Higher Daily High Outdoor Temperature Predicts Moderately Higher Immediate Standing Diastolic Blood Pressure for Population

Higher Daily High Outdoor Temperature Predicts Moderately Higher Immediate Standing Diastolic Blood Pressure for Population

Contents

Variables

A
Daily High Outdoor Temperature 112
A
Immediate Standing Diastolic Blood Pressure 104

Categories

A
Environment 564
A
Vital Signs 110

Tags

Low Confidence
Moderate Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 1.3% average increase in Immediate Standing Diastolic Blood Pressure following above average Daily High Outdoor Temperature.

Abstract

Immediate Standing Diastolic Blood Pressure was generally 5% higher than average after an average of 79.2 degrees fahrenheit of Daily High Outdoor Temperature over the previous 7 days.

Aggregated data from 1 study participants suggests with a LOW degree of confidence (p=0.243, 95% CI -1.795 to 2.873) that Daily High Outdoor Temperature has a moderately positive predictive relationship (R=0.539) with Immediate Standing Diastolic Blood Pressure.

The highest quartile of Immediate Standing Diastolic Blood Pressure measurements were observed following an average 78.9 degrees fahrenheit Daily High Outdoor Temperature.

The lowest quartile of Immediate Standing Diastolic Blood Pressure measurements were observed following an average 79.4 degrees fahrenheit of Daily High Outdoor Temperature.

After an onset delay of 0 seconds, Immediate Standing Diastolic Blood Pressure is typically 5% higher than average over the 7 days following around 79.4 degrees fahrenheit of Daily High Outdoor Temperature Daily High Outdoor Temperature.

Keywords: Daily High Outdoor Temperature, Immediate Standing Diastolic Blood Pressure, 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 4 paired observations from 1 participants revealed a minimal improvement in Immediate Standing Diastolic Blood Pressure following above-average Daily High Outdoor Temperature exposure.

+1.3%
Change from Baseline
Minimal effect on Immediate Standing Diastolic Blood Pressure
0.03
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
0.539
Correlation (r)
p = 0.001
Significance
z = 1.00
Effect Magnitude
φ = 1.00
Temporality

What This Means

When participants had above-average Daily High Outdoor Temperature:

  • Immediate Standing Diastolic Blood Pressure increased by 1.3% on average
  • Temporal analysis supports Daily High 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 Daily High Outdoor Temperature values associated with high and low Immediate Standing Diastolic Blood Pressure 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 High Outdoor Temperature Distribution

Immediate Standing Diastolic Blood Pressure Distribution

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Daily High Outdoor Temperature
Effect Variable Name Immediate Standing Diastolic Blood Pressure
Sinn Predictive Coefficient 0.025646314636817
Confidence Level LOW
Confidence Interval 2.3335662809528
Forward Pearson Predictive Coefficient 0.539
Critical T Value 2.132
Average Daily High Outdoor Temperature Over Previous 7 days Before ABOVE Average Immediate Standing Diastolic Blood Pressure 78.9 degrees fahrenheit
Average Daily High Outdoor Temperature Over Previous 7 days Before BELOW Average Immediate Standing Diastolic Blood Pressure 79.4 degrees fahrenheit
Duration of Action 7 days
Effect Size moderately positive
Number of Paired Measurements 4
Optimal Pearson Product -0.040951265344107
P Value 0.24334354373793
Statistical Significance 0.001
Strength of Relationship 2.3335662809528
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 1

Daily High Outdoor Temperature Info

Property Value
Variable Name Daily High Outdoor Temperature
Aggregation Method MEAN
Analysis Performed At 2021-03-23
Duration of Action 7 days
Kurtosis 2.6047200144081
Maximum Allowed Value 134 degrees fahrenheit
Mean 64.494335169492 degrees fahrenheit
Median 64.867302259887 degrees fahrenheit
Minimum Allowed Value -87 degrees fahrenheit
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 112
Number of Measurements 22277
Number of Measurements (including those generated by tagged, joined, or child variables) 22277
Public true
Onset Delay 0 seconds
Standard Deviation 7.9765055521619
Unit Degrees Fahrenheit
User Variables 708
Variable Category Environment
Variable ID 6038777
Variance 120.14325825086

