This individual's Overall Mood is generally highest after an average of 2 out of 5 of Inflammation over the previous 24 hours.
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Blue represents the mean of Inflammation over the previous 24 hours
An increase in 24 hours cumulative Inflammation is usually followed by an decrease in Overall Mood. (R = -0.136)
Typical values for Overall Mood following a given amount of Inflammation over the previous 24 hours.
Typical Inflammation seen over the previous 24 hours preceding the given Overall Mood value.
Correlation between outcome and aggregated predictor measurements over given number of days
Peak correlation suggests the delay between predictor and observable outcome
This chart shows how Inflammation changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Inflammation on each day of the week.
This chart shows the typical value recorded for Inflammation for each month of the year.
This chart shows how Overall Mood changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Overall Mood on each day of the week.
This chart shows the typical value recorded for Overall Mood for each month of the year.

Abstract

This individual's Overall Mood is generally 1% higher than normal after an average of 2 out of 5 Inflammation over the previous 24 hours. This individual's data suggests with a medium degree of confidence (p=0.18331495800186, 95% CI -0.245 to -0.027) that Inflammation has a weakly negative predictive relationship (R=-0.14) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 2.96 out of 5 Inflammation. The lowest quartile of Overall Mood measurements were observed following an average 3.2448979591837 /5 Inflammation. Overall Mood is generally 2% lower than normal after an average of 3.2448979591837 out of 5 of Inflammation over the previous 24 hours. Overall Mood is generally 1% higher after an average of 2.96 out of 5 of Inflammation over the previous 24 hours.

Objective

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

Participant Instructions

Record your Inflammation daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Overall Mood 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

Inflammation Pre-Processing
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.
Inflammation Analysis Settings

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Inflammation would produce an observable change in Overall Mood. It was assumed that Inflammation could produce an observable change in Overall Mood for as much as 1 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
207 raw Inflammation measurements with 91 changes spanning 350 days from 2018-07-24 to 2019-07-09 were used in this analysis. 14019 raw Overall Mood measurements with 1249 changes spanning 2620 days from 2012-05-06 to 2019-07-09 were used in this analysis.

Data Sources

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.

Overall Mood 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 Inflammation and Overall Mood

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. 192 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Inflammation 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 Inflammation and Overall Mood 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 Inflammation
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.1281
Confidence Level medium
Confidence Interval 0.10859309903328
Forward Pearson Predictive Coefficient -0.136
Critical T Value 1.646
Average Inflammation Over Previous 24 hours Before ABOVE Average Overall Mood 2.96 out of 5
Average Inflammation Over Previous 24 hours Before BELOW Average Overall Mood 3.245 out of 5
Duration of Action 24 hours
Effect Size weakly negative
Number of Paired Measurements 192
Optimal Pearson Product 0.038296903296972
P Value 0.18331495800186
Statistical Significance 0.9419
Strength of Relationship 0.10859309903328
Study Type individual
Analysis Performed At 2019-07-06

Inflammation Statistics

Property Value
Variable Name Inflammation
Aggregation Method MEAN
Analysis Performed At 2019-07-09
Duration of Action 24 hours
Kurtosis 2.2497509392948
Maximum Allowed Value 5 out of 5
Mean 3.0244 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 91
Number of Correlations 307
Number of Measurements 207
Onset Delay 0 seconds
Standard Deviation 1.0118853708923
Unit 1 to 5 Rating
Variable ID 89245
Variance 1.0239120038259

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-07-09
Duration of Action 24 hours
Kurtosis 6.8457368686411
Maximum Allowed Value 5 out of 5
Mean 2.9083 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1249
Number of Correlations 3620
Number of Measurements 14019
Onset Delay 0 seconds
Standard Deviation 0.52278567060155
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.27330485738631

Tracking Inflammation

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

Tracking Overall Mood

Record your Overall Mood 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