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This individual's REM Sleep Duration is generally highest after an average of 50 percent of Cloud Cover over the previous 7 days.
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Blue represents the mean of Cloud Cover over the previous 7 days
An increase in 7 days cumulative Cloud Cover is usually followed by an increase in REM Sleep Duration. (R = 0.207)
Typical values for REM Sleep Duration following a given amount of Cloud Cover over the previous 7 days.
Typical Cloud Cover seen over the previous 7 days preceding the given REM Sleep Duration value.
This chart shows how your Cloud Cover changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Cloud Cover on each day of the week.
This chart shows the typical value recorded for Cloud Cover for each month of the year.
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.

Abstract

This individual's REM Sleep Duration is generally 4% higher than normal after an average of 49.86 percent Cloud Cover over the previous 7 days. This individual's data suggests with a medium degree of confidence (p=0.021634552915819, 95% CI -22.943 to 23.357) that Cloud Cover has a weakly positive predictive relationship (R=0.21) with REM Sleep Duration. The highest quartile of REM Sleep Duration measurements were observed following an average 49.48 percent Cloud Cover. The lowest quartile of REM Sleep Duration measurements were observed following an average 38.282051282051 % Cloud Cover.REM Sleep Duration is generally 3% lower than normal after an average of 38.282051282051 percent of Cloud Cover over the previous 7 days. REM Sleep Duration is generally 4% higher after an average of 49.48 percent of Cloud Cover over the previous 7 days.

Objective

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

Participant Instructions

Grant access to your weather and air quality data on the Import Data page. This individual's data will automatically be imported and analyzed.
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.

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

Cloud Cover Pre-Processing
No minimum allowed measurement value was defined for Cloud Cover. No maximum allowed measurement value was defined for Cloud Cover. No missing data filling value was defined for Cloud Cover so any gaps in data were just not analyzed instead of assuming zero values for those times.
Cloud Cover Analysis Settings

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Cloud Cover would produce an observable change in REM Sleep Duration. It was assumed that Cloud Cover could produce an observable change in REM Sleep Duration for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
843 raw Cloud Cover measurements with 726 changes spanning 1003 days from 2016-07-01 to 2019-03-31 were used in this analysis. 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.

Data Sources

Cloud Cover data was primarily collected using Weather. Automatically import temperature, humidity, hours of daylight, air quality and pollen count.

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

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 Cloud Cover and REM Sleep Duration

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. 160 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Cloud Cover 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 Cloud Cover and REM Sleep Duration 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 Cloud Cover
Effect Variable Name REM Sleep Duration
Sinn Predictive Coefficient 0.2032
Confidence Level medium
Confidence Interval 23.149508690177
Forward Pearson Correlation Coefficient 0.207
Critical T Value 1.646
Average Cloud Cover Over Previous 7 days Before ABOVE Average REM Sleep Duration 49.48 percent
Average Cloud Cover Over Previous 7 days Before BELOW Average REM Sleep Duration 38.282 percent
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 160
Optimal Pearson Product 0.064492892226517
P Value 0.021634552915819
Statistical Significance 0.9816
Strength of Relationship 23.149508690177
Study Type individual
Analysis Performed At 2019-04-04

Cloud Cover Statistics

Property Value
Variable Name Cloud Cover
Aggregation Method MEAN
Analysis Performed At 2019-03-31
Duration of Action 7 days
Kurtosis 1.7838650762642
Mean 43.906 percent
Median 42 percent
Number of Changes 726
Number of Correlations 321
Number of Measurements 843
Onset Delay 0 seconds
Standard Deviation 30.622430946386
Unit Percent
UPC 736902427712
Variable ID 5957090
Variance 937.73327706617

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

Tracking Cloud Cover

Grant access to your weather and air quality data on the Import Data page. This individual's data will automatically be imported and analyzed.

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.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn