This individual's Resilience is generally 7.5% lower after 164 pounds Body Weight over the previous 7 days.
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Blue represents the mean of Body Weight over the previous 7 days
An increase in 7 days cumulative Body Weight is usually followed by an decrease in Resilience. (R = -0.281)
Typical values for Resilience following a given amount of Body Weight over the previous 7 days.
Typical Body Weight seen over the previous 7 days preceding the given Resilience value.
Correlation between outcome and aggregated predictor measurements over given number of days
Peak correlation suggests the delay between predictor and observable outcome
This chart shows how Body Weight changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Body Weight on each day of the week.
This chart shows the typical value recorded for Body Weight for each month of the year.
This chart shows how Resilience changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Resilience on each day of the week.
This chart shows the typical value recorded for Resilience for each month of the year.

Abstract

This individual's Resilience is generally 4% higher than normal after an average of 156.6 pounds Body Weight over the previous 7 days. This individual's data suggests with a medium degree of confidence (p=0.018023742495495, 95% CI -0.418 to -0.144) that Body Weight has a weakly negative predictive relationship (R=-0.28) with Resilience. The highest quartile of Resilience measurements were observed following an average 156.68 pounds Body Weight. The lowest quartile of Resilience measurements were observed following an average 160.41386810127 lb Body Weight. Resilience is generally 3% lower than normal after an average of 160.41386810127 pounds of Body Weight over the previous 7 days. Resilience is generally 4% higher after an average of 156.68 pounds of Body Weight over the previous 7 days.

Objective

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

Participant Instructions

Get Fitbit here and use it to record your Body Weight. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
Record your Resilience daily in the reminder inbox or using the interactive web or mobile notifications.

Design

This study is based on data donated by one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

Body Weight Pre-Processing
Body Weight measurement values below 0 pounds were assumed erroneous and removed. Body Weight measurement values above 1000 pounds were assumed erroneous and removed. No missing data filling value was defined for Body Weight so any gaps in data were just not analyzed instead of assuming zero values for those times.
Body Weight Analysis Settings

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

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

Data Quantity
4417 raw Body Weight measurements with 1699 changes spanning 2662 days from 2012-04-18 to 2019-08-02 were used in this analysis. 787 raw Resilience measurements with 118 changes spanning 948 days from 2013-08-30 to 2016-04-05 were used in this analysis.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Resilience is statistically significant at 95% confidence interval. After treatment, a 7.5% decrease (-0.21747029564823 out of 5) from the mean baseline 2.91781891254 out of 5 was observed. The relative standard deviation at baseline was 17.7%. The observed change was 0.42211188108068 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 may have only a 5% likelihood of resulting from random fluctuation (a p-value

Data Sources

Body Weight data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Resilience 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 negative relationship between Body Weight and Resilience

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. 190 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Body Weight 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, 2 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Body Weight and Resilience 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 Body Weight
Effect Variable Name Resilience
Sinn Predictive Coefficient 0.291
Confidence Level medium
Confidence Interval 0.13654918693967
Forward Pearson Predictive Coefficient -0.281
Critical T Value 1.646
Average Body Weight Over Previous 7 days Before ABOVE Average Resilience 156.68 pounds
Average Body Weight Over Previous 7 days Before BELOW Average Resilience 160.414 pounds
Duration of Action 7 days
Effect Size weakly negative
Number of Paired Measurements 190
Optimal Pearson Product 0.21760261325367
P Value 0.018023742495495
Statistical Significance 0.9964
Strength of Relationship 0.13654918693967
Study Type individual
Analysis Performed At 2019-08-01
Number of Pairs 190
Number of Raw Predictor Measurements ( Including Tags, Joins, and Children) 7564
Baseline Relative Standard Deviation of Outcome Measurements 17.7
Experiment Duration (days) 948
Number of Raw Outcome Measurements 787
Z Score 0.42211188108068
Last Analysis 2019-08-01
Experiment Began 2013-08-30 19:14:00
Experiment Ended 2016-04-05 02:31:10
P Value 0.018023742495495
Predictor Category Physique
Duration of Action (h) 168
Significance 0.9964
Outcome Relative Standard Deviation at Baseline 17.7
Outcome Standard Deviation at Baseline 0.51519586487704/5
Outcome Mean at Baseline 2.91781891254/5
Average Followup Change From Baseline -7.5&
Average Absolute Followup Change From Baseline 2.7003486168917/5
Z- Score 0.42211188108068
Average Predictor Treatment Value 164lb over 7 days

Body Weight Statistics

Property Value
Variable Name Body Weight
Aggregation Method MEAN
Analysis Performed At 2019-08-18
Duration of Action 7 days
Kurtosis 2.5214093424243
Maximum Allowed Value 1000 pounds
Mean 161.96 pounds
Median 161.64211395295 pounds
Minimum Allowed Value 0 pounds
Number of Changes 1699
Number of Correlations 1212
Number of Measurements 4417
Onset Delay 0 seconds
Standard Deviation 5.5853948995709
Unit Pounds
UPC 875011003902
Variable ID 1486
Variance 31.196636184152

Resilience Statistics

Property Value
Variable Name Resilience
Aggregation Method MEAN
Analysis Performed At 2019-07-31
Duration of Action 24 hours
Kurtosis 4.6519368236661
Maximum Allowed Value 5 out of 5
Mean 2.7332 out of 5
Median 2.7777777777778 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 118
Number of Correlations 755
Number of Measurements 787
Onset Delay 0 seconds
Standard Deviation 0.58943687828993
Unit 1 to 5 Rating
Variable ID 1436
Variance 0.34743583348818

Tracking Body Weight

Get Fitbit here and use it to record your Body Weight. Once you have a Fitbit account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Tracking Resilience

Record your Resilience 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