Higher General Communication & Scheduling Activities Predicts Very Slightly Higher Sleep Duration for Population

Higher General Communication & Scheduling Activities Predicts Very Slightly Higher Sleep Duration for Population

Contents

Variables

A
General Communication & Scheduling Activities 163
A
Sleep Duration 3379

Categories

A
Activities 1637
A
Sleep 111

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High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 17.1% average increase in Sleep Duration following above average General Communication & Scheduling Activities.

Abstract

Sleep Duration was generally 10% higher than average after a total of 29 minutes of General Communication & Scheduling Activities over the previous 7 days.

Aggregated data from 15 study participants suggests with a HIGH degree of confidence (p=0.26, 95% CI -0.878 to 0.983) that General Communication & Scheduling Activities has a very weakly positive predictive relationship (R=0.0526) with Sleep Duration.

The highest quartile of Sleep Duration measurements were observed following an average 49 minutes General Communication & Scheduling Activities per day.

The lowest quartile of Sleep Duration measurements were observed following an average 33 minutes of General Communication & Scheduling Activities per day.

After an onset delay of 0 seconds, Sleep Duration is typically 10% lower than average over the 7 days following around 33 minutes of General Communication & Scheduling Activities General Communication & Scheduling Activities.

Keywords: General Communication & Scheduling Activities, Sleep Duration, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 15 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 4,240 paired observations from 15 participants revealed a minimal improvement in Sleep Duration following above-average General Communication & Scheduling Activities exposure.

+0.0%
Change from Baseline
Minimal effect on Sleep Duration
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.053
Correlation (r)
p = 0.465
Significance
z = 0.27
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average General Communication & Scheduling Activities:

  • Sleep Duration increased by 0.0% on average
  • Temporal analysis supports General Communication & Scheduling Activities 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 15 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 15 participants and 4,240 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

0.5 h
Value Predicting Higher Sleep Duration
Average General Communication & Scheduling Activities when Sleep Duration exceeded its mean
0.7 h
Value Predicting Lower Sleep Duration
Average General Communication & Scheduling Activities when Sleep Duration was below its mean

What This Suggests

Sleep Duration tended to be highest when General Communication & Scheduling Activities was around 0.5 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

General Communication & Scheduling Activities Distribution

Sleep Duration Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name General Communication & Scheduling Activities
Effect Variable Name Sleep Duration
Sinn Predictive Coefficient 0.020431676808934
Confidence Level HIGH
Confidence Interval 0.93033074041135
Forward Pearson Predictive Coefficient 0.0526
Critical T Value 1.6816666666667
Total General Communication & Scheduling Activities Over Previous 7 days Before ABOVE Average Sleep Duration 49 minutes
Total General Communication & Scheduling Activities Over Previous 7 days Before BELOW Average Sleep Duration 33 minutes
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 4240
Optimal Pearson Product 0.024336183303759
P Value 0.25985665218867
Statistical Significance 0.4647
Strength of Relationship 0.93033074041135
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 15

General Communication & Scheduling Activities Info

Property Value
Variable Name General Communication & Scheduling Activities
Aggregation Method SUM
Analysis Performed At 2020-10-11
Duration of Action 7 days
Filling Value 0
Kurtosis 211.90508800785
Maximum Allowed Value 7 days
Mean 24 minutes
Median 10 seconds
Minimum Allowed Value 0 seconds
Number of Aggregate Predictors 15
Number of Aggregate Outcomes 148
Number of Measurements 14262
Number of Measurements (including those generated by tagged, joined, or child variables) 2298
Public true
Onset Delay 0 seconds
Standard Deviation 1.7674984006996
Unit Hours
User Variables 58
Variable Category Activities
Variable ID 5956882
Variance 28.332940205375

Sleep Duration Info

Property Value
Variable Name Sleep Duration
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 3.1431132810239
Maximum Allowed Value 16 hours
Mean 7 hours
Median 7 hours
Minimum Allowed Value 6 minutes
Number of Aggregate Predictors 3162
Number of Aggregate Outcomes 217
Number of Measurements 43055
Number of Measurements (including those generated by tagged, joined, or child variables) 16052
Public true
Onset Delay 0 seconds
Standard Deviation 1.2204190250991
Unit Hours
User Variables 404
UPC 067981966602
Variable Category Sleep
Variable ID 1867
Variance 2.1855287054824

Introduction

Background

General Communication & Scheduling Activities (Activities) and Sleep Duration (Sleep) 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 General Communication & Scheduling Activities affect Sleep Duration?

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 General Communication & Scheduling Activities for maximizing Sleep Duration?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between General Communication & Scheduling Activities and Sleep Duration. Additionally, we attempt to determine the General Communication & Scheduling Activities values most likely to produce optimal Sleep Duration 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 0.0% improvement in Sleep Duration following above-average General Communication & Scheduling Activities 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 Sleep Duration is not statistically significant at a 95% confidence interval. This suggests that the General Communication & Scheduling Activities value may not have a significant influence on the Sleep Duration value, or that more data is needed to detect an effect.

After treatment, a 17.1% increase (16 minutes) from the mean baseline 5 hours was observed. The relative standard deviation at baseline was 77.4867%. The observed change was 0.269423 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: 1.039
Critical t-value: 1.682

Since t = 1.04 < 1.68, 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 General Communication & Scheduling Activities might influence Sleep Duration.

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 General Communication & Scheduling 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 General Communication & Scheduling Activities
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 15 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 General Communication & Scheduling Activities was associated with a 0.0% improvement in Sleep Duration—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 0.0% 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 General Communication & Scheduling Activities may influence Sleep Duration 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 15 participants. Thus, the study design is equivalent to the aggregation of 15 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 General Communication & Scheduling Activities would produce an observable change in Sleep Duration.
  • Duration of Action: It was assumed that General Communication & Scheduling Activities could produce an observable change in Sleep Duration for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between General Communication & Scheduling Activities values and subsequent Sleep Duration 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

General Communication & Scheduling 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.

Sleep Duration data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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 General Communication & Scheduling Activities Affect Sleep Duration?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5956882-effect-1867-population-study
BibTeX 
@misc{sinn_cause_5956882_effect_1867_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does General Communication & Scheduling Activities Affect Sleep Duration?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5956882-effect-1867-population-study},
  note = {Accessed: January 6, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does General Communication & Scheduling Activities Affect Sleep Duration?." The Journal of Citizen Science. Accessed January 6, 2026. https://studies.crowdsourcingcures.org/study/cause-5956882-effect-1867-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does General Communication & Scheduling Activities Affect Sleep Duration?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5956882-effect-1867-population-study [Accessed January 6, 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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