For most, Resting Heart Rate (Pulse) is generally highest 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 lower Resting Heart Rate (Pulse)
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 Resting Heart Rate (Pulse) on each day of the week.
This chart shows the typical value recorded for Resting Heart Rate (Pulse) for each month of the year.

Abstract

Aggregated data from 49 study participants suggests with a medium degree of confidence (p=0.1667153260791, 95% CI -1.371 to 1.348) that Barometric Pressure has a very weakly negative predictive relationship (R=-0.01) with Resting Heart Rate (Pulse). The highest quartile of Resting Heart Rate (Pulse) measurements were observed following an average 101 pascal Barometric Pressure. The lowest quartile of Resting Heart Rate (Pulse) measurements were observed following an average 101709.06308331 Pa Barometric Pressure.

Objective

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

Participant Instructions

Record your Barometric Pressure daily in the reminder inbox or using the interactive web or mobile notifications.
Get Fitbit here and use it to record your Resting Heart Rate (Pulse). Once you have a Fitbit account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.

Design

This study is based on data donated by 49 participants. Thus, the study design is equivalent to the aggregation of 49 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

Resting Heart Rate (Pulse) Pre-Processing
Resting Heart Rate (Pulse) measurement values below 0 beats per minute were assumed erroneous and removed. No maximum allowed measurement value was defined for Resting Heart Rate (Pulse). No missing data filling value was defined for Resting Heart Rate (Pulse) so any gaps in data were just not analyzed instead of assuming zero values for those times.
Resting Heart Rate (Pulse) Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Barometric Pressure would produce an observable change in Resting Heart Rate (Pulse). It was assumed that Barometric Pressure could produce an observable change in Resting Heart Rate (Pulse) 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.

Resting Heart Rate (Pulse) data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply correlation. 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 Resting Heart Rate (Pulse)

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. 190 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 Resting Heart Rate (Pulse) 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 Resting Heart Rate (Pulse)
Sinn Predictive Coefficient 0.0039583494330716
Confidence Level medium
Confidence Interval 1.3591769670884
Forward Pearson Correlation Coefficient -0.0114
Critical T Value 1.6709183673469
Average Barometric Pressure Over Previous 7 days Before ABOVE Average Resting Heart Rate ( Pulse) 101 pascal
Average Barometric Pressure Over Previous 7 days Before BELOW Average Resting Heart Rate ( Pulse) 101709.06308331 pascal
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 190
Optimal Pearson Product 0.078091596606026
P Value 0.1667153260791
Statistical Significance 0.64851224627726
Strength of Relationship 1.3591769670884
Study Type population
Analysis Performed At 2019-02-02
Number of Participants 49

Barometric Pressure Statistics

Property Value
Variable Name Barometric Pressure
Aggregation Method MEAN
Analysis Performed At 2019-01-18
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

Resting Heart Rate (Pulse) Statistics

Property Value
Variable Name Resting Heart Rate (Pulse)
Aggregation Method MEAN
Analysis Performed At 2019-01-30
Duration of Action 24 hours
Kurtosis 3.1226736840716
Mean 85.394203252033 beats per minute
Median 85.260501355014 beats per minute
Minimum Allowed Value 0 beats per minute
Number of Correlations 745
Number of Measurements 19869
Onset Delay 0 seconds
Standard Deviation 3.4978694251243
Unit Beats per Minute
UPC 714169039954
Variable ID 5211891
Variance 16.184445493263

https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn