Mike P. Sinn
PRINCIPAL INVESTIGATOR
Mike P. Sinn

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For most, Inspiration is generally highest after an average of 54 percent of Cloud Cover Amount over the previous 7 days.
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People with higher Cloud Cover Amount usually have higher Inspiration
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Cloud Cover Amount on each day of the week.
This chart shows the typical value recorded for Cloud Cover Amount for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Inspiration on each day of the week.
This chart shows the typical value recorded for Inspiration for each month of the year.

Abstract

Aggregated data from 51 study participants suggests with a medium degree of confidence (p=0.2331220271533, 95% CI -0.359 to 0.349) that Cloud Cover Amount has a very weakly negative predictive relationship (R=-0.01) with Inspiration. The highest quartile of Inspiration measurements were observed following an average 50.67 percent Cloud Cover Amount. The lowest quartile of Inspiration measurements were observed following an average 43.272583217304 % Cloud Cover Amount.

Objective

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

Participant Instructions

Grant access to your weather and air quality data on the Import Data page. This individual's data will automatically be imported and analyzed.
Record your Inspiration daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 51 participants. Thus, the study design is equivalent to the aggregation of 51 separate n=1 observational natural experiments.

Data Analysis

Cloud Cover Amount Pre-Processing
No minimum allowed measurement value was defined for Cloud Cover Amount. No maximum allowed measurement value was defined for Cloud Cover Amount. No missing data filling value was defined for Cloud Cover Amount so any gaps in data were just not analyzed instead of assuming zero values for those times.
Cloud Cover Amount Analysis Settings

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Cloud Cover Amount would produce an observable change in Inspiration. It was assumed that Cloud Cover Amount could produce an observable change in Inspiration for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Cloud Cover Amount data was primarily collected using Weather. Automatically import temperature, humidity, hours of daylight, air quality and pollen count.

Inspiration 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 Cloud Cover Amount and Inspiration

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. 45 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Cloud Cover Amount 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 Cloud Cover Amount and Inspiration 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 Cloud Cover Amount
Effect Variable Name Inspiration
Sinn Predictive Coefficient 0.0024485696823597
Confidence Level medium
Confidence Interval 0.3543896969429
Forward Pearson Correlation Coefficient -0.005
Critical T Value 1.6976078431373
Average Cloud Cover Amount Over Previous 7 days Before ABOVE Average Inspiration 50.67 percent
Average Cloud Cover Amount Over Previous 7 days Before BELOW Average Inspiration 43.272583217304 percent
Duration of Action 7 days
Effect Size very weakly negative
Number of Paired Measurements 45
Optimal Pearson Product 0.079369761295426
P Value 0.2331220271533
Statistical Significance 0.25515294188232
Strength of Relationship 0.3543896969429
Study Type population
Analysis Performed At 2019-01-29
Number of Participants 51

Cloud Cover Amount Statistics

Property Value
Variable Name Cloud Cover Amount
Aggregation Method MEAN
Analysis Performed At 2019-01-27
Duration of Action 7 days
Kurtosis 2.1676602426388
Mean 46.062541516245 percent
Median 44.287680505415 percent
Number of Correlations 166
Number of Measurements 1058765
Onset Delay 0 seconds
Standard Deviation 29.381798754552
Unit Percent
Variable ID 5954747
Variance 893.11768234549

Inspiration Statistics

Property Value
Variable Name Inspiration
Aggregation Method MEAN
Analysis Performed At 2019-01-27
Duration of Action 24 hours
Kurtosis 2.961633165699
Maximum Allowed Value 5 out of 5
Mean 2.4171389017788 out of 5
Median 2.3874866116675 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 305
Number of Measurements 21779
Onset Delay 0 seconds
Standard Deviation 0.48943130717992
Unit 1 to 5 Rating
Variable ID 1355
Variance 0.53329535802421

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