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Based on data from 22 participants, Upsettedness is generally lowest after an average of 5 hours of Sleep Duration over the previous 7 days.
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People with higher Sleep Duration usually have higher Upsettedness
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sleep Duration on each day of the week.
This chart shows the typical value recorded for Sleep Duration for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Upsettedness on each day of the week.
This chart shows the typical value recorded for Upsettedness for each month of the year.

Abstract

Aggregated data from 22 study participants suggests with a medium degree of confidence (p=0.24140071163602, 95% CI -0.842 to 0.719) that Sleep Duration has a very weakly negative predictive relationship (R=-0.06) with Upsettedness. The highest quartile of Upsettedness measurements were observed following an average 5 hours Sleep Duration. The lowest quartile of Upsettedness measurements were observed following an average 5.4017661569288 h Sleep Duration.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your 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.
Record your Upsettedness daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 22 participants. Thus, the study design is equivalent to the aggregation of 22 separate n=1 observational natural experiments.

Data Analysis

Sleep Duration Pre-Processing
Sleep Duration measurement values below 0 seconds were assumed erroneous and removed. Sleep Duration measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Sleep Duration data were unrecorded 0 seconds measurement values.
Sleep Duration Analysis Settings

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

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

Data Sources

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

Upsettedness 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.

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 Sleep Duration and Upsettedness

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. 40 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Sleep Duration 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 Sleep Duration and Upsettedness 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 Sleep Duration
Effect Variable Name Upsettedness
Sinn Predictive Coefficient 0.018047904179115
Confidence Level medium
Confidence Interval 0.7803891537478
Forward Pearson Correlation Coefficient -0.0618
Critical T Value 1.7596363636364
Average Sleep Duration Over Previous 7 days Before ABOVE Average Upsettedness 5 hours
Average Sleep Duration Over Previous 7 days Before BELOW Average Upsettedness 5 hours
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 40
Optimal Pearson Product 0.069548704250944
P Value 0.24140071163602
Statistical Significance 0.22670909075532
Strength of Relationship 0.7803891537478
Study Type population
Analysis Performed At 2019-04-06
Number of Participants 22

Sleep Duration Statistics

Property Value
Variable Name Sleep Duration
Aggregation Method MEAN
Analysis Performed At 2019-01-30
Duration of Action 7 days
Kurtosis 26.117213119491
Maximum Allowed Value 7 days
Mean 5 hours
Median 5 hours
Minimum Allowed Value 0 seconds
Number of Correlations 2022
Number of Measurements 40974
Onset Delay 0 seconds
Standard Deviation 4.2072240524865
Unit Hours
UPC 067981966602
Variable ID 1867
Variance 294.95711944016

Upsettedness Statistics

Property Value
Variable Name Upsettedness
Aggregation Method MEAN
Analysis Performed At 2018-12-22
Duration of Action 24 hours
Kurtosis 2.8949165007044
Maximum Allowed Value 5 out of 5
Mean 2.4309300217549 out of 5
Median 2.3593266173034 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 323
Number of Measurements 32691
Onset Delay 0 seconds
Standard Deviation 0.59349512450857
Unit 1 to 5 Rating
Variable ID 1475
Variance 0.70280272714181

Tracking Sleep Duration

Get Fitbit here and use it to record your 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.

Tracking Upsettedness

Record your Upsettedness daily in the reminder inbox or using the interactive web or mobile notifications.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn