Higher Net Caloric Intake Intake Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Net Caloric Intake Intake Predicts ↓lower Overall Mood

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For most, Overall Mood is generally highest after a total of 976.84 kilocalories of Net Caloric Intake intake over the previous 7 days.

Trait Correlation Between Net Caloric Intake intake and Overall Mood

The chart above indicates that people with higher average Net Caloric Intake intake usually have lower average Overall Mood. (R = -0.96659919963035)

Net Caloric Intake Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Net Caloric Intake by Day of Week

Average Net Caloric Intake by Month

This chart shows the typical value recorded for Net Caloric Intake for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 2 study participants suggests with a medium degree of confidence (p=0.0435, 95% CI -0.329 to -0.113) that Net Caloric Intake has a weakly negative predictive relationship (R=-0.2213) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 976.84 kilocalories Net Caloric Intake per day. The lowest quartile of Overall Mood measurements were observed following an average 1863.3800527709 kcal Net Caloric Intake per day.

Objective

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

Participant Instructions

Get MyFitnessPal here and use it to record your Net Caloric Intake. Once you have a MyFitnessPal account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
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 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Net Caloric Intake would produce an observable change in Overall Mood. It was assumed that Net Caloric Intake could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply correlation. 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 168 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Net Caloric 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Net Caloric Intake intake
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.032638514722368
Confidence Level medium
Confidence Interval 0.10784097255003
Forward Pearson Correlation Coefficient -0.2213
Critical T Value 1.646
Total Net Caloric Intake intake Over Previous 7 days Before ABOVE Average Overall Mood 976.84 kilocalories
Total Net Caloric Intake intake Over Previous 7 days Before BELOW Average Overall Mood 1863.3800527709 kilocalories
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 168
Optimal Pearson Product 0.45183796120911
P Value 0.0435
Statistical Significance 0.9821
Strength of Relationship 0.10784097255003
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 2


Net Caloric Intake Statistics

Property Value
Variable Name Net Caloric Intake
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 1.7340314720432
Maximum Allowed Value kilocalories
Mean 1093.4443333333 kilocalories
Median 1035 kilocalories
Minimum Allowed Value kilocalories
Number of Correlations 113
Number of Measurements 391
Onset Delay 0 seconds
Standard Deviation 940.50062694858
Unit Kilocalories
UPC 017800165426
Variable ID 1507
Variance 902936.20050112


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

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