Compliance against scheduled prompts

Computes EMA compliance as the proportion of scheduled prompts that received a response inside their response window.

Usage

compliance(data, start_date, n_days, times, window = 3, through = NULL)

Arguments

data

A data frame from read_ema(), containing timestamp.

start_date

Date the study began (day 1).

n_days

Study length in days.

times

Character vector of prompt times in "HH:MM", e.g. c("09:00", "15:00", "21:00").

window

Response window in hours. A record counts toward a prompt if it arrives between the prompt time and window hours later.

through

Compute compliance as of this moment. Defaults to the last timestamp in the data, so a study still running is not penalised for prompts that have not happened yet.

Value

An object of class nof1_compliance: a list with rate, n_answered, n_expected, n_off_window, and a data frame prompts with one row per scheduled prompt and a logical answered.

Details

The definition matters more than the arithmetic. Compliance is often reported as the number of records divided by the number of prompts, which lets a participant who answers one prompt three times appear more compliant than one who answers three prompts once. Late entries, likewise, are data but not evidence that a prompt was answered when it was asked.

This function therefore counts each scheduled prompt at most once, and only when a record falls within window hours of it. Records outside every window are returned separately as n_off_window: they are not discarded, they are simply not evidence of compliance.

Examples

d <- data.frame(timestamp = as.POSIXct(
  c("2026-02-18 09:30:00", "2026-02-18 15:10:00", "2026-02-19 09:05:00"),
  tz = "UTC"))
compliance(d, start_date = "2026-02-18", n_days = 2,
           times = c("09:00", "15:00", "21:00"))
#> Compliance: 75.0%  (3 of 4 prompts answered in window)