Charts
Trait Correlation Between Acne Severity and Calories Burned
Acne Severity Charts
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Calories Burned Charts
Daily Distribution
Average by Day of Week
Average by Month
Average by Year
Relationship Charts
Calories Burned Following Acne Severity
Correlation Between Acne Severity and Calories Burned by Duration of Action
Correlation Between Acne Severity and Calories Burned by Onset Delay
Average Acne Severity Preceding Calories Burned
Average Calories Burned by Previous Acne Severity
Abstract
Not Enough Shared Data
Please create a study and share it with your friends so we can collect enough data to determine the effect of Acne Severity on Calories Burned. Create a StudySolution: Create a Study
Please create a study and share it with your friends so we can collect enough data to determine the effect of Acne Severity on Calories Burned. Create a StudyCalories Burned was generally 1% higher than average after an average of 2.54 out of 5 of Acne Severity over the previous 7 days.
Aggregated data from 2 study participants suggests with a MEDIUM degree of confidence (p=0.387, 95% CI -166.398 to 166.595) that Acne Severity has a very weakly positive predictive relationship (R=0.0985) with Calories Burned.
The highest quartile of Calories Burned measurements were observed following an average 2.58 out of 5 Acne Severity.
The lowest quartile of Calories Burned measurements were observed following an average 2.1 out of 5 of Acne Severity.
After an onset delay of 0 seconds, Calories Burned is typically 0% lower than average over the 7 days following around 2.1 out of 5 Acne Severity.
Objective
Participant Instructions
Manual Recording Option
Create a reminder for Acne Severity here
and record it daily by enabling notifications or using
the reminder inbox here
.
Automatic Import of Calories Burned via Fitbit
Get Fitbit here
and use it to record your Calories Burned. Then,
import your data here
.
Manual Recording Option
Create a reminder for Calories Burned here
and record it daily by enabling notifications or using
the reminder inbox here
.
Automatic Import of Calories Burned via Google Fit
Get Google Fit here
and use it to record your Calories Burned. Then,
import your data here
.
Automatic Import of Calories Burned via Withings
Get Withings here
and use it to record your Calories Burned. Then,
import your data here
.
Design
This study is based on data donated by 2 participants. Thus, the study design is equivalent to the aggregation of 2 separate n=1 observational natural experiments.
Data Analysis
Acne Severity Pre-Processing
Acne Severity measurement values below 1 out of 5 were assumed erroneous and removed. Acne Severity measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Acne Severity so any gaps in data were just not analyzed instead of assuming zero values for those times.
Calories Burned Pre-Processing
Calories Burned measurement values below 100 kilocalories were assumed erroneous and removed. Calories Burned measurement values above 20000 kilocalories were assumed erroneous and removed. No missing data filling value was defined for Calories Burned 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 Acne Severity would produce an observable change in Calories Burned.
It was assumed that Acne Severity could produce an observable change in Calories Burned for as much as 7 days after the stimulus event.
Statistical Significance
Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Calories Burned is not statistically significant at a 95% confidence interval. This suggests that the Acne Severity value does not have a significant influence on the Calories Burned value.
Data Sources
Acne Severity 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.
Calories Burned data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.
Limitations
The accuracy of this study may be limited by the fact that
Not Enough Shared Data
Please create a study and share it with your friends so we can collect enough data to determine the effect of Acne Severity on Calories Burned. Create a StudySolution: Create a Study
Please create a study and share it with your friends so we can collect enough data to determine the effect of Acne Severity on Calories Burned. Create a Study . A greater amount of data and more variance in the data would help to resolve this issue.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 very weakly positive (R = 0.0985) relationship between Acne Severity and Calories Burned.
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. 225 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Acne Severity 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 Acne Severity and Calories Burned.
0 humans feel that any relationship observed between Acne Severity and Calories Burned 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
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 Acne Severity and Calories Burned is coincidental.
Relationship Statistics
| Property | Value |
|---|---|
| Cause Variable Name | Acne Severity |
| Effect Variable Name | Calories Burned |
| Sinn Predictive Coefficient | 0.008927510270451 |
| Confidence Level | MEDIUM |
| Confidence Interval | 166.49616149742 |
| Forward Pearson Predictive Coefficient | 0.0985 |
| Critical T Value | 1.655 |
| Average Acne Severity Over Previous 7 days Before ABOVE Average Calories Burned | 2.58 out of 5 |
| Average Acne Severity Over Previous 7 days Before BELOW Average Calories Burned | 2.1 out of 5 |
| Duration of Action | 7 days |
| Effect Size | very weakly positive |
| Number of Paired Measurements | 225 |
| Optimal Pearson Product | 0.047120350258797 |
| P Value | 0.38713800686205 |
| Statistical Significance | 0.7307 |
| Strength of Relationship | 166.49616149742 |
| Study Type | population |
| Analysis Performed At | 2021-08-30 |
| Number of Participants | 2 |
Acne Severity Info
| Property | Value |
|---|---|
| Variable Name | Acne Severity |
| Aggregation Method | MEAN |
| Analysis Performed At | 2021-03-01 |
| Duration of Action | 24 hours |
| Kurtosis | 2.1540296927304 |
| Maximum Allowed Value | 5 out of 5 |
| Mean | 2.936194214876 out of 5 |
| Median | 2.8976859504132 out of 5 |
| Minimum Allowed Value | 1 out of 5 |
| Number of Aggregate Predictors | 1373 |
| Number of Aggregate Outcomes | 134 |
| Number of Measurements | 2760 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 2760 |
| Public | true |
| Onset Delay | 0 seconds |
| Standard Deviation | 0.23031831853763 |
| Unit | 1 to 5 Rating |
| User Variables | 156 |
| UPC | 837654497159 |
| Variable Category | Symptoms |
| Variable ID | 1251 |
| Variance | 0.20680264733649 |
Calories Burned Info
| Property | Value |
|---|---|
| Variable Name | Calories Burned |
| Aggregation Method | SUM |
| Analysis Performed At | 2020-09-23 |
| Duration of Action | 7 days |
| Kurtosis | 10.719482104079 |
| Maximum Allowed Value | 20000 kilocalories |
| Mean | 1693.6119981094 kilocalories |
| Median | 1643.7344918892 kilocalories |
| Minimum Allowed Value | 100 kilocalories |
| Number of Aggregate Predictors | 667 |
| Number of Aggregate Outcomes | 211 |
| Number of Measurements | 122895 |
| Number of Measurements (including those generated by tagged, joined, or child variables) | 21949 |
| Public | true |
| Onset Delay | 0 seconds |
| Standard Deviation | 420.78331639612 |
| Unit | Kilocalories |
| User Variables | 393 |
| UPC | 0 |
| Variable Category | Physical Activity |
| Variable ID | 1280 |
| Variance | 236738.41913029 |