Higher Calories Burned Predicts Very Slightly Lower Sleep Efficiency for Population

Sleep Efficiency was generally 2% higher than average after a total of 1360 kilocalories of Calories Burned over the previous 7 days.

Aggregated data from 128 study participants suggests with a HIGH degree of confidence (p=0.191, 95% CI -1.674 to 1.607) that Calories Burned has a very weakly negative predictive relationship (R=-0.0334) with Sleep Efficiency.

The highest quartile of Sleep Efficiency measurements were observed following an average 2 kilocalories Calories Burned per day.

The lowest quartile of Sleep Efficiency measurements were observed following an average 2350 kilocalories of Calories Burned per day.

After an onset delay of 0 seconds, Sleep Efficiency is typically 2% lower than average over the 7 days following around 2350 kilocalories Calories Burned.

The objective of this study is to determine the nature of the relationship (if any) between Calories Burned and Sleep Efficiency. Additionally, we attempt to determine the Calories Burned values most likely to produce optimal Sleep Efficiency values.

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Manual Recording Option

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This study is based on data donated by 128 participants. Thus, the study design is equivalent to the aggregation of 128 separate n=1 observational natural experiments.

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.

Sleep Efficiency Pre-Processing

Sleep Efficiency measurement values below 1 percent were assumed erroneous and removed. No maximum allowed measurement value was defined for Sleep Efficiency. No missing data filling value was defined for Sleep Efficiency 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 Calories Burned would produce an observable change in Sleep Efficiency.

It was assumed that Calories Burned could produce an observable change in Sleep Efficiency for as much as 7 days after the stimulus event.

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Sleep Efficiency is statistically significant at 95% confidence interval.

Calories Burned data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

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

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 negative (R = -0.0334) relationship between Calories Burned and Sleep Efficiency.

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. 234 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Calories Burned 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 Calories Burned and Sleep Efficiency.

0 humans feel that any relationship observed between Calories Burned and Sleep Efficiency 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.

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 Calories Burned and Sleep Efficiency is coincidental.