↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Overall Mood















↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Overall Mood

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For most, Overall Mood is generally highest after a total of 428.26 milligrams of Acetyl-L-Carnitine intake over the previous 7 days.


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↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Overall Mood


Abstract

Aggregated data from 2 study participants suggests with a high degree of confidence (p=1.3054943304997E-18, 95% CI 0.166 to 0.529) that Acetyl-L-Carnitine has a moderately positive predictive relationship (R=0.35) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 428.26 milligrams Acetyl-L-Carnitine per day. The lowest quartile of Overall Mood measurements were observed following an average 52.90791592548 mg Acetyl-L-Carnitine per day.

Objective

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

Participant Instructions

Get Google Calendar here and use it to record your Acetyl-L-Carnitine. Once you have a Google Calendar 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 Acetyl-L-Carnitine would produce an observable change in Overall Mood. It was assumed that Acetyl-L-Carnitine 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. 130 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Acetyl-L-Carnitine 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.

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Charts

Statistics

Property Value
Aggregate QM Score 0.044817998748539
Qm Score 0.044817998748539
Number Of Correlations 2
Population Trait Correlation Pearson Correlation Coefficient 0.84557088547186
Average Daily High Cause 1241
Average Daily Low Cause 50
Average Effect 3
Average Effect Following High Cause 4
Average Effect Following Low Cause 3
Cause Changes 29
Cause Variable Category Name Treatments
Cause Variable Combination Operation SUM
Cause Variable Common Unit Abbreviated Name mg
Cause Variable Common Unit Name Milligrams
Cause Variable Display Name Acetyl-L-Carnitine intake
Cause Variable Name Acetyl-L-Carnitine
Confidence Interval 0.1817674996408
Confidence Level high
Correlation Coefficient 0.3474
Created At 2018-10-20 20:48:09
Critical T Value 1.655
Cumulative Value Over Duration Of Action Predicting High Outcome 428.26
Cumulative Value Over Duration Of Action Predicting Low Outcome 52.90791592548
Direction higher
Duration Of Action 604800
Duration Of Action In Hours 168
Effect Changes 178
Effect Size moderately positive
Effect Variable Category Name Emotions
Effect Variable Common Unit Abbreviated Name /5
Effect Variable Common Unit Name 1 to 5 Rating
Effect Variable Display Name Overall Mood
Effect Variable Name Overall Mood
Effect Variable Valence positive
Forward Pearson Correlation Coefficient 0.3474
Grouped Cause Value Closest To Value Predicting High Outcome 500
Number Of Pairs 130
Number Of Users 2
Onset Delay 1800
Onset Delay In Hours 0.5
Optimal Pearson Product 0.63699675343992
Predictive Pearson Correlation Coefficient 0.011748519617646
Predictor Explanation ↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Overall Mood
Predicts High Effect Change 8
Predicts Low Effect Change -4
P Value 1.3054943304997E-18
Reverse Pearson Correlation Coefficient 0.3802498061214
Significant Difference 1
Statistical Significance 0.3095
Strength Level weak
T Value 8.895826458937
Type population
Updated At 2018-10-20 20:48:09
Id 1398

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↑Higher CauseVariableName Intake Predicts ↑higher EffectVariableName

















↑Higher CauseVariableName Intake Predicts ↑higher EffectVariableName

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Your last CauseVariableName recording was 0 milligrams 2025 days ago. Your EffectVariableName is generally lowest after a total of 44 milligrams of CauseVariableName intake over the previous 24 hours.


