↑Higher Daily Step Count Predicts Significantly ↑Higher Heart Rate Variability

Heart Rate Variability (HRV) was generally 7% higher than average after a total of 40800 count of Daily Step Count over the previous 7 days.

This data suggests with a LOW degree of confidence (p=0.221, 95% CI -4064.514 to 4065.864) that Daily Step Count has a strongly positive predictive relationship (R=0.675) with Heart Rate Variability (HRV).

The highest quartile of Heart Rate Variability measurements were observed following an average 6610 count Daily Step Count per day.

The lowest quartile of Heart Rate Variability measurements were observed following an average 1120 count of Daily Step Count per day.

After an onset delay of 0 seconds, Heart Rate Variability is typically 18% lower than average over the 7 days following around 1120 count Daily Step Count.

The objective of this study is to determine the nature of the relationship (if any) between Daily Step Count and Heart Rate Variability. Additionally, we attempt to determine the Daily Step Count values most likely to produce optimal Heart Rate Variability values.

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

Daily Step Count Pre-Processing

Daily Step Count measurement values below 1 count were assumed erroneous and removed. No maximum allowed measurement value was defined for Daily Step Count. No missing data filling value was defined for Daily Step Count so any gaps in data were just not analyzed instead of assuming zero values for those times.

Heart Rate Variability (HRV) Pre-Processing

Heart Rate Variability (HRV) measurement values below 0 milliseconds were assumed erroneous and removed. Heart Rate Variability (HRV) measurement values above 864000000 milliseconds were assumed erroneous and removed. It was assumed that any gaps in Heart Rate Variability (HRV) data were unrecorded 0 milliseconds measurement values.

Predictive Analytics

It was assumed that 0 seconds would pass before a change in Daily Step Count would produce an observable change in Heart Rate Variability (HRV).

It was assumed that Daily Step Count could produce an observable change in Heart Rate Variability (HRV) for as much as 7 days after the stimulus event.

Daily Step Count Data Quantity

There are currently 7 raw measurements with 6 changes spanning 6 days from 2022-05-07 to 2022-05-13.

Heart Rate Variability (HRV) Data Quantity

There are currently 7 raw measurements with 6 changes spanning 6 days from 2022-05-07 to 2022-05-13.

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Heart Rate Variability is not statistically significant at a 95% confidence interval. This suggests that the Daily Step Count value does not have a significant influence on the Heart Rate Variability value.

After treatment, a 32% increase (1380 milliseconds) from the mean baseline 4330 milliseconds was observed. The relative standard deviation at baseline was 49.9%. The observed change was 0.64 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).

Daily Step Count data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Heart Rate Variability (HRV) 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.

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 strongly positive (R = 0.67464277155072) relationship between Daily Step Count and Heart Rate Variability (HRV).

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. 7 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Daily Step Count 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 Daily Step Count and Heart Rate Variability (HRV).

Seems right?

Up Vote ➤

0 humans feel that any relationship observed between Daily Step Count and Heart Rate Variability (HRV) is coincidental.

Seems coincidental?

Down Vote ➤

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.

Potential Issues

The following issues were identified during analysis:

• Not Enough Variance In Data:

  There are only 0 changes in the effect values from negative 32 days onset delay and 24 hours duration of action paired data. 1 are required. There is not enough variance in the data to create a study on the relationship between Daily Step Count and Heart Rate Variability (HRV). There are 6 changes in the value of the Daily Step Count and an unknown number of changes in Heart Rate Variability (HRV) during the 14 days from 2022-05-07 00:00:00 to 2022-05-21 00:00:00. At least 1 changes in values are necessary because we're trying to see what happens to Heart Rate Variability (HRV) when Daily Step Count is above average and what happens when it's below average.

  An onset delay of 0 seconds and duration of action 7 days were being used.

• Not Enough Variance In Data:

  There are only 0 changes in the effect values from negative 16 days onset delay and 24 hours duration of action paired data. 1 are required. There is not enough variance in the data to create a study on the relationship between Daily Step Count and Heart Rate Variability (HRV). There are 6 changes in the value of the Daily Step Count and an unknown number of changes in Heart Rate Variability (HRV) during the 14 days from 2022-05-07 00:00:00 to 2022-05-21 00:00:00. At least 1 changes in values are necessary because we're trying to see what happens to Heart Rate Variability (HRV) when Daily Step Count is above average and what happens when it's below average.

  An onset delay of 0 seconds and duration of action 7 days were being used.

• Not Enough Variance In Data:

  There are only 0 changes in the effect values from negative 8 days onset delay and 24 hours duration of action paired data. 1 are required. There is not enough variance in the data to create a study on the relationship between Daily Step Count and Heart Rate Variability (HRV). There are 6 changes in the value of the Daily Step Count and an unknown number of changes in Heart Rate Variability (HRV) during the 14 days from 2022-05-07 00:00:00 to 2022-05-21 00:00:00. At least 1 changes in values are necessary because we're trying to see what happens to Heart Rate Variability (HRV) when Daily Step Count is above average and what happens when it's below average.

  An onset delay of 0 seconds and duration of action 7 days were being used.

• Not Enough Overlapping Data:

  Daily Step Count and Heart Rate Variability (HRV) have less than 4 days of overlapping data in common after pairing. https://v7.curedao.org/study/cause-1451-effect-6068048-user-93369-user-study

  An onset delay of 0 seconds and duration of action 7 days were being used.

• Only 0 pairs:

  An onset delay of 0 seconds and duration of action 7 days were being used.

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 Daily Step Count and Heart Rate Variability (HRV) is coincidental.