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Based on data from 49 participants, Overall Mood is generally highest after an average of 2.9 out of 5 of Tiredness / Fatigue over the previous 7 days.
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People with higher Tiredness / Fatigue usually have lower Overall Mood
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Tiredness / Fatigue on each day of the week.
This chart shows the typical value recorded for Tiredness / Fatigue for each month of the year.
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

Aggregated data from 49 study participants suggests with a medium degree of confidence (p=0.20878867245434, 95% CI -0.514 to 0.142) that Tiredness / Fatigue has a weakly negative predictive relationship (R=-0.19) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 2.77 out of 5 Tiredness / Fatigue. The lowest quartile of Overall Mood measurements were observed following an average 3.2343696881517 /5 Tiredness / Fatigue.

Objective

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

Participant Instructions

Record your Tiredness / Fatigue 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 49 participants. Thus, the study design is equivalent to the aggregation of 49 separate n=1 observational natural experiments.

Data Analysis

Tiredness / Fatigue Pre-Processing
Tiredness / Fatigue measurement values below 1 out of 5 were assumed erroneous and removed. Tiredness / Fatigue measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Tiredness / Fatigue so any gaps in data were just not analyzed instead of assuming zero values for those times.
Tiredness / Fatigue 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 Tiredness / Fatigue would produce an observable change in Overall Mood. It was assumed that Tiredness / Fatigue could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Tiredness / Fatigue 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 Tiredness / Fatigue 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. 48 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Tiredness / Fatigue 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, 16 humans feel that there is a plausible mechanism of action and 1 feel that any relationship observed between Tiredness / Fatigue 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 Tiredness / Fatigue
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.031634145004121
Confidence Level medium
Confidence Interval 0.32769446835378
Forward Pearson Correlation Coefficient -0.186
Critical T Value 1.7257959183673
Average Tiredness / Fatigue Over Previous 7 days Before ABOVE Average Overall Mood 2.77 out of 5
Average Tiredness / Fatigue Over Previous 7 days Before BELOW Average Overall Mood 3.2343696881517 out of 5
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 48
Optimal Pearson Product 0.097877691954137
P Value 0.20878867245434
Statistical Significance 0.22347959004133
Strength of Relationship 0.32769446835378
Study Type population
Analysis Performed At 2019-05-15
Number of Participants 49

Tiredness / Fatigue Statistics

Property Value
Variable Name Tiredness / Fatigue
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 7 days
Kurtosis 2.0842183459689
Maximum Allowed Value 5 out of 5
Mean 3.3735747731397 out of 5
Median 3.3707500321069 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 312
Number of Measurements 8772
Onset Delay 0 seconds
Standard Deviation 0.35540512470403
Unit 1 to 5 Rating
UPC 635797687433
Variable ID 87760
Variance 0.36873751536251

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-05-03
Duration of Action 24 hours
Kurtosis 3.7383708126619
Maximum Allowed Value 5 out of 5
Mean 3.1156748504321 out of 5
Median 3.1369047348216 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1149
Number of Measurements 605816
Onset Delay 0 seconds
Standard Deviation 0.56833853113207
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.43884384603152

Tracking Tiredness / Fatigue

Record your Tiredness / Fatigue 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