stepmetrics

Overview

The stepmetrics package provides tools to calculate step- and cadence-based metrics from wearable device data. It supports data aggregated at epochs of 1–60 seconds, and automatically re-aggregates sub-minute data to 60-second epochs before computing metrics.

Currently, the package has been tested with data from:

• Fitbit exports

• ActiGraph CSV and AGD files

• GGIR outputs (with externally computed step counts, e.g., Verisense algorithm)

Main functionalities

• Summarizing total steps per day

• Quantifying time and steps across cadence bands

• Computing cadence peaks (e.g., 1-, 30-, 60-minute peaks)

• Deriving moderate, vigorous, and MVPA minutes and steps

• Producing daily- and person-level summary datasets

Installation

The stable release of stepmetrics can be installed from CRAN:

# install.packages("devtools")
install.packages("stepmetrics")

You can install the development version of stepmetrics from GitHub with:

# install.packages("devtools")
devtools::install_github("jhmigueles/stepmetrics")

Core Workflow

The main function is step.metrics(), which processes raw step data and exports day-level and person-level summaries.

library(stepmetrics)
step.metrics(datadir = "C:/mydata/",
             outputdir = "C:/myoutput/",
             idloc = "_",
             cadence_bands = c(0, 1, 20, 40, 60, 80, 100, 120, Inf),
             cadence_peaks = c(1, 30, 60),
             cadence_MOD = 100,
             cadence_VIG = 130,
             includedaycrit = 10,
             exclude_pk30_0 = TRUE,
             exclude_pk60_0 = TRUE,
             time_format = NULL)

This function does not return an object into the R session. Instead, it generates:

• Day-level CSVs: one file per participant in outputdir/daySummary/

• Person-level CSV: aggregated averages in outputdir/personSummary.csv

Working with GGIR output

If your step counts were generated in GGIR (e.g., with an external algorithm such as Verisense):

• Set datadir to the GGIR output directory (the folder whose name begins with "output_").

• The function will automatically detect and validate whether it’s a GGIR output folder.

Important: stepmetrics looks for a column with "step" in its name. Valid examples:

• steps

• step_column

• step_count

• step_per_epoch

When computing steps externally for GGIR, ensure your chosen column name follows this convention.

Key Functions

• readFile(): Reads and standardizes timestamp/step data from CSV, AGD, or GGIR .RData files. Handles ActiGraph headers, multiple files per participant, and aggregates sub-minute epochs to 60 seconds.

• define_day_indices(): Converts ISO-8601 timestamps to sequential day indices (useful for looping over days).

• get_cadence_bands(): Calculates minutes and steps spent in predefined cadence bands (e.g., 0–19, 20–39 spm).

• get_cadence_peaks(): Computes cadence peaks (mean of the top 1, 30, 60 minutes of the day) and counts how many of those minutes had 0 steps.

• isGGIRoutput(): Verifies whether a folder is a valid GGIR output directory suitable for stepmetrics (checks for part 2 data and step columns).

Example Outputs

Day-level output (*_DaySum.csv) includes:

• Participant ID, date, weekday

• Recording duration, valid wear time, awake time (if GGIR available)

• Total steps

• Cadence peaks (values + number of zeroes)

• Minutes and steps per cadence band

• MPA, VPA, MVPA (minutes and steps)

Person-level output (personSummary.csv) includes:

• Overall averages across valid days

• Weighted weekday/weekend averages

• Stratified weekday-only and weekend-only averages

Citation

If you use stepmetrics in your research, please cite:

Migueles, JH. stepmetrics: Calculate Step and Cadence Metrics from Wearable Data. Zenodo. DOI: 10.5281/zenodo.7858094