Higher Energy Predicts Slightly Lower Hours Of Sleep for Population

Higher Energy Predicts Slightly Lower Hours Of Sleep for Population

Contents

Variables

A
Energy 2158
A
Hours of Sleep 80

Categories

A
Emotions 2028
A
Sleep 111

Tags

Low Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 31.4% average increase in Hours of Sleep following above average Energy.

Abstract

Hours of Sleep was generally 30% higher than average after an average of 3.3 out of 5 of Energy over the previous 24 hours.

Aggregated data from 2 study participants suggests with a LOW degree of confidence (p=0.0794, 95% CI -3.447 to 3.1) that Energy has a weakly negative predictive relationship (R=-0.173) with Hours of Sleep.

The highest quartile of Hours of Sleep measurements were observed following an average 3.18 out of 5 Energy.

The lowest quartile of Hours of Sleep measurements were observed following an average 3 out of 5 of Energy.

After an onset delay of 0 seconds, Hours of Sleep is typically 28% lower than average over the 24 hours following around 3 out of 5 of Energy Energy.

Keywords: Energy, Hours of Sleep, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 2 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 16 paired observations from 2 participants revealed a substantial improvement in Hours of Sleep following above-average Energy exposure.

+31.1%
Change from Baseline
Substantial effect on Hours of Sleep
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
-0.173
Correlation (r)
p = 0.012
Significance
z = 2.85
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Energy:

  • Hours of Sleep increased by 31.1% on average
  • Temporal analysis supports Energy as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.012)

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 2 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 Energy values associated with high and low Hours of Sleep 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

Energy Distribution

Hours of Sleep Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Energy
Effect Variable Name Hours of Sleep
Sinn Predictive Coefficient 0.0015697916452829
Confidence Level LOW
Confidence Interval 3.2733
Forward Pearson Predictive Coefficient -0.1732
Critical T Value 1.745
Average Energy Over Previous 24 hours Before ABOVE Average Hours of Sleep 3.18 out of 5
Average Energy Over Previous 24 hours Before BELOW Average Hours of Sleep 3 out of 5
Duration of Action 24 hours
Effect Size weakly negative
Number of Paired Measurements 16
Optimal Pearson Product 0.45431652348037
P Value 0.079389
Statistical Significance 0.0116
Strength of Relationship 3.2733
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 2

Energy Info

Property Value
Variable Name Energy
Aggregation Method MEAN
Analysis Performed At 2022-08-31
Duration of Action 24 hours
Kurtosis 1.7963079252106
Maximum Allowed Value 5 out of 5
Mean 2.8605791891892 out of 5
Median 2.8725064864865 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 1883
Number of Aggregate Outcomes 275
Number of Measurements 8144
Number of Measurements (including those generated by tagged, joined, or child variables) 8144
Public true
Onset Delay 0 seconds
Standard Deviation 0.36971120901836
Unit 1 to 5 Rating
User Variables 458
UPC 637769766115
Variable Category Emotions
Variable ID 1306
Variance 0.38180313102113

Hours of Sleep Info

Property Value
Variable Name Hours of Sleep
Aggregation Method SUM
Analysis Performed At 2020-10-09
Duration of Action 7 days
Filling Value 0
Kurtosis 7.2856341981839
Maximum Allowed Value 7 days
Mean 4 hours
Median 3 hours
Minimum Allowed Value 0 seconds
Number of Aggregate Predictors 63
Number of Aggregate Outcomes 17
Number of Measurements 55
Number of Measurements (including those generated by tagged, joined, or child variables) 55
Public true
Onset Delay 0 seconds
Standard Deviation 2.1012450044516
Unit Hours
User Variables 14
Variable Category Sleep
Variable ID 103960
Variance 7.5570692422646

Introduction

Background

Energy (Emotions) and Hours of Sleep (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

Does Energy affect Hours of Sleep?

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 Energy for maximizing Hours of Sleep?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Energy and Hours of Sleep. Additionally, we attempt to determine the Energy values most likely to produce optimal Hours of Sleep 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 31.1% improvement in Hours of Sleep following above-average Energy exposure. The Predictor Impact Score (PIS) of 0.00 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.012).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Hours of Sleep is statistically significant at a 95% confidence interval. The p-value of 0.0116 indicates there is less than a 1.16% probability that this result occurred by chance.

After treatment, a 31.4% increase (11 minutes) from the mean baseline 6 hours was observed. The relative standard deviation at baseline was 46.7%. The observed change was 2.8548 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: 1.819
Critical t-value: 1.745

Since t = 1.82 > 1.75, 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 Energy might influence Hours of Sleep.

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

Conclusion

📊 Preliminary Findings: With 2 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 Energy was associated with a 31.1% improvement in Hours of Sleep—a substantial effect. The Predictor Impact Score of 0.00 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.00 and a 31.1% 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 Energy may influence Hours of Sleep in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

Help End Unnecessary Suffering

Current clinical trials are 82x more expensive than necessary and take 17 years to bring treatments to market. Pragmatic trials integrated into standard healthcare could reduce costs from $41,000 to $500 per participant and compress timelines to just 2 years. Learn how redirecting just 1% of global military spending could accelerate cures for the 2 billion people suffering from treatable diseases.

Make Suffering Optional

Methods

Study Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 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 Energy would produce an observable change in Hours of Sleep.
  • Duration of Action: It was assumed that Energy could produce an observable change in Hours of Sleep for as much as 24 hours after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Energy values and subsequent Hours of Sleep 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

Energy 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.

Hours of Sleep 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 Energy Affect Hours of Sleep?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1306-effect-103960-population-study
BibTeX 
@misc{sinn_cause_1306_effect_103960_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Energy Affect Hours of Sleep?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1306-effect-103960-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Energy Affect Hours of Sleep?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1306-effect-103960-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Energy Affect Hours of Sleep?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1306-effect-103960-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

LinkedIn Reddit