Higher Dizziness Or Lightheadedness Predicts Slightly Higher Sleep Duration for Population
Contents
Mike Sinn
PRINCIPAL INVESTIGATOR
Mike Sinn

Variables

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Dizziness or Lightheadedness 141
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Sleep Duration 3379

Categories

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Symptoms 13336
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Sleep 111

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Your Data

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Low Confidence
Weak Effect Size
Positive Relationship
Population Study
cause image gauge image effect image
Participants reported a 21.6% average increase in Sleep Duration following above average Dizziness Or Lightheadedness.
Abstract

Abstract

Sleep Duration was generally 8% higher than average after an average of 2.5 out of 5 of Dizziness Or Lightheadedness over the previous 24 hours.

Aggregated data from 1 study participants suggests with a LOW degree of confidence (p=0.195, 95% CI -0.786 to 1.328) that Dizziness Or Lightheadedness has a weakly positive predictive relationship (R=0.271) with Sleep Duration.

The highest quartile of Sleep Duration measurements were observed following an average 2.21 out of 5 Dizziness Or Lightheadedness.

The lowest quartile of Sleep Duration measurements were observed following an average 1.8 out of 5 of Dizziness Or Lightheadedness.

After an onset delay of 0 seconds, Sleep Duration is typically 3% lower than average over the 24 hours following around 1.8 out of 5 Dizziness Or Lightheadedness.

Objective

Objective

The objective of this study is to determine the nature of the relationship (if any) between Dizziness Or Lightheadedness and Sleep Duration. Additionally, we attempt to determine the Dizziness Or Lightheadedness values most likely to produce optimal Sleep Duration values.
Participant Instructions

Participant Instructions

Manual Recording Option

A Create a reminder for Dizziness or Lightheadedness here and record it daily by enabling notifications or using A the reminder inbox here .


Sleep Duration Automatic Import of Sleep Duration via Fitbit

A Get Fitbit here and use it to record your Sleep Duration. Then, A import your data here .

Manual Recording Option

A Create a reminder for Sleep Duration here and record it daily by enabling notifications or using A the reminder inbox here .

Design

Design

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

Data Analysis

Data Analysis

Dizziness or Lightheadedness Pre-Processing

Dizziness or Lightheadedness measurement values below 1 out of 5 were assumed erroneous and removed. Dizziness or Lightheadedness measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Dizziness or Lightheadedness so any gaps in data were just not analyzed instead of assuming zero values for those times.

Sleep Duration Pre-Processing

Sleep Duration measurement values below 6 minutes were assumed erroneous and removed. Sleep Duration measurement values above 16 hours were assumed erroneous and removed. No missing data filling value was defined for Sleep Duration so any gaps in data were just not analyzed instead of assuming zero values for those times.

Predictive Analytics

It was assumed that 0 seconds would pass before a change in Dizziness Or Lightheadedness would produce an observable change in Sleep Duration.

It was assumed that Dizziness Or Lightheadedness could produce an observable change in Sleep Duration for as much as 24 hours after the stimulus event.

Statistical Significance

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Sleep Duration is not statistically significant at a 95% confidence interval. This suggests that the Dizziness Or Lightheadedness value does not have a significant influence on the Sleep Duration value.

After treatment, a 21.6% increase (45 minutes) from the mean baseline 7 hours was observed. The relative standard deviation at baseline was 30.8%. The observed change was 0.35501 times the standard deviation.

A common rule of thumb considers a change greater than twice the baseline standard deviation on two separate pre-post experiments may be considered significant. This occurrence would have only a 5% likelihood of resulting from random fluctuation (a p-value < 0.05).

Data Sources

Data Sources

Dizziness Or Lightheadedness 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.

Sleep Duration data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Limitations

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.

Criteria For Causal Inference

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 (R = 0.271) relationship between Dizziness Or Lightheadedness and Sleep Duration.

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. 39 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Dizziness Or Lightheadedness 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,

0 humans feel that there is a plausible mechanism of action for a relationship between Dizziness Or Lightheadedness and Sleep Duration.

0 humans feel that any relationship observed between Dizziness Or Lightheadedness and Sleep Duration 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.

Plausibility

Plausibility

A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 0 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Dizziness Or Lightheadedness and Sleep Duration is coincidental.

Relationship Statistics

Property Value
Cause Variable Name Dizziness Or Lightheadedness
Effect Variable Name Sleep Duration
Sinn Predictive Coefficient 0.012894529754029
Confidence Level LOW
Confidence Interval 1.057
Forward Pearson Predictive Coefficient 0.271
Critical T Value 1.684
Average Dizziness Or Lightheadedness Over Previous 24 hours Before ABOVE Average Sleep Duration 2.21 out of 5
Average Dizziness Or Lightheadedness Over Previous 24 hours Before BELOW Average Sleep Duration 1.8 out of 5
Duration of Action 24 hours
Effect Size weakly positive
Number of Paired Measurements 39
Optimal Pearson Product 0.15292460452362
P Value 0.1953
Statistical Significance 0.1672
Strength of Relationship 1.057
Study Type population
Analysis Performed At 2021-08-30
Number of Participants 1

Dizziness or Lightheadedness Info

Property Value
Variable Name Dizziness Or Lightheadedness
Aggregation Method MEAN
Analysis Performed At 2020-09-23
Duration of Action 24 hours
Kurtosis 2.5136680998726
Maximum Allowed Value 5 out of 5
Mean 2.8866631578947 out of 5
Median 2.8947368421053 out of 5
Minimum Allowed Value 1 out of 5
Number of Aggregate Predictors 133
Number of Aggregate Outcomes 8
Number of Measurements 118
Number of Measurements (including those generated by tagged, joined, or child variables) 118
Public true
Onset Delay 0 seconds
Standard Deviation 0.2905191190176
Unit 1 to 5 Rating
User Variables 31
UPC 712383996411
Variable Category Symptoms
Variable ID 87214
Variance 0.21861861952395

Sleep Duration Info

Property Value
Variable Name Sleep Duration
Aggregation Method MEAN
Analysis Performed At 2020-10-11
Duration of Action 7 days
Kurtosis 3.1431132810239
Maximum Allowed Value 16 hours
Mean 7 hours
Median 7 hours
Minimum Allowed Value 6 minutes
Number of Aggregate Predictors 3162
Number of Aggregate Outcomes 217
Number of Measurements 43055
Number of Measurements (including those generated by tagged, joined, or child variables) 16052
Public true
Onset Delay 0 seconds
Standard Deviation 1.2204190250991
Unit Hours
User Variables 404
UPC 067981966602
Variable Category Sleep
Variable ID 1867
Variance 2.1855287054824