For most, Time Spent On Software Development is generally highest after a daily total of 6 minutes of Cardio Heart Rate Zone over the previous 3 days.
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People with higher Cardio Heart Rate Zone usually have higher Time Spent On Software Development
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Cardio Heart Rate Zone on each day of the week.
This chart shows the typical value recorded for Cardio Heart Rate Zone for each month of the year.
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.

Abstract

Aggregated data from 8 study participants suggests with a high degree of confidence (p=0.083560062770906, 95% CI -0.115 to 0.291) that Cardio Heart Rate Zone Minutes has a very weakly positive predictive relationship (R=0.09) with Time Spent On Software Development. The highest quartile of Time Spent On Software Development measurements were observed following an average 12 minutes Cardio Heart Rate Zone Minutes per day. The lowest quartile of Time Spent On Software Development measurements were observed following an average 16.339346271759 min Cardio Heart Rate Zone Minutes per day.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Cardio Heart Rate Zone. 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.
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.

Design

This study is based on data donated by 8 participants. Thus, the study design is equivalent to the aggregation of 8 separate n=1 observational natural experiments.

Data Analysis

Cardio Heart Rate Zone Pre-Processing
Cardio Heart Rate Zone measurement values below 0 seconds were assumed erroneous and removed. Cardio Heart Rate Zone measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Cardio Heart Rate Zone data were unrecorded 0 seconds measurement values.
Cardio Heart Rate Zone Analysis Settings

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Cardio Heart Rate Zone Minutes would produce an observable change in Time Spent On Software Development. It was assumed that Cardio Heart Rate Zone Minutes could produce an observable change in Time Spent On Software Development for as much as 3.25 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Cardio Heart Rate Zone Minutes data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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.

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 Cardio Heart Rate Zone and Time Spent On Software Development

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. 595 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Cardio Heart Rate Zone 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 Cardio Heart Rate Zone and Time Spent On Software Development 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 Cardio Heart Rate Zone
Effect Variable Name Time Spent On Software Development
Sinn Predictive Coefficient 0.048514632287032
Confidence Level high
Confidence Interval 0.20306620244259
Forward Pearson Correlation Coefficient 0.0883
Critical T Value 1.67225
Total Cardio Heart Rate Zone Over Previous 3 days Before ABOVE Average Time Spent On Software Development 12 minutes
Total Cardio Heart Rate Zone Over Previous 3 days Before BELOW Average Time Spent On Software Development 16 minutes
Duration of Action 3 days
Effect Size very weakly positive
Number of Paired Measurements 595
Optimal Pearson Product 0.11492007497845
P Value 0.083560062770906
Statistical Significance 0.60127499559894
Strength of Relationship 0.20306620244259
Study Type population
Analysis Performed At 2019-01-30
Number of Participants 8

Cardio Heart Rate Zone Statistics

Property Value
Variable Name Cardio Heart Rate Zone Minutes
Aggregation Method SUM
Analysis Performed At 2019-01-28
Duration of Action 7 days
Kurtosis 52.14885347965
Maximum Allowed Value 7 days
Mean 7 minutes
Median 4 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 256
Number of Measurements 10834
Onset Delay 0 seconds
Standard Deviation 10.097880997098
Unit Minutes
Variable ID 5211881
Variance 204.88475506934

Time Spent On Software Development Statistics

Property Value
Variable Name Time Spent On Software Development
Aggregation Method SUM
Analysis Performed At 2019-01-25
Duration of Action 7 days
Kurtosis 133.45581963553
Maximum Allowed Value 7 days
Mean 15 minutes
Median 4 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 269
Number of Measurements 21530
Onset Delay 0 seconds
Standard Deviation 0.45846642088526
Unit Hours
Variable ID 111632
Variance 0.90147225397025

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