Average daily minutes of moderate-to-vigorous physical activity (MVPA) from accelerometer data

This function calculates the average daily minutes of moderate-to-vigorous physical activity (MVPA) across a week of accelerometer measurement. It takes seven parameters, each representing the MVPA minutes on a specific day (Day 1 to Day 7). The function computes the daily average across the week.

Usage

calculate_exercise_daily_avg(
  ammdmva1,
  ammdmva2,
  ammdmva3,
  ammdmva4,
  ammdmva5,
  ammdmva6,
  ammdmva7
)

Arguments

ammdmva1

numeric A numeric representing minutes of moderate-to-vigorous physical activity (MVPA) on Day 1 of accelerometer measurement.

ammdmva2

numeric A numeric representing minutes of MVPA on Day 2 of accelerometer measurement.

ammdmva3

numeric A numeric representing minutes of MVPA on Day 3 of accelerometer measurement.

ammdmva4

numeric A numeric representing minutes of MVPA on Day 4 of accelerometer measurement.

ammdmva5

numeric A numeric representing minutes of MVPA on Day 5 of accelerometer measurement.

ammdmva6

numeric A numeric representing minutes of MVPA on Day 6 of accelerometer measurement.

ammdmva7

numeric A numeric representing minutes of MVPA on Day 7 of accelerometer measurement.

Value

numeric The average daily minutes of MVPA across a week of accelerometer use. If inputs are invalid or out of bounds, the function returns a tagged NA.

Details

This function processes physical activity data from accelerometer measurements to create a weekly activity summary.

     **Data Quality Requirements:**
     - Requires complete 7-day data (missing days result in tagged NA)
     - This conservative approach ensures reliable activity estimates
     - Zero values are preserved (represent valid no-activity days)

     **Missing Data Codes:**
     - For all input variables:
       - `9996`: Valid skip. Handled as `haven::tagged_na("a")`.
       - `9997-9999`: Don't know, refusal, or not stated. Handled as `haven::tagged_na("b")`.

See also

calculate_exercise_weekly() for activity unit conversion, categorize_exercise() for activity level classification

Examples

# Scalar usage: Single respondent
# Example: Calculate the average minutes of exercise per day for a week of accelerometer data.
calculate_exercise_daily_avg(30, 40, 25, 35, 20, 45, 50)
#> [1] 35
# Output: 35

# Example: Respondent has non-response values for all inputs.
result <- calculate_exercise_daily_avg(9998, 9998, 9998, 9998, 9998, 9998, 9998)
result # Shows: NA
#> [1] NA
haven::is_tagged_na(result, "b") # Shows: TRUE (confirms it's tagged NA(b))
#> [1] TRUE
format(result, tag = TRUE) # Shows: "NA(b)" (displays the tag)
#> [1] "NA"

# Multiple respondents
calculate_exercise_daily_avg(
  c(30, 20), c(40, 30), c(25, 35), c(35, 45),
  c(20, 25), c(45, 55), c(50, 60)
)
#> [1] 35.00000 38.57143
# Returns: c(35, 39.28571)

# Database usage: Applied to survey datasets
# library(dplyr)
# dataset |>
#   mutate(avg_exercise = calculate_exercise_daily_avg(ammdmva1, ammdmva2,
#     ammdmva3, ammdmva4, ammdmva5, ammdmva6, ammdmva7))