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This individual's Overall Mood is generally highest after an average of 91 percent of Sleep Efficiency over the previous 24 hours.
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Blue represents the mean of Sleep Efficiency over the previous 24 hours
An increase in 24 hours cumulative Sleep Efficiency is usually followed by an increase in Overall Mood. (R = 0.087)
Typical values for Overall Mood following a given amount of Sleep Efficiency over the previous 24 hours.
Typical Sleep Efficiency seen over the previous 24 hours preceding the given Overall Mood value.
This chart shows how your Sleep Efficiency changes over time.
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.
This chart shows how your Overall Mood changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Overall Mood on each day of the week.
This chart shows the typical value recorded for Overall Mood for each month of the year.

Abstract

This individual's Overall Mood is generally 1% higher than normal after an average of 91 percent Sleep Efficiency over the previous 24 hours. This individual's data suggests with a high degree of confidence (p=0.076449316215496, 95% CI 0.053 to 0.121) that Sleep Efficiency has a very weakly positive predictive relationship (R=0.09) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 90.9 percent Sleep Efficiency. The lowest quartile of Overall Mood measurements were observed following an average 90.662379421222 % Sleep Efficiency.Overall Mood is generally 1% lower than normal after an average of 90.662379421222 percent of Sleep Efficiency over the previous 24 hours. Overall Mood is generally 1% higher after an average of 90.9 percent of Sleep Efficiency over the previous 24 hours.

Objective

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

Participant Instructions

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

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

Sleep Efficiency Pre-Processing
Sleep Efficiency measurement values below 1 percent were assumed erroneous and removed. 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

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

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

Data Quantity
1358 raw Sleep Efficiency measurements with 1337 changes spanning 1951 days from 2013-11-26 to 2019-03-31 were used in this analysis. 13635 raw Overall Mood measurements with 1192 changes spanning 2522 days from 2012-05-06 to 2019-04-01 were used in this analysis.

Data Sources

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

Overall Mood 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 positive relationship between Sleep Efficiency and Overall Mood

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. 1173 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Sleep Efficiency 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, 9 humans feel that there is a plausible mechanism of action and 2 feel that any relationship observed between Sleep Efficiency and Overall Mood 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 Efficiency
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.1055
Confidence Level high
Confidence Interval 0.034332365255707
Forward Pearson Correlation Coefficient 0.087
Critical T Value 1.646
Average Sleep Efficiency Over Previous 24 hours Before ABOVE Average Overall Mood 90.9 percent
Average Sleep Efficiency Over Previous 24 hours Before BELOW Average Overall Mood 90.662 percent
Duration of Action 24 hours
Effect Size very weakly positive
Number of Paired Measurements 1173
Optimal Pearson Product 0.0047502709679911
P Value 0.076449316215496
Statistical Significance 1
Strength of Relationship 0.034332365255707
Study Type individual
Analysis Performed At 2019-04-04

Sleep Efficiency Statistics

Property Value
Variable Name Sleep Efficiency
Aggregation Method MEAN
Analysis Performed At 2019-03-31
Duration of Action 24 hours
Kurtosis 2.8726751424007
Mean 90.764 percent
Median 91 percent
Minimum Allowed Value 1 percent
Number of Changes 1337
Number of Correlations 2869
Number of Measurements 1358
Onset Delay 0 seconds
Standard Deviation 4.384865042623
Unit Percent
UPC 878881000699
Variable ID 5211811
Variance 19.227041442017

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-04-01
Duration of Action 24 hours
Kurtosis 6.8454669666675
Maximum Allowed Value 5 out of 5
Mean 2.9135 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1192
Number of Correlations 4086
Number of Measurements 13635
Onset Delay 0 seconds
Standard Deviation 0.52653757307699
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.2772418158618

Tracking Sleep Efficiency

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.

Tracking Overall Mood

Record your Overall Mood 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