Higher Sleep Start Time Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Sleep Start Time Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after an average of 11 hours of Sleep Start Time over the previous 8 days.

Trait Correlation Between Sleep Start Time and Overall Mood

The chart above indicates that people with higher average Sleep Start Time usually have higher average Overall Mood. (R = 0.1107440525909)

Sleep Start Time Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Sleep Start Time by Day of Week

Average Sleep Start Time by Month

This chart shows the typical value recorded for Sleep Start Time for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 23 study participants suggests with a medium degree of confidence (p=0.21698961901618, 95% CI -0.488 to 0.391) that Sleep Start Time has a very weakly negative predictive relationship (R=-0.0486) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 11 hours Sleep Start Time. The lowest quartile of Overall Mood measurements were observed following an average 11.700896733211 h Sleep Start Time.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Sleep Start Time. Once you have a Fitbit 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.

Design

This study is based on data donated by 23 participants. Thus, the study design is equivalent to the aggregation of 23 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Sleep Start Time would produce an observable change in Overall Mood. It was assumed that Sleep Start Time could produce an observable change in Overall Mood for as much as 8 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 127 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Sleep Start Time 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Sleep Start Time
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.031447377475967
Confidence Level medium
Confidence Interval 0.43969942146969
Forward Pearson Correlation Coefficient -0.0486
Critical T Value 1.697202935331
Average Sleep Start Time Over Previous 8 days Before ABOVE Average Overall Mood 11 hours
Average Sleep Start Time Over Previous 8 days Before BELOW Average Overall Mood 12 hours
Duration of Action 8 days
Effect Size very weakly negative
Number of Paired Measurements 127
Optimal Pearson Product 0.097371075511997
P Value 0.21698961901618
Statistical Significance 0.4822
Strength of Relationship 0.43969942146969
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 23


Sleep Start Time Statistics

Property Value
Variable Name Sleep Start Time
Aggregation Method MEAN
Analysis Performed At 2018-11-20
Duration of Action 24 hours
Kurtosis 68.400490488415
Maximum Allowed Value 7 days
Mean 10 hours
Median 10 hours
Minimum Allowed Value 0 seconds
Number of Correlations 648
Number of Measurements 95941
Onset Delay 0 seconds
Standard Deviation 1.8514009058263
Unit Hours
Variable ID 5211821
Variance 6.2550638829994


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-19
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Business Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Business Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 8 minutes of Business over the previous 7 days.

Trait Correlation Between Business and Overall Mood

The chart above indicates that people with higher average Business usually have lower average Overall Mood. (R = -0.026269494470406)

Business Hours Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Business Hours by Day of Week

Average Business Hours by Month

This chart shows the typical value recorded for Business Hours for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 23 study participants suggests with a medium degree of confidence (p=0.19060321090787, 95% CI -3.326 to 3.239) that Business Hours has a very weakly negative predictive relationship (R=-0.0435) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 8 minutes Business Hours per day. The lowest quartile of Overall Mood measurements were observed following an average 0.13257103353231 h Business Hours per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Business Hours and Overall Mood. Additionally, we attempt to determine the Business Hours values most likely to produce optimal Overall Mood values.

Participant Instructions

Get RescueTime here and use it to record your Business. 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.

Design

This study is based on data donated by 23 participants. Thus, the study design is equivalent to the aggregation of 23 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Business Hours would produce an observable change in Overall Mood. It was assumed that Business Hours could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 90 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Business Hours 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Business
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.023226115304077
Confidence Level medium
Confidence Interval 3.2824418830212
Forward Pearson Correlation Coefficient -0.0435
Critical T Value 1.6784099965731
Total Business Over Previous 7 days Before ABOVE Average Overall Mood 8 minutes
Total Business Over Previous 7 days Before BELOW Average Overall Mood 8 minutes
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 90
Optimal Pearson Product 0.047480828039542
P Value 0.19060321090787
Statistical Significance 0.5163
Strength of Relationship 3.2824418830212
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 23


Business Statistics

Property Value
Variable Name Business Hours
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 59.500351521425
Maximum Allowed Value 7 days
Mean 18 minutes
Median 12 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 502
Number of Measurements 20182
Onset Delay 0 seconds
Standard Deviation 0.31785669146198
Unit Hours
UPC 039956980715
Variable ID 111572
Variance 0.2018559422041


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Time Spent Productively Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Time Spent Productively Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 3 hours of Time Spent Productively over the previous 8 days.

