For most, Overall Mood is generally highest after a daily total of 4 hours of Time Spent Moderately Productively over the previous 7 days.


Abstract
Aggregated data from 35 study participants suggests with a medium degree of confidence (p=0.27556370976104, 95% CI 2.572 to 2.608) that Time Spent Moderately Productively has a very weakly positive predictive relationship (R=0.02) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 3 hours Time Spent Moderately Productively per day. The lowest quartile of Overall Mood measurements were observed following an average 2.6372436896455 h Time Spent Moderately Productively per day.
Objective
The objective of this study is to determine the nature of the relationship (if any) between Time Spent Moderately Productively and Overall Mood. Additionally, we attempt to determine the Time Spent Moderately Productively values most likely to produce optimal Overall Mood values.
Participant Instructions
Get RescueTime here and use it to record your Time Spent Moderately Productively. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Record your Overall Mood daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Overall Mood daily in the reminder inbox or using the interactive web or mobile notifications.
Design
This study is based on data donated by 35 participants. Thus, the study design is equivalent to the aggregation of 35 separate n=1 observational natural experiments.
Data Analysis
Time Spent Moderately Productively PreProcessing
Time Spent Moderately Productively measurement values below 0 seconds were assumed erroneous and removed. Time Spent Moderately Productively measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Time Spent Moderately Productively data were unrecorded 0 seconds measurement values.
Overall Mood PreProcessing
Overall Mood measurement values below 1 out of 5 were assumed erroneous and removed. Overall Mood measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Overall Mood 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 Time Spent Moderately Productively would produce an observable change in Overall Mood. It was assumed that Time Spent Moderately Productively could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.
Time Spent Moderately Productively measurement values below 0 seconds were assumed erroneous and removed. Time Spent Moderately Productively measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Time Spent Moderately Productively data were unrecorded 0 seconds measurement values.

Overall Mood PreProcessing
Overall Mood measurement values below 1 out of 5 were assumed erroneous and removed. Overall Mood measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Overall Mood 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 Time Spent Moderately Productively would produce an observable change in Overall Mood. It was assumed that Time Spent Moderately Productively could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources
Time Spent Moderately Productively data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into predefined categories with builtin productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.
Overall Mood 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.
Overall Mood 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 correlation. 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 very weakly positive relationship between Time Spent Moderately Productively and Overall Mood
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. 134 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Time Spent Moderately Productively 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 1 feel that any relationship observed between Time Spent Moderately Productively and Overall Mood 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 very weakly positive relationship between Time Spent Moderately Productively and Overall Mood
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. 134 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Time Spent Moderately Productively 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 1 feel that any relationship observed between Time Spent Moderately Productively and Overall Mood 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  Time Spent Moderately Productively 
Effect Variable Name  Overall Mood 
Sinn Predictive Coefficient  0.0036163974704256 
Confidence Level  medium 
Confidence Interval  2.590320229466 
Forward Pearson Correlation Coefficient  0.018 
Critical T Value  1.6914088235294 
Total Time Spent Moderately Productively Over Previous 7 days Before ABOVE Average Overall Mood  3 hours 
Total Time Spent Moderately Productively Over Previous 7 days Before BELOW Average Overall Mood  3 hours 
Duration of Action  7 days 
Effect Size  very weakly positive 
Number of Paired Measurements  134 
Optimal Pearson Product  0.036422346230432 
P Value  0.27556370976104 
Statistical Significance  0.42315428456558 
Strength of Relationship  2.590320229466 
Study Type  population 
Analysis Performed At  20190129 
Number of Participants  35 
Time Spent Moderately Productively Statistics
Property  Value 

Variable Name  Time Spent Moderately Productively 
Aggregation Method  SUM 
Analysis Performed At  20190129 
Duration of Action  7 days 
Kurtosis  23.530544427522 
Maximum Allowed Value  7 days 
Mean  29 minutes 
Median  17 minutes 
Minimum Allowed Value  0 seconds 
Number of Correlations  662 
Number of Measurements  20562 
Onset Delay  0 seconds 
Standard Deviation  0.56907461022317 
Unit  Hours 
Variable ID  111502 
Variance  0.52030089591372 
Overall Mood Statistics
Property  Value 

Variable Name  Overall Mood 
Aggregation Method  MEAN 
Analysis Performed At  20190127 
Duration of Action  24 hours 
Kurtosis  3.7383708126619 
Maximum Allowed Value  5 out of 5 
Mean  3.1156748504321 out of 5 
Median  3.1369047348216 out of 5 
Minimum Allowed Value  1 out of 5 
Number of Correlations  1149 
Number of Measurements  605816 
Onset Delay  0 seconds 
Standard Deviation  0.56833853113207 
Unit  1 to 5 Rating 
UPC  767674073845 
Variable ID  1398 
Variance  0.43884384603152 
Principal Investigator  Mike Sinn