Higher Exercise Predicts higher Overall Mood





↑Higher Exercise Predicts ↑higher Overall Mood
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Principal Investigator – Mike Sinn

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For most, Overall Mood is generally .

Created with Highstock 6.2.0Average Exercise (min) for ParticipantAverage Overall Mood (/5) for ParticipantTrait Correlation Between Exercise and Overall MoodPeople with higher average Exercise usually have higher average Overall Mood. (R = 0.055666607004605)01020304050602.533.544.55
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Created with Highstock 6.2.0Average Daily Value (min)Number of DaysDaily Exercise DistributionEach column represents the number of days this value occurred.00.51258102030405060708090100050100
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Created with Highstock 6.2.0DayAverage (min)Average Exercise by Day of WeekThis chart shows the typical value recorded for Exercise on each day of the week.SunMonTueWedThuFriSat2530354045
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Created with Highstock 6.2.0MonthDaily Average (min)Average Exercise by MonthThis chart shows the typical value recorded for Exercise for each month of the year.JanuaryFebruaryMarchAprilMayJulyAugustSeptemberOctoberNovemberDecember0255075
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Created with Highstock 6.2.0Average Daily Value (/5)Number of DaysDaily Overall Mood DistributionEach column represents the number of days this value occurred.1234501k2k
https://quantimo.do
Created with Highstock 6.2.0DayAverage (/5)Average Overall Mood by Day of WeekThis chart shows the typical value recorded for Overall Mood on each day of the week.SunMonTueWedThuFriSat2.852.8752.92.9252.95
https://quantimo.do
Created with Highstock 6.2.0MonthAverage (/5)Average Overall Mood by MonthThis chart shows the typical value recorded for Overall Mood for each month of the year.JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember32.52.753.25
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Abstract

Aggregated data from 4 study participants suggests with a medium degree of confidence (p=0.36, 95% CI 0.106 to 0.636) that Exercise has a moderately positive predictive relationship (R=0.37) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 19 minutes Exercise per day. The lowest quartile of Overall Mood measurements were observed following an average 17.286932339576 min Exercise per day.

Objective

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

Participant Instructions

Record your Exercise 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 4 participants. Thus, the study design is equivalent to the aggregation of 4 separate n=1 observational natural experiments.

Data Analysis

Exercise Pre-Processing
Exercise measurement values below 0 seconds were assumed erroneous and removed. Exercise measurement values above 7 days were assumed erroneous and removed. It was assumed that any gaps in Exercise data were unrecorded 0 seconds measurement values.

Overall Mood Pre-Processing
Overall Mood measurement values below 1 out of 5 were assumed erroneous and removed. Overall Mood measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Overall Mood so any gaps in data were just not analyzed instead of assuming zero values for those times.

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

Data Sources

Exercise data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Overall Mood data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply correlation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out 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:
Strength (A.K.A. Effect Size)
A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a moderately positive relationship between Exercise and Overall Mood

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 368 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Exercise values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Specificity
Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

Temporality
The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient
Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 32 humans feel that there is a plausible mechanism of action and 5 feel that any relationship observed between Exercise and Overall Mood is coincidental.

Coherence
Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants
with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment
All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy
The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Exercise
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.012540092612063
Confidence Level medium
Confidence Interval 0.26509740417056
Forward Pearson Correlation Coefficient 0.37109968275393
Critical T Value 1.733
Total Exercise Over Previous 7 days Before ABOVE Average Overall Mood 19 minutes
Total Exercise Over Previous 7 days Before BELOW Average Overall Mood 17 minutes
Duration of Action 7 days
Effect Size moderately positive
Number of Paired Measurements 368
Optimal Pearson Product -0.00046061526444267
P Value 0.36
Statistical Significance 0.46757499454543
Strength of Relationship 0.26509740417056
Study Type population
Analysis Performed At 2019-01-12
Number of Participants 4

Exercise Statistics

Property Value
Variable Name Exercise
Aggregation Method MEAN
Analysis Performed At 2019-01-12
Duration of Action 7 days
Kurtosis 2.4622420993866
Maximum Allowed Value 7 days
Mean 23 minutes
Median 24 minutes
Minimum Allowed Value 0 seconds
Number of Correlations 50
Number of Measurements 247
Onset Delay 0 seconds
Standard Deviation 13.434816311673
Unit Minutes
UPC 641945618877
Variable ID 93529
Variance 436.1962285333

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-01-13
Duration of Action 24 hours
Kurtosis 3.7383708126619
Maximum Allowed Value 5 out of 5
Mean 3.1156748504321 out of 5
Median 3.1369047348216 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 1149
Number of Measurements 605816
Onset Delay 0 seconds
Standard Deviation 0.56833853113207
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.43884384603152


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