This individual's Deep Sleep is generally highest after an average of 0.11 count of Lifting Weights over the previous 7 days.


Abstract
This individual's Deep Sleep is generally 3% higher than normal after an average of 0.1111 count Lifting Weights over the previous 7 days. This individual's data suggests with a medium degree of confidence (p=0.15820106393194, 95% CI 0.067 to 0.143) that Lifting Weights has a weakly positive predictive relationship (R=0.11) with Deep Sleep. The highest quartile of Deep Sleep measurements were observed following an average 0.11 count Lifting Weights. The lowest quartile of Deep Sleep measurements were observed following an average 0.062857142857143 count Lifting Weights.Deep Sleep is generally 3% lower than normal after an average of 0.062857142857143 count of Lifting Weights over the previous 7 days. Deep Sleep is generally 3% higher after an average of 0.11 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 Deep Sleep. Additionally, we attempt to determine the Lifting Weights values most likely to produce optimal Deep Sleep values.
Participant Instructions
Record your Lifting Weights daily in the reminder inbox or using the interactive web or mobile notifications.
Get Sleep as Android here and use it to record your Deep Sleep. Once you have a Sleep as Android account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
Get Sleep as Android here and use it to record your Deep Sleep. Once you have a Sleep as Android 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 PreProcessing
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.
Deep Sleep PreProcessing
Deep Sleep measurement values below 0 out of 1 were assumed erroneous and removed. Deep Sleep measurement values above 1 out of 1 were assumed erroneous and removed. No missing data filling value was defined for Deep Sleep so any gaps in data were just not analyzed instead of assuming zero values for those times.
Predictive Analytics
It was assumed that 0 hours would pass before a change in Lifting Weights would produce an observable change in Deep Sleep. It was assumed that Lifting Weights could produce an observable change in Deep Sleep for as much as 7 days after the stimulus event.
Data Quantity
902 raw Lifting Weights measurements with 300 changes spanning 1263 days from 20160114 to 20190630 were used in this analysis. 632 raw Deep Sleep measurements with 359 changes spanning 1225 days from 20131123 to 20170401 were used in this analysis.
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.

Deep Sleep PreProcessing
Deep Sleep measurement values below 0 out of 1 were assumed erroneous and removed. Deep Sleep measurement values above 1 out of 1 were assumed erroneous and removed. No missing data filling value was defined for Deep Sleep so any gaps in data were just not analyzed instead of assuming zero values for those times.

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

Data Quantity
902 raw Lifting Weights measurements with 300 changes spanning 1263 days from 20160114 to 20190630 were used in this analysis. 632 raw Deep Sleep measurements with 359 changes spanning 1225 days from 20131123 to 20170401 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.
Deep Sleep data was primarily collected using Sleep as Android. Smart alarm clock with sleep cycle tracking. Wakes you gently in optimal moment for pleasant mornings.
Deep Sleep data was primarily collected using Sleep as Android. Smart alarm clock with sleep cycle tracking. Wakes you gently in optimal moment for pleasant mornings.
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 nonexistent 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 positive relationship between Lifting Weights and Deep Sleep
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. 119 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 timeprecedence 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 biochemical 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 Deep Sleep 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.
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 positive relationship between Lifting Weights and Deep Sleep
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. 119 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 timeprecedence 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 biochemical 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 Deep Sleep 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  Deep Sleep 
Sinn Predictive Coefficient  0.0757 
Confidence Level  medium 
Confidence Interval  0.037754975158644 
Forward Pearson Correlation Coefficient  0.105 
Critical T Value  1.646 
Average Lifting Weights Over Previous 7 days Before ABOVE Average Deep Sleep  0.11 count 
Average Lifting Weights Over Previous 7 days Before BELOW Average Deep Sleep  0.063 count 
Duration of Action  7 days 
Effect Size  weakly positive 
Number of Paired Measurements  119 
Optimal Pearson Product  0.038985492405985 
P Value  0.15820106393194 
Statistical Significance  0.6756 
Strength of Relationship  0.037754975158644 
Study Type  individual 
Analysis Performed At  20190630 
Lifting Weights Statistics
Property  Value 

Variable Name  Lifting Weights 
Aggregation Method  MEAN 
Analysis Performed At  20190701 
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 
Deep Sleep Statistics
Property  Value 

Variable Name  Deep Sleep 
Aggregation Method  MEAN 
Analysis Performed At  20190314 
Duration of Action  7 days 
Kurtosis  4.4611235779915 
Maximum Allowed Value  1 out of 1 
Mean  0.52175 out of 1 
Median  0.53571426868439 out of 1 
Minimum Allowed Value  0 out of 1 
Number of Changes  359 
Number of Correlations  2711 
Number of Measurements  632 
Onset Delay  0 seconds 
Standard Deviation  0.1316962526704 
Unit  0 to 1 Rating 
UPC  884904543333 
Variable ID  53709 
Variance  0.017343902967425 
Tracking Lifting Weights
Record your Lifting Weights daily in the reminder inbox or using the interactive web or mobile notifications.Tracking Deep Sleep
Get Sleep as Android here and use it to record your Deep Sleep. Once you have a Sleep as Android account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Principal Investigator  Mike Sinn