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↑Higher CauseVariableName Intake Predicts ↑higher EffectVariableName


Abstract

You recorded 0 milligrams CauseVariableName 2124 days ago. Your EffectVariableName is generally -5.68% lower than normal after 44 milligrams CauseVariableName per 24 hours. Your data suggests with a low degree of confidence (p=0.24959098488184, 95% CI -9.644 to 9.882) that CauseVariableName has a weakly positive predictive relationship (R=0.12) with EffectVariableName. The highest quartile of EffectVariableName measurements were observed following an average 52.77 milligrams CauseVariableName per day. The lowest quartile of EffectVariableName measurements were observed following an average 44 mg CauseVariableName per day.EffectVariableName is generally 5.68% lower than normal after around 52.77 milligrams CauseVariableName over the previous 24 hours. EffectVariableName is generally 6.28% higher after around 52.77 milligramsCauseVariableName over the previous 24 hours.

Objective

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

Participant Instructions

You currently have 100 CauseVariableName measurements.
You currently have 100 EffectVariableName measurements.

Design

This study is based on data donated by one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

You currently have 100 EffectVariableName measurements. You currently have 100 CauseVariableName measurements. It was assumed that 0.5 hours would pass before a change in CauseVariableName would produce an observable change in EffectVariableName. It was assumed that CauseVariableName could produce an observable change in EffectVariableName for as much as 1 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. 99 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their CauseVariableName 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.

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Charts


Statistics

Property Value
All Pairs Significance 0.96311683259876
Cause Changes 98
Cause Changes Statistical Significance 0.96186667345295
Cause Number Of Processed Daily Measurements 199
Cause Number Of Raw Measurements 100
Correlation Coefficient 0.119
Duration Of Action 86400
Effect Changes 98
Effect Number Of Processed Daily Measurements 100
Effect Number Of Raw Measurements 100
Effect Variable Is Outcome Of Interest 1
Experiment End Time 2012-12-27T16:37:20+00:00
Experiment Start Time 2012-09-19T16:37:20+00:00
Forward Spearman Correlation Coefficient 0.12209647495362
Number Of Days 98
Number Of Days Significance 0.96186667345295
Number Of Pairs 99
Onset Delay 1800
Optimal Change Spread 11.96
Optimal Change Spread Significance 0.98143851727597
Qm Score 0.099
Raw Cause Measurement Significance 0.96432600665275
Raw Effect Measurement Significance 0.96432600665275
Statistical Significance 0.82862205227685
Vote Statistical Significance 1
Average Daily High Cause 75.266666666667
Average Daily Low Cause 25.574074074074
Average Effect 47.646464646465
Average Effect Following High Cause 52.022222222222
Average Effect Following Low Cause 44
Cause Value Spread 100
Cause Variable Category Name Treatments
Cause Variable Common Unit Abbreviated Name mg
Cause Variable Common Unit Name Milligrams
Cause Variable Display Name CauseVariableName intake
Cause Variable Name CauseVariableName
Confidence Interval 9.7629043881773
Confidence Level low
Critical T Value 1.66
Cumulative Value Over Duration Of Action Predicting High Outcome 52.77
Cumulative Value Over Duration Of Action Predicting Low Outcome 44
Degrees Of Freedom 98
Direction higher
Duration Of Action In Hours 24
Effect Size weakly positive
Effect Variable Category Name Symptoms
Effect Variable Common Unit Abbreviated Name %
Effect Variable Common Unit Name Percent
Effect Variable Display Name EffectVariableName
Effect Variable Name EffectVariableName
Effect Variable Valence negative
Forward Pearson Correlation Coefficient 0.119
Grouped Cause Value Closest To Value Predicting High Outcome 53
Grouped Cause Value Closest To Value Predicting Low Outcome 44
Maximum Cause Value 100
Minimum Probability 0.05
Number Of Cause Changes For Optimal Values 98
Number Of Effect Changes For Optimal Values 98
Number Of High Effect Pairs 47
Number Of Low Effect Pairs 52
Number Of Unique Cause Values For Optimal Values 67
Number Of Unique Effect Values For Optimal Values 67
Onset Delay In Hours 0.5
Optimal Pearson Product 0.035440250269802
Predictor Explanation ↑Higher CauseVariableName Intake Predicts ↑higher EffectVariableName
Predicts High Effect Change 6.28
Predicts Low Effect Change -5.68
P Value 0.24959098488184
Strength Level very weak
T Value 0.96849700897456
Type individual

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↑Higher Entertainment Predicts ↑higher Very Distracting

















↑Higher Entertainment Predicts ↑higher Very Distracting

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For most, Very Distracting is generally highest after a total of 2 hours of Entertainment over the previous 7 days.


