This individual's Guiltiness is generally lowest after an average of 21 index of Body Mass Index Or BMI over the previous 7 days.


Abstract
This individual's Guiltiness is generally 8% lower than normal after 20.737203325544 index Body Mass Index Or BMI per 7 days. This individual's data suggests with a high degree of confidence (p=8.6211986610405E14, 95% CI 0.291 to 0.147) that Body Mass Index Or BMI has a weakly negative predictive relationship (R=0.22) with Guiltiness. The highest quartile of Guiltiness measurements were observed following an average 20.46 index Body Mass Index Or BMI. The lowest quartile of Guiltiness measurements were observed following an average 20.720179681183 index Body Mass Index Or BMI.Guiltiness is generally 8% lower than normal after an average of 20.720179681183 index of Body Mass Index Or BMI over the previous 7 days. Guiltiness is generally 7% higher after an average of 20.46 index of Body Mass Index Or BMI over the previous 7 days.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Body Mass Index Or BMI and Guiltiness. Additionally, we attempt to determine the Body Mass Index Or BMI values most likely to produce optimal Guiltiness values.
Participant Instructions
Get Fitbit here and use it to record your Body Mass Index Or BMI. 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.
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
Body Mass Index Or BMI PreProcessing
Body Mass Index Or BMI measurement values below 1 index were assumed erroneous and removed. No maximum allowed measurement value was defined for Body Mass Index Or BMI. No missing data filling value was defined for Body Mass Index Or BMI so any gaps in data were just not analyzed instead of assuming zero values for those times.
Guiltiness PreProcessing
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.
Predictive Analytics
It was assumed that 0 hours would pass before a change in Body Mass Index Or BMI would produce an observable change in Guiltiness. It was assumed that Body Mass Index Or BMI could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.
Data Quantity
2355 raw Body Mass Index Or BMI measurements with 1756 changes spanning 2538 days from 20120418 to 20190331 were used in this analysis. 2972 raw Guiltiness measurements with 716 changes spanning 2045 days from 20131117 to 20190625 were used in this analysis.
Body Mass Index Or BMI measurement values below 1 index were assumed erroneous and removed. No maximum allowed measurement value was defined for Body Mass Index Or BMI. No missing data filling value was defined for Body Mass Index Or BMI so any gaps in data were just not analyzed instead of assuming zero values for those times.

Guiltiness PreProcessing
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.

Predictive Analytics
It was assumed that 0 hours would pass before a change in Body Mass Index Or BMI would produce an observable change in Guiltiness. It was assumed that Body Mass Index Or BMI could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.

Data Quantity
2355 raw Body Mass Index Or BMI measurements with 1756 changes spanning 2538 days from 20120418 to 20190331 were used in this analysis. 2972 raw Guiltiness measurements with 716 changes spanning 2045 days from 20131117 to 20190625 were used in this analysis.
Data Sources
Body Mass Index Or BMI 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.
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 nonexistent 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 negative relationship between Body Mass Index Or BMI 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. 1196 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Body Mass Index Or BMI 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 timeprecedence 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 biochemical 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 Body Mass Index Or BMI 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.
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 negative relationship between Body Mass Index Or BMI 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. 1196 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Body Mass Index Or BMI 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 timeprecedence 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 biochemical 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 Body Mass Index Or BMI 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  Body Mass Index Or BMI 
Effect Variable Name  Guiltiness 
Sinn Predictive Coefficient  0.3034 
Confidence Level  high 
Confidence Interval  0.071655962681047 
Forward Pearson Correlation Coefficient  0.219 
Critical T Value  1.646 
Average Body Mass Index Or BMI Over Previous 7 days Before ABOVE Average Guiltiness  20.46 index 
Average Body Mass Index Or BMI Over Previous 7 days Before BELOW Average Guiltiness  20.72 index 
Duration of Action  7 days 
Effect Size  weakly negative 
Number of Paired Measurements  1196 
Optimal Pearson Product  0.075583665159531 
P Value  8.6211986610405E14 
Statistical Significance  1 
Strength of Relationship  0.071655962681047 
Study Type  individual 
Analysis Performed At  20190626 
Body Mass Index Or BMI Statistics
Property  Value 

Variable Name  Body Mass Index Or BMI 
Aggregation Method  MEAN 
Analysis Performed At  20190502 
Duration of Action  7 days 
Kurtosis  2.7834166526091 
Mean  20.593 index 
Median  20.532480239868 index 
Minimum Allowed Value  1 index 
Number of Changes  1756 
Number of Correlations  2499 
Number of Measurements  2355 
Onset Delay  0 seconds 
Standard Deviation  0.71676319189842 
Unit  Index 
UPC  712038762439 
Variable ID  1272 
Variance  0.51374947326041 
Guiltiness Statistics
Property  Value 

Variable Name  Guiltiness 
Aggregation Method  MEAN 
Analysis Performed At  20190625 
Duration of Action  24 hours 
Kurtosis  3.0693668747023 
Maximum Allowed Value  5 out of 5 
Mean  2.1685 out of 5 
Median  2 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Changes  716 
Number of Correlations  5425 
Number of Measurements  2972 
Onset Delay  0 seconds 
Standard Deviation  0.93526347749501 
Unit  1 to 5 Rating 
Variable ID  1335 
Variance  0.87471777233607 
Tracking Body Mass Index Or BMI
Get Fitbit here and use it to record your Body Mass Index Or BMI. 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.

Principal Investigator  Mike Sinn