This individual's Arthritic Pains is generally lowest after an average of 0.33 count of Lifting Weights over the previous 7 days.


Abstract
This individual's Arthritic Pains is generally 5% lower than normal after 0.33333333333333 count Lifting Weights per 7 days. This individual's data suggests with a medium degree of confidence (p=0.18555264451608, 95% CI 0.152 to 0.438) that Lifting Weights has a weakly positive predictive relationship (R=0.14) with Arthritic Pains. The highest quartile of Arthritic Pains measurements were observed following an average 0.5 count Lifting Weights. The lowest quartile of Arthritic Pains measurements were observed following an average 0.36082474226804 count Lifting Weights.Arthritic Pains is generally 5% lower than normal after an average of 0.36082474226804 count of Lifting Weights over the previous 7 days. Arthritic Pains is generally 7% higher after an average of 0.5 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 Arthritic Pains. Additionally, we attempt to determine the Lifting Weights values most likely to produce optimal Arthritic Pains values.
Participant Instructions
Record your Lifting Weights daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Arthritic Pains daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Arthritic Pains 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
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.
Arthritic Pains PreProcessing
Arthritic Pains measurement values below 1 out of 5 were assumed erroneous and removed. Arthritic Pains measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Arthritic Pains 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 Arthritic Pains. It was assumed that Lifting Weights could produce an observable change in Arthritic Pains 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. 208 raw Arthritic Pains measurements with 66 changes spanning 344 days from 20180721 to 20190630 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.

Arthritic Pains PreProcessing
Arthritic Pains measurement values below 1 out of 5 were assumed erroneous and removed. Arthritic Pains measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Arthritic Pains 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 Arthritic Pains. It was assumed that Lifting Weights could produce an observable change in Arthritic Pains 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. 208 raw Arthritic Pains measurements with 66 changes spanning 344 days from 20180721 to 20190630 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.
Arthritic Pains 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.
Arthritic Pains 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 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 Arthritic Pains
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. 206 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 Arthritic Pains 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 Arthritic Pains
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. 206 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 Arthritic Pains 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  Arthritic Pains 
Sinn Predictive Coefficient  0.1336 
Confidence Level  medium 
Confidence Interval  0.29475705272892 
Forward Pearson Correlation Coefficient  0.143 
Critical T Value  1.646 
Average Lifting Weights Over Previous 7 days Before ABOVE Average Arthritic Pains  0.5 count 
Average Lifting Weights Over Previous 7 days Before BELOW Average Arthritic Pains  0.361 count 
Duration of Action  7 days 
Effect Size  weakly positive 
Number of Paired Measurements  206 
Optimal Pearson Product  0.058229344552843 
P Value  0.18555264451608 
Statistical Significance  0.9344 
Strength of Relationship  0.29475705272892 
Study Type  individual 
Analysis Performed At  20190615 
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 
Arthritic Pains Statistics
Property  Value 

Variable Name  Arthritic Pains 
Aggregation Method  MEAN 
Analysis Performed At  20190630 
Duration of Action  24 hours 
Kurtosis  3.1590825369279 
Maximum Allowed Value  5 out of 5 
Mean  1.9659 out of 5 
Median  2 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Changes  66 
Number of Correlations  328 
Number of Measurements  208 
Onset Delay  0 seconds 
Standard Deviation  1.1979577712159 
Unit  1 to 5 Rating 
Variable ID  5795361 
Variance  1.4351028216165 
Tracking Lifting Weights
Record your Lifting Weights daily in the reminder inbox or using the interactive web or mobile notifications.Tracking Arthritic Pains
Record your Arthritic Pains daily in the reminder inbox or using the interactive web or mobile notifications.

Principal Investigator  Mike Sinn