Trait Correlation Between Time Spent Productively and Overall Mood

The chart above indicates that people with higher average Time Spent Productively usually have higher average Overall Mood. (R = 0.16206009901553)

Time Spent Productively Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Time Spent Productively by Day of Week

Average Time Spent Productively by Month

This chart shows the typical value recorded for Time Spent Productively for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 23 study participants suggests with a medium degree of confidence (p=0.23694208935426, 95% CI -2.767 to 2.7) that Time Spent Productively has a very weakly negative predictive relationship (R=-0.0333) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 3 hours Time Spent Productively per day. The lowest quartile of Overall Mood measurements were observed following an average 2.709557504516 h Time Spent Productively per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Time Spent Productively and Overall Mood. Additionally, we attempt to determine the Time Spent Productively values most likely to produce optimal Overall Mood values.

Participant Instructions

Get RescueTime here and use it to record your Time Spent 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.

Design

This study is based on data donated by 23 participants. Thus, the study design is equivalent to the aggregation of 23 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Time Spent Productively would produce an observable change in Overall Mood. It was assumed that Time Spent Productively could produce an observable change in Overall Mood for as much as 8 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 90 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Time Spent 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Time Spent Productively
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.017779992854099
Confidence Level medium
Confidence Interval 2.7337778266763
Forward Pearson Correlation Coefficient -0.0333
Critical T Value 1.6815733299065
Total Time Spent Productively Over Previous 8 days Before ABOVE Average Overall Mood 3 hours
Total Time Spent Productively Over Previous 8 days Before BELOW Average Overall Mood 3 hours
Duration of Action 8 days
Effect Size very weakly negative
Number of Paired Measurements 90
Optimal Pearson Product 0.06895251317437
P Value 0.23694208935426
Statistical Significance 0.4962
Strength of Relationship 2.7337778266763
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 23


Time Spent Productively Statistics

Property Value
Variable Name Time Spent Productively
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 9.8901913493741
Maximum Allowed Value 7 days
Mean 94 minutes
Median 63 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 656
Number of Measurements 20534
Onset Delay 0 seconds
Standard Deviation 1.5882812405017
Unit Hours
Variable ID 111542
Variance 4.0931522051249


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Time Spent On Social Networking Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Time Spent On Social Networking Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 34 minutes of Time Spent On Social Networking over the previous 7 days.

Trait Correlation Between Time Spent On Social Networking and Overall Mood

The chart above indicates that people with higher average Time Spent On Social Networking usually have lower average Overall Mood. (R = -0.26296815287854)

Time Spent On Social Networking Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Time Spent On Social Networking by Day of Week

Average Time Spent On Social Networking by Month

This chart shows the typical value recorded for Time Spent On Social Networking for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 22 study participants suggests with a medium degree of confidence (p=0.20141350374427, 95% CI -3.003 to 2.817) that Time Spent On Social Networking has a very weakly negative predictive relationship (R=-0.0929) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 34 minutes Time Spent On Social Networking per day. The lowest quartile of Overall Mood measurements were observed following an average 0.9030622251818 h Time Spent On Social Networking per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Time Spent On Social Networking and Overall Mood. Additionally, we attempt to determine the Time Spent On Social Networking values most likely to produce optimal Overall Mood values.

Participant Instructions

Get RescueTime here and use it to record your Time Spent On Social Networking. 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.

Design

This study is based on data donated by 22 participants. Thus, the study design is equivalent to the aggregation of 22 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Time Spent On Social Networking would produce an observable change in Overall Mood. It was assumed that Time Spent On Social Networking could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 70 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Time Spent On Social Networking 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Time Spent On Social Networking
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.041585269238615
Confidence Level medium
Confidence Interval 2.910127563647
Forward Pearson Correlation Coefficient -0.0929
Critical T Value 1.6838344792136
Total Time Spent On Social Networking Over Previous 7 days Before ABOVE Average Overall Mood 34 minutes
Total Time Spent On Social Networking Over Previous 7 days Before BELOW Average Overall Mood 54 minutes
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 70
Optimal Pearson Product 0.068805645178049
P Value 0.20141350374427
Statistical Significance 0.4956
Strength of Relationship 2.910127563647
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 22


Time Spent On Social Networking Statistics

Property Value
Variable Name Time Spent On Social Networking
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 42.370644342356
Maximum Allowed Value 7 days
Mean 23 minutes
Median 12 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 509
Number of Measurements 20512
Onset Delay 0 seconds
Standard Deviation 0.48697771726047
Unit Hours
Variable ID 111592
Variance 0.4917779947824


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Time Spent Moderately Productively Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Time Spent Moderately Productively Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 30 minutes of Time Spent Moderately Productively over the previous 7 days.

Trait Correlation Between Time Spent Moderately Productively and Overall Mood

The chart above indicates that people with higher average Time Spent Moderately Productively usually have higher average Overall Mood. (R = 0.31283853947979)

Time Spent Moderately Productively Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Time Spent Moderately Productively by Day of Week

Average Time Spent Moderately Productively by Month

This chart shows the typical value recorded for Time Spent Moderately Productively for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 23 study participants suggests with a medium degree of confidence (p=0.21184544735295, 95% CI -2.846 to 2.827) that Time Spent Moderately Productively has a very weakly negative predictive relationship (R=-0.0093) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 30 minutes Time Spent Moderately Productively per day. The lowest quartile of Overall Mood measurements were observed following an average 0.48532659762803 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.

Design

This study is based on data donated by 23 participants. Thus, the study design is equivalent to the aggregation of 23 separate n=1 observational natural experiments.

Data Analysis

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

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 90 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Time Spent Moderately Productively
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.0049655834330885
Confidence Level medium
Confidence Interval 2.8365412265594
Forward Pearson Correlation Coefficient -0.0093
Critical T Value 1.6815733299065
Total Time Spent Moderately Productively Over Previous 7 days Before ABOVE Average Overall Mood 30 minutes
Total Time Spent Moderately Productively Over Previous 7 days Before BELOW Average Overall Mood 29 minutes
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 90
Optimal Pearson Product 0.065638264789061
P Value 0.21184544735295
Statistical Significance 0.5298
Strength of Relationship 2.8365412265594
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 23


Time Spent Moderately Productively Statistics

Property Value
Variable Name Time Spent Moderately Productively
Aggregation Method SUM
Analysis Performed At 2018-11-16
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 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Sleep Duration Predicts higher Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Sleep Duration Predicts ↑higher Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after an average of 7 hours of Sleep Duration over the previous 7 days.

Trait Correlation Between Sleep Duration and Overall Mood

The chart above indicates that people with higher average Sleep Duration usually have higher average Overall Mood. (R = 0.003317809519714)

Sleep Duration Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Sleep Duration by Day of Week

Average Sleep Duration by Month

This chart shows the typical value recorded for Sleep Duration for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 58 study participants suggests with a medium degree of confidence (p=0.24758140265156, 95% CI -0.432 to 0.439) that Sleep Duration has a very weakly positive predictive relationship (R=0) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 7 hours Sleep Duration. The lowest quartile of Overall Mood measurements were observed following an average 6.7997037229224 h Sleep Duration.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Sleep Duration. Once you have a Fitbit 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.

Design

This study is based on data donated by 58 participants. Thus, the study design is equivalent to the aggregation of 58 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Sleep Duration would produce an observable change in Overall Mood. It was assumed that Sleep Duration could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 85 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Sleep Duration 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Sleep Duration
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.00211215068166
Confidence Level medium
Confidence Interval 0.43577428495293
Forward Pearson Correlation Coefficient 0.0037
Critical T Value 1.6922672419548
Average Sleep Duration Over Previous 7 days Before ABOVE Average Overall Mood 7 hours
Average Sleep Duration Over Previous 7 days Before BELOW Average Overall Mood 7 hours
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 85
Optimal Pearson Product 0.10157864499827
P Value 0.24758140265156
Statistical Significance 0.4463
Strength of Relationship 0.43577428495293
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 58


