This individual's Sleep Efficiency is generally 0.4% lower after 162 pounds Body Weight over the previous 7 days.
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Blue represents the mean of Body Weight over the previous 7 days
An increase in 7 days cumulative Body Weight is usually followed by an decrease in Sleep Efficiency. (R = -0.278)
Typical values for Sleep Efficiency following a given amount of Body Weight over the previous 7 days.
Typical Body Weight seen over the previous 7 days preceding the given Sleep Efficiency 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 Body Weight changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Body Weight on each day of the week.
This chart shows the typical value recorded for Body Weight for each month of the year.
This chart shows how 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.

Abstract

This individual's Sleep Efficiency is generally 0% higher than normal after an average of 156.8 pounds Body Weight over the previous 7 days. This individual's data suggests with a high degree of confidence (p=0.17487489829245, 95% CI -0.614 to 0.058) that Body Weight has a weakly negative predictive relationship (R=-0.28) with Sleep Efficiency. The highest quartile of Sleep Efficiency measurements were observed following an average 156.86 pounds Body Weight. The lowest quartile of Sleep Efficiency measurements were observed following an average 157.96 lb Body Weight. Sleep Efficiency is generally 0% lower than normal after an average of 157.96 pounds of Body Weight over the previous 7 days. Sleep Efficiency is generally 0% higher after an average of 156.86 pounds of Body Weight over the previous 7 days.

Objective

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

Participant Instructions

Get Withings here and use it to record your Body Weight. Once you have a Withings 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 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.

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

Body Weight Pre-Processing
Body Weight measurement values below 0 pounds were assumed erroneous and removed. Body Weight measurement values above 1000 pounds were assumed erroneous and removed. No missing data filling value was defined for Body Weight so any gaps in data were just not analyzed instead of assuming zero values for those times.
Body Weight Analysis Settings

Sleep Efficiency Pre-Processing
No minimum allowed measurement value was defined for Sleep Efficiency. 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

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

Data Quantity
4417 raw Body Weight measurements with 1699 changes spanning 2662 days from 2012-04-18 to 2019-08-02 were used in this analysis. 1363 raw Sleep Efficiency measurements with 1348 changes spanning 1956 days from 2013-11-26 to 2019-04-05 were used in this analysis.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Sleep Efficiency is not statistically significant at a 95% confidence interval. This suggests that the Body Weight value does not have a significant influence on the Sleep Efficiency value.After treatment, a 0.4% decrease (-0.43759689922483 percent) from the mean baseline 85.62569213732 percent was observed. The relative standard deviation at baseline was 1%. The observed change was 0.42837758987033 times the standard deviation. A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would may have only a 5% likelihood of resulting from random fluctuation (a p-value

Data Sources

Body Weight data was primarily collected using Withings. Withings creates smart products and apps to take care of yourself and your loved ones in a new and easy way. Discover the Withings Pulse, Wi-Fi Body Scale, and Blood Pressure Monitor.

Sleep Efficiency 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 Body Weight and Sleep Efficiency

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. 1338 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Body Weight 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 Body Weight and Sleep Efficiency 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 Body Weight
Effect Variable Name Sleep Efficiency
Sinn Predictive Coefficient 0.1942
Confidence Level low
Confidence Interval 0.33566238456456
Forward Pearson Predictive Coefficient -0.278
Critical T Value 1.66
Average Body Weight Over Previous 7 days Before ABOVE Average Sleep Efficiency 156.86 pounds
Average Body Weight Over Previous 7 days Before BELOW Average Sleep Efficiency 157.96 pounds
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 1338
Optimal Pearson Product 0.10347392397967
P Value 0.17487489829245
Statistical Significance 0.7639
Strength of Relationship 0.33566238456456
Study Type individual
Analysis Performed At 2019-05-23
Number of Pairs 1338
Number of Raw Predictor Measurements ( Including Tags, Joins, and Children) 190
Baseline Relative Standard Deviation of Outcome Measurements 1
Experiment Duration (days) 1956
Number of Raw Outcome Measurements 1363
Z Score 0.42837758987033
Last Analysis 2019-05-23
Experiment Began 2013-11-26 00:00:00
Experiment Ended 2019-04-05 00:00:00
P Value 0.17487489829245
Predictor Category Physique
Duration of Action (h) 168
Significance 0.7639
Outcome Relative Standard Deviation at Baseline 1
Outcome Standard Deviation at Baseline 1.0215214557729%
Outcome Mean at Baseline 85.62569213732%
Average Followup Change From Baseline -0.4&
Average Absolute Followup Change From Baseline 85.188095238095%
Z- Score 0.42837758987033
Average Predictor Treatment Value 162lb over 7 days

Body Weight Statistics

Property Value
Variable Name Body Weight
Aggregation Method MEAN
Analysis Performed At 2019-08-18
Duration of Action 7 days
Kurtosis 2.5214093424243
Maximum Allowed Value 1000 pounds
Mean 161.96 pounds
Median 161.64211395295 pounds
Minimum Allowed Value 0 pounds
Number of Changes 1699
Number of Correlations 1212
Number of Measurements 4417
Onset Delay 0 seconds
Standard Deviation 5.5853948995709
Unit Pounds
UPC 875011003902
Variable ID 1486
Variance 31.196636184152

Sleep Efficiency Statistics

Property Value
Variable Name Sleep Efficiency
Aggregation Method MEAN
Analysis Performed At 2019-08-11
Duration of Action 24 hours
Kurtosis 2.8469281084588
Mean 90.723 percent
Median 91 percent
Number of Changes 1348
Number of Correlations 2906
Number of Measurements 1363
Onset Delay 0 seconds
Standard Deviation 4.3980680479499
Unit Percent
UPC 878881000699
Variable ID 5211811
Variance 19.343002554398

Tracking Body Weight

Get Withings here and use it to record your Body Weight. Once you have a Withings account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

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.
Join This Study

https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn