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This individual's Sleep Start Time is generally highest after a daily total of 2 days of Time Spent On Software Development over the previous 7 days.
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Blue represents the sum of Time Spent On Software Development over the previous 7 days
An increase in 7 days cumulative Time Spent On Software Development is usually followed by an increase in Sleep Start Time. (R = 0.048)
Typical values for Sleep Start Time following a given amount of Time Spent On Software Development over the previous 7 days.
Typical Time Spent On Software Development seen over the previous 7 days preceding the given Sleep Start Time value.
This chart shows how your Time Spent On Software Development changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Time Spent On Software Development on each day of the week.
This chart shows the typical value recorded for Time Spent On Software Development 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 0% higher than normal after a total of 2 days Time Spent On Software Development over the previous 7 days. This individual's data suggests with a high degree of confidence (p=0.25771282115217, 95% CI -0.156 to 0.252) that Time Spent On Software Development has a very weakly positive predictive relationship (R=0.05) with Sleep Start Time. The highest quartile of Sleep Start Time measurements were observed following an average 2 days Time Spent On Software Development per day. The lowest quartile of Sleep Start Time measurements were observed following an average 45.438846153846 h Time Spent On Software Development per day.Sleep Start Time is generally 1% lower than normal after a total of 45 hours of Time Spent On Software Development over the previous 7 days. Sleep Start Time is generally 0% higher after a total of 2 days of Time Spent On Software Development over the previous 7 days.

Objective

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

Participant Instructions

Get RescueTime here and use it to record your Time Spent On Software Development. Once you have a RescueTime 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

Time Spent On Software Development Pre-Processing
Time Spent On Software Development measurement values below 0 seconds were assumed erroneous and removed. Time Spent On Software Development measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Time Spent On Software Development data were unrecorded 0 seconds measurement values.
Time Spent On Software Development 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 Time Spent On Software Development would produce an observable change in Sleep Start Time. It was assumed that Time Spent On Software Development could produce an observable change in Sleep Start Time for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
559 raw Time Spent On Software Development measurements with 564 changes spanning 682 days from 2016-06-03 to 2018-04-16 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

Time Spent On Software Development data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.

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 Time Spent On Software Development 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. 713 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Time Spent On Software Development 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 Time Spent On Software Development 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 Time Spent On Software Development
Effect Variable Name Sleep Start Time
Sinn Predictive Coefficient 0.048
Confidence Level high
Confidence Interval 0.20402628962528
Forward Pearson Correlation Coefficient 0.048
Critical T Value 1.646
Total Time Spent On Software Development Over Previous 7 days Before ABOVE Average Sleep Start Time 2 days
Total Time Spent On Software Development Over Previous 7 days Before BELOW Average Sleep Start Time 45 hours
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 713
Optimal Pearson Product 0.0056014536343731
P Value 0.25771282115217
Statistical Significance 1
Strength of Relationship 0.20402628962528
Study Type individual
Analysis Performed At 2019-04-04

Time Spent On Software Development Statistics

Property Value
Variable Name Time Spent On Software Development
Aggregation Method SUM
Analysis Performed At 2019-03-30
Duration of Action 7 days
Kurtosis 3.9509291749464
Maximum Allowed Value 7 days
Mean 3 hours
Median 0 seconds
Minimum Allowed Value 0 seconds
Number of Changes 564
Number of Correlations 803
Number of Measurements 559
Onset Delay 0 seconds
Standard Deviation 4.5869899791775
Unit Hours
Variable ID 111632
Variance 21.040477069075

Sleep Start Time Statistics

Property Value
Variable Name Sleep Start Time
Aggregation Method MEAN
Analysis Performed At 2019-03-31
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 Time Spent On Software Development

Get RescueTime here and use it to record your Time Spent On Software Development. Once you have a RescueTime 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