This individual's Deep Sleep Duration is generally highest after an average of 54 percent of Indoor Humidity over the previous 7 days.
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Blue represents the mean of Indoor Humidity over the previous 7 days
An increase in 7 days cumulative Indoor Humidity is usually followed by an decrease in Deep Sleep Duration. (R = -0.153)
Typical values for Deep Sleep Duration following a given amount of Indoor Humidity over the previous 7 days.
Typical Indoor Humidity seen over the previous 7 days preceding the given Deep Sleep Duration value.
Correlation between outcome and aggregated predictor measurements over given number of days
Peak correlation suggests the delay between predictor and observable outcome
This chart shows how Indoor Humidity changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Indoor Humidity on each day of the week.
This chart shows the typical value recorded for Indoor Humidity for each month of the year.
This chart shows how Deep Sleep Duration changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Deep Sleep Duration on each day of the week.
This chart shows the typical value recorded for Deep Sleep Duration for each month of the year.

Abstract

This individual's Deep Sleep Duration is generally 4.67% higher than normal after an average of 54.29 percent Indoor Humidity over the previous 7 days. This individual's data suggests with a high degree of confidence (p=0.0045443267412635, 95% CI -23.603 to 23.297) that Indoor Humidity has a weakly negative predictive relationship (R=-0.15) with Deep Sleep Duration. The highest quartile of Deep Sleep Duration measurements were observed following an average 55.12 percent Indoor Humidity. The lowest quartile of Deep Sleep Duration measurements were observed following an average 59.983606557377 % Indoor Humidity.Deep Sleep Duration is generally 4.11% lower than normal after an average of 59.983606557377 percent of Indoor Humidity over the previous 7 days. Deep Sleep Duration is generally 4.67% higher after an average of 55.12 percent of Indoor Humidity over the previous 7 days.

Objective

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

Participant Instructions

Get Netatmo here and use it to record your Indoor Humidity. Once you have a Netatmo 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 Deep 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

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

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

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

Data Quantity
253 raw Indoor Humidity measurements with 222 changes spanning 278 days from 2018-04-17 to 2019-01-20 were used in this analysis. 391 raw Deep Sleep Duration measurements with 307 changes spanning 420 days from 2018-05-06 to 2019-06-30 were used in this analysis.

Data Sources

Indoor Humidity data was primarily collected using Netatmo. Experience the comfort of a Smart Home: Smart Thermostat, Security Camera with Face Recognition, Weather Station.

Deep 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 negative relationship between Indoor Humidity and Deep 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. 233 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Indoor Humidity 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 Indoor Humidity and Deep 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 Indoor Humidity
Effect Variable Name Deep Sleep Duration
Sinn Predictive Coefficient 0.1527
Confidence Level high
Confidence Interval 23.4499355458
Forward Pearson Correlation Coefficient -0.153
Critical T Value 1.646
Average Indoor Humidity Over Previous 7 days Before ABOVE Average Deep Sleep Duration 55.12 percent
Average Indoor Humidity Over Previous 7 days Before BELOW Average Deep Sleep Duration 59.984 percent
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 233
Optimal Pearson Product 0.053545393453919
P Value 0.0045443267412635
Statistical Significance 0.99798348372017
Strength of Relationship 23.4499355458
Study Type individual
Analysis Performed At 2019-07-02

Indoor Humidity Statistics

Property Value
Variable Name Indoor Humidity
Aggregation Method MEAN
Analysis Performed At 2019-06-26
Duration of Action 7 days
Kurtosis 1.7049810010258
Mean 56.787 percent
Median 57 percent
Number of Changes 222
Number of Correlations 91
Number of Measurements 253
Onset Delay 0 seconds
Standard Deviation 13.794477027474
Unit Percent
Variable ID 6034983
Variance 190.28759646151

Deep Sleep Duration Statistics

Property Value
Variable Name Deep Sleep Duration
Aggregation Method SUM
Analysis Performed At 2019-07-01
Duration of Action 24 hours
Kurtosis 3.0359623344059
Maximum Allowed Value 7 days
Mean 70 minutes
Median 72 minutes
Minimum Allowed Value 60 seconds
Number of Changes 307
Number of Measurements 391
Onset Delay 0 seconds
Standard Deviation 31.262599866283
Unit Minutes
Variable ID 6054282
Variance 977.35015039934

Tracking Indoor Humidity

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

Tracking Deep Sleep Duration

Get Fitbit here and use it to record your Deep 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