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Based on data from 10 participants, Lack of Motivation is generally lowest after an average of 2.1 out of 5 of Loneliness over the previous 38 hours.
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People with higher Loneliness usually have higher Lack of Motivation
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Loneliness on each day of the week.
This chart shows the typical value recorded for Loneliness for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Lack of Motivation on each day of the week.
This chart shows the typical value recorded for Lack of Motivation for each month of the year.

Abstract

Aggregated data from 10 study participants suggests with a low degree of confidence (p=0.0643459902791, 95% CI -0.136 to 0.763) that Loneliness has a moderately positive predictive relationship (R=0.31) with Lack of Motivation. The highest quartile of Lack Of Motivation measurements were observed following an average 3.12 out of 5 Loneliness. The lowest quartile of Lack Of Motivation measurements were observed following an average 2.368663003663 /5 Loneliness.

Objective

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

Participant Instructions

Record your Loneliness daily in the reminder inbox or using the interactive web or mobile notifications.
Record your Lack of Motivation daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by 10 participants. Thus, the study design is equivalent to the aggregation of 10 separate n=1 observational natural experiments.

Data Analysis

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

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

Predictive Analytics
It was assumed that 0 hours would pass before a change in Loneliness would produce an observable change in Lack Of Motivation. It was assumed that Loneliness could produce an observable change in Lack Of Motivation for as much as 1.6 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Loneliness 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.

Lack Of Motivation 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 moderately positive relationship between Loneliness and Lack of Motivation

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. 44 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Loneliness 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, 3 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Loneliness and Lack of Motivation 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 Loneliness
Effect Variable Name Lack of Motivation
Sinn Predictive Coefficient 0.070583654632441
Confidence Level low
Confidence Interval 0.44947854886449
Forward Pearson Correlation Coefficient 0.3137
Critical T Value 1.7521
Average Loneliness Over Previous 38 hours Before ABOVE Average Lack of Motivation 3.12 out of 5
Average Loneliness Over Previous 38 hours Before BELOW Average Lack of Motivation 2.368663003663 out of 5
Duration of Action 38 hours
Effect Size moderately positive
Number of Paired Measurements 44
Optimal Pearson Product 0.19455898724887
P Value 0.0643459902791
Statistical Significance 0.11784999885131
Strength of Relationship 0.44947854886449
Study Type population
Analysis Performed At 2019-04-09
Number of Participants 10

Loneliness Statistics

Property Value
Variable Name Loneliness
Aggregation Method MEAN
Analysis Performed At 2019-02-02
Duration of Action 24 hours
Kurtosis 2.129823259139
Maximum Allowed Value 5 out of 5
Mean 3.1070804651163 out of 5
Median 3.1133204134367 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 90
Number of Measurements 3284
Onset Delay 0 seconds
Standard Deviation 0.37253680726713
Unit 1 to 5 Rating
UPC 783324854602
Variable ID 89438
Variance 0.43373220938802

Lack of Motivation Statistics

Property Value
Variable Name Lack of Motivation
Aggregation Method MEAN
Analysis Performed At 2019-04-06
Duration of Action 7 days
Kurtosis 2.016838045956
Maximum Allowed Value 5 out of 5
Mean 3.3784796703297 out of 5
Median 3.3545819854748 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 123
Number of Measurements 3339
Onset Delay 0 seconds
Standard Deviation 0.42789866056371
Unit 1 to 5 Rating
Variable ID 89387
Variance 0.44134223873528

Tracking Loneliness

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

Tracking Lack of Motivation

Record your Lack of Motivation 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