This individual's Overall Mood is generally highest after an average of 63 grams of Sugar intake over the previous 7 days.
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Blue represents the mean of Sugar intake over the previous 7 days
An increase in 7 days cumulative Sugar intake is usually followed by an increase in Overall Mood. (R = 0.191)
Typical values for Overall Mood following a given amount of Sugar intake over the previous 7 days.
Typical Sugar intake seen over the previous 7 days 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 your Sugar changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sugar on each day of the week.
This chart shows the typical value recorded for Sugar for each month of the year.
This chart shows how your 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 6% higher than normal after a total of 63.32 grams Sugar intake over the previous 7 days. This individual's data suggests with a high degree of confidence (p=2.4055696060101E-7, 95% CI 0.107 to 0.275) that Sugar (g) has a weakly positive predictive relationship (R=0.19) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 61.93 grams Sugar (g) per day. The lowest quartile of Overall Mood measurements were observed following an average 45.996056815071 g Sugar (g) per day.Overall Mood is generally 3% lower than normal after a total of 45.996056815071 grams of Sugar intake over the previous 7 days. Overall Mood is generally 6% higher after a total of 61.93 grams of Sugar intake over the previous 7 days.

Objective

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

Participant Instructions

Record your Sugar 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

Sugar Pre-Processing
Sugar measurement values below 0 grams were assumed erroneous and removed. No maximum allowed measurement value was defined for Sugar. No missing data filling value was defined for Sugar so any gaps in data were just not analyzed instead of assuming zero values for those times.
Sugar 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 Sugar (g) would produce an observable change in Overall Mood. It was assumed that Sugar (g) could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
1430 raw Sugar (g) measurements with 475 changes spanning 2084 days from 2013-01-12 to 2018-09-28 were used in this analysis. 13980 raw Overall Mood measurements with 1242 changes spanning 2609 days from 2012-05-06 to 2019-06-27 were used in this analysis.

Data Sources

Sugar (g) 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 positive relationship between Sugar intake 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. 609 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Sugar 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, 2 humans feel that there is a plausible mechanism of action and 2 feel that any relationship observed between Sugar intake 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 Sugar intake
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.1961
Confidence Level high
Confidence Interval 0.083576690160933
Forward Pearson Correlation Coefficient 0.191
Critical T Value 1.646
Total Sugar intake Over Previous 7 days Before ABOVE Average Overall Mood 61.93 grams
Total Sugar intake Over Previous 7 days Before BELOW Average Overall Mood 45.996 grams
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 609
Optimal Pearson Product 0.078682994073737
P Value 2.4055696060101E-7
Statistical Significance 1
Strength of Relationship 0.083576690160933
Study Type individual
Analysis Performed At 2019-06-28

Sugar Statistics

Property Value
Variable Name Sugar (g)
Aggregation Method MEAN
Analysis Performed At 2019-06-28
Duration of Action 7 days
Kurtosis 4.6353556645492
Mean 53.614 grams
Median 36 grams
Minimum Allowed Value 0 grams
Number of Changes 475
Number of Correlations 52
Number of Measurements 1430
Onset Delay 0 seconds
Standard Deviation 38.04538256676
Unit Grams
UPC 492000455032
Variable ID 106942
Variance 1447.4511346511

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-06-28
Duration of Action 24 hours
Kurtosis 6.8362298874825
Maximum Allowed Value 5 out of 5
Mean 2.9081 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1242
Number of Correlations 4028
Number of Measurements 13980
Onset Delay 0 seconds
Standard Deviation 0.52351955717749
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.27407272674732

Tracking Sugar

Record your Sugar 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