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↑Higher Entertainment Predicts ↑higher Very Distracting


Abstract

Aggregated data from 48 study participants suggests with a medium degree of confidence (p=0.0570625, 95% CI -1.798 to 2.589) that Entertainment Hours has a moderately positive predictive relationship (R=0.4) with Very Distracting Hours. The highest quartile of Very Distracting Hours measurements were observed following an average 2 hours Entertainment Hours per day. The lowest quartile of Very Distracting Hours measurements were observed following an average 0.40272555235811 h Entertainment Hours per day.

Objective

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

Participant Instructions

Get RescueTime here and use it to record your Entertainment. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Get RescueTime here and use it to record your Very Distracting. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.

Design

This study is based on data donated by 48 participants. Thus, the study design is equivalent to the aggregation of 48 separate n=1 observational natural experiments.

Data Analysis

It was assumed that 0 hours would pass before a change in Entertainment Hours would produce an observable change in Very Distracting Hours. It was assumed that Entertainment Hours could produce an observable change in Very Distracting Hours 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. 251 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Entertainment 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.

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Study

Charts

Statistics

Property Value
Aggregate QM Score 0.36027693527377
Qm Score 0.36027693527377
Number Of Correlations 48
Population Trait Correlation Pearson Correlation Coefficient 0.37936563133989
Average Daily High Cause 5
Average Daily Low Cause 1
Average Effect 10
Average Effect Following High Cause 19
Average Effect Following Low Cause 10
Cause Changes 11070
Cause Variable Category Name Activities
Cause Variable Combination Operation SUM
Cause Variable Common Unit Abbreviated Name h
Cause Variable Common Unit Name Hours
Cause Variable Display Name Entertainment
Cause Variable Name Entertainment Hours
Confidence Interval 2.1936128738177
Confidence Level medium
Correlation Coefficient 0.3954
Created At 2017-11-23 03:30:33
Critical T Value 1.6564166666667
Cumulative Value Over Duration Of Action Predicting High Outcome 2.19
Cumulative Value Over Duration Of Action Predicting Low Outcome 0.40272555235811
Data Analysis It was assumed that 0 hours would pass before a change in Entertainment Hours would produce an observable change in Very Distracting Hours. It was assumed that Entertainment Hours could produce an observable change in Very Distracting Hours for as much as 7 days after the stimulus event.
Data Sources The QuantiModo platform was used to aggregate data from these data sources.
Data Sources Paragraph For Cause Entertainment Hours data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.
Data Sources Paragraph For Effect Very Distracting Hours data was primarily collected using RescueTime. Detailed reports show which applications and websites you spent time on. Activities are automatically grouped into pre-defined categories with built-in productivity scores covering thousands of websites and applications. You can customize categories and productivity scores to meet your needs.
Direction higher
Duration Of Action 604800
Duration Of Action In Hours 168
Effect Changes 11716
Effect Size moderately positive
Effect Variable Category Name Goals
Effect Variable Common Unit Abbreviated Name h
Effect Variable Common Unit Name Hours
Effect Variable Display Name Very Distracting
Effect Variable Name Very Distracting Hours
Forward Pearson Correlation Coefficient 0.3954
Number Of Pairs 251
Number Of Users 48
Optimal Pearson Product 0.82828907003028
Predictor Explanation ↑Higher Entertainment Predicts ↑higher Very Distracting
Predicts High Effect Change 64
Predicts Low Effect Change -12
P Value 0.0570625
Sharing Description Does Entertainment affect Very Distracting?
Sharing Title Does Entertainment affect Very Distracting?
Significant Difference 1
Statistical Significance 0.8456
Strength Level weak
T Value 4.8537774828509
Type population
Updated At 2017-12-12 00:23:45
Id 111532
Average Effect Following High Cause Explanation Very Distracting Hours is 19 h (90% higher) on average after days with around 5h Entertainment Hours
Cumulative Value Over Duration Of Action Predicting High Outcome Explanation Very Distracting Hours is generally 64% higher after around 2 hoursEntertainment Hours over the previous 7 days.
Cumulative Value Over Duration Of Action Predicting Low Outcome Explanation Very Distracting Hours is generally 12% lower than normal after around 24 minutes Entertainment Hours over the previous 7 days.
Participant Instructions Get RescueTime here and use it to record your Entertainment. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Get RescueTime here and use it to record your Very Distracting. Once you have a RescueTime account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Significance Explanation Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Very Distracting Hours is statistically significant at 95% confidence interval.
Study Abstract Aggregated data from 48 study participants suggests with a medium degree of confidence (p=0.0570625, 95% CI -1.798 to 2.589) that Entertainment Hours has a moderately positive predictive relationship (R=0.4) with Very Distracting Hours. The highest quartile of Very Distracting Hours measurements were observed following an average 2 hours Entertainment Hours per day. The lowest quartile of Very Distracting Hours measurements were observed following an average 0.40272555235811 h Entertainment Hours per day.
Study Background In order to reduce suffering through the advancement of human knowledge, I have chosen to share my findings regarding the relationship between Entertainment and Very Distracting.
Study Design This study is based on data donated by 48 participants. Thus, the study design is equivalent to the aggregation of 48 separate n=1 observational natural experiments.
Study Invitation Donate a few seconds a day to help us discover if Entertainment affects Very Distracting!
Study 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. 251 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Entertainment 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.

