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Based on data from 62 participants, Sleep Start Time is generally highest after an average of 2.1 millimeters of Precipitation over the previous 7 days.
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People with higher Precipitation usually have lower Sleep Start Time
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Precipitation on each day of the week.
This chart shows the typical value recorded for Precipitation for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sleep Start Time on each day of the week.
This chart shows the typical value recorded for Sleep Start Time for each month of the year.

Abstract

Aggregated data from 62 study participants suggests with a medium degree of confidence (p=0.18471887858292, 95% CI -0.368 to 0.378) that Precipitation has a very weakly positive predictive relationship (R=0.01) with Sleep Start Time. The highest quartile of Sleep Start Time measurements were observed following an average 2.86 millimeters Precipitation. The lowest quartile of Sleep Start Time measurements were observed following an average 2.0224121676401 mm Precipitation.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Precipitation and Sleep Start Time. Additionally, we attempt to determine the Precipitation values most likely to produce optimal Sleep Start Time values.

Participant Instructions

Grant access to your weather and air quality data on the Import Data page. This individual's data will automatically be imported and analyzed.
Get Fitbit here and use it to record your Sleep Start Time. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Design

This study is based on data donated by 62 participants. Thus, the study design is equivalent to the aggregation of 62 separate n=1 observational natural experiments.

Data Analysis

Precipitation Pre-Processing
Precipitation measurement values below 0 millimeters were assumed erroneous and removed. No maximum allowed measurement value was defined for Precipitation. No missing data filling value was defined for Precipitation so any gaps in data were just not analyzed instead of assuming zero values for those times.
Precipitation Analysis Settings

Sleep Start Time Pre-Processing
Sleep Start Time measurement values below 0 seconds were assumed erroneous and removed. Sleep Start Time measurement values above 7 days were assumed erroneous and removed. No missing data filling value was defined for Sleep Start Time so any gaps in data were just not analyzed instead of assuming zero values for those times.
Sleep Start Time Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Precipitation would produce an observable change in Sleep Start Time. It was assumed that Precipitation could produce an observable change in Sleep Start Time for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Precipitation data was primarily collected using Weather. Automatically import temperature, humidity, hours of daylight, air quality and pollen count.

Sleep Start Time 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 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 positive relationship between Precipitation and Sleep Start Time

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. 444 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Precipitation 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 Precipitation and Sleep Start Time 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 Precipitation
Effect Variable Name Sleep Start Time
Sinn Predictive Coefficient 0.0051146946513483
Confidence Level medium
Confidence Interval 0.3726339234758
Forward Pearson Correlation Coefficient 0.0051
Critical T Value 1.6542258064516
Average Precipitation Over Previous 7 days Before ABOVE Average Sleep Start Time 2.86 millimeters
Average Precipitation Over Previous 7 days Before BELOW Average Sleep Start Time 2.0224121676401 millimeters
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 444
Optimal Pearson Product 0.026440574472085
P Value 0.18471887858292
Statistical Significance 0.84838386733205
Strength of Relationship 0.3726339234758
Study Type population
Analysis Performed At 2019-04-06
Number of Participants 62

Precipitation Statistics

Property Value
Variable Name Precipitation
Aggregation Method MEAN
Analysis Performed At 2019-03-28
Duration of Action 7 days
Kurtosis 55.837468890152
Mean 1.9130805084337 millimeters
Median 0.41915017211704 millimeters
Minimum Allowed Value 0 millimeters
Number of Correlations 203
Number of Measurements 1211238
Onset Delay 0 seconds
Standard Deviation 4.171951057237
Unit Millimeters
UPC 721866373106
Variable ID 5954746
Variance 29.482937133789

Sleep Start Time Statistics

Property Value
Variable Name Sleep Start Time
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 24 hours
Kurtosis 68.395831412521
Maximum Allowed Value 7 days
Mean 10 hours
Median 10 hours
Minimum Allowed Value 0 seconds
Number of Correlations 642
Number of Measurements 95932
Onset Delay 0 seconds
Standard Deviation 1.8542834812607
Unit Hours
Variable ID 5211821
Variance 6.2884253085362

Tracking Precipitation

Grant access to your weather and air quality data on the Import Data page. This individual's data will automatically be imported and analyzed.

Tracking Sleep Start Time

Get Fitbit here and use it to record your Sleep Start Time. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn