Higher Daily Low Outdoor Temperature Predicts Very Slightly Higher Sleep Efficiency for Population

Higher Daily Low Outdoor Temperature Predicts Very Slightly Higher Sleep Efficiency for Population

Contents

Variables

A
Daily Low Outdoor Temperature 108
A
Sleep Efficiency 1854

Categories

A
Environment 564
A
Sleep 111

Tags

Medium Confidence
Very Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.2% average decrease in Sleep Efficiency following above average Daily Low Outdoor Temperature.

Abstract

Sleep Efficiency was generally 0% higher than average after an average of 56 degrees fahrenheit of Daily Low Outdoor Temperature over the previous 7 days.

Aggregated data from 1 study participants suggests with a MEDIUM degree of confidence (p=0.36, 95% CI -0.344 to 0.352) that Daily Low Outdoor Temperature has a very weakly positive predictive relationship (R=0.0041) with Sleep Efficiency.

The highest quartile of Sleep Efficiency measurements were observed following an average 54.4 degrees fahrenheit Daily Low Outdoor Temperature.

The lowest quartile of Sleep Efficiency measurements were observed following an average 54.3 degrees fahrenheit of Daily Low Outdoor Temperature.

After an onset delay of 0 seconds, Sleep Efficiency is typically 0% lower than average over the 7 days following around 54.3 degrees fahrenheit of Daily Low Outdoor Temperature Daily Low Outdoor Temperature.

Keywords: Daily Low Outdoor Temperature, Sleep Efficiency, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 1 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 145 paired observations from 1 participants revealed a minimal reduction in Sleep Efficiency following above-average Daily Low Outdoor Temperature exposure.

-0.1%
Change from Baseline
Minimal effect on Sleep Efficiency
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

Medium
Confidence
0.004
Correlation (r)
p = 0.981
Significance
z = 0.10
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Daily Low Outdoor Temperature:

  • Sleep Efficiency decreased by 0.1% on average
  • Temporal analysis supports Daily Low 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 1 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 Daily Low Outdoor Temperature values associated with high and low Sleep Efficiency 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

Daily Low Outdoor Temperature Distribution

Sleep Efficiency Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Daily Low Outdoor Temperature
Effect Variable Name Sleep Efficiency
Sinn Predictive Coefficient 0.00019508329398346
Confidence Level MEDIUM
Confidence Interval 0.34772660538749
Forward Pearson Predictive Coefficient 0.0041
Critical T Value 1.646
Average Daily Low Outdoor Temperature Over Previous 7 days Before ABOVE Average Sleep Efficiency 54.4 degrees fahrenheit
Average Daily Low Outdoor Temperature Over Previous 7 days Before BELOW Average Sleep Efficiency 54.3 degrees fahrenheit
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 145
Optimal Pearson Product 1.9584917015938E-5
P Value 0.36016687324167
Statistical Significance 0.9806
Strength of Relationship 0.34772660538749
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Daily Low Outdoor Temperature Info

Property Value
Variable Name Daily Low Outdoor Temperature
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 2.6648923815535
Maximum Allowed Value 134 degrees fahrenheit
Mean 61.142252264887 degrees fahrenheit
Median 61.70563082122 degrees fahrenheit
Minimum Allowed Value -87 degrees fahrenheit
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 108
Number of Measurements 95009
Number of Measurements (including those generated by tagged, joined, or child variables) 22138
Public true
Onset Delay 0 seconds
Standard Deviation 7.8068296380477
Unit Degrees Fahrenheit
User Variables 658
Variable Category Environment
Variable ID 6038778
Variance 119.68320186752

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

Daily Low Outdoor Temperature (Environment) 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

Does Daily Low Outdoor Temperature 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 Daily Low Outdoor Temperature for maximizing Sleep Efficiency?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Daily Low Outdoor Temperature and Sleep Efficiency. Additionally, we attempt to determine the Daily Low Outdoor Temperature 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 0.1% reduction in Sleep Efficiency following above-average Daily Low Outdoor Temperature exposure. The Predictor Impact Score (PIS) of 0.00 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 not statistically significant at a 95% confidence interval. This suggests that the Daily Low Outdoor Temperature value may not have a significant influence on the Sleep Efficiency value, or that more data is needed to detect an effect.

After treatment, a 0.2% decrease (-0.116 percent) from the mean baseline 85.7 percent was observed. The relative standard deviation at baseline was 1%. The observed change was 0.1 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: 0.452
Critical t-value: 1.646

Since t = 0.45 < 1.65, 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 Daily Low Outdoor Temperature 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 Daily Low 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 Daily Low Outdoor Temperature
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 1 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 Daily Low Outdoor Temperature was associated with a 0.1% reduction in Sleep Efficiency—a minimal 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 0.1% 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 Daily Low Outdoor Temperature 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 1 participants. Thus, the study design is equivalent to the aggregation of 1 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 Daily Low Outdoor Temperature would produce an observable change in Sleep Efficiency.
  • Duration of Action: It was assumed that Daily Low Outdoor Temperature could produce an observable change in Sleep Efficiency for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Daily Low Outdoor Temperature 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

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

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 Daily Low Outdoor Temperature Affect Sleep Efficiency?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-6038778-effect-5211811-population-study
BibTeX 
@misc{sinn_cause_6038778_effect_5211811_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Daily Low Outdoor Temperature Affect Sleep Efficiency?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-6038778-effect-5211811-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Daily Low Outdoor Temperature Affect Sleep Efficiency?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-6038778-effect-5211811-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Daily Low Outdoor Temperature Affect Sleep Efficiency?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-6038778-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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