Study Objective The objective of this study is to determine the nature of the relationship (if any) between Entertainment and Very Distracting. Additionally, we attempt to determine the Entertainment values most likely to produce optimal Very Distracting values.
Study Question Does Entertainment affect Very Distracting?
Study Results This analysis suggests that higher Entertainment Hours (Activities) generally predicts higher Very Distracting Hours (p=0.0570625, 95% CI -1.798 to 2.589) Very Distracting Hours is, on average, 64% higher after around 2 hours Entertainment Hours. After an onset delay of 0 seconds, Very Distracting Hours is, on average, 12% than its average over the 7 days following around 24 minutes Entertainment Hours. 251 data points were used in this analysis. The value for Entertainment Hours changed 11070 times, effectively running 5535 separate natural experiments. The top quartile outcome values are preceded by an average 2 hours of Entertainment Hours. The bottom quartile outcome values are preceded by an average 24 minutes of Entertainment Hours. The Forward Pearson Correlation Coefficient was 0.395((p=0.0570625, 95% CI -1.798 to 2.589) , onset delay = 0 seconds, duration of action = 7 days). The Reverse Pearson Correlation Coefficient was (0.0570625, 95% CI -2.194 to 2.194, onset delay = -0 seconds, duration of action = -7 days). When the Entertainment Hours value is closer to 2 hours than 24 minutes, the Very Distracting Hours value which follows is, on average, 64% percent higher than its typical value. When the Entertainment Hours value is closer to 24 minutes than 2 hours, the Very Distracting Hours value which follows is , on average, 12% than its typical value. Very Distracting Hours is 19 h (90% higher) on average after days with around 5h Entertainment Hours
Study Title ↑Higher Entertainment Predicts ↑higher Very Distracting
Tag Line For most, Very Distracting is generally highest after a total of 2 hours of Entertainment over the previous 7 days.
Cause Variable Image Url https://quantimodo.quantimo.do/ionic/Modo/www/img/variable_categories/activity.svg
Cause Variable Svg Url https://quantimodo.quantimo.do/ionic/Modo/www/img/variable_categories/activity.svg
Cause Variable Png Url https://quantimodo.quantimo.do/ionic/Modo/www/img/variable_categories/activity.png
Cause Variable Ion Icon ion-ios-body-outline
Effect Variable Image Url https://quantimodo.quantimo.do/ionic/Modo/www/img/variable_categories/work.svg
Effect Variable Svg Url https://quantimodo.quantimo.do/ionic/Modo/www/img/variable_categories/work.svg
Effect Variable Png Url https://quantimodo.quantimo.do/ionic/Modo/www/img/variable_categories/work.png
Effect Variable Ion Icon ion-laptop
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Study Join Link https://pImUNsI6T5Ysd81k.quantimo.do/ionic/Modo/www/index.html#/app/study-join?appName=MoodiModo&appVersion=2.8.501&clientId=moodimodo&causeVariableId=111612&effectVariableId=111532
Study Link Dynamic https://pImUNsI6T5Ysd81k.quantimo.do/ionic/Modo/www/index.html#/app/study?appName=MoodiModo&appVersion=2.8.501&clientId=moodimodo&causeVariableId=111612&effectVariableId=111532
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Study Link Twitter https://twitter.com/home?status=%E2%86%91Higher%20Entertainment%20Predicts%20%E2%86%91higher%20Very%20Distracting%20https%3A%2F%2Fapp.quantimo.do%2Fapi%2Fv2%2Fstudy%3FcauseVariableId%3D111612%26effectVariableId%3D111532%20%40quantimodo

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↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Energy















↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Energy

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For most, Energy is generally highest after a total of 369.57 milligrams of Acetyl-L-Carnitine intake over the previous 7 days.


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↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Energy


Abstract

Aggregated data from 2 study participants suggests with a high degree of confidence (p=0.001, 95% CI 0.178 to 0.558) that Acetyl-L-Carnitine has a moderately positive predictive relationship (R=0.37) with Energy. The highest quartile of Energy measurements were observed following an average 369.57 milligrams Acetyl-L-Carnitine per day. The lowest quartile of Energy measurements were observed following an average 0 mg Acetyl-L-Carnitine per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Acetyl-L-Carnitine and Energy. Additionally, we attempt to determine the Acetyl-L-Carnitine values most likely to produce optimal Energy values.

Participant Instructions

Get Google Calendar here and use it to record your Acetyl-L-Carnitine. Once you have a Google Calendar account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Record your Energy 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 Acetyl-L-Carnitine would produce an observable change in Energy. It was assumed that Acetyl-L-Carnitine could produce an observable change in Energy 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. 93 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Acetyl-L-Carnitine 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.

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Statistics

Property Value
Aggregate QM Score 0.040387555892441
Qm Score 0.040387555892441
Number Of Correlations 2
Population Trait Correlation Pearson Correlation Coefficient -0.75998508190589
Average Daily High Cause 500
Average Effect 3
Average Effect Following High Cause 4
Average Effect Following Low Cause 3
Cause Changes 16
Cause Variable Category Name Treatments
Cause Variable Combination Operation SUM
Cause Variable Common Unit Abbreviated Name mg
Cause Variable Common Unit Name Milligrams
Cause Variable Display Name Acetyl-L-Carnitine intake
Cause Variable Name Acetyl-L-Carnitine
Confidence Interval 0.18999300104513
Confidence Level high
Correlation Coefficient 0.368
Created At 2017-10-07 05:24:44
Critical T Value 1.66
Cumulative Value Over Duration Of Action Predicting High Outcome 369.57
Direction higher
Duration Of Action 604800
Duration Of Action In Hours 168
Effect Changes 158
Effect Size moderately positive
Effect Variable Category Name Emotions
Effect Variable Common Unit Abbreviated Name /5
Effect Variable Common Unit Name 1 to 5 Rating
Effect Variable Display Name Energy
Effect Variable Name Energy
Effect Variable Valence positive
Forward Pearson Correlation Coefficient 0.368
Number Of Pairs 93
Number Of Users 2
Onset Delay 1800
Onset Delay In Hours 0.5
Optimal Pearson Product 0.5617444982872
Predictor Explanation ↑Higher Acetyl-L-Carnitine Intake Predicts ↑higher Energy
Predicts High Effect Change 4
Predicts Low Effect Change -7
P Value 0.001
Significant Difference 1
Statistical Significance 0.2133
Strength Level weak
T Value 3.3599393856449
Type population
Updated At 2017-12-12 00:23:45
Id 1306

