cause image
gauge image
effect image

This individual's Guiltiness is generally lowest after an average of 1300 count of Daily Step over the previous 7 days.
Join This Study
Go To Interactive Study
Blue represents the mean of Daily Step over the previous 7 days
An increase in 7 days cumulative Daily Step is usually followed by an decrease in Guiltiness. (R = -0.092)
Typical values for Guiltiness following a given amount of Daily Step over the previous 7 days.
Typical Daily Step seen over the previous 7 days preceding the given Guiltiness value.
This chart shows how your Steps changes over time.
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.
This chart shows how your Guiltiness changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Guiltiness on each day of the week.
This chart shows the typical value recorded for Guiltiness for each month of the year.

Abstract

This individual's Guiltiness is generally 2% lower than normal after 1274.5 count Daily Step Count per 7 days. This individual's data suggests with a high degree of confidence (p=0.051998465828567, 95% CI -0.17 to -0.014) that Daily Step Count has a very weakly negative predictive relationship (R=-0.09) with Guiltiness. The highest quartile of Guiltiness measurements were observed following an average 9 count Daily Step Count. The lowest quartile of Guiltiness measurements were observed following an average 9986.8837209302 count Daily Step Count.Guiltiness is generally 2% lower than normal after an average of 9986.8837209302 count of Daily Step over the previous 7 days. Guiltiness is generally 2% higher after an average of 9 count of Daily Step over the previous 7 days.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Daily Step Count and Guiltiness. Additionally, we attempt to determine the Daily Step Count values most likely to produce optimal Guiltiness 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 Guiltiness daily in the reminder inbox or using the interactive web or mobile notifications.

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

Steps Pre-Processing
Steps measurement values below 1000 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

Guiltiness Pre-Processing
Guiltiness measurement values below 1 out of 5 were assumed erroneous and removed. Guiltiness measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Guiltiness so any gaps in data were just not analyzed instead of assuming zero values for those times.
Guiltiness 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 Guiltiness. It was assumed that Daily Step Count could produce an observable change in Guiltiness for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
2022 raw Daily Step Count measurements with 1986 changes spanning 2534 days from 2012-04-25 to 2019-04-03 were used in this analysis. 2800 raw Guiltiness measurements with 661 changes spanning 1964 days from 2013-11-17 to 2019-04-04 were used in this analysis.

Data Sources

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

Guiltiness 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 negative relationship between Daily Step and Guiltiness

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. 1130 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, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Daily Step and Guiltiness 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 Guiltiness
Sinn Predictive Coefficient 0.092
Confidence Level high
Confidence Interval 0.077602783538993
Forward Pearson Correlation Coefficient -0.092
Critical T Value 1.646
Average Daily Step Over Previous 7 days Before ABOVE Average Guiltiness 9 count
Average Daily Step Over Previous 7 days Before BELOW Average Guiltiness 9 count
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 1130
Optimal Pearson Product 0.013227019425408
P Value 0.051998465828567
Statistical Significance 1
Strength of Relationship 0.077602783538993
Study Type individual
Analysis Performed At 2019-04-04

Steps Statistics

Property Value
Variable Name Daily Step Count
Aggregation Method MEAN
Analysis Performed At 2019-04-05
Duration of Action 7 days
Kurtosis 3.3732116804392
Mean 8637.2 count
Median 8432 count
Minimum Allowed Value 1000 count
Number of Changes 1986
Number of Correlations 1845
Number of Measurements 2022
Onset Delay 0 seconds
Standard Deviation 4577.2698673382
Unit Count
UPC 734010049130
Variable ID 1451
Variance 20951399.438442

Guiltiness Statistics

Property Value
Variable Name Guiltiness
Aggregation Method MEAN
Analysis Performed At 2019-04-04
Duration of Action 24 hours
Kurtosis 3.1224153723756
Maximum Allowed Value 5 out of 5
Mean 2.1627 out of 5
Median 2 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 661
Number of Correlations 5379
Number of Measurements 2800
Onset Delay 0 seconds
Standard Deviation 0.92130141512025
Unit 1 to 5 Rating
Variable ID 1335
Variance 0.84879629750257

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 Guiltiness

Record your Guiltiness daily in the reminder inbox or using the interactive web or mobile notifications.
Join This Study

https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn