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This individual's Inflammatory Pain is generally lowest after a daily total of 170 grams of Fiber intake over the previous 7 days.
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Blue represents the sum of Fiber intake over the previous 7 days
An increase in 7 days cumulative Fiber intake is usually followed by an decrease in Inflammatory Pain. (R = -0.103)
Typical values for Inflammatory Pain following a given amount of Fiber intake over the previous 7 days.
Typical Fiber intake seen over the previous 7 days preceding the given Inflammatory Pain value.
This chart shows how your Fiber changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Fiber on each day of the week.
This chart shows the typical value recorded for Fiber for each month of the year.
This chart shows how your Inflammatory Pain changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Inflammatory Pain on each day of the week.
This chart shows the typical value recorded for Inflammatory Pain for each month of the year.

Abstract

This individual's Inflammatory Pain is generally 1% lower than normal after 168.976 grams Fiber per 7 days. This individual's data suggests with a low degree of confidence (p=0.37544473492945, 95% CI -0.346 to 0.14) that Fiber has a weakly negative predictive relationship (R=-0.1) with Inflammatory Pain. The highest quartile of Inflammatory Pain measurements were observed following an average 147.4 grams Fiber per day. The lowest quartile of Inflammatory Pain measurements were observed following an average 167.41666666667 g Fiber per day.Inflammatory Pain is generally 1% lower than normal after a total of 167.41666666667 grams of Fiber intake over the previous 7 days. Inflammatory Pain is generally 1% higher after a total of 147.4 grams of Fiber intake over the previous 7 days.

Objective

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

Participant Instructions

Record your Fiber daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Inflammatory Pain 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

Fiber Pre-Processing
Fiber measurement values below 0 grams were assumed erroneous and removed. No maximum allowed measurement value was defined for Fiber. It was assumed that any gaps in Fiber data were unrecorded 0 grams measurement values.
Fiber Analysis Settings

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

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

Data Quantity
1762 raw Fiber measurements with 517 changes spanning 2085 days from 2013-01-12 to 2018-09-28 were used in this analysis. 93 raw Inflammatory Pain measurements with 37 changes spanning 87 days from 2013-01-12 to 2013-04-09 were used in this analysis.

Data Sources

Fiber 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.

Inflammatory Pain 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 weakly negative relationship between Fiber intake and Inflammatory Pain

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. 51 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Fiber intake 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 Fiber intake and Inflammatory Pain 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 Fiber intake
Effect Variable Name Inflammatory Pain
Sinn Predictive Coefficient 0.071
Confidence Level low
Confidence Interval 0.24282740506454
Forward Pearson Correlation Coefficient -0.103
Critical T Value 1.671
Total Fiber intake Over Previous 7 days Before ABOVE Average Inflammatory Pain 147.4 grams
Total Fiber intake Over Previous 7 days Before BELOW Average Inflammatory Pain 167.417 grams
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 51
Optimal Pearson Product 0.02438016619029
P Value 0.37544473492945
Statistical Significance 0.5679
Strength of Relationship 0.24282740506454
Study Type individual
Analysis Performed At 2019-04-04

Fiber Statistics

Property Value
Variable Name Fiber
Aggregation Method SUM
Analysis Performed At 2019-01-25
Duration of Action 7 days
Kurtosis 10.752895863851
Mean 9.2083 grams
Median 0 grams
Minimum Allowed Value 0 grams
Number of Changes 517
Number of Correlations 246
Number of Measurements 1762
Onset Delay 0 seconds
Standard Deviation 17.744462766631
Unit Grams
UPC 806149257127
Variable ID 1315
Variance 314.86595887634

Inflammatory Pain Statistics

Property Value
Variable Name Inflammatory Pain
Aggregation Method MEAN
Analysis Performed At 2019-01-28
Duration of Action 7 days
Kurtosis 2.7508536592149
Maximum Allowed Value 5 out of 5
Mean 2.202 out of 5
Median 2.2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 37
Number of Correlations 242
Number of Measurements 93
Onset Delay 0 seconds
Standard Deviation 0.5272642923496
Unit 1 to 5 Rating
UPC 753970618156
Variable ID 1340
Variance 0.27800763398693

Tracking Fiber

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

Tracking Inflammatory Pain

Record your Inflammatory Pain 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