Higher Resting Heart Rate (Pulse) Predicts Very Slightly Lower Calories Burned for Population

Calories Burned was generally 2% higher than average after an average of 68.5 beats per minute of Resting Heart Rate over the previous 24 hours.

Aggregated data from 32 study participants suggests with a HIGH degree of confidence (p=0.221, 95% CI -146.679 to 146.604) that Resting Heart Rate has a very weakly negative predictive relationship (R=-0.0375) with Calories Burned.

The highest quartile of Calories Burned measurements were observed following an average 69.4 beats per minute Resting Heart Rate.

The lowest quartile of Calories Burned measurements were observed following an average 69.8 beats per minute of Resting Heart Rate.

After an onset delay of 0 seconds, Calories Burned is typically 2% lower than average over the 24 hours following around 69.8 beats per minute Resting Heart Rate.

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

Manual Recording Option

Create a reminder for Resting Heart Rate (Pulse) here and record it daily by enabling notifications or using the reminder inbox here .

Manual Recording Option

Create a reminder for Calories Burned here and record it daily by enabling notifications or using the reminder inbox here .

This study is based on data donated by 32 participants. Thus, the study design is equivalent to the aggregation of 32 separate n=1 observational natural experiments.

Resting Heart Rate (Pulse) Pre-Processing

Resting Heart Rate (Pulse) measurement values below 20 beats per minute were assumed erroneous and removed. Resting Heart Rate (Pulse) measurement values above 300 beats per minute were assumed erroneous and removed. No missing data filling value was defined for Resting Heart Rate (Pulse) 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 Resting Heart Rate (Pulse) would produce an observable change in Calories Burned.

It was assumed that Resting Heart Rate (Pulse) could produce an observable change in Calories Burned for as much as 24 hours after the stimulus event.

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 Resting Heart Rate value does not have a significant influence on the Calories Burned value.

After treatment, a 0.8% decrease (-19.2 kilocalories) from the mean baseline 2410 kilocalories was observed. The relative standard deviation at baseline was 18.175%. The observed change was 0.226521 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).

Resting Heart Rate (Pulse) data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Calories Burned 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 = -0.0375) relationship between Resting Heart Rate (Pulse) 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. 6147 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Resting Heart Rate (Pulse) 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 Resting Heart Rate (Pulse) and Calories Burned.

0 humans feel that any relationship observed between Resting Heart Rate (Pulse) 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.

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 Resting Heart Rate (Pulse) and Calories Burned is coincidental.