Before ABOVE Average Interest 0 seconds
Total Unknown Activities Over Previous 7 days Before BELOW Average Interest 529 milliseconds
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 96
Optimal Pearson Product 0.085576243379973
P Value 0.22948
Statistical Significance 0.0436
Strength of Relationship 0.46223
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 5

Unknown Activities Info

Property Value
Variable Name Unknown Activities
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Filling Value 0
Kurtosis 214.53731282657
Maximum Allowed Value 7 days
Mean 1 seconds
Median 0 seconds
Minimum Allowed Value 0 seconds
Number of Aggregate Predictors 2
Number of Aggregate Outcomes 107
Number of Measurements 467
Number of Measurements (including those generated by tagged, joined, or child variables) 307
Public true
Onset Delay 0 seconds
Standard Deviation 0.0021466688694363
Unit Hours
User Variables 25
Variable Category Activities
Variable ID 5956915
Variance 1.5265768329219E-5

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

Introduction

Background

Unknown Activities (Activities) and Interest (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 Unknown Activities affect Interest?

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 Unknown Activities for maximizing Interest?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Unknown Activities and Interest. Additionally, we attempt to determine the Unknown Activities values most likely to produce optimal Interest 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 1.8% improvement in Interest following above-average Unknown Activities exposure. The Predictor Impact Score (PIS) of 0.06 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.044).

Statistical Significance

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

After treatment, a 5.5% increase (0.124 out of 5) from the mean baseline 3.05 out of 5 was observed. The relative standard deviation at baseline was 21.125%. The observed change was 0.38775 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: 1.044
Critical t-value: 1.722

Since t = 1.04 < 1.72, 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 Unknown Activities might influence Interest.

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

Conclusion

📊 Preliminary Findings: With 5 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 Unknown Activities was associated with a 1.8% improvement in Interest—a minimal effect. The Predictor Impact Score of 0.06 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.06 and a 1.8% 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 Unknown Activities may influence Interest 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 5 participants. Thus, the study design is equivalent to the aggregation of 5 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 Unknown Activities would produce an observable change in Interest.
  • Duration of Action: It was assumed that Unknown Activities could produce an observable change in Interest for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Unknown Activities values and subsequent Interest 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

Unknown Activities data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.

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.

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 Unknown Activities Affect Interest?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5956915-effect-1356-population-study
BibTeX 
@misc{sinn_cause_5956915_effect_1356_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Unknown Activities Affect Interest?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5956915-effect-1356-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Unknown Activities Affect Interest?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5956915-effect-1356-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Unknown Activities Affect Interest?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5956915-effect-1356-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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