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Based on data from 15 participants, Tiredness / Fatigue is generally lowest after an average of 2.2 out of 5 of Distress over the previous 24 hours.
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People with higher Distress usually have higher Tiredness / Fatigue
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Distress on each day of the week.
This chart shows the typical value recorded for Distress for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Tiredness / Fatigue on each day of the week.
This chart shows the typical value recorded for Tiredness / Fatigue for each month of the year.

Abstract

Aggregated data from 15 study participants suggests with a medium degree of confidence (p=0.19264205714878, 95% CI -0.361 to 0.696) that Distress has a weakly positive predictive relationship (R=0.17) with Tiredness / Fatigue. The highest quartile of Tiredness / Fatigue measurements were observed following an average 2.13 out of 5 Distress. The lowest quartile of Tiredness / Fatigue measurements were observed following an average 1.8195093795094 /5 Distress.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Distress and Tiredness / Fatigue. Additionally, we attempt to determine the Distress values most likely to produce optimal Tiredness / Fatigue values.

Participant Instructions

Record your Distress daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Tiredness / Fatigue daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 15 participants. Thus, the study design is equivalent to the aggregation of 15 separate n=1 observational natural experiments.

Data Analysis

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

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Distress would produce an observable change in Tiredness / Fatigue. It was assumed that Distress could produce an observable change in Tiredness / Fatigue for as much as 1 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Distress 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.

Tiredness / Fatigue 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 weakly positive relationship between Distress and Tiredness / Fatigue

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. 34 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Distress 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 Distress and Tiredness / Fatigue 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 Distress
Effect Variable Name Tiredness / Fatigue
Sinn Predictive Coefficient 0.037440676019473
Confidence Level medium
Confidence Interval 0.52820556641802
Forward Pearson Correlation Coefficient 0.1677
Critical T Value 1.7524
Average Distress Over Previous 24 hours Before ABOVE Average Tiredness / Fatigue 2.13 out of 5
Average Distress Over Previous 24 hours Before BELOW Average Tiredness / Fatigue 1.8195093795094 out of 5
Duration of Action 24 hours
Effect Size weakly positive
Number of Paired Measurements 34
Optimal Pearson Product 0.10161577344123
P Value 0.19264205714878
Statistical Significance 0.14786666962318
Strength of Relationship 0.52820556641802
Study Type population
Analysis Performed At 2019-04-09
Number of Participants 15

Distress Statistics

Property Value
Variable Name Distress
Aggregation Method MEAN
Analysis Performed At 2019-02-21
Duration of Action 24 hours
Kurtosis 2.8813080392775
Maximum Allowed Value 5 out of 5
Mean 2.4661185369318 out of 5
Median 2.4103051371244 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 345
Number of Measurements 32940
Onset Delay 0 seconds
Standard Deviation 0.52551215340851
Unit 1 to 5 Rating
UPC 647297398818
Variable ID 1305
Variance 0.61453835687818

Tiredness / Fatigue Statistics

Property Value
Variable Name Tiredness / Fatigue
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 7 days
Kurtosis 2.0842183459689
Maximum Allowed Value 5 out of 5
Mean 3.3735747731397 out of 5
Median 3.3707500321069 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 312
Number of Measurements 8772
Onset Delay 0 seconds
Standard Deviation 0.35540512470403
Unit 1 to 5 Rating
UPC 635797687433
Variable ID 87760
Variance 0.36873751536251

Tracking Distress

Record your Distress daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Tiredness / Fatigue

Record your Tiredness / Fatigue 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