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Based on data from 81 participants, Overall Mood is generally highest after an average of 1500 count of Daily Step over the previous 7 days.
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People with higher Daily Step usually have higher Overall Mood
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Steps on each day of the week.
This chart shows the typical value recorded for Steps for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Overall Mood on each day of the week.
This chart shows the typical value recorded for Overall Mood for each month of the year.

Abstract

Aggregated data from 81 study participants suggests with a medium degree of confidence (p=0.22965500382689, 95% CI -0.392 to 0.486) that Daily Step Count has a very weakly positive predictive relationship (R=0.05) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 5 count Daily Step Count. The lowest quartile of Overall Mood measurements were observed following an average 5248.0921638779 count Daily Step Count.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Steps. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's 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 81 participants. Thus, the study design is equivalent to the aggregation of 81 separate n=1 observational natural experiments.

Data Analysis

Steps Pre-Processing
Steps measurement values below 0 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Steps. No missing data filling value was defined for Steps so any gaps in data were just not analyzed instead of assuming zero values for those times.
Steps Analysis Settings

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.
Overall Mood Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Daily Step Count would produce an observable change in Overall Mood. It was assumed that Daily Step Count could produce an observable change in Overall Mood for as much as 6.93 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Daily Step Count data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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 causation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out 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 very weakly positive relationship between Daily Step 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. 279 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Daily Step 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, 4 humans feel that there is a plausible mechanism of action and 1 feel that any relationship observed between Daily Step 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 Daily Step
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.039535453153914
Confidence Level medium
Confidence Interval 0.43904165415134
Forward Pearson Correlation Coefficient 0.0468
Critical T Value 1.7257740728684
Average Daily Step Over Previous 7 days Before ABOVE Average Overall Mood 5 count
Average Daily Step Over Previous 7 days Before BELOW Average Overall Mood 5248.0921638779 count
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 279
Optimal Pearson Product 0.052660565508273
P Value 0.22965500382689
Statistical Significance 0.29818888769175
Strength of Relationship 0.43904165415134
Study Type population
Analysis Performed At 2019-04-04
Number of Participants 81

Steps Statistics

Property Value
Variable Name Daily Step Count
Aggregation Method MEAN
Analysis Performed At 2019-03-31
Duration of Action 7 days
Kurtosis 34.268362395225
Mean 2965.1029013514 count
Median 2070.8941441441 count
Minimum Allowed Value 0 count
Number of Correlations 754
Number of Measurements 58312
Onset Delay 0 seconds
Standard Deviation 3113.6074061369
Unit Count
UPC 734010049130
Variable ID 1451
Variance 13251645.990518

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-04-03
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


Tracking Steps

Get Fitbit here and use it to record your Steps. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Tracking Overall Mood

Record your Overall Mood daily in the reminder inbox or using the interactive web or mobile notifications.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn