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This individual's Sickness Severity is generally lowest after a daily total of 9 grams of Monounsaturated Fat intake over the previous 7 days.
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Blue represents the sum of Monounsaturated Fat intake over the previous 7 days
An increase in 7 days cumulative Monounsaturated Fat intake is usually followed by an decrease in Sickness Severity. (R = -0.11)
Typical values for Sickness Severity following a given amount of Monounsaturated Fat intake over the previous 7 days.
Typical Monounsaturated Fat intake seen over the previous 7 days preceding the given Sickness Severity value.
This chart shows how your Monounsaturated Fat changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Monounsaturated Fat on each day of the week.
This chart shows the typical value recorded for Monounsaturated Fat for each month of the year.
This chart shows how your Sickness Severity changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sickness Severity on each day of the week.
This chart shows the typical value recorded for Sickness Severity for each month of the year.

Abstract

This individual's Sickness Severity is generally -3% lower than normal after 9 grams Monounsaturated Fat per 7 days. This individual's data suggests with a low degree of confidence (p=0.36817878389173, 95% CI -0.69 to 0.47) that Monounsaturated Fat has a weakly negative predictive relationship (R=-0.11) with Sickness Severity. The highest quartile of Sickness Severity measurements were observed following an average 6 grams Monounsaturated Fat per day. The lowest quartile of Sickness Severity measurements were observed following an average 7.8 g Monounsaturated Fat per day.Sickness Severity is generally 3% lower than normal after a total of 7.8 grams of Monounsaturated Fat intake over the previous 7 days. Sickness Severity is generally 2% higher after a total of 6 grams of Monounsaturated Fat intake over the previous 7 days.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Monounsaturated Fat and Sickness Severity. Additionally, we attempt to determine the Monounsaturated Fat values most likely to produce optimal Sickness Severity values.

Participant Instructions

Record your Monounsaturated Fat daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Sickness Severity 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

Monounsaturated Fat Pre-Processing
Monounsaturated Fat measurement values below 0.1 grams were assumed erroneous and removed. No maximum allowed measurement value was defined for Monounsaturated Fat. No missing data filling value was defined for Monounsaturated Fat so any gaps in data were just not analyzed instead of assuming zero values for those times.
Monounsaturated Fat Analysis Settings

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

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

Data Quantity
1127 raw Monounsaturated Fat measurements with 351 changes spanning 886 days from 2013-01-15 to 2015-06-20 were used in this analysis. 55 raw Sickness Severity measurements with 24 changes spanning 82 days from 2013-01-12 to 2013-04-04 were used in this analysis.

Data Sources

Monounsaturated Fat 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.

Sickness Severity 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 Monounsaturated Fat intake and Sickness Severity

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. 12 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Monounsaturated Fat 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 Monounsaturated Fat intake and Sickness Severity 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 Monounsaturated Fat intake
Effect Variable Name Sickness Severity
Sinn Predictive Coefficient 0.0101
Confidence Level low
Confidence Interval 0.58036209994904
Forward Pearson Correlation Coefficient -0.11
Critical T Value 1.782
Total Monounsaturated Fat intake Over Previous 7 days Before ABOVE Average Sickness Severity 6 grams
Total Monounsaturated Fat intake Over Previous 7 days Before BELOW Average Sickness Severity 7.8 grams
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 12
Optimal Pearson Product 0.035561849801301
P Value 0.36817878389173
Statistical Significance 0.0664
Strength of Relationship 0.58036209994904
Study Type individual
Analysis Performed At 2019-04-04

Monounsaturated Fat Statistics

Property Value
Variable Name Monounsaturated Fat
Aggregation Method SUM
Analysis Performed At 2019-03-29
Duration of Action 7 days
Kurtosis 8.3045267684777
Mean 9.6134 grams
Median 0 grams
Minimum Allowed Value 0.1 grams
Number of Changes 351
Number of Correlations 229
Number of Measurements 1127
Onset Delay 0 seconds
Standard Deviation 22.373202242961
Unit Grams
Variable ID 1383
Variance 500.56017860443

Sickness Severity Statistics

Property Value
Variable Name Sickness Severity
Aggregation Method MEAN
Analysis Performed At 2019-03-29
Duration of Action 7 days
Kurtosis 5.2166694866718
Maximum Allowed Value 5 out of 5
Mean 2.1952 out of 5
Median 2.2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 24
Number of Correlations 212
Number of Measurements 55
Onset Delay 0 seconds
Standard Deviation 0.38486963061416
Unit 1 to 5 Rating
UPC 810758020393
Variable ID 1444
Variance 0.14812463256908

Tracking Monounsaturated Fat

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

Tracking Sickness Severity

Record your Sickness Severity 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