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This individual's Anger is generally lowest after a daily total of 74 minutes of REM Sleep Duration over the previous 24 hours.
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Blue represents the sum of REM Sleep Duration over the previous 24 hours
An increase in 24 hours cumulative REM Sleep Duration is usually followed by an increase in Anger. (R = 0.152)
Typical values for Anger following a given amount of REM Sleep Duration over the previous 24 hours.
Typical REM Sleep Duration seen over the previous 24 hours preceding the given Anger value.
This chart shows how your REM Sleep Duration changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for REM Sleep Duration on each day of the week.
This chart shows the typical value recorded for REM Sleep Duration for each month of the year.
This chart shows how your Anger changes over time.
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.

Abstract

This individual's Anger is generally 8% lower than normal after 74 minutes REM Sleep Duration per 24 hours. This individual's data suggests with a low degree of confidence (p=0.18570516499987, 95% CI -0.439 to 0.743) that REM Sleep Duration has a weakly positive predictive relationship (R=0.15) with Anger. The highest quartile of Anger measurements were observed following an average 81 minutes REM Sleep Duration per day. The lowest quartile of Anger measurements were observed following an average 74.155555555556 min REM Sleep Duration per day.Anger is generally 8% lower than normal after a total of 74 minutes of REM Sleep Duration over the previous 24 hours. Anger is generally 8% higher after a total of 81 minutes of REM Sleep Duration over the previous 24 hours.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your REM Sleep Duration. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
Record your Anger daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

REM Sleep Duration Pre-Processing
REM Sleep Duration measurement values below 60 seconds were assumed erroneous and removed. REM Sleep Duration measurement values above 7 days were assumed erroneous and removed. No missing data filling value was defined for REM Sleep Duration so any gaps in data were just not analyzed instead of assuming zero values for those times.
REM Sleep Duration Analysis Settings

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

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

Data Quantity
314 raw REM Sleep Duration measurements with 290 changes spanning 332 days from 2018-05-06 to 2019-04-03 were used in this analysis. 156 raw Anger measurements with 73 changes spanning 1484 days from 2014-11-25 to 2018-12-19 were used in this analysis.

Data Sources

REM Sleep Duration data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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.

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 positive relationship between REM Sleep Duration and Anger

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. 79 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their REM Sleep Duration 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 REM Sleep Duration and Anger 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 REM Sleep Duration
Effect Variable Name Anger
Sinn Predictive Coefficient 0.1283
Confidence Level low
Confidence Interval 0.59084166747395
Forward Pearson Correlation Coefficient 0.152
Critical T Value 1.664
Total REM Sleep Duration Over Previous 24 hours Before ABOVE Average Anger 81 minutes
Total REM Sleep Duration Over Previous 24 hours Before BELOW Average Anger 74 minutes
Duration of Action 24 hours
Effect Size weakly positive
Number of Paired Measurements 79
Optimal Pearson Product 0.030313433167131
P Value 0.18570516499987
Statistical Significance 0.8443
Strength of Relationship 0.59084166747395
Study Type individual
Analysis Performed At 2019-04-04

REM Sleep Duration Statistics

Property Value
Variable Name REM Sleep Duration
Aggregation Method SUM
Analysis Performed At 2019-04-03
Duration of Action 24 hours
Kurtosis 3.1877848056985
Maximum Allowed Value 7 days
Mean 78 minutes
Median 74 minutes
Minimum Allowed Value 60 seconds
Number of Changes 290
Number of Correlations 1
Number of Measurements 314
Onset Delay 0 seconds
Standard Deviation 33.196219964213
Unit Minutes
Variable ID 6054281
Variance 1101.9890199124

Anger Statistics

Property Value
Variable Name Anger
Aggregation Method MEAN
Analysis Performed At 2019-03-31
Duration of Action 24 hours
Kurtosis 1.9605412643714
Maximum Allowed Value 5 out of 5
Mean 2.6526 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 73
Number of Correlations 706
Number of Measurements 156
Onset Delay 0 seconds
Standard Deviation 1.4064834957382
Unit 1 to 5 Rating
Variable ID 86779
Variance 1.9781958237841

Tracking REM Sleep Duration

Get Fitbit here and use it to record your REM Sleep Duration. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Tracking Anger

Record your Anger 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