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Based on data from 8 participants, Lack of Motivation is generally lowest after an average of 2.1 out of 5 of Anger over the previous 24 hours.
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People with higher Anger usually have higher Lack of Motivation
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Anger on each day of the week.
This chart shows the typical value recorded for Anger for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Lack of Motivation on each day of the week.
This chart shows the typical value recorded for Lack of Motivation for each month of the year.

Abstract

Aggregated data from 8 study participants suggests with a low degree of confidence (p=0.14805126364212, 95% CI -0.364 to 1.082) that Anger has a moderately positive predictive relationship (R=0.36) with Lack of Motivation. The highest quartile of Lack Of Motivation measurements were observed following an average 3.08 out of 5 Anger. The lowest quartile of Lack Of Motivation measurements were observed following an average 2.4378382034632 /5 Anger.

Objective

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

Participant Instructions

Record your Anger daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Lack of Motivation daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 8 participants. Thus, the study design is equivalent to the aggregation of 8 separate n=1 observational natural experiments.

Data Analysis

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

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Anger would produce an observable change in Lack Of Motivation. It was assumed that Anger could produce an observable change in Lack Of Motivation for as much as 1 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Anger 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.

Lack Of Motivation 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 moderately positive relationship between Anger and Lack of Motivation

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. 25 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Anger 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 Anger and Lack of Motivation 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 Anger
Effect Variable Name Lack of Motivation
Sinn Predictive Coefficient 0.043766950462303
Confidence Level low
Confidence Interval 0.72301907836007
Forward Pearson Correlation Coefficient 0.3594
Critical T Value 1.7375
Average Anger Over Previous 24 hours Before ABOVE Average Lack of Motivation 3.08 out of 5
Average Anger Over Previous 24 hours Before BELOW Average Lack of Motivation 2.4378382034632 out of 5
Duration of Action 24 hours
Effect Size moderately positive
Number of Paired Measurements 25
Optimal Pearson Product 0.26756907857968
P Value 0.14805126364212
Statistical Significance 0.11615000318852
Strength of Relationship 0.72301907836007
Study Type population
Analysis Performed At 2019-04-09
Number of Participants 8

Anger Statistics

Property Value
Variable Name Anger
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 24 hours
Kurtosis 2.7309978915133
Maximum Allowed Value 5 out of 5
Mean 2.77166 out of 5
Median 2.7365087145969 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 231
Number of Measurements 1759
Onset Delay 0 seconds
Standard Deviation 0.39346666933253
Unit 1 to 5 Rating
Variable ID 86779
Variance 0.42886863323092

Lack of Motivation Statistics

Property Value
Variable Name Lack of Motivation
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 7 days
Kurtosis 2.016838045956
Maximum Allowed Value 5 out of 5
Mean 3.3784796703297 out of 5
Median 3.3545819854748 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 123
Number of Measurements 3339
Onset Delay 0 seconds
Standard Deviation 0.42789866056371
Unit 1 to 5 Rating
Variable ID 89387
Variance 0.44134223873528

Tracking Anger

Record your Anger daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Lack of Motivation

Record your Lack of Motivation 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