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↑Higher 5 HTP Intake Predicts ↓lower Overall Mood















↑Higher 5 HTP Intake Predicts ↓lower Overall Mood

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For most, Overall Mood is generally highest after a total of 0.01 milligrams of 5 HTP intake over the previous 7 days.


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↑Higher 5 HTP Intake Predicts ↓lower Overall Mood


Abstract

Aggregated data from 2 study participants suggests with a high degree of confidence (701 overlapping data points) that 5 HTP has a weakly negative predictive relationship (R=-0.2455) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 0.01 milligrams 5 HTP per day. The lowest quartile of Overall Mood measurements were observed following an average 17.454859799172 mg 5 HTP per day.

Objective

The objective of this study is to determine the nature of the relationship (if any) between 5 HTP and Overall Mood. Additionally, we attempt to determine the 5 HTP values most likely to produce optimal Overall Mood values.

Participant Instructions

Get GitHub here and use it to record your 5 HTP. Once you have a GitHub 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 5 HTP would produce an observable change in Overall Mood. It was assumed that 5 HTP 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. 701 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their 5 HTP 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.

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Study

Charts

Statistics

Property Value
Aggregate QM Score 0.044461423390318
Qm Score 0.044461423390318
Number Of Correlations 2
Population Trait Correlation Pearson Correlation Coefficient 0.99976689745948
Average Daily High Cause 200
Average Daily Low Cause 5
Average Effect 3
Average Effect Following High Cause 2
Average Effect Following Low Cause 3
Cause Changes 138
Cause Variable Category Name Treatments
Cause Variable Combination Operation SUM
Cause Variable Common Unit Abbreviated Name mg
Cause Variable Common Unit Name Milligrams
Cause Variable Display Name 5 HTP intake
Cause Variable Name 5 HTP
Confidence Interval 0.072774541869103
Confidence Level high
Correlation Coefficient -0.2455
Created At 2017-09-20 19:21:47
Critical T Value 1.646
Cumulative Value Over Duration Of Action Predicting High Outcome 0.01
Cumulative Value Over Duration Of Action Predicting Low Outcome 17.454859799172
Direction lower
Duration Of Action 604800
Duration Of Action In Hours 168
Effect Changes 1213
Effect Size weakly negative
Effect Variable Category Name Emotions
Effect Variable Common Unit Abbreviated Name /5
Effect Variable Common Unit Name 1 to 5 Rating
Effect Variable Display Name Overall Mood
Effect Variable Name Overall Mood
Effect Variable Valence positive
Forward Pearson Correlation Coefficient -0.2455
Number Of Pairs 701
Number Of Users 2
Onset Delay 1800
Onset Delay In Hours 0.5
Optimal Pearson Product 0.08006052282559
Predictive Pearson Correlation Coefficient -0.14146717929012
Predictor Explanation ↑Higher 5 HTP Intake Predicts ↓lower Overall Mood
Predicts High Effect Change 2
Predicts Low Effect Change -17
Reverse Pearson Correlation Coefficient -0.10398862280164
Significant Difference 1
Statistical Significance 0.5498
Strength Level weak
T Value 13.10621066028
Type population
Updated At 2017-12-12 00:23:45
Id 1398

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