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Based on data from 20 participants, Headache Severity is generally lowest after an average of 100000 pascal of Barometric Pressure over the previous 7 days.
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People with higher Barometric Pressure usually have higher Headache Severity
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Barometric Pressure on each day of the week.
This chart shows the typical value recorded for Barometric Pressure for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Headache Severity on each day of the week.
This chart shows the typical value recorded for Headache Severity for each month of the year.

Abstract

Aggregated data from 20 study participants suggests with a medium degree of confidence (p=0.24370287205271, 95% CI -0.352 to 0.342) that Barometric Pressure has a very weakly negative predictive relationship (R=-0.01) with Headache Severity. The highest quartile of Headache Severity measurements were observed following an average 101 pascal Barometric Pressure. The lowest quartile of Headache Severity measurements were observed following an average 101929.50282386 Pa Barometric Pressure.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Barometric Pressure and Headache Severity. Additionally, we attempt to determine the Barometric Pressure values most likely to produce optimal Headache Severity values.

Participant Instructions

Record your Barometric Pressure daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Headache Severity daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 20 participants. Thus, the study design is equivalent to the aggregation of 20 separate n=1 observational natural experiments.

Data Analysis

Barometric Pressure Pre-Processing
No minimum allowed measurement value was defined for Barometric Pressure. No maximum allowed measurement value was defined for Barometric Pressure. No missing data filling value was defined for Barometric Pressure so any gaps in data were just not analyzed instead of assuming zero values for those times.
Barometric Pressure Analysis Settings

Headache Severity Pre-Processing
Headache Severity measurement values below 1 out of 5 were assumed erroneous and removed. Headache Severity measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Headache Severity so any gaps in data were just not analyzed instead of assuming zero values for those times.
Headache Severity Analysis Settings

Predictive Analytics
It was assumed that 0.05 hours would pass before a change in Barometric Pressure would produce an observable change in Headache Severity. It was assumed that Barometric Pressure could produce an observable change in Headache Severity for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Barometric 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.

Headache Severity 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.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply causation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.
We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence.

The list of the criteria is as follows:
Strength (A.K.A. Effect Size)
A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a very weakly negative relationship between Barometric Pressure and Headache Severity

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 119 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Barometric Pressure values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Specificity
Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

Temporality
The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient
Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Barometric Pressure and Headache Severity is coincidental.

Coherence
Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment
All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy
The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Barometric Pressure
Effect Variable Name Headache Severity
Sinn Predictive Coefficient 0.0024102655230032
Confidence Level medium
Confidence Interval 0.3465983324374
Forward Pearson Correlation Coefficient -0.005
Critical T Value 1.7003
Average Barometric Pressure Over Previous 7 days Before ABOVE Average Headache Severity 101 pascal
Average Barometric Pressure Over Previous 7 days Before BELOW Average Headache Severity 101929.50282386 pascal
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 119
Optimal Pearson Product 0.042936178456554
P Value 0.24370287205271
Statistical Significance 0.39767999923788
Strength of Relationship 0.3465983324374
Study Type population
Analysis Performed At 2019-04-06
Number of Participants 20

Barometric Pressure Statistics

Property Value
Variable Name Barometric Pressure
Aggregation Method MEAN
Analysis Performed At 2019-03-13
Duration of Action 7 days
Kurtosis 3.8477427800269
Mean 101650.24563591 pascal
Median 101644.79145885 pascal
Number of Correlations 212
Number of Measurements 1224608
Onset Delay 0 seconds
Standard Deviation 498.27123110278
Unit Pascal
UPC 794628323701
Variable ID 96380
Variance 399230.17717796

Headache Severity Statistics

Property Value
Variable Name Headache Severity
Aggregation Method MEAN
Analysis Performed At 2019-03-27
Duration of Action 7 days
Kurtosis 2.5633473307877
Maximum Allowed Value 5 out of 5
Mean 2.666209047619 out of 5
Median 2.6205298941799 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 907
Number of Measurements 4584
Onset Delay 0 seconds
Standard Deviation 0.33509926286233
Unit 1 to 5 Rating
Variable ID 87323
Variance 0.35059244480326

Tracking Barometric Pressure

Record your Barometric Pressure daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Headache Severity

Record your Headache Severity daily in the reminder inbox or using the interactive web or mobile notifications.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn