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This individual's Overall Mood is generally highest after a daily total of 14 count of Awakenings over the previous 7 days.
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Blue represents the sum of Awakenings over the previous 7 days
An increase in 7 days cumulative Awakenings is usually followed by an decrease in Overall Mood. (R = -0.096)
Typical values for Overall Mood following a given amount of Awakenings over the previous 7 days.
Typical Awakenings seen over the previous 7 days preceding the given Overall Mood value.
This chart shows how your Awakenings changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Awakenings on each day of the week.
This chart shows the typical value recorded for Awakenings for each month of the year.
This chart shows how your Overall Mood changes over time.
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

This individual's Overall Mood is generally 1% higher than normal after a total of 14 count Awakenings over the previous 7 days. This individual's data suggests with a high degree of confidence (p=0.0025501757879041, 95% CI -0.116 to -0.076) that Awakenings has a very weakly negative predictive relationship (R=-0.1) with Overall Mood. The highest quartile of Overall Mood measurements were observed following an average 23.54 count Awakenings per day. The lowest quartile of Overall Mood measurements were observed following an average 24.72520661157 count Awakenings per day.Overall Mood is generally 1% lower than normal after a total of 24.72520661157 count of Awakenings over the previous 7 days. Overall Mood is generally 1% higher after a total of 23.54 count of Awakenings over the previous 7 days.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Awakenings. 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 one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

Awakenings Pre-Processing
Awakenings measurement values below 1 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Awakenings. No missing data filling value was defined for Awakenings so any gaps in data were just not analyzed instead of assuming zero values for those times.
Awakenings 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 Awakenings would produce an observable change in Overall Mood. It was assumed that Awakenings could produce an observable change in Overall Mood for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
1404 raw Awakenings measurements with 1390 changes spanning 1932 days from 2013-11-26 to 2019-03-12 were used in this analysis. 13614 raw Overall Mood measurements with 1188 changes spanning 2518 days from 2012-05-06 to 2019-03-29 were used in this analysis.

Data Sources

Awakenings 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 negative relationship between Awakenings 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. 1388 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Awakenings 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 2 feel that any relationship observed between Awakenings 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 Awakenings
Effect Variable Name Overall Mood
Sinn Predictive Coefficient 0.1064
Confidence Level high
Confidence Interval 0.020109562077553
Forward Pearson Correlation Coefficient -0.096
Critical T Value 1.646
Total Awakenings Over Previous 7 days Before ABOVE Average Overall Mood 23.54 count
Total Awakenings Over Previous 7 days Before BELOW Average Overall Mood 24.725 count
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 1388
Optimal Pearson Product 0.0099752610321247
P Value 0.0025501757879041
Statistical Significance 1
Strength of Relationship 0.020109562077553
Study Type individual
Analysis Performed At 2019-04-04

Awakenings Statistics

Property Value
Variable Name Awakenings
Aggregation Method SUM
Analysis Performed At 2019-03-29
Duration of Action 7 days
Kurtosis 3.5568308675072
Mean 23.904 count
Median 23 count
Minimum Allowed Value 1 count
Number of Changes 1390
Number of Correlations 2818
Number of Measurements 1404
Onset Delay 0 seconds
Standard Deviation 11.429476940955
Unit Count
Variable ID 1906
Variance 130.63294314381

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-03-29
Duration of Action 24 hours
Kurtosis 6.846028106746
Maximum Allowed Value 5 out of 5
Mean 2.9141 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1188
Number of Correlations 4066
Number of Measurements 13614
Onset Delay 0 seconds
Standard Deviation 0.52670621573651
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.27741943769547

Tracking Awakenings

Get Fitbit here and use it to record your Awakenings. 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