Higher Average Daily Outdoor Temperature Predicts Very Slightly Lower Sleep Efficiency From Fitbit for Population

Higher Average Daily Outdoor Temperature Predicts Very Slightly Lower Sleep Efficiency From Fitbit for Population

Contents

Variables

A
Average Daily Outdoor Temperature 107
A
Sleep Efficiency From Fitbit 195

Categories

A
Environment 564
A
Sleep 111

Tags

Low Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 0.1% average increase in Sleep Efficiency From Fitbit following above average Average Daily Outdoor Temperature.

Abstract

Sleep Efficiency From Fitbit was generally 0% higher than average after an average of 64.7 degrees fahrenheit of Average Daily Outdoor Temperature over the previous 7 days.

Aggregated data from 1 study participants suggests with a LOW degree of confidence (p=0.317, 95% CI -0.716 to 0.627) that Average Daily Outdoor Temperature has a very weakly negative predictive relationship (R=-0.0442) with Sleep Efficiency From Fitbit.

The highest quartile of Sleep Efficiency From Fitbit measurements were observed following an average 66.2 degrees fahrenheit Average Daily Outdoor Temperature.

The lowest quartile of Sleep Efficiency From Fitbit measurements were observed following an average 62.6 degrees fahrenheit of Average Daily Outdoor Temperature.

After an onset delay of 0 seconds, Sleep Efficiency From Fitbit is typically 0% lower than average over the 7 days following around 62.6 degrees fahrenheit of Average Daily Outdoor Temperature Average Daily Outdoor Temperature.

Keywords: Average Daily Outdoor Temperature, Sleep Efficiency From Fitbit, 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 5 paired observations from 1 participants revealed a minimal reduction in Sleep Efficiency From Fitbit following above-average Average Daily Outdoor Temperature exposure.

-0.2%
Change from Baseline
Minimal effect on Sleep Efficiency From Fitbit
0.00
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.044
Correlation (r)
p = 0.001
Significance
z = 0.50
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Average Daily Outdoor Temperature:

  • Sleep Efficiency From Fitbit decreased by 0.2% on average
  • Temporal analysis supports Average Daily Outdoor Temperature as the predictor (not the outcome)
  • This relationship is statistically significant (p = 0.001)

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 Average Daily Outdoor Temperature values associated with high and low Sleep Efficiency From Fitbit 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

Average Daily Outdoor Temperature Distribution

Sleep Efficiency From Fitbit Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Average Daily Outdoor Temperature
Effect Variable Name Sleep Efficiency From Fitbit
Sinn Predictive Coefficient 0.0021030930338955
Confidence Level LOW
Confidence Interval 0.67169712704929
Forward Pearson Predictive Coefficient -0.0442
Critical T Value 2.015
Average Average Daily Outdoor Temperature Over Previous 7 days Before ABOVE Average Sleep Efficiency From Fitbit 66.2 degrees fahrenheit
Average Average Daily Outdoor Temperature Over Previous 7 days Before BELOW Average Sleep Efficiency From Fitbit 62.6 degrees fahrenheit
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 5
Optimal Pearson Product -0.037500918360104
P Value 0.31741837544477
Statistical Significance 0.001
Strength of Relationship 0.67169712704929
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Average Daily Outdoor Temperature Info

Property Value
Variable Name Average Daily Outdoor Temperature
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 2.6039819469393
Maximum Allowed Value 134 degrees fahrenheit
Mean 63.749346093146 degrees fahrenheit
Median 64.281163531761 degrees fahrenheit
Minimum Allowed Value -87 degrees fahrenheit
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 107
Number of Measurements 95016
Number of Measurements (including those generated by tagged, joined, or child variables) 22144
Public true
Onset Delay 0 seconds
Standard Deviation 7.7762207716368
Unit Degrees Fahrenheit
User Variables 658
Variable Category Environment
Variable ID 6038776
Variance 118.83499021982

Sleep Efficiency From Fitbit Info

Property Value
Variable Name Sleep Efficiency From Fitbit
Aggregation Method MEAN
Analysis Performed At 2020-09-23
Duration of Action 24 hours
Kurtosis 24.227663163315
Mean 26.276693513514 percent
Median 23.27027027027 percent
Minimum Allowed Value 1 percent
Number of Aggregate Predictors 176
Number of Aggregate Outcomes 19
Number of Measurements 34624
Number of Measurements (including those generated by tagged, joined, or child variables) 3894
Public true
Onset Delay 0 seconds
Standard Deviation 18.585603366682
Unit Percent
User Variables 37
Variable Category Sleep
Variable ID 6057041
Variance 585.5176254255

Introduction

Background

Average Daily Outdoor Temperature (Environment) and Sleep Efficiency From Fitbit (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 Average Daily Outdoor Temperature affect Sleep Efficiency From Fitbit?

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 Average Daily Outdoor Temperature for maximizing Sleep Efficiency From Fitbit?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Average Daily Outdoor Temperature and Sleep Efficiency From Fitbit. Additionally, we attempt to determine the Average Daily Outdoor Temperature values most likely to produce optimal Sleep Efficiency From Fitbit 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 0.2% reduction in Sleep Efficiency From Fitbit following above-average Average Daily Outdoor Temperature exposure. The Predictor Impact Score (PIS) of 0.00 indicates insufficient evidence for a causal relationship. This finding is statistically significant (p = 0.001).

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Sleep Efficiency From Fitbit is not statistically significant at a 95% confidence interval. This suggests that the Average Daily Outdoor Temperature value may not have a significant influence on the Sleep Efficiency From Fitbit value, or that more data is needed to detect an effect.

After treatment, a 0.1% increase (-0.211 percent) from the mean baseline 90.1 percent was observed. The relative standard deviation at baseline was 0.4%. The observed change was 0.5 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: 0.676
Critical t-value: 2.015

Since t = 0.68 < 2.02, 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 Average Daily Outdoor Temperature might influence Sleep Efficiency From Fitbit.

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 Average Daily 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 Average Daily 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 Average Daily Outdoor Temperature was associated with a 0.2% reduction in Sleep Efficiency From Fitbit—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 0.2% 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 Average Daily Outdoor Temperature may influence Sleep Efficiency From Fitbit in real-world conditions. The within-subject design and temporal analysis provide confidence in these relationships, though observational limitations remain.

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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 Average Daily Outdoor Temperature would produce an observable change in Sleep Efficiency From Fitbit.
  • Duration of Action: It was assumed that Average Daily Outdoor Temperature could produce an observable change in Sleep Efficiency From Fitbit for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Average Daily Outdoor Temperature values and subsequent Sleep Efficiency From Fitbit 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

Average Daily Outdoor Temperature 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 From Fitbit 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 Average Daily Outdoor Temperature Affect Sleep Efficiency From Fitbit?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6057041-population-study
BibTeX 
@misc{sinn_cause_6038776_effect_6057041_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Average Daily Outdoor Temperature Affect Sleep Efficiency From Fitbit?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6057041-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Average Daily Outdoor Temperature Affect Sleep Efficiency From Fitbit?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6057041-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Average Daily Outdoor Temperature Affect Sleep Efficiency From Fitbit?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-6038776-effect-6057041-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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