Higher Outdoor Temperature Predicts Slightly Higher Sleep Noise Level for Population

Higher Outdoor Temperature Predicts Slightly Higher Sleep Noise Level for Population

Contents

Variables

A
Outdoor Temperature 454
A
Sleep Noise Level 1026

Categories

A
Environment 564
A
Sleep 111

Tags

High Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 2.8% average increase in Sleep Noise Level following above average Outdoor Temperature.

Abstract

Sleep Noise Level was generally 11% higher than average after an average of 19.8 degrees celsius of Outdoor Temperature over the previous 7 days.

Aggregated data from 24 study participants suggests with a HIGH degree of confidence (p=0.0962, 95% CI 0.09 to 0.127) that Outdoor Temperature has a weakly positive predictive relationship (R=0.109) with Sleep Noise Level.

The highest quartile of Sleep Noise Level measurements were observed following an average 18.7 degrees celsius Outdoor Temperature.

The lowest quartile of Sleep Noise Level measurements were observed following an average 17.2 degrees celsius of Outdoor Temperature.

After an onset delay of 0 seconds, Sleep Noise Level is typically 9% lower than average over the 7 days following around 17.2 degrees celsius of Outdoor Temperature Outdoor Temperature.

Keywords: Outdoor Temperature, Sleep Noise Level, N-of-1 trials, real-world evidence, causal inference, observational study

Moderate Confidence: Based on 24 participants. More data would increase certainty.

Results

Primary Findings

Analysis of 4,873 paired observations from 24 participants revealed a modest improvement in Sleep Noise Level following above-average Outdoor Temperature exposure.

+9.2%
Change from Baseline
Modest effect on Sleep Noise Level
0.05
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.109
Correlation (r)
p = 0.604
Significance
z = 0.60
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Outdoor Temperature:

  • Sleep Noise Level increased by 9.2% on average
  • Temporal analysis supports Outdoor Temperature 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 24 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 Outdoor Temperature values associated with high and low Sleep Noise Level 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

Outdoor Temperature Distribution

Sleep Noise Level Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Outdoor Temperature
Effect Variable Name Sleep Noise Level
Sinn Predictive Coefficient 0.049510408868784
Confidence Level HIGH
Confidence Interval 0.018460704241862
Forward Pearson Predictive Coefficient 0.1089
Critical T Value 1.6792916666667
Average Outdoor Temperature Over Previous 7 days Before ABOVE Average Sleep Noise Level 18.7 degrees celsius
Average Outdoor Temperature Over Previous 7 days Before BELOW Average Sleep Noise Level 17.2 degrees celsius
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 4873
Optimal Pearson Product 0.16521823443837
P Value 0.096246031705099
Statistical Significance 0.6043
Strength of Relationship 0.018460704241862
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 24

Outdoor Temperature Info

Property Value
Variable Name Outdoor Temperature
Aggregation Method MEAN
Analysis Performed At 2020-09-15
Duration of Action 7 days
Kurtosis 2.5869393546724
Maximum Allowed Value 101 degrees celsius
Mean 16.934805022738 degrees celsius
Median 16.806543406516 degrees celsius
Minimum Allowed Value -66 degrees celsius
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 454
Number of Measurements 312494
Number of Measurements (including those generated by tagged, joined, or child variables) 60483
Public true
Onset Delay 0 seconds
Standard Deviation 5.8976135603379
Unit Degrees Celsius
User Variables 927
UPC 0
Variable Category Environment
Variable ID 5954773
Variance 55.947734908476

Sleep Noise Level Info

Property Value
Variable Name Sleep Noise Level
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 4.9021550437484
Maximum Allowed Value 1 out of 1
Mean 0.089598090756021 out of 1
Median 0.07586629665245 out of 1
Minimum Allowed Value 0 out of 1
Number of Aggregate Predictors 927
Number of Aggregate Outcomes 99
Number of Measurements 7783
Number of Measurements (including those generated by tagged, joined, or child variables) 6508
Public true
Onset Delay 0 seconds
Standard Deviation 0.059484303836982
Unit 0 to 1 Rating
User Variables 63
UPC 897392002268
Variable Category Sleep
Variable ID 1889
Variance 0.0060052663677998

Introduction

Background

Outdoor Temperature (Environment) and Sleep Noise Level (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 Outdoor Temperature affect Sleep Noise Level?

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 Outdoor Temperature for maximizing Sleep Noise Level?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Outdoor Temperature and Sleep Noise Level. Additionally, we attempt to determine the Outdoor Temperature values most likely to produce optimal Sleep Noise Level 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 9.2% improvement in Sleep Noise Level following above-average Outdoor Temperature exposure. The Predictor Impact Score (PIS) of 0.05 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 Noise Level is statistically significant at a 95% confidence interval. The p-value of 0.6043 indicates there is less than a 60.43% probability that this result occurred by chance.

After treatment, a 2.8% increase (0.00269 out of 1) from the mean baseline 0.105 out of 1 was observed. The relative standard deviation at baseline was 39.2958%. The observed change was 0.603633 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: 2.941
Critical t-value: 1.679

Since t = 2.94 > 1.68, 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 Outdoor Temperature might influence Sleep Noise Level.

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

Conclusion

📊 Preliminary Findings: With 24 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 Outdoor Temperature was associated with a 9.2% improvement in Sleep Noise Level—a modest effect. The Predictor Impact Score of 0.05 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.05 and a 9.2% 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 Outdoor Temperature may influence Sleep Noise Level 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 24 participants. Thus, the study design is equivalent to the aggregation of 24 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 Outdoor Temperature would produce an observable change in Sleep Noise Level.
  • Duration of Action: It was assumed that Outdoor Temperature could produce an observable change in Sleep Noise Level for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Outdoor Temperature values and subsequent Sleep Noise Level 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

Outdoor Temperature data was primarily collected using Weather. Automatically import temperature, humidity, and ultraviolet light exposure.

Sleep Noise Level 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 Outdoor Temperature Affect Sleep Noise Level?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-5954773-effect-1889-population-study
BibTeX 
@misc{sinn_cause_5954773_effect_1889_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Outdoor Temperature Affect Sleep Noise Level?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-5954773-effect-1889-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Outdoor Temperature Affect Sleep Noise Level?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-5954773-effect-1889-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Outdoor Temperature Affect Sleep Noise Level?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-5954773-effect-1889-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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