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This individual's Sleep Start Time is generally highest after a daily total of 1 count of Ate Lunch over the previous 7 days.
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Blue represents the sum of Ate Lunch over the previous 7 days
An increase in 7 days cumulative Ate Lunch is usually followed by an decrease in Sleep Start Time. (R = -0.165)
Typical values for Sleep Start Time following a given amount of Ate Lunch over the previous 7 days.
Typical Ate Lunch seen over the previous 7 days preceding the given Sleep Start Time value.
This chart shows how your Ate Lunch changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Ate Lunch on each day of the week.
This chart shows the typical value recorded for Ate Lunch 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 1% higher than normal after a total of 1 count Ate Lunch over the previous 7 days. This individual's data suggests with a high degree of confidence (p=4.8574527442338E-6, 95% CI -0.348 to 0.018) that Ate Lunch has a weakly negative predictive relationship (R=-0.17) with Sleep Start Time. The highest quartile of Sleep Start Time measurements were observed following an average 1.05 count Ate Lunch per day. The lowest quartile of Sleep Start Time measurements were observed following an average 1.6472081218274 count Ate Lunch per day.Sleep Start Time is generally 3% lower than normal after a total of 1.6472081218274 count of Ate Lunch over the previous 7 days. Sleep Start Time is generally 1% higher after a total of 1.05 count of Ate Lunch over the previous 7 days.

Objective

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

Participant Instructions

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

Ate Lunch Pre-Processing
Ate Lunch measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Ate Lunch. It was assumed that any gaps in Ate Lunch data were unrecorded 0 count measurement values.
Ate Lunch 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 0.5 hours would pass before a change in Ate Lunch would produce an observable change in Sleep Start Time. It was assumed that Ate Lunch could produce an observable change in Sleep Start Time for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
533 raw Ate Lunch measurements with 150 changes spanning 825 days from 2016-12-26 to 2019-03-31 were used in this analysis. 1952 raw Sleep Start Time measurements with 1162 changes spanning 1951 days from 2013-11-26 to 2019-03-31 were used in this analysis.

Data Sources

Ate Lunch 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.

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 weakly negative relationship between Ate Lunch 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. 848 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Ate Lunch 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 Ate Lunch 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 Ate Lunch
Effect Variable Name Sleep Start Time
Sinn Predictive Coefficient 0.1647
Confidence Level high
Confidence Interval 0.18251988110173
Forward Pearson Correlation Coefficient -0.165
Critical T Value 1.646
Total Ate Lunch Over Previous 7 days Before ABOVE Average Sleep Start Time 1.05 count
Total Ate Lunch Over Previous 7 days Before BELOW Average Sleep Start Time 1.647 count
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 848
Optimal Pearson Product 0.053420825978704
P Value 4.8574527442338E-6
Statistical Significance 0.9982
Strength of Relationship 0.18251988110173
Study Type individual
Analysis Performed At 2019-04-04

Ate Lunch Statistics

Property Value
Variable Name Ate Lunch
Aggregation Method SUM
Analysis Performed At 2019-04-01
Duration of Action 7 days
Kurtosis 4.572301987665
Mean 0.18897 count
Median 0 count
Minimum Allowed Value 0 count
Number of Changes 150
Number of Correlations 179
Number of Measurements 533
Onset Delay 30 minutes
Standard Deviation 0.40061100672246
Unit Count
UPC 825703610598
Variable ID 5956846
Variance 0.16048917870718

Sleep Start Time Statistics

Property Value
Variable Name Sleep Start Time
Aggregation Method MEAN
Analysis Performed At 2019-03-31
Duration of Action 24 hours
Kurtosis 7.217559329383
Maximum Allowed Value 7 days
Mean 12 hours
Median 12 hours
Minimum Allowed Value 60 minutes
Number of Changes 1162
Number of Correlations 3135
Number of Measurements 1952
Onset Delay 0 seconds
Standard Deviation 1.7437107114378
Unit Hours
Variable ID 5211821
Variance 3.0405270451829

Tracking Ate Lunch

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

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