This individual's Sleep Efficiency is generally highest after an average of 0.2 count of Lifting Weights over the previous 7 days.
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Blue represents the mean of Lifting Weights over the previous 7 days
An increase in 7 days cumulative Lifting Weights is usually followed by an decrease in Sleep Efficiency. (R = -0.056)
Typical values for Sleep Efficiency following a given amount of Lifting Weights over the previous 7 days.
Typical Lifting Weights 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 Lifting Weights changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Lifting Weights on each day of the week.
This chart shows the typical value recorded for Lifting Weights 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 0.24% higher than normal after an average of 0.2 count Lifting Weights over the previous 7 days. This individual's data suggests with a high degree of confidence (p=0.040167303950079, 95% CI -0.502 to 0.39) that Lifting Weights has a very weakly negative predictive relationship (R=-0.06) with Sleep Efficiency. The highest quartile of Sleep Efficiency measurements were observed following an average 0.21 count Lifting Weights. The lowest quartile of Sleep Efficiency measurements were observed following an average 0.24675040355125 count Lifting Weights.Sleep Efficiency is generally 0.4% lower than normal after an average of 0.24675040355125 count of Lifting Weights over the previous 7 days. Sleep Efficiency is generally 0.24% higher after an average of 0.21 count of Lifting Weights over the previous 7 days.

Objective

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

Participant Instructions

Record your Lifting Weights daily in the reminder inbox or using the interactive web or mobile notifications.
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

Lifting Weights Pre-Processing
Lifting Weights measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Lifting Weights. It was assumed that any gaps in Lifting Weights data were unrecorded 0 count measurement values.
Lifting Weights Analysis Settings

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

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

Data Quantity
902 raw Lifting Weights measurements with 300 changes spanning 1263 days from 2016-01-14 to 2019-06-30 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.

Data Sources

Lifting Weights 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 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 very weakly negative relationship between Lifting Weights 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. 1086 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Lifting Weights 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 Lifting Weights 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 Lifting Weights
Effect Variable Name Sleep Efficiency
Sinn Predictive Coefficient 0.0645
Confidence Level high
Confidence Interval 0.44625075053847
Forward Pearson Correlation Coefficient -0.056
Critical T Value 1.646
Average Lifting Weights Over Previous 7 days Before ABOVE Average Sleep Efficiency 0.21 count
Average Lifting Weights Over Previous 7 days Before BELOW Average Sleep Efficiency 0.247 count
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 1086
Optimal Pearson Product 0.0065132782778263
P Value 0.040167303950079
Statistical Significance 0.99998677409732
Strength of Relationship 0.44625075053847
Study Type individual
Analysis Performed At 2019-07-02

Lifting Weights Statistics

Property Value
Variable Name Lifting Weights
Aggregation Method MEAN
Analysis Performed At 2019-07-01
Duration of Action 7 days
Kurtosis 2.4816231633429
Mean 0.24107 count
Median 0 count
Minimum Allowed Value 0 count
Number of Changes 300
Number of Correlations 801
Number of Measurements 902
Onset Delay 0 seconds
Standard Deviation 0.42525654899846
Unit Count
UPC 716788836488
Variable ID 94185
Variance 0.18084313246608

Sleep Efficiency Statistics

Property Value
Variable Name Sleep Efficiency
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 24 hours
Kurtosis 2.8469281084588
Mean 90.723 percent
Median 91 percent
Minimum Allowed Value 1 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 Lifting Weights

Record your Lifting Weights daily in the reminder inbox or using the interactive web or mobile notifications.

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