Mike P. Sinn
PRINCIPAL INVESTIGATOR
Mike P. Sinn

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For most, Sleep Efficiency is generally highest after a daily total of 95 meters of Elevation over the previous 7 days.
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People with higher Elevation usually have higher Sleep Efficiency
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Elevation on each day of the week.
This chart shows the typical value recorded for Elevation for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sleep Efficiency on each day of the week.
This chart shows the typical value recorded for Sleep Efficiency for each month of the year.

Abstract

Aggregated data from 99 study participants suggests with a medium degree of confidence (p=0.20190842037756, 95% CI -1.893 to 1.889) that Elevation has a very weakly negative predictive relationship (R=-0) with Sleep Efficiency. The highest quartile of Sleep Efficiency measurements were observed following an average 37.66 meters Elevation per day. The lowest quartile of Sleep Efficiency measurements were observed following an average 43.398387096774 m Elevation per day.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Elevation. 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.
Get Fitbit here and use it to record your Sleep Efficiency. 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 99 participants. Thus, the study design is equivalent to the aggregation of 99 separate n=1 observational natural experiments.

Data Analysis

Elevation Pre-Processing
Elevation measurement values below 0 meters were assumed erroneous and removed. No maximum allowed measurement value was defined for Elevation. No missing data filling value was defined for Elevation so any gaps in data were just not analyzed instead of assuming zero values for those times.
Elevation Analysis Settings

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

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

Data Sources

Elevation data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Sleep Efficiency 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 very weakly negative relationship between Elevation and Sleep Efficiency

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. 200 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Elevation 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 Elevation and Sleep Efficiency 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 Elevation
Effect Variable Name Sleep Efficiency
Sinn Predictive Coefficient 0.00075077744155606
Confidence Level medium
Confidence Interval 1.8908223140404
Forward Pearson Correlation Coefficient -0.0017
Critical T Value 1.6659494949495
Total Elevation Over Previous 7 days Before ABOVE Average Sleep Efficiency 37.66 meters
Total Elevation Over Previous 7 days Before BELOW Average Sleep Efficiency 43.398387096774 meters
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 200
Optimal Pearson Product 0.074519994252955
P Value 0.20190842037756
Statistical Significance 0.7537929289892
Strength of Relationship 1.8908223140404
Study Type population
Analysis Performed At 2019-02-02
Number of Participants 99

Elevation Statistics

Property Value
Variable Name Elevation
Aggregation Method SUM
Analysis Performed At 2019-01-27
Duration of Action 7 days
Kurtosis 17.320281587911
Mean 43.106004895105 meters
Median 34.458391608392 meters
Minimum Allowed Value 0 meters
Number of Correlations 693
Number of Measurements 46444
Onset Delay 0 seconds
Standard Deviation 42.406346044079
Unit Meters
Variable ID 1907
Variance 6869.5948660679

Sleep Efficiency Statistics

Property Value
Variable Name Sleep Efficiency
Aggregation Method MEAN
Analysis Performed At 2018-12-22
Duration of Action 24 hours
Kurtosis 10.116231860745
Mean 87.550074074074 percent
Median 88.418518518519 percent
Number of Correlations 339
Number of Measurements 22405
Onset Delay 0 seconds
Standard Deviation 6.0946131489375
Unit Percent
UPC 878881000699
Variable ID 5211811
Variance 78.854434460161

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