Based on data from 10 participants, Tiredness / Fatigue is generally lowest after an average of 3.5 out of 5 of Attentiveness over the previous 24 hours.


Abstract
Aggregated data from 10 study participants suggests with a low degree of confidence (p=0.22097442356926, 95% CI 0.571 to 0.391) that Attentiveness has a very weakly negative predictive relationship (R=0.09) with Tiredness / Fatigue. The highest quartile of Tiredness / Fatigue measurements were observed following an average 2.72 out of 5 Attentiveness. The lowest quartile of Tiredness / Fatigue measurements were observed following an average 3.152562667779 /5 Attentiveness.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Attentiveness and Tiredness / Fatigue. Additionally, we attempt to determine the Attentiveness values most likely to produce optimal Tiredness / Fatigue values.
Participant Instructions
Record your Attentiveness daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Tiredness / Fatigue daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Tiredness / Fatigue daily in the reminder inbox or using the interactive web or mobile notifications.
Design
This study is based on data donated by 10 participants. Thus, the study design is equivalent to the aggregation of 10 separate n=1 observational natural experiments.
Data Analysis
Attentiveness PreProcessing
Attentiveness measurement values below 1 out of 5 were assumed erroneous and removed. Attentiveness measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Attentiveness so any gaps in data were just not analyzed instead of assuming zero values for those times.
Tiredness / Fatigue PreProcessing
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.
Predictive Analytics
It was assumed that 0 hours would pass before a change in Attentiveness would produce an observable change in Tiredness / Fatigue. It was assumed that Attentiveness could produce an observable change in Tiredness / Fatigue for as much as 1 days after the stimulus event.
Attentiveness measurement values below 1 out of 5 were assumed erroneous and removed. Attentiveness measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Attentiveness so any gaps in data were just not analyzed instead of assuming zero values for those times.

Tiredness / Fatigue PreProcessing
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.

Predictive Analytics
It was assumed that 0 hours would pass before a change in Attentiveness would produce an observable change in Tiredness / Fatigue. It was assumed that Attentiveness could produce an observable change in Tiredness / Fatigue for as much as 1 days after the stimulus event.

Data Sources
Attentiveness 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.
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.
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.
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 nonexistent 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 very weakly negative relationship between Attentiveness and Tiredness / Fatigue
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. 43 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Attentiveness 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 timeprecedence 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 biochemical 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 Attentiveness and Tiredness / Fatigue 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.
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 very weakly negative relationship between Attentiveness and Tiredness / Fatigue
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. 43 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Attentiveness 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 timeprecedence 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 biochemical 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 Attentiveness and Tiredness / Fatigue 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  Attentiveness 
Effect Variable Name  Tiredness / Fatigue 
Sinn Predictive Coefficient  0.019641663276525 
Confidence Level  low 
Confidence Interval  0.48074386331713 
Forward Pearson Correlation Coefficient  0.0901 
Critical T Value  1.7359 
Average Attentiveness Over Previous 24 hours Before ABOVE Average Tiredness / Fatigue  2.72 out of 5 
Average Attentiveness Over Previous 24 hours Before BELOW Average Tiredness / Fatigue  3.152562667779 out of 5 
Duration of Action  24 hours 
Effect Size  very weakly negative 
Number of Paired Measurements  43 
Optimal Pearson Product  0.031339898273614 
P Value  0.22097442356926 
Statistical Significance  0.21221999812406 
Strength of Relationship  0.48074386331713 
Study Type  population 
Analysis Performed At  20190515 
Number of Participants  10 
Attentiveness Statistics
Property  Value 

Variable Name  Attentiveness 
Aggregation Method  MEAN 
Analysis Performed At  20190415 
Duration of Action  24 hours 
Kurtosis  2.9010308452766 
Maximum Allowed Value  5 out of 5 
Mean  2.6094145789101 out of 5 
Median  2.6030904693325 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Correlations  302 
Number of Measurements  20496 
Onset Delay  0 seconds 
Standard Deviation  0.4502096987717 
Unit  1 to 5 Rating 
Variable ID  1267 
Variance  0.45353341406611 
Tiredness / Fatigue Statistics
Property  Value 

Variable Name  Tiredness / Fatigue 
Aggregation Method  MEAN 
Analysis Performed At  20190406 
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 
Tracking Attentiveness
Record your Attentiveness daily in the reminder inbox or using the interactive web or mobile notifications.Tracking Tiredness / Fatigue
Record your Tiredness / Fatigue daily in the reminder inbox or using the interactive web or mobile notifications.

Principal Investigator  Mike Sinn