Immediate Standing Diastolic Blood Pressure Info

Property Value
Variable Name Immediate Standing Diastolic Blood Pressure
Aggregation Method MEAN
Analysis Performed At 2022-08-10
Duration of Action 24 hours
Kurtosis 2.7744655248043
Maximum Allowed Value 100000 millimeters merc
Mean 84.471 millimeters merc
Median 84.5 millimeters merc
Minimum Allowed Value 1 millimeters merc
Number of Aggregate Predictors 55
Number of Aggregate Outcomes 49
Number of Measurements 20
Number of Measurements (including those generated by tagged, joined, or child variables) 20
Public true
Onset Delay 0 seconds
Standard Deviation 5.6940654151828
Unit Millimeters Merc
User Variables 1
Variable Category Vital Signs
Variable ID 6066333
Variance 32.422380952381

Introduction

Background

Daily High Outdoor Temperature (Environment) and Immediate Standing Diastolic Blood Pressure (Vital Signs) 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 High Outdoor Temperature affect Immediate Standing Diastolic Blood Pressure?

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 High Outdoor Temperature for maximizing Immediate Standing Diastolic Blood Pressure?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Daily High Outdoor Temperature and Immediate Standing Diastolic Blood Pressure. Additionally, we attempt to determine the Daily High Outdoor Temperature values most likely to produce optimal Immediate Standing Diastolic Blood Pressure 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 1.3% improvement in Immediate Standing Diastolic Blood Pressure following above-average Daily High Outdoor Temperature exposure. The Predictor Impact Score (PIS) of 0.03 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 Immediate Standing Diastolic Blood Pressure is not statistically significant at a 95% confidence interval. This suggests that the Daily High Outdoor Temperature value may not have a significant influence on the Immediate Standing Diastolic Blood Pressure value, or that more data is needed to detect an effect.

After treatment, a 1.3% increase (1.08 millimeters merc) from the mean baseline 82 millimeters merc was observed. The relative standard deviation at baseline was 1.3%. The observed change was 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: 0.994
Critical t-value: 2.132

Since t = 0.99 < 2.13, 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 High Outdoor Temperature might influence Immediate Standing Diastolic Blood Pressure.

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 High 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 High 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 High Outdoor Temperature was associated with a 1.3% improvement in Immediate Standing Diastolic Blood Pressure—a minimal effect. The Predictor Impact Score of 0.03 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.03 and a 1.3% 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 High Outdoor Temperature may influence Immediate Standing Diastolic Blood Pressure 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 Daily High Outdoor Temperature would produce an observable change in Immediate Standing Diastolic Blood Pressure.
  • Duration of Action: It was assumed that Daily High Outdoor Temperature could produce an observable change in Immediate Standing Diastolic Blood Pressure for as much as 7 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Daily High Outdoor Temperature values and subsequent Immediate Standing Diastolic Blood Pressure 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 High Outdoor Temperature data was primarily collected using Weather. Automatically import temperature, humidity, and ultraviolet light exposure.

Immediate Standing Diastolic Blood Pressure 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 Daily High Outdoor Temperature Affect Immediate Standing Diastolic Blood Pressure?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-6038777-effect-6066333-population-study
BibTeX 
@misc{sinn_cause_6038777_effect_6066333_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Daily High Outdoor Temperature Affect Immediate Standing Diastolic Blood Pressure?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-6038777-effect-6066333-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Daily High Outdoor Temperature Affect Immediate Standing Diastolic Blood Pressure?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-6038777-effect-6066333-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Daily High Outdoor Temperature Affect Immediate Standing Diastolic Blood Pressure?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-6038777-effect-6066333-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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