This individual's Overall Mood is generally 4.1% higher after 8.04 serving Garlic over the previous 21 days.
Join This Study
Go To Interactive Study
Blue represents the sum of Garlic consumption over the previous 21 days
An increase in 21 days cumulative Garlic consumption is usually followed by an increase in Overall Mood. (R = 0.121)
Typical values for Overall Mood following a given amount of Garlic consumption over the previous 21 days.
Typical Garlic consumption seen over the previous 21 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 Garlic changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Garlic on each day of the week.
This chart shows the typical value recorded for Garlic 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 a total of 8 serving Garlic consumption over the previous 21 days. This individual's data suggests with a low degree of confidence (p=0.21152275271278, 95% CI 0.017 to 0.225) that Garlic (Count) has a weakly positive predictive relationship (R=0.12) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 4.17 serving Garlic (Count) per day. The lowest quartile of Overall Mood measurements were observed following an average 3.4166666666667 serving Garlic (Count) per day. Overall Mood is generally 1% lower than normal after a total of 3.4166666666667 serving of Garlic consumption over the previous 21 days. Overall Mood is generally 1% higher after a total of 4.17 serving of Garlic consumption over the previous 21 days.

Objective

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

Participant Instructions

Record your Garlic 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

Garlic Pre-Processing
Garlic measurement values below 0 serving were assumed erroneous and removed. Garlic measurement values above 40 serving were assumed erroneous and removed. It was assumed that any gaps in Garlic data were unrecorded 0 serving measurement values.
Garlic 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 0.5 hours would pass before a change in Garlic (Count) would produce an observable change in Overall Mood. It was assumed that Garlic (Count) could produce an observable change in Overall Mood for as much as 21 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
41 raw Garlic (Count) measurements with 12 changes spanning 47 days from 2019-03-02 to 2019-04-18 were used in this analysis. 14190 raw Overall Mood measurements with 1276 changes spanning 2668 days from 2012-05-06 to 2019-08-25 were used in this analysis.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Overall Mood is not statistically significant at a 95% confidence interval. This suggests that the Garlic (Count) value does not have a significant influence on the Overall Mood value.After treatment, a 4.1% increase (0.11604609929078 out of 5) from the mean baseline 2.8208333333333 out of 5 was observed. The relative standard deviation at baseline was 11.4%. The observed change was 0.36240443411258 times the standard deviation. A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would may have only a 5% likelihood of resulting from random fluctuation (a p-value

Data Sources

Garlic (Count) 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 Garlic consumption 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. 95 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Garlic consumption 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 Garlic consumption 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 Garlic consumption
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.0503
Confidence Level low
Confidence Interval 0.10352608217934
Forward Pearson Predictive Coefficient 0.121
Critical T Value 1.66
Total Garlic consumption Over Previous 21 days Before ABOVE Average Overall Mood 4.17 serving
Total Garlic consumption Over Previous 21 days Before BELOW Average Overall Mood 3.417 serving
Duration of Action 21 days
Effect Size weakly positive
Number of Paired Measurements 95
Optimal Pearson Product 0.018541369723426
P Value 0.21152275271278
Statistical Significance 0.4154
Strength of Relationship 0.10352608217934
Study Type individual
Analysis Performed At 2019-08-08
Number of Pairs 95
Number of Raw Predictor Measurements ( Including Tags, Joins, and Children) 41
Baseline Relative Standard Deviation of Outcome Measurements 11.4
Experiment Duration (days) 98
Number of Raw Outcome Measurements 14129
Z Score 0.36240443411258
Last Analysis 2019-08-08
Experiment Began 2019-01-31 02:00:00
Experiment Ended 2019-05-09 02:30:00
P Value 0.21152275271278
Predictor Category Treatments
Duration of Action (h) 504
Onset Delay (h) 0.5
Significance 0.4154
Outcome Relative Standard Deviation at Baseline 11.4
Outcome Standard Deviation at Baseline 0.3202115878492/5
Outcome Mean at Baseline 2.8208333333333/5
Average Followup Change From Baseline 4.1&
Average Absolute Followup Change From Baseline 2.9368794326241/5
Z- Score 0.36240443411258
Average Predictor Treatment Value 8.04serving over 21 days

Garlic Statistics

Property Value
Variable Name Garlic (Count)
Aggregation Method SUM
Analysis Performed At 2019-08-07
Duration of Action 21 days
Kurtosis 2.3996675246675
Maximum Allowed Value 40 serving
Mean 0.24324 serving
Median 0 serving
Minimum Allowed Value 0 serving
Number of Changes 12
Number of Correlations 51
Number of Measurements 41
Onset Delay 30 minutes
Standard Deviation 0.43196939296734
Unit Serving
UPC 616320074241
Variable ID 6054146
Variance 0.18659755646057

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-08-26
Duration of Action 24 hours
Kurtosis 6.8571064844775
Maximum Allowed Value 5 out of 5
Mean 2.9061 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1276
Number of Correlations 4589
Number of Measurements 14190
Onset Delay 0 seconds
Standard Deviation 0.5210978398367
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.27154295868247

Tracking Garlic

Record your Garlic 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.
Join This Study

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