This individual's Inflammation is generally 37.3% lower after 14.7 count Rainbow Light ProbioActive Probiotic over the previous 21 days.


Abstract
This individual's Inflammation is generally 18% lower than normal after 14 count Rainbow Light ProbioActive Probiotic per 21 days. This individual's data suggests with a high degree of confidence (p=0.001, 95% CI 0.915 to 0.083) that Rainbow Light ProbioActive Probiotic has a moderately negative predictive relationship (R=0.5) with Inflammation. The highest quartile of Inflammation measurements were observed following an average 6.29 count Rainbow Light ProbioActive Probiotic per day. The lowest quartile of Inflammation measurements were observed following an average 13.85 count Rainbow Light ProbioActive Probiotic per day. Inflammation is generally 18% lower than normal after a total of 13.85 count of Rainbow Light ProbioActive Probiotic over the previous 21 days. Inflammation is generally 23% higher after a total of 6.29 count of Rainbow Light ProbioActive Probiotic over the previous 21 days.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Rainbow Light ProbioActive Probiotic and Inflammation. Additionally, we attempt to determine the Rainbow Light ProbioActive Probiotic values most likely to produce optimal Inflammation values.
Participant Instructions
Record your Rainbow Light ProbioActive Probiotic daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Inflammation daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Inflammation 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
Rainbow Light ProbioActive Probiotic PreProcessing
Rainbow Light ProbioActive Probiotic measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Rainbow Light ProbioActive Probiotic. It was assumed that any gaps in Rainbow Light ProbioActive Probiotic data were unrecorded 0 count measurement values.
Inflammation PreProcessing
Inflammation measurement values below 1 out of 5 were assumed erroneous and removed. Inflammation measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Inflammation so any gaps in data were just not analyzed instead of assuming zero values for those times.
Predictive Analytics
It was assumed that 0.5 hours would pass before a change in Rainbow Light ProbioActive Probiotic would produce an observable change in Inflammation. It was assumed that Rainbow Light ProbioActive Probiotic could produce an observable change in Inflammation for as much as 21 days after the stimulus event.
Data Quantity
68 raw Rainbow Light ProbioActive Probiotic measurements with 14 changes spanning 74 days from 20190611 to 20190824 were used in this analysis. 244 raw Inflammation measurements with 105 changes spanning 396 days from 20180724 to 20190824 were used in this analysis.
Rainbow Light ProbioActive Probiotic measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Rainbow Light ProbioActive Probiotic. It was assumed that any gaps in Rainbow Light ProbioActive Probiotic data were unrecorded 0 count measurement values.

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

Predictive Analytics
It was assumed that 0.5 hours would pass before a change in Rainbow Light ProbioActive Probiotic would produce an observable change in Inflammation. It was assumed that Rainbow Light ProbioActive Probiotic could produce an observable change in Inflammation for as much as 21 days after the stimulus event.

Data Quantity
68 raw Rainbow Light ProbioActive Probiotic measurements with 14 changes spanning 74 days from 20190611 to 20190824 were used in this analysis. 244 raw Inflammation measurements with 105 changes spanning 396 days from 20180724 to 20190824 were used in this analysis.
Statistical Significance
Using a twotailed ttest with alpha = 0.05, it was determined that the change in Inflammation is statistically significant at 95% confidence interval. After treatment, a 37.3% decrease (1.3039215686275 out of 5) from the mean baseline 3.5 out of 5 was observed. The relative standard deviation at baseline was 32.8%. The observed change was 1.13673246959 times the standard deviation. A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate prepost experiments may be considered significant. This occurrence would may have only a 5% likelihood of resulting from random fluctuation (a pvalue
Data Sources
Rainbow Light ProbioActive Probiotic 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.
Inflammation 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.
Inflammation 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 moderately negative relationship between Rainbow Light ProbioActive Probiotic and Inflammation
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. 78 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Rainbow Light ProbioActive Probiotic 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 Rainbow Light ProbioActive Probiotic and Inflammation 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 moderately negative relationship between Rainbow Light ProbioActive Probiotic and Inflammation
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. 78 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Rainbow Light ProbioActive Probiotic 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 Rainbow Light ProbioActive Probiotic and Inflammation 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  Rainbow Light ProbioActive Probiotic 
Effect Variable Name  Inflammation 
Sinn Predictive Coefficient  0.1831 
Confidence Level  high 
Confidence Interval  0.41635525587078215 
Forward Pearson Predictive Coefficient  0.499 
Critical T Value  1.664 
Total Rainbow Light Probio Active Probiotic Over Previous 21 days Before ABOVE Average Inflammation  6.29 count 
Total Rainbow Light Probio Active Probiotic Over Previous 21 days Before BELOW Average Inflammation  13.85 count 
Duration of Action  21 days 
Effect Size  moderately negative 
Number of Paired Measurements  76 
Optimal Pearson Product  0.45274490842002896 
P Value  0.001 
Statistical Significance  0.3669 
Strength of Relationship  0.41635525587078215 
Study Type  individual 
Analysis Performed At  20190819 
Number of Pairs  76 
Number of Raw Predictor Measurements ( Including Tags, Joins, and Children)  61 
Baseline Relative Standard Deviation of Outcome Measurements  32.8 
Experiment Duration (days)  103 
Number of Raw Outcome Measurements  239 
Z Score  1.1367324695900187 
Last Analysis  20190819 
Experiment Began  20190512 01:00:00 
Experiment Ended  20190824 00:00:00 
P Value  0.001 
Predictor Category  Treatments 
Duration of Action (h)  504 
Onset Delay (h)  0.5 
Significance  0.3669 
Outcome Relative Standard Deviation at Baseline  32.8 
Outcome Standard Deviation at Baseline  1.1470786693528088/5 
Outcome Mean at Baseline  3.5/5 
Average Followup Change From Baseline  37.3& 
Average Absolute Followup Change From Baseline  2.196078431372549/5 
Z Score  1.1367324695900187 
Average Predictor Treatment Value  14.7count over 21 days 
Rainbow Light ProbioActive Probiotic Statistics
Property  Value 

Variable Name  Rainbow Light ProbioActive Probiotic 
Aggregation Method  SUM 
Analysis Performed At  20190826 
Duration of Action  21 days 
Kurtosis  1.1082139102528643 
Mean  0.58491 count 
Median  1 count 
Minimum Allowed Value  0 count 
Number of Changes  14 
Number of Correlations  51 
Number of Measurements  68 
Onset Delay  30 minutes 
Standard Deviation  0.4950791091821659 
Unit  Count 
Variable ID  6056499 
Variance  0.24510332434860696 
Inflammation Statistics
Property  Value 

Variable Name  Inflammation 
Aggregation Method  MEAN 
Analysis Performed At  20190824 
Duration of Action  24 hours 
Kurtosis  2.2340112026830266 
Maximum Allowed Value  5 out of 5 
Mean  2.9114 out of 5 
Median  3 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Changes  105 
Number of Correlations  346 
Number of Measurements  244 
Onset Delay  0 seconds 
Standard Deviation  1.0478776785789965 
Unit  1 to 5 Rating 
Variable ID  89245 
Variance  1.0980476292641066 
Tracking Rainbow Light ProbioActive Probiotic
Record your Rainbow Light ProbioActive Probiotic daily in the reminder inbox or using the interactive web or mobile notifications.Tracking Inflammation
Record your Inflammation daily in the reminder inbox or using the interactive web or mobile notifications.

Principal Investigator  Mike Sinn