Sleep Duration Statistics

Property Value
Variable Name Sleep Duration
Aggregation Method MEAN
Analysis Performed At 2018-11-18
Duration of Action 7 days
Kurtosis 26.117213119491
Maximum Allowed Value 7 days
Mean 5 hours
Median 5 hours
Minimum Allowed Value 0 seconds
Number of Correlations 2025
Number of Measurements 40974
Onset Delay 0 seconds
Standard Deviation 4.2072240524865
Unit Hours
UPC 067981966602
Variable ID 1867
Variance 294.95711944016


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Net Caloric Intake Intake Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Net Caloric Intake Intake Predicts ↓lower Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 976.84 kilocalories of Net Caloric Intake intake over the previous 7 days.

Trait Correlation Between Net Caloric Intake intake and Overall Mood

The chart above indicates that people with higher average Net Caloric Intake intake usually have lower average Overall Mood. (R = -0.96659919963035)

Net Caloric Intake Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Net Caloric Intake by Day of Week

Average Net Caloric Intake by Month

This chart shows the typical value recorded for Net Caloric Intake for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 2 study participants suggests with a medium degree of confidence (p=0.0435, 95% CI -0.329 to -0.113) that Net Caloric Intake has a weakly negative predictive relationship (R=-0.2213) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 976.84 kilocalories Net Caloric Intake per day. The lowest quartile of Overall Mood measurements were observed following an average 1863.3800527709 kcal Net Caloric Intake per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Net Caloric Intake and Overall Mood. Additionally, we attempt to determine the Net Caloric Intake values most likely to produce optimal Overall Mood values.

Participant Instructions

Get MyFitnessPal here and use it to record your Net Caloric Intake. Once you have a MyFitnessPal 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.

Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Net Caloric Intake would produce an observable change in Overall Mood. It was assumed that Net Caloric Intake could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 168 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Net Caloric Intake 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Net Caloric Intake intake
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.032638514722368
Confidence Level medium
Confidence Interval 0.10784097255003
Forward Pearson Correlation Coefficient -0.2213
Critical T Value 1.646
Total Net Caloric Intake intake Over Previous 7 days Before ABOVE Average Overall Mood 976.84 kilocalories
Total Net Caloric Intake intake Over Previous 7 days Before BELOW Average Overall Mood 1863.3800527709 kilocalories
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 168
Optimal Pearson Product 0.45183796120911
P Value 0.0435
Statistical Significance 0.9821
Strength of Relationship 0.10784097255003
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 2


Net Caloric Intake Statistics

Property Value
Variable Name Net Caloric Intake
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 1.7340314720432
Maximum Allowed Value kilocalories
Mean 1093.4443333333 kilocalories
Median 1035 kilocalories
Minimum Allowed Value kilocalories
Number of Correlations 113
Number of Measurements 391
Onset Delay 0 seconds
Standard Deviation 940.50062694858
Unit Kilocalories
UPC 017800165426
Variable ID 1507
Variance 902936.20050112


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Egg Consumption Predicts higher Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Egg Consumption Predicts ↑higher Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 1.44 serving of Egg consumption over the previous 7 days.

Trait Correlation Between Egg consumption and Overall Mood

The chart above indicates that people with higher average Egg consumption usually have higher average Overall Mood. (R = 0.12417335992676)

Egg Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Egg by Day of Week

Average Egg by Month

This chart shows the typical value recorded for Egg for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 2 study participants suggests with a low degree of confidence (p=0.3205, 95% CI -0.021 to 0.172) that Egg has a very weakly positive predictive relationship (R=0.08) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 1.44 serving Egg per day. The lowest quartile of Overall Mood measurements were observed following an average 1.6904761904762 serving Egg per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Egg and Overall Mood. Additionally, we attempt to determine the Egg values most likely to produce optimal Overall Mood values.

