Higher Sickness Severity Predicts Slightly Higher Determination for Population

Determination was generally 5% higher than average after an average of 2.25 out of 5 of Sickness Severity over the previous 4 days.

Aggregated data from 2 study participants suggests with a LOW degree of confidence (p=0.188, 95% CI -0.11 to 0.412) that Sickness Severity has a weakly positive predictive relationship (R=0.151) with Determination.

The highest quartile of Determination measurements were observed following an average 2.12 out of 5 Sickness Severity.

The lowest quartile of Determination measurements were observed following an average 1.91 out of 5 of Sickness Severity.

After an onset delay of 0 seconds, Determination is typically 2% lower than average over the 4 days following around 1.91 out of 5 Sickness Severity.

The objective of this study is to determine the nature of the relationship (if any) between Sickness Severity and Determination. Additionally, we attempt to determine the Sickness Severity values most likely to produce optimal Determination values.

Manual Recording Option

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Manual Recording Option

Create a reminder for Determination here and record it daily by enabling notifications or using the reminder inbox here .

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.

Sickness Severity Pre-Processing

Sickness Severity measurement values below 1 out of 5 were assumed erroneous and removed. Sickness Severity measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Sickness Severity so any gaps in data were just not analyzed instead of assuming zero values for those times.

Determination Pre-Processing

Determination measurement values below 1 out of 5 were assumed erroneous and removed. Determination measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Determination so any gaps in data were just not analyzed instead of assuming zero values for those times.

Predictive Analytics

It was assumed that 0 seconds would pass before a change in Sickness Severity would produce an observable change in Determination.

It was assumed that Sickness Severity could produce an observable change in Determination for as much as 4 days after the stimulus event.

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Determination is not statistically significant at a 95% confidence interval. This suggests that the Sickness Severity value does not have a significant influence on the Determination value.

After treatment, a 5.9% increase (0.237 out of 5) from the mean baseline 3.53 out of 5 was observed. The relative standard deviation at baseline was 13.35%. The observed change was 0.45766 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 have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

Sickness Severity 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.

Determination 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.

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.

Criteria For Causal Inference

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 (R = 0.1511) relationship between Sickness Severity and Determination.

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. 32 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Sickness Severity 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,

0 humans feel that there is a plausible mechanism of action for a relationship between Sickness Severity and Determination.

0 humans feel that any relationship observed between Sickness Severity and Determination 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.

A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 0 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Sickness Severity and Determination is coincidental.