Higher Coffee Consumption Predicts Very Slightly Lower Efficiency Score From Rescuetime for Population

Higher Coffee Consumption Predicts Very Slightly Lower Efficiency Score From Rescuetime for Population

Contents

Variables

A
Coffee 326
A
Efficiency Score From Rescuetime 3196

Categories

A
Foods 13415
A
Goals 126

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 5.7% average decrease in Efficiency Score From Rescuetime following above average Coffee Consumption.

Abstract

Efficiency Score From Rescuetime was generally 3% higher than average after a total of 6.17 serving of Coffee over the previous 14 days.

Aggregated data from 6 study participants suggests with a HIGH degree of confidence (p=0.198, 95% CI -4.703 to 4.52) that Coffee has a very weakly negative predictive relationship (R=-0.0914) with Efficiency Score From Rescuetime.

The highest quartile of Efficiency Score From Rescuetime measurements were observed following an average 10.5 serving Coffee per day.

The lowest quartile of Efficiency Score From Rescuetime measurements were observed following an average 9.96 serving of Coffee per day.

After an onset delay of 30 minutes, Efficiency Score From Rescuetime is typically 4% lower than average over the 14 days following around 9.96 serving of Coffee Coffee.

Keywords: Coffee, Efficiency Score From Rescuetime, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 6 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 3,176 paired observations from 6 participants revealed a minimal reduction in Efficiency Score From Rescuetime following above-average Coffee exposure.

-1.2%
Change from Baseline
Minimal effect on Efficiency Score From Rescuetime
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.091
Correlation (r)
p = 0.460
Significance
z = 0.34
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Coffee:

  • Efficiency Score From Rescuetime decreased by 1.2% on average
  • Temporal analysis supports Coffee 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 6 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 Coffee values associated with high and low Efficiency Score From Rescuetime 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

Coffee Distribution

Efficiency Score From Rescuetime Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Coffee Consumption
Effect Variable Name Efficiency Score From Rescuetime
Sinn Predictive Coefficient 0.020619307660373
Confidence Level HIGH
Confidence Interval 4.6117228275012
Forward Pearson Predictive Coefficient -0.0914
Critical T Value 1.6566666666667
Total Coffee Consumption Over Previous 14 days Before ABOVE Average Efficiency Score From Rescuetime 10.5 serving
Total Coffee Consumption Over Previous 14 days Before BELOW Average Efficiency Score From Rescuetime 9.96 serving
Duration of Action 14 days
Effect Size very weakly negative
Number of Paired Measurements 3176
Optimal Pearson Product 0.048011864564148
P Value 0.19812636888345
Statistical Significance 0.4599
Strength of Relationship 4.6117228275012
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 6

Coffee Info

Property Value
Variable Name Coffee
Aggregation Method SUM
Analysis Performed At 2021-07-18
Duration of Action 14 days
Filling Value 0
Kurtosis 12.33836841945
Maximum Allowed Value 40 serving
Mean 0.64430984666667 serving
Median 0.55333333333333 serving
Minimum Allowed Value 0 serving
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 326
Number of Measurements 4720
Number of Measurements (including those generated by tagged, joined, or child variables) 4174
Public true
Onset Delay 30 minutes
Standard Deviation 0.40051808662978
Unit Serving
User Variables 559
UPC 813957023530
Variable Category Foods
Variable ID 84985
Variance 0.44569240881787

Efficiency Score From Rescuetime Info

Property Value
Variable Name Efficiency Score From Rescuetime
Aggregation Method MEAN
Analysis Performed At 2022-08-12
Duration of Action 7 days
Kurtosis 2.8950328674772
Mean 49.4088875 percent
Median 50.140875 percent
Number of Aggregate Predictors 3036
Number of Aggregate Outcomes 160
Number of Measurements 3557
Number of Measurements (including those generated by tagged, joined, or child variables) 3557
Public true
Onset Delay 0 seconds
Standard Deviation 18.807714053283
Unit Percent
User Variables 83
UPC 0
Variable Category Goals
Variable ID 5956874
Variance 387.41867577137

Introduction

Background

Coffee (Foods) and Efficiency Score From Rescuetime (Goals) 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 Coffee affect Efficiency Score From Rescuetime?

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 Coffee for maximizing Efficiency Score From Rescuetime?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Coffee and Efficiency Score From Rescuetime. Additionally, we attempt to determine the Coffee values most likely to produce optimal Efficiency Score From Rescuetime 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 1.2% reduction in Efficiency Score From Rescuetime following above-average Coffee exposure. The Predictor Impact Score (PIS) of 0.02 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 Efficiency Score From Rescuetime is statistically significant at a 95% confidence interval. The p-value of 0.4599 indicates there is less than a 45.99% probability that this result occurred by chance.

After treatment, a 5.7% decrease (-1.17 percent) from the mean baseline 54.8 percent was observed. The relative standard deviation at baseline was 13.9333%. The observed change was 0.336522 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: 2.083
Critical t-value: 1.657

Since t = 2.08 > 1.66, 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 Coffee might influence Efficiency Score From Rescuetime.

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

Conclusion

📊 Preliminary Findings: With 6 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 Coffee was associated with a 1.2% reduction in Efficiency Score From Rescuetime—a minimal effect. The Predictor Impact Score of 0.02 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.02 and a 1.2% 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 Coffee may influence Efficiency Score From Rescuetime 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 6 participants. Thus, the study design is equivalent to the aggregation of 6 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 30 minutes would pass before a change in Coffee would produce an observable change in Efficiency Score From Rescuetime.
  • Duration of Action: It was assumed that Coffee could produce an observable change in Efficiency Score From Rescuetime for as much as 14 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Coffee values and subsequent Efficiency Score From Rescuetime 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

Coffee 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.

Efficiency Score From Rescuetime 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.

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 Coffee Affect Efficiency Score From Rescuetime?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-84985-effect-5956874-population-study
BibTeX 
@misc{sinn_cause_84985_effect_5956874_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Coffee Affect Efficiency Score From Rescuetime?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-84985-effect-5956874-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Coffee Affect Efficiency Score From Rescuetime?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-84985-effect-5956874-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Coffee Affect Efficiency Score From Rescuetime?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-84985-effect-5956874-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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