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This individual's Sleep Start Time is generally highest after a daily total of 580 event of Code Commits over the previous 7 days.
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Blue represents the sum of Code Commits over the previous 7 days
An increase in 7 days cumulative Code Commits is usually followed by an increase in Sleep Start Time. (R = 0.247)
Typical values for Sleep Start Time following a given amount of Code Commits over the previous 7 days.
Typical Code Commits seen over the previous 7 days preceding the given Sleep Start Time value.
This chart shows how your Code Commits changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Code Commits on each day of the week.
This chart shows the typical value recorded for Code Commits for each month of the year.
This chart shows how your Sleep Start Time changes over time.
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

This individual's Sleep Start Time is generally 7% higher than normal after a total of 577 event Code Commits over the previous 7 days. This individual's data suggests with a high degree of confidence (p=3.7347574750426E-31, 95% CI 0.094 to 0.4) that Code Commits has a weakly positive predictive relationship (R=0.25) with Sleep Start Time. The highest quartile of Sleep Start Time measurements were observed following an average 530.76 event Code Commits per day. The lowest quartile of Sleep Start Time measurements were observed following an average 184.10719754977 event Code Commits per day.Sleep Start Time is generally 2% lower than normal after a total of 184.10719754977 event of Code Commits over the previous 7 days. Sleep Start Time is generally 7% higher after a total of 530.76 event of Code Commits over the previous 7 days.

Objective

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

Participant Instructions

Get GitHub here and use it to record your Code Commits. Once you have a GitHub account, you can import your data from 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 one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

Code Commits Pre-Processing
Code Commits measurement values below 0 event were assumed erroneous and removed. No maximum allowed measurement value was defined for Code Commits. It was assumed that any gaps in Code Commits data were unrecorded 0 event measurement values.
Code Commits Analysis Settings

Sleep Start Time Pre-Processing
Sleep Start Time measurement values below 60 minutes 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 Code Commits would produce an observable change in Sleep Start Time. It was assumed that Code Commits could produce an observable change in Sleep Start Time for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
87338 raw Code Commits measurements with 1633 changes spanning 2081 days from 2013-07-19 to 2019-03-31 were used in this analysis. 1952 raw Sleep Start Time measurements with 1162 changes spanning 1951 days from 2013-11-26 to 2019-03-31 were used in this analysis.

Data Sources

Code Commits data was primarily collected using GitHub. GitHub is the best place to share code with friends, co-workers, classmates, and complete strangers. Over four million people use GitHub to build amazing things together.

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 weakly positive relationship between Code Commits 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. 1945 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Code Commits 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 Code Commits 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 Code Commits
Effect Variable Name Sleep Start Time
Sinn Predictive Coefficient 0.247
Confidence Level high
Confidence Interval 0.15285853491531
Forward Pearson Correlation Coefficient 0.247
Critical T Value 1.646
Total Code Commits Over Previous 7 days Before ABOVE Average Sleep Start Time 530.76 event
Total Code Commits Over Previous 7 days Before BELOW Average Sleep Start Time 184.107 event
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 1945
Optimal Pearson Product 0.17722708506363
P Value 3.7347574750426E-31
Statistical Significance 1
Strength of Relationship 0.15285853491531
Study Type individual
Analysis Performed At 2019-04-04

Code Commits Statistics

Property Value
Variable Name Code Commits
Aggregation Method SUM
Analysis Performed At 2019-03-31
Duration of Action 7 days
Kurtosis 101.83500926551
Mean 41.325 event
Median 15 event
Minimum Allowed Value 0 event
Number of Changes 1633
Number of Correlations 2584
Number of Measurements 87338
Onset Delay 0 seconds
Standard Deviation 93.522658821126
Unit Event
Variable ID 5955693
Variance 8746.4877129727

Sleep Start Time Statistics

Property Value
Variable Name Sleep Start Time
Aggregation Method MEAN
Analysis Performed At 2019-04-02
Duration of Action 24 hours
Kurtosis 7.217559329383
Maximum Allowed Value 7 days
Mean 12 hours
Median 12 hours
Minimum Allowed Value 60 minutes
Number of Changes 1162
Number of Correlations 3135
Number of Measurements 1952
Onset Delay 0 seconds
Standard Deviation 1.7437107114378
Unit Hours
Variable ID 5211821
Variance 3.0405270451829

Tracking Code Commits

Get GitHub here and use it to record your Code Commits. Once you have a GitHub account, you can import your data from 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