Higher Insomnia Or Sleep Disturbances Predicts Moderately Higher Sleep Efficiency for Population

Higher Insomnia Or Sleep Disturbances Predicts Moderately Higher Sleep Efficiency for Population

Contents

Variables

A
Insomnia or Sleep Disturbances 786
A
Sleep Efficiency 1854

Categories

A
Symptoms 13336
A
Sleep 111

Tags

Low Confidence
Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 3% average increase in Sleep Efficiency following above average Insomnia Or Sleep Disturbances.

Abstract

Sleep Efficiency was generally 4% higher than average after an average of 3.5 out of 5 of Insomnia Or Sleep Disturbances over the previous 3 days.

Aggregated data from 3 study participants suggests with a LOW degree of confidence (p=0.184, 95% CI -1.969 to 2.597) that Insomnia Or Sleep Disturbances has a moderately positive predictive relationship (R=0.314) with Sleep Efficiency.

The highest quartile of Sleep Efficiency measurements were observed following an average 4.42 out of 5 Insomnia Or Sleep Disturbances.

The lowest quartile of Sleep Efficiency measurements were observed following an average 3.57 out of 5 of Insomnia Or Sleep Disturbances.

After an onset delay of 0 seconds, Sleep Efficiency is typically 0% lower than average over the 3 days following around 3.57 out of 5 of Insomnia Or Sleep Disturbances Insomnia Or Sleep Disturbances.

Keywords: Insomnia Or Sleep Disturbances, Sleep Efficiency, 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 56 paired observations from 3 participants revealed a minimal reduction in Sleep Efficiency following above-average Insomnia Or Sleep Disturbances exposure.

-2.4%
Change from Baseline
Minimal effect on Sleep Efficiency
0.04
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Low
Confidence
0.314
Correlation (r)
p = 0.066
Significance
z = 1.36
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Insomnia Or Sleep Disturbances:

  • Sleep Efficiency decreased by 2.4% on average
  • Temporal analysis supports Insomnia Or Sleep Disturbances 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 (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 3 participants and 56 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

3.5 /5
Value Predicting Higher Sleep Efficiency
Average Insomnia Or Sleep Disturbances when Sleep Efficiency exceeded its mean
3.2 /5
Value Predicting Lower Sleep Efficiency
Average Insomnia Or Sleep Disturbances when Sleep Efficiency was below its mean

What This Suggests

Sleep Efficiency tended to be highest when Insomnia Or Sleep Disturbances was around 3.5 /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

Insomnia Or Sleep Disturbances Distribution

Sleep Efficiency Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Insomnia Or Sleep Disturbances
Effect Variable Name Sleep Efficiency
Sinn Predictive Coefficient 0.040704498089712
Confidence Level LOW
Confidence Interval 2.2826663517025
Forward Pearson Predictive Coefficient 0.3141
Critical T Value 1.7663333333333
Average Insomnia Or Sleep Disturbances Over Previous 3 days Before ABOVE Average Sleep Efficiency 4.42 out of 5
Average Insomnia Or Sleep Disturbances Over Previous 3 days Before BELOW Average Sleep Efficiency 3.57 out of 5
Duration of Action 3 days
Effect Size moderately positive
Number of Paired Measurements 56
Optimal Pearson Product 0.20787709437996
P Value 0.18428978363875
Statistical Significance 0.0655
Strength of Relationship 2.2826663517025
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 3

Insomnia or Sleep Disturbances Info

Property Value
Variable Name Insomnia Or Sleep Disturbances
Aggregation Method MEAN
Analysis Performed At 2021-07-06
Duration of Action 24 hours
Kurtosis 1.7235876061481
Maximum Allowed Value 5 out of 5
Mean 3.3539501449275 out of 5
Median 3.3433075362319 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 643
Number of Aggregate Outcomes 143
Number of Measurements 2231
Number of Measurements (including those generated by tagged, joined, or child variables) 2231
Public true
Onset Delay 0 seconds
Standard Deviation 0.46613871337773
Unit 1 to 5 Rating
User Variables 527
UPC 646437277334
Variable Category Symptoms
Variable ID 89251
Variance 0.57196769644228

Sleep Efficiency Info

Property Value
Variable Name Sleep Efficiency
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 24 hours
Kurtosis 7.3216313398995
Mean 86.964972477359 percent
Median 87.801418439716 percent
Minimum Allowed Value 1 percent
Number of Aggregate Predictors 1698
Number of Aggregate Outcomes 156
Number of Measurements 22620
Number of Measurements (including those generated by tagged, joined, or child variables) 1914
Public true
Onset Delay 0 seconds
Standard Deviation 5.9209578901457
Unit Percent
User Variables 147
UPC 878881000699
Variable Category Sleep
Variable ID 5211811
Variance 75.785458915098

Introduction

Background

Insomnia Or Sleep Disturbances (Symptoms) and Sleep Efficiency (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 Insomnia Or Sleep Disturbances affect Sleep Efficiency?

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 Insomnia Or Sleep Disturbances for maximizing Sleep Efficiency?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Insomnia Or Sleep Disturbances and Sleep Efficiency. Additionally, we attempt to determine the Insomnia Or Sleep Disturbances values most likely to produce optimal Sleep Efficiency 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 2.4% reduction in Sleep Efficiency following above-average Insomnia Or Sleep Disturbances exposure. The Predictor Impact Score (PIS) of 0.04 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 Efficiency is statistically significant at a 95% confidence interval. The p-value of 0.0655 indicates there is less than a 6.55% probability that this result occurred by chance.

After treatment, a 3% increase (-2.39 percent) from the mean baseline 94 percent was observed. The relative standard deviation at baseline was 3.2%. The observed change was 1.36325 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: 2.482
Critical t-value: 1.766

Since t = 2.48 > 1.77, 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 Insomnia Or Sleep Disturbances might influence Sleep Efficiency.

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 Insomnia Or Sleep Disturbances 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 Insomnia Or Sleep Disturbances
  • 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 Insomnia Or Sleep Disturbances was associated with a 2.4% reduction in Sleep Efficiency—a minimal effect. The Predictor Impact Score of 0.04 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.04 and a 2.4% 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 Insomnia Or Sleep Disturbances may influence Sleep Efficiency 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 Insomnia Or Sleep Disturbances would produce an observable change in Sleep Efficiency.
  • Duration of Action: It was assumed that Insomnia Or Sleep Disturbances could produce an observable change in Sleep Efficiency for as much as 3 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Insomnia Or Sleep Disturbances values and subsequent Sleep Efficiency 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

Insomnia Or Sleep Disturbances 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.

Sleep Efficiency 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 Insomnia Or Sleep Disturbances Affect Sleep Efficiency?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-89251-effect-5211811-population-study
BibTeX 
@misc{sinn_cause_89251_effect_5211811_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Insomnia Or Sleep Disturbances Affect Sleep Efficiency?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-89251-effect-5211811-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Insomnia Or Sleep Disturbances Affect Sleep Efficiency?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-89251-effect-5211811-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Insomnia Or Sleep Disturbances Affect Sleep Efficiency?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-89251-effect-5211811-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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