Participant Instructions

Record your Egg 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 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0.5 hours would pass before a change in Egg would produce an observable change in Overall Mood. It was assumed that Egg could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 100 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Egg 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Egg consumption
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.0086510929374148
Confidence Level low
Confidence Interval 0.096315836496126
Forward Pearson Correlation Coefficient 0.0755
Critical T Value 1.66
Total Egg consumption Over Previous 7 days Before ABOVE Average Overall Mood 1.44 serving
Total Egg consumption Over Previous 7 days Before BELOW Average Overall Mood 1.6904761904762 serving
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 100
Optimal Pearson Product -0.012595860063328
P Value 0.3205
Statistical Significance 0.6753
Strength of Relationship 0.096315836496126
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 2


Egg Statistics

Property Value
Variable Name Egg
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 9.5123338331015
Maximum Allowed Value 20 serving
Mean 1.009778 serving
Median 0.4 serving
Minimum Allowed Value 0 serving
Number of Correlations 108
Number of Measurements 105
Onset Delay 30 minutes
Standard Deviation 0.97863778045265
Unit Serving
UPC 032991770303
Variable ID 1729
Variance 1.2564555695201


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Pulp Free Orange Juice Consumption Predicts higher Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Pulp Free Orange Juice Consumption Predicts ↑higher Overall Mood

cause image
gauge image
effect image


For most, Overall Mood is generally highest after a total of 0.62 serving of Pulp Free Orange Juice consumption over the previous 7 days.

Trait Correlation Between Pulp Free Orange Juice consumption and Overall Mood

The chart above indicates that people with higher average Pulp Free Orange Juice consumption usually have lower average Overall Mood. (R = -0.41835016108548)

Pulp Free Orange Juice Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Pulp Free Orange Juice by Day of Week

Average Pulp Free Orange Juice by Month

This chart shows the typical value recorded for Pulp Free Orange Juice for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 2 study participants suggests with a high degree of confidence (p=0.0005, 95% CI 0.206 to 0.362) that Pulp Free Orange Juice has a weakly positive predictive relationship (R=0.28) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 0.62 serving Pulp Free Orange Juice per day. The lowest quartile of Overall Mood measurements were observed following an average 0.18030888030888 serving Pulp Free Orange Juice per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Pulp Free Orange Juice and Overall Mood. Additionally, we attempt to determine the Pulp Free Orange Juice values most likely to produce optimal Overall Mood values.

Participant Instructions

Record your Pulp Free Orange Juice 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 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0.5 hours would pass before a change in Pulp Free Orange Juice would produce an observable change in Overall Mood. It was assumed that Pulp Free Orange Juice could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 227 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Pulp Free Orange Juice 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Pulp Free Orange Juice consumption
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.046161908450065
Confidence Level high
Confidence Interval 0.078305199090747
Forward Pearson Correlation Coefficient 0.284
Critical T Value 1.646
Total Pulp Free Orange Juice consumption Over Previous 7 days Before ABOVE Average Overall Mood 0.62 serving
Total Pulp Free Orange Juice consumption Over Previous 7 days Before BELOW Average Overall Mood 0.18030888030888 serving
Duration of Action 7 days
Effect Size weakly positive
Number of Paired Measurements 227
Optimal Pearson Product 0.28211228554039
P Value 0.0005
Statistical Significance 0.8278
Strength of Relationship 0.078305199090747
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 2


Pulp Free Orange Juice Statistics

Property Value
Variable Name Pulp Free Orange Juice
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 8.587029706249
Maximum Allowed Value 20 serving
Mean 0.872245 serving
Median 0.75 serving
Minimum Allowed Value 0 serving
Number of Correlations 97
Number of Measurements 114
Onset Delay 30 minutes
Standard Deviation 0.45229243342115
Unit Serving
Variable ID 1725
Variance 0.23620342936598


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354

Higher Dark Mint Chocolate Chip Protein Bar Consumption Predicts lower Overall Mood

Mike Sinn

Principal Investigator Mike Sinn

↑Higher Dark Mint Chocolate Chip Protein Bar Consumption Predicts ↓lower Overall Mood

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For most, Overall Mood is generally highest after a total of 0.19 serving of Dark Mint Chocolate Chip Protein Bar consumption over the previous 7 days.

