This individual's Guiltiness is generally lowest after a daily total of 30 minutes of Active Time over the previous 7 days.
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Blue represents the sum of Active Time over the previous 7 days
An increase in 7 days cumulative Active Time is usually followed by an decrease in Guiltiness. (R = -0.546)
Typical values for Guiltiness following a given amount of Active Time over the previous 7 days.
Typical Active Time seen over the previous 7 days preceding the given Guiltiness value.
This chart shows how your Active Time changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Active Time on each day of the week.
This chart shows the typical value recorded for Active Time for each month of the year.
This chart shows how your Guiltiness changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Guiltiness on each day of the week.
This chart shows the typical value recorded for Guiltiness for each month of the year.

Abstract

This individual's Guiltiness is generally 43% lower than normal after 29 minutes Active Time per 7 days. This individual's data suggests with a high degree of confidence (p=0.0013902191827805, 95% CI -1.439 to 0.347) that Active Time has a moderately negative predictive relationship (R=-0.55) with Guiltiness. The highest quartile of Guiltiness measurements were observed following an average 9 minutes Active Time per day. The lowest quartile of Guiltiness measurements were observed following an average 2139.75 s Active Time per day.Guiltiness is generally 43% lower than normal after a total of 36 minutes of Active Time over the previous 7 days. Guiltiness is generally 27% higher after a total of 9 minutes of Active Time over the previous 7 days.

Objective

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

Participant Instructions

Record your Active Time daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Guiltiness daily in the reminder inbox or using the interactive web or mobile notifications.

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

Active Time Pre-Processing
Active Time measurement values below 0 microseconds were assumed erroneous and removed. Active Time measurement values above 24 hours were assumed erroneous and removed. No missing data filling value was defined for Active Time so any gaps in data were just not analyzed instead of assuming zero values for those times.
Active Time Analysis Settings

Guiltiness Pre-Processing
Guiltiness measurement values below 1 out of 5 were assumed erroneous and removed. Guiltiness measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Guiltiness so any gaps in data were just not analyzed instead of assuming zero values for those times.
Guiltiness Analysis Settings

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

Data Quantity
107 raw Active Time measurements with 56 changes spanning 283 days from 2013-08-30 to 2014-06-09 were used in this analysis. 2932 raw Guiltiness measurements with 699 changes spanning 2015 days from 2013-11-17 to 2019-05-25 were used in this analysis.

Data Sources

Active Time 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.

Guiltiness 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.

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 moderately negative relationship between Active Time and Guiltiness

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. 13 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Active Time 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 Active Time and Guiltiness 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 Active Time
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.0614
Confidence Level high
Confidence Interval 0.89266221658304
Forward Pearson Correlation Coefficient -0.546
Critical T Value 1.771
Total Active Time Over Previous 7 days Before ABOVE Average Guiltiness 9 minutes
Total Active Time Over Previous 7 days Before BELOW Average Guiltiness 2 seconds
Duration of Action 7 days
Effect Size moderately negative
Number of Paired Measurements 13
Optimal Pearson Product 0.51252284404734
P Value 0.0013902191827805
Statistical Significance 0.1125
Strength of Relationship 0.89266221658304
Study Type individual
Analysis Performed At 2019-05-26

Active Time Statistics

Property Value
Variable Name Active Time
Aggregation Method SUM
Analysis Performed At 2019-05-26
Duration of Action 7 days
Kurtosis 489.96725788803
Maximum Allowed Value 24 hours
Mean 12 seconds
Median 0 microseconds
Minimum Allowed Value 0 microseconds
Number of Changes 56
Number of Correlations 390
Number of Measurements 107
Onset Delay 0 seconds
Standard Deviation 178.41526055149
Unit Seconds
Variable ID 1872
Variance 31832.005197656

Guiltiness Statistics

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2019-05-26
Duration of Action 24 hours
Kurtosis 3.0892766518944
Maximum Allowed Value 5 out of 5
Mean 2.1552 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 699
Number of Correlations 5420
Number of Measurements 2932
Onset Delay 0 seconds
Standard Deviation 0.92251985273351
Unit 1 to 5 Rating
Variable ID 1335
Variance 0.85104287868746

Tracking Active Time

Record your Active Time daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Guiltiness

Record your Guiltiness daily in the reminder inbox or using the interactive web or mobile notifications.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn