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This individual's Awakenings is generally highest after an average of 23 degrees celsius of Indoor Temperature over the previous 24 hours.
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Blue represents the mean of Indoor Temperature over the previous 24 hours
An increase in 24 hours cumulative Indoor Temperature is usually followed by an increase in Awakenings. (R = 0.067)
Typical values for Awakenings following a given amount of Indoor Temperature over the previous 24 hours.
Typical Indoor Temperature seen over the previous 24 hours preceding the given Awakenings value.
This chart shows how your Indoor Temperature changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Indoor Temperature on each day of the week.
This chart shows the typical value recorded for Indoor Temperature for each month of the year.
This chart shows how your Awakenings changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Awakenings on each day of the week.
This chart shows the typical value recorded for Awakenings for each month of the year.

Abstract

This individual's Awakenings is generally 3% higher than normal after an average of 22.6 degrees celsius Indoor Temperature over the previous 24 hours. This individual's data suggests with a medium degree of confidence (p=0.14973518392503, 95% CI -2.012 to 2.146) that Indoor Temperature has a very weakly positive predictive relationship (R=0.07) with Awakenings. The highest quartile of Awakenings measurements were observed following an average 22.53 degrees celsius Indoor Temperature. The lowest quartile of Awakenings measurements were observed following an average 22.097368421053 C Indoor Temperature.Awakenings is generally 3% lower than normal after an average of 22.097368421053 degrees celsius of Indoor Temperature over the previous 24 hours. Awakenings is generally 3% higher after an average of 22.53 degrees celsius of Indoor Temperature over the previous 24 hours.

Objective

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

Participant Instructions

Get Netatmo here and use it to record your Indoor Temperature. 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 Awakenings. 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 Temperature Pre-Processing
No minimum allowed measurement value was defined for Indoor Temperature. No maximum allowed measurement value was defined for Indoor Temperature. No missing data filling value was defined for Indoor Temperature so any gaps in data were just not analyzed instead of assuming zero values for those times.
Indoor Temperature Analysis Settings

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

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

Data Quantity
260 raw Indoor Temperature measurements with 246 changes spanning 278 days from 2018-04-17 to 2019-01-20 were used in this analysis. 1423 raw Awakenings measurements with 1390 changes spanning 1951 days from 2013-11-26 to 2019-03-31 were used in this analysis.

Data Sources

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

Awakenings 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 positive relationship between Indoor Temperature and Awakenings

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. 245 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Indoor Temperature 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 Temperature and Awakenings 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 Temperature
Effect Variable Name Awakenings
Sinn Predictive Coefficient 0.0669
Confidence Level medium
Confidence Interval 2.0792356899209
Forward Pearson Correlation Coefficient 0.067
Critical T Value 1.646
Average Indoor Temperature Over Previous 24 hours Before ABOVE Average Awakenings 22.53 degrees celsius
Average Indoor Temperature Over Previous 24 hours Before BELOW Average Awakenings 22.097 degrees celsius
Duration of Action 24 hours
Effect Size very weakly positive
Number of Paired Measurements 245
Optimal Pearson Product 0.012113146933156
P Value 0.14973518392503
Statistical Significance 0.999
Strength of Relationship 2.0792356899209
Study Type individual
Analysis Performed At 2019-04-05

Indoor Temperature Statistics

Property Value
Variable Name Indoor Temperature
Aggregation Method MEAN
Analysis Performed At 2019-03-31
Duration of Action 24 hours
Kurtosis 2.4238568282385
Mean 22.387 degrees celsius
Median 22.6 degrees celsius
Number of Changes 246
Number of Correlations 91
Number of Measurements 260
Onset Delay 0 seconds
Standard Deviation 2.3785464197663
Unit Degrees Celsius
Variable ID 6034981
Variance 5.6574830709831

Awakenings Statistics

Property Value
Variable Name Awakenings
Aggregation Method SUM
Analysis Performed At 2019-03-31
Duration of Action 7 days
Kurtosis 3.5568308675072
Mean 23.904 count
Median 23 count
Minimum Allowed Value 1 count
Number of Changes 1390
Number of Correlations 2818
Number of Measurements 1423
Onset Delay 0 seconds
Standard Deviation 11.429476940955
Unit Count
Variable ID 1906
Variance 130.63294314381

Tracking Indoor Temperature

Get Netatmo here and use it to record your Indoor Temperature. 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 Awakenings

Get Fitbit here and use it to record your Awakenings. 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