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This individual's Guiltiness is generally lowest after an average of 1700 kilocalories of Calories Burned over the previous 7 days.
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Blue represents the mean of Calories Burned over the previous 7 days
An increase in 7 days cumulative Calories Burned is usually followed by an decrease in Guiltiness. (R = -0.023)
Typical values for Guiltiness following a given amount of Calories Burned over the previous 7 days.
Typical Calories Burned seen over the previous 7 days preceding the given Guiltiness value.
This chart shows how your Calories Burned changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Calories Burned on each day of the week.
This chart shows the typical value recorded for Calories Burned 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 3% lower than normal after 1673.0913007829 kilocalories Calories Burned per 7 days. This individual's data suggests with a medium degree of confidence (p=0.28416175225377, 95% CI -0.261 to 0.215) that Calories Burned has a very weakly negative predictive relationship (R=-0.02) with Guiltiness. The highest quartile of Guiltiness measurements were observed following an average 2 kilocalories Calories Burned. The lowest quartile of Guiltiness measurements were observed following an average 2189.9752843984 kcal Calories Burned.Guiltiness is generally 3% lower than normal after an average of 2189.9752843984 kilocalories of Calories Burned over the previous 7 days. Guiltiness is generally 3% higher after an average of 2 kilocalories of Calories Burned over the previous 7 days.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Calories Burned. 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.
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

Calories Burned Pre-Processing
No minimum allowed measurement value was defined for Calories Burned. No maximum allowed measurement value was defined for Calories Burned. No missing data filling value was defined for Calories Burned so any gaps in data were just not analyzed instead of assuming zero values for those times.
Calories Burned 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 Calories Burned would produce an observable change in Guiltiness. It was assumed that Calories Burned could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
161 raw Calories Burned measurements with 131 changes spanning 160 days from 2018-10-26 to 2019-04-03 were used in this analysis. 2800 raw Guiltiness measurements with 661 changes spanning 1964 days from 2013-11-17 to 2019-04-04 were used in this analysis.

Data Sources

Calories Burned data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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 very weakly negative relationship between Calories Burned 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. 109 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Calories Burned 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 Calories Burned 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 Calories Burned
Effect Variable Name Guiltiness
Sinn Predictive Coefficient 0.0617
Confidence Level medium
Confidence Interval 0.23799323298611
Forward Pearson Correlation Coefficient -0.023
Critical T Value 1.646
Average Calories Burned Over Previous 7 days Before ABOVE Average Guiltiness 2 kilocalories
Average Calories Burned Over Previous 7 days Before BELOW Average Guiltiness 2 kilocalories
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 109
Optimal Pearson Product 0.007558011065971
P Value 0.28416175225377
Statistical Significance 0.9378
Strength of Relationship 0.23799323298611
Study Type individual
Analysis Performed At 2019-04-04

Calories Burned Statistics

Property Value
Variable Name Calories Burned
Aggregation Method MEAN
Analysis Performed At 2019-04-04
Duration of Action 7 days
Kurtosis 3.9268618973341
Mean 2111.8 kilocalories
Median 2120 kilocalories
Number of Changes 131
Number of Correlations 3278
Number of Measurements 161
Onset Delay 0 seconds
Standard Deviation 490.17973231988
Unit Kilocalories
Variable ID 1280
Variance 240276.16997719

Guiltiness Statistics

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2019-04-04
Duration of Action 24 hours
Kurtosis 3.1224153723756
Maximum Allowed Value 5 out of 5
Mean 2.1627 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 661
Number of Correlations 5379
Number of Measurements 2800
Onset Delay 0 seconds
Standard Deviation 0.92130141512025
Unit 1 to 5 Rating
Variable ID 1335
Variance 0.84879629750257

Tracking Calories Burned

Get Fitbit here and use it to record your Calories Burned. 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.

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