Trait Correlation Between Dark Mint Chocolate Chip Protein Bar consumption and Overall Mood

The chart above indicates that people with higher average Dark Mint Chocolate Chip Protein Bar consumption usually have higher average Overall Mood. (R = 0.98870119675472)

Dark Mint Chocolate Chip Protein Bar Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Dark Mint Chocolate Chip Protein Bar by Day of Week

Average Dark Mint Chocolate Chip Protein Bar by Month

This chart shows the typical value recorded for Dark Mint Chocolate Chip Protein Bar for each month of the year.

Overall Mood Distribution

A frequency distribution displays the frequency of various values. Each column represents the number of the occurrences of a value.

Average Overall Mood by Day of Week

Average Overall Mood by Month

This chart shows the typical value recorded for Overall Mood for each month of the year.



Abstract

Aggregated data from 2 study participants suggests with a high degree of confidence (p=3.6998838542834E-50, 95% CI -0.567 to -0.342) that Dark Mint Chocolate Chip Protein Bar has a moderately negative predictive relationship (R=-0.4544) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 0.19 serving Dark Mint Chocolate Chip Protein Bar per day. The lowest quartile of Overall Mood measurements were observed following an average 1.5157112725747 serving Dark Mint Chocolate Chip Protein Bar per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Dark Mint Chocolate Chip Protein Bar and Overall Mood. Additionally, we attempt to determine the Dark Mint Chocolate Chip Protein Bar values most likely to produce optimal Overall Mood values.

Participant Instructions

Get MyFitnessPal here and use it to record your Dark Mint Chocolate Chip Protein Bar. Once you have a MyFitnessPal 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.

Design

This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0.5 hours would pass before a change in Dark Mint Chocolate Chip Protein Bar would produce an observable change in Overall Mood. It was assumed that Dark Mint Chocolate Chip Protein Bar could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.

Data Sources

The QuantiModo platform was used to aggregate data from these data sources.

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 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:
1. Strength (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.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. 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.
4. 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).
5. 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful.
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect.
8. Experiment: Occasionally it is possible to appeal to experimental evidence.
9. Analogy: The effect of similar factors may be considered.
The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 395 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Dark Mint Chocolate Chip Protein Bar 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. Furthermore, it will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.


Relationship Statistics

Property Value
Cause Variable Name Dark Mint Chocolate Chip Protein Bar consumption
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.040391380345466
Confidence Level high
Confidence Interval 0.11215694510457
Forward Pearson Correlation Coefficient -0.4544
Critical T Value 1.646
Total Dark Mint Chocolate Chip Protein Bar consumption Over Previous 7 days Before ABOVE Average Overall Mood 0.19 serving
Total Dark Mint Chocolate Chip Protein Bar consumption Over Previous 7 days Before BELOW Average Overall Mood 1.5157112725747 serving
Duration of Action 7 days
Effect Size moderately negative
Number of Paired Measurements 395
Optimal Pearson Product 0.0086931384067512
P Value 3.6998838542834E-50
Statistical Significance 0.5547
Strength of Relationship 0.11215694510457
Study Type population
Analysis Performed At 2017-12-12
Number of Participants 2


Dark Mint Chocolate Chip Protein Bar Statistics

Property Value
Variable Name Dark Mint Chocolate Chip Protein Bar
Aggregation Method SUM
Analysis Performed At 2018-11-16
Duration of Action 7 days
Kurtosis 28.767551260641
Maximum Allowed Value 20 serving
Mean 0.42645333333333 serving
Median 0.33333333333333 serving
Minimum Allowed Value 0 serving
Number of Correlations 65
Number of Measurements 633
Onset Delay 30 minutes
Standard Deviation 0.38824771792749
Unit Serving
Variable ID 1630
Variance 0.22668717796951


Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2018-11-17
Duration of Action 24 hours
Kurtosis 3.7416256154524
Maximum Allowed Value 5 out of 5
Mean 3.1155337108594 out of 5
Median 3.1365425423606 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1151
Number of Measurements 606419
Onset Delay 0 seconds
Standard Deviation 0.57006455569065
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.4399449452354