In Part 2 we will use a Personal application API key to access Intraday Time Series Data, giving us heart rate data with a greater temporal resolution e.g. detail-level = 1sec OR 1min.

Intra-day Time Series

We will use the get_heart_rate_intraday_time_series() function which has the following arguments:
* date The date, in the format yyyy-MM-dd or today.
* detail_level Number of data points to include. Either 1sec or 1min. Optional.
* start_time The start of the period, in the format HH:mm. Optional.
* end_time The end of the period, in the format HH:mm. Optional.

library(fitbitr)
str(get_heart_rate_intraday_time_series(token, date = date, detail_level = "1min"))

Pretty straightforward, we can see a data.frame consisting of 2 variables; a character vector time (from start_time to end_time, defaulting to a 24-hour period from “00:00:00” on the date specified) and an integer vector value corresponding to the Heart Rate for that time.

Let’s import the data into a data.frame and rename value to the more descriptive HR. We must also convert time from a character to something more correct. Otherwise when we call geom_smooth() in ggplot it won’t produce any result. I’ve used the as.difftime() function in base R, rather than the more common strptime(), as it gives me a time since midnight in units of my choice, rather than appending the time to todays date which would introduce confusion when analysing data from multiple days.

df <- get_heart_rate_intraday_time_series(token, date = date, detail_level = "1min")
# Rename col 'value' to 'HR
names(df)[2] <- "HR"
# Convert time from class = character to difftime, i.e. mins since 00:00
df$time <- as.difftime(df$time, units = "mins")

Plotting Intra-day Time Series

We can plot this data as a simple scatter plot in ggplot using geom_point(). I’ve coloured it red, given the cardiac nature of the data, and changed it’s transparency alpha = 0.4 to improve legibility given the close distribution of the points along the x axis.

Finally I’ve added a smoother (? correct term) using geom_smooth() to aid the eye in seeing patterns given the ‘overplotting’ described above. This defaults to method = "gam" with formula = y ~ s(x, bs = "cs"). I don’t have the depth of mathematical knowledge to say if this is a correct method, but visually ‘eyeballing’ the resultant line it looks pretty appropriate to me!

I’ve elected to show a 95% (default) Confidence Interval using the argument se = TRUE . labs() will add a title and label the x-axis, helping to clarify that time is in minutes as difftime is not the most obvious.

library(ggplot2)
library(ggthemes)
p <- ggplot(df, aes(x = time, y = HR)) + geom_point(col = "red", alpha = 0.4) + geom_smooth(se = TRUE) + labs(title = "Heart Rate over Time", x = "Time (mins)") + theme_few()
p

Exploring different temporal resolutions

As described above the get_heart_rate_intraday_time_series() function has an argument detail_level which specifies the ‘Number of data points to include’, i.e. the temporal resolution. This can be specified as either 1sec (and seemingly not any other number of minutes) or 1min.

If we could reduce the temporal resolution even from 1 minute to 2 minutes this would halve the amount of data to process, particularly important when we start analysing larger time periods and more than one subject! Clearly this will come at the expense of a loss of detail so I thought it would be interesting to plot the same heart rate data at different detail levels (1 min, 5 min & 15 min) and compare the results. Particularly the smoothed line.

Unfortunately get_heart_rate_intraday_time_series() won’t accept a vector for detail_level so we have to resort to separate function calls, storing the outputs in their own data frames. We will then merge() these (again unfortunately only can merge two at a time, so have to repeat twice) into a single dataframe, using the arguments all = TRUE as we don’t want to select only those time points that intersect (this would lose the greater resolution, defeating the point of the exercise!). I had specified no.dups = FALSE although in retrospect this is unecessary as they’re all pulling data from the same source so the temporal resolution shouldn’t change the heart rate.

We then need to do a bit of tidying, we will rename the columns to their detail_level: "HR_1min", "HR_5min", "HR_15min" respectively and convert the time to difftime to allow us to perform the geom_smooth() regression line.

df.1m <- get_heart_rate_intraday_time_series(token, date = date, detail_level = "1min")
df.5m <- get_heart_rate_intraday_time_series(token, date = date, detail_level = "5min")
df.15m <- get_heart_rate_intraday_time_series(token, date = date, detail_level = "15min")

df.detail.levels <- merge(df.1m, df.5m, by = "time", all = TRUE, no.dups = FALSE)
df.detail.levels <- merge(df.detail.levels, df.15m, by = "time", all = TRUE, no.dups = FALSE)

names(df.detail.levels)[2:4] <- c("HR_1min", "HR_5min", "HR_15min")
df.detail.levels$time <- as.difftime(df.detail.levels$time, units = "mins")

There would be various ways of representing the resultant information. I could produce a 1x3 panel plot. Instead I decided to follow the principle of showing as much useful info in a single figure as possible and instead plot a line for each detail_level on the same axes.

head(df.detail.levels, 3)

It is easier for us to plot if we convert it into ‘Tall Format’ using the melt() function in the reshape2 package with the argument id.vars = "time" to show that we are preserving time for our x-axis.

library(reshape2)
melted <- melt(df.detail.levels, id.vars = "time")
head(melted, 3)

Now we can create the plot in ggplot using the aesthetic col = variable to colour the points by the melted column variable which consists of the detail_level.

ggplot(melted, aes(x = time, y = value, col = variable)) + geom_point(alpha = 0.5) + geom_smooth(aes(col = variable), se = FALSE) + labs(title = "Heart Rate over Time", subtitle = "By varying detail level", x = "Time (mins)", y = "HR", colour = "Detail Level") + theme_few()

Discussion

We can see the following observations:
* A greater detail level gives a greater range of values. This is most significant for the higher heart rates, presumably because they are more transient, in contrast to the low heart rates which were probably sustained during sleep.
* Overall there is little difference in the averaged data between the various detail levels.

This is all fairly intuitive and we can also be seen using the extremely useful summary()

Periop Wearables: Heart Rate Data from FitBit

Intra-day Time Series

Plotting Intra-day Time Series

Exploring different temporal resolutions

Discussion

Conclusions