cause image
gauge image
effect image

Based on data from 2 participants, Inflammatory Pain is generally lowest after an average of 21 index of Body Mass Index Or BMI over the previous 7 days.
Join This Study
Go To Interactive Study
People with higher Body Mass Index Or BMI usually have higher Inflammatory Pain
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Body Mass Index Or BMI on each day of the week.
This chart shows the typical value recorded for Body Mass Index Or BMI for each month of the year.
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

Aggregated data from 2 study participants suggests with a low degree of confidence (p=0.070320018651077, 95% CI 0.149 to 0.446) that Body Mass Index Or BMI has a weakly positive predictive relationship (R=0.3) with Inflammatory Pain. The highest quartile of Inflammatory Pain measurements were observed following an average 21.19 index Body Mass Index Or BMI. The lowest quartile of Inflammatory Pain measurements were observed following an average 20.986626018486 index Body Mass Index Or BMI.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Body Mass Index Or BMI and Inflammatory Pain. Additionally, we attempt to determine the Body Mass Index Or BMI values most likely to produce optimal Inflammatory Pain 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 Inflammatory Pain daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

Data Analysis

Body Mass Index Or BMI Pre-Processing
Body Mass Index Or BMI measurement values below 0 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.
Body Mass Index Or BMI 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 Body Mass Index Or BMI would produce an observable change in Inflammatory Pain. It was assumed that Body Mass Index Or BMI could produce an observable change in Inflammatory Pain for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Body Mass Index Or BMI data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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 positive relationship between Body Mass Index Or BMI 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. 87 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 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 Body Mass Index Or BMI 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 Body Mass Index Or BMI
Effect Variable Name Inflammatory Pain
Sinn Predictive Coefficient 0.030191217305785
Confidence Level low
Confidence Interval 0.14865586306599
Forward Pearson Correlation Coefficient 0.2972
Critical T Value 1.66
Average Body Mass Index Or BMI Over Previous 7 days Before ABOVE Average Inflammatory Pain 21.19 index
Average Body Mass Index Or BMI Over Previous 7 days Before BELOW Average Inflammatory Pain 20.986626018486 index
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 87
Optimal Pearson Product 0.13332468098327
P Value 0.070320018651077
Statistical Significance 0.64219999313354
Strength of Relationship 0.14865586306599
Study Type population
Analysis Performed At 2019-04-06
Number of Participants 2

Body Mass Index Or BMI Statistics

Property Value
Variable Name Body Mass Index Or BMI
Aggregation Method MEAN
Analysis Performed At 2019-03-30
Duration of Action 7 days
Kurtosis 3.5047848345785
Mean 27.016748603352 index
Median 27.012478760234 index
Minimum Allowed Value 0 index
Number of Correlations 867
Number of Measurements 63821
Onset Delay 0 seconds
Standard Deviation 0.63231300374885
Unit Index
UPC 712038762439
Variable ID 1272
Variance 0.9025023057101

Inflammatory Pain Statistics

Property Value
Variable Name Inflammatory Pain
Aggregation Method MEAN
Analysis Performed At 2019-04-05
Duration of Action 7 days
Kurtosis 2.5167595823337
Maximum Allowed Value 5 out of 5
Mean 2.386725 out of 5
Median 2.35 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 137
Number of Measurements 195
Onset Delay 0 seconds
Standard Deviation 0.58153138134718
Unit 1 to 5 Rating
UPC 753970618156
Variable ID 1340
Variance 0.34406858138811

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 Inflammatory Pain

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

https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn