This individual's Hunger is generally lowest after a daily total of 77 minutes of Deep Sleep Duration over the previous 24 hours.
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Blue represents the sum of Deep Sleep Duration over the previous 24 hours
An increase in 24 hours cumulative Deep Sleep Duration is usually followed by an decrease in Hunger. (R = -0.018)
Typical values for Hunger following a given amount of Deep Sleep Duration over the previous 24 hours.
Typical Deep Sleep Duration seen over the previous 24 hours preceding the given Hunger value.
Correlation between outcome and aggregated predictor measurements over given number of days
Peak correlation suggests the delay between predictor and observable outcome
This chart shows how Deep Sleep Duration changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Deep Sleep Duration on each day of the week.
This chart shows the typical value recorded for Deep Sleep Duration for each month of the year.
This chart shows how Hunger changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Hunger on each day of the week.
This chart shows the typical value recorded for Hunger for each month of the year.

Abstract

This individual's Hunger is generally -0.3% lower than normal after 77 minutes Deep Sleep Duration per 24 hours. This individual's data suggests with a medium degree of confidence (p=0.39687690557275, 95% CI -0.248 to 0.212) that Deep Sleep Duration has a very weakly negative predictive relationship (R=-0.02) with Hunger. The highest quartile of Hunger measurements were observed following an average 77 minutes Deep Sleep Duration per day. The lowest quartile of Hunger measurements were observed following an average 76.273504273504 min Deep Sleep Duration per day.Hunger is generally 0.3% lower than normal after a total of 76 minutes of Deep Sleep Duration over the previous 24 hours. Hunger is generally 0.29% higher after a total of 77 minutes of Deep Sleep Duration over the previous 24 hours.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Deep 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 Hunger 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

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

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

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

Data Quantity
391 raw Deep Sleep Duration measurements with 307 changes spanning 420 days from 2018-05-06 to 2019-06-30 were used in this analysis. 539 raw Hunger measurements with 275 changes spanning 1693 days from 2014-11-09 to 2019-06-30 were used in this analysis.

Data Sources

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

Hunger 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 Deep Sleep Duration and Hunger

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. 234 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Deep 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 Deep Sleep Duration and Hunger 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.

Potential Issues Identified During Analysis
The average of effect values expected to be lower than average (2.4224137931034) are actually higher than the average (2.4152421652422). This suggests a weak relationship or insufficient data.
The average of effect values expected to be higher than average (2.4081920903955) are actually lower than the average (2.4152421652422). This suggests a weak relationship or insufficient data.
The low effect change is greater than the high effect change.

Relationship Statistics

Property Value
Cause Variable Name Deep Sleep Duration
Effect Variable Name Hunger
Sinn Predictive Coefficient 0.018
Confidence Level medium
Confidence Interval 0.22975168551979
Forward Pearson Correlation Coefficient -0.018
Critical T Value 1.646
Total Deep Sleep Duration Over Previous 24 hours Before ABOVE Average Hunger 77 minutes
Total Deep Sleep Duration Over Previous 24 hours Before BELOW Average Hunger 76 minutes
Duration of Action 24 hours
Effect Size very weakly negative
Number of Paired Measurements 234
Optimal Pearson Product -0.00023358663304492
P Value 0.39687690557275
Statistical Significance 0.99914943180639
Strength of Relationship 0.22975168551979
Study Type individual
Analysis Performed At 2019-07-02

Deep Sleep Duration Statistics

Property Value
Variable Name Deep Sleep Duration
Aggregation Method SUM
Analysis Performed At 2019-07-01
Duration of Action 24 hours
Kurtosis 3.0359623344059
Maximum Allowed Value 7 days
Mean 70 minutes
Median 72 minutes
Minimum Allowed Value 60 seconds
Number of Changes 307
Number of Measurements 391
Onset Delay 0 seconds
Standard Deviation 31.262599866283
Unit Minutes
Variable ID 6054282
Variance 977.35015039934

Hunger Statistics

Property Value
Variable Name Hunger
Aggregation Method MEAN
Analysis Performed At 2019-06-30
Duration of Action 7 days
Kurtosis 2.0302835564081
Maximum Allowed Value 5 out of 5
Mean 2.3984 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 275
Number of Correlations 1262
Number of Measurements 539
Onset Delay 0 seconds
Standard Deviation 1.0446834363744
Unit 1 to 5 Rating
Variable ID 102685
Variance 1.091363482235

Tracking Deep Sleep Duration

Get Fitbit here and use it to record your Deep 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 Hunger

Record your Hunger 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