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Based on data from 7 participants, Deep Sleep is generally highest after an average of 3.3 out of 5 of Alertness over the previous 24 hours.
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People with higher Alertness usually have lower Deep Sleep
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Alertness on each day of the week.
This chart shows the typical value recorded for Alertness for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Deep Sleep on each day of the week.
This chart shows the typical value recorded for Deep Sleep for each month of the year.

Abstract

Aggregated data from 7 study participants suggests with a low degree of confidence (p=0.22072636654776, 95% CI -0.203 to -0.001) that Alertness has a weakly negative predictive relationship (R=-0.1) with Deep Sleep. The highest quartile of Deep Sleep measurements were observed following an average 3.31 out of 5 Alertness. The lowest quartile of Deep Sleep measurements were observed following an average 3.5039377289377 /5 Alertness.

Objective

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

Participant Instructions

Record your Alertness daily in the reminder inbox or using the interactive web or mobile notifications.
Get Sleep as Android here and use it to record your Deep Sleep. Once you have a Sleep as Android 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 7 participants. Thus, the study design is equivalent to the aggregation of 7 separate n=1 observational natural experiments.

Data Analysis

Alertness Pre-Processing
Alertness measurement values below 1 out of 5 were assumed erroneous and removed. Alertness measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Alertness so any gaps in data were just not analyzed instead of assuming zero values for those times.
Alertness Analysis Settings

Deep Sleep Pre-Processing
Deep Sleep measurement values below 0 out of 1 were assumed erroneous and removed. Deep Sleep measurement values above 1 out of 1 were assumed erroneous and removed. No missing data filling value was defined for Deep Sleep so any gaps in data were just not analyzed instead of assuming zero values for those times.
Deep Sleep Analysis Settings

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

Data Sources

Alertness 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.

Deep Sleep data was primarily collected using Sleep as Android. Smart alarm clock with sleep cycle tracking. Wakes you gently in optimal moment for pleasant mornings.

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 Alertness and Deep Sleep

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. 19 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Alertness 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 Alertness and Deep Sleep 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 Alertness
Effect Variable Name Deep Sleep
Sinn Predictive Coefficient 0.008819026968468
Confidence Level low
Confidence Interval 0.10107013023684
Forward Pearson Correlation Coefficient -0.1022
Critical T Value 1.749
Average Alertness Over Previous 24 hours Before ABOVE Average Deep Sleep 3.31 out of 5
Average Alertness Over Previous 24 hours Before BELOW Average Deep Sleep 3.5039377289377 out of 5
Duration of Action 24 hours
Effect Size weakly negative
Number of Paired Measurements 19
Optimal Pearson Product 0.16138299823408
P Value 0.22072636654776
Statistical Significance 0.11128571543044
Strength of Relationship 0.10107013023684
Study Type population
Analysis Performed At 2019-04-06
Number of Participants 7

Alertness Statistics

Property Value
Variable Name Alertness
Aggregation Method MEAN
Analysis Performed At 2019-03-05
Duration of Action 24 hours
Kurtosis 3.4479346555058
Maximum Allowed Value 5 out of 5
Mean 2.7603797120419 out of 5
Median 2.7562056384801 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 441
Number of Measurements 25812
Onset Delay 0 seconds
Standard Deviation 0.48580945567481
Unit 1 to 5 Rating
UPC 794504377927
Variable ID 1258
Variance 0.50450583357547

Deep Sleep Statistics

Property Value
Variable Name Deep Sleep
Aggregation Method MEAN
Analysis Performed At 2019-03-14
Duration of Action 7 days
Kurtosis 3.8596872305367
Maximum Allowed Value 1 out of 1
Mean 0.54978631578947 out of 1
Median 0.55367427203788 out of 1
Minimum Allowed Value 0 out of 1
Number of Correlations 353
Number of Measurements 9468
Onset Delay 0 seconds
Standard Deviation 0.12990270571797
Unit 0 to 1 Rating
UPC 884904543333
Variable ID 53709
Variance 0.019099911694479

Tracking Alertness

Record your Alertness daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Deep Sleep

Get Sleep as Android here and use it to record your Deep Sleep. Once you have a Sleep as Android 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