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This individual's Sleep Start Time is generally highest after an average of 1700 kilocalories of Calories Burned over the previous 7 days.
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Blue represents the mean of Calories Burned over the previous 7 days
An increase in 7 days cumulative Calories Burned is usually followed by an increase in Sleep Start Time. (R = 0.002)
Typical values for Sleep Start Time following a given amount of Calories Burned over the previous 7 days.
Typical Calories Burned seen over the previous 7 days preceding the given Sleep Start Time value.
This chart shows how your Calories Burned changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Calories Burned on each day of the week.
This chart shows the typical value recorded for Calories Burned for each month of the year.
This chart shows how your Sleep Start Time changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sleep Start Time on each day of the week.
This chart shows the typical value recorded for Sleep Start Time for each month of the year.

Abstract

This individual's Sleep Start Time is generally 2% higher than normal after an average of 1673 kilocalories Calories Burned over the previous 7 days. This individual's data suggests with a medium degree of confidence (p=0.052491236898529, 95% CI -0.375 to 0.379) that Calories Burned has a very weakly positive predictive relationship (R=0) with Sleep Start Time. The highest quartile of Sleep Start Time measurements were observed following an average 2 kilocalories Calories Burned. The lowest quartile of Sleep Start Time measurements were observed following an average 2138.5811415268 kcal Calories Burned.Sleep Start Time is generally 2% lower than normal after an average of 2138.5811415268 kilocalories of Calories Burned over the previous 7 days. Sleep Start Time is generally 2% higher after an average of 2 kilocalories of Calories Burned over the previous 7 days.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Calories Burned. 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.
Get Fitbit here and use it to record your Sleep Start Time. 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

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

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

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

Data Quantity
161 raw Calories Burned measurements with 131 changes spanning 160 days from 2018-10-26 to 2019-04-03 were used in this analysis. 1955 raw Sleep Start Time measurements with 1171 changes spanning 1954 days from 2013-11-26 to 2019-04-03 were used in this analysis.

Data Sources

Calories Burned data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Sleep Start Time 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 Calories Burned and Sleep Start Time

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. 125 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Calories Burned 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, 2 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Calories Burned and Sleep Start Time 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 Calories Burned
Effect Variable Name Sleep Start Time
Sinn Predictive Coefficient 0.0373
Confidence Level medium
Confidence Interval 0.37692033620734
Forward Pearson Correlation Coefficient 0.002
Critical T Value 1.646
Average Calories Burned Over Previous 7 days Before ABOVE Average Sleep Start Time 2 kilocalories
Average Calories Burned Over Previous 7 days Before BELOW Average Sleep Start Time 2 kilocalories
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 125
Optimal Pearson Product 0.00012333017822528
P Value 0.052491236898529
Statistical Significance 0.9589
Strength of Relationship 0.37692033620734
Study Type individual
Analysis Performed At 2019-04-04

Calories Burned Statistics

Property Value
Variable Name Calories Burned
Aggregation Method MEAN
Analysis Performed At 2019-04-04
Duration of Action 7 days
Kurtosis 3.9268618973341
Mean 2111.8 kilocalories
Median 2120 kilocalories
Number of Changes 131
Number of Correlations 3278
Number of Measurements 161
Onset Delay 0 seconds
Standard Deviation 490.17973231988
Unit Kilocalories
Variable ID 1280
Variance 240276.16997719

Sleep Start Time Statistics

Property Value
Variable Name Sleep Start Time
Aggregation Method MEAN
Analysis Performed At 2019-04-05
Duration of Action 24 hours
Kurtosis 7.2125987470022
Maximum Allowed Value 7 days
Mean 12 hours
Median 12 hours
Minimum Allowed Value 60 minutes
Number of Changes 1171
Number of Correlations 3164
Number of Measurements 1955
Onset Delay 0 seconds
Standard Deviation 1.7421264575675
Unit Hours
Variable ID 5211821
Variance 3.0350045941567

Tracking Calories Burned

Get Fitbit here and use it to record your Calories Burned. 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 Sleep Start Time

Get Fitbit here and use it to record your Sleep Start Time. 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