---
title: "5. Accounting for Study Protocol"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{5. Accounting for Study Protocol}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

When you use an accelerometer in a study, you are likely to give your
study participants specific instructions on when they should start
wearing the accelerometer, for how many days, and whether or not they
are expected to take the accelerometer off during specific activity
types or parts of the day. Further, it may be that you turned on the
accelerometers hours or even days before you gave it to the participant
or stopped it hours or days after you received it back. Your knowledge
about all these aspects of your study protocol can be used by GGIR to
mask certain periods of time in the recording. This is important because
this information is not necessarily obvious from the recorded data. For
instance, when a recording is started and dispatched to the participant
via mail, the time during which the devices are in transit and not worn
may be impossible to distinguish from a participant wearing the
accelerometer and commuting.

## Selecting/Masking the data

It is important that GGIR analyses masks all data outside the time
window for which the participant was instructed to wear the
accelererometer. Study protocols differ in duration and expected wear
period, which is why GGIR offers a variety of ways to account for study
protocol.

The main parameter to do this is `data_masking_strategy`, and it
requires a numeric value indicating one of the following strategies:

-   **data_masking_strategy = 1** to indicate the a specific number of
    hours should be masked from the start and/or the end of the
    recording, specified with parameters `hrs.del.start` and
    `hrs.del.end`, respectively.

-   **data_masking_strategy = 2** to indicate that only the data between
    the first and the last midnight in the recording should be
    considered.

-   **data_masking_strategy = 3** to indicate that only the most active
    X 24-h blocks starting any time in the day should be used, where X
    is specified by parameter `ndayswindow`. Note this can be combined
    with beforementioned parameters `hrs.del.start` and `hrs.del.end`,
    which will trim this window at the start and end.

-   **data_masking_strategy = 4** to indicate that only the data after
    the first midnight should be considered.

-   **data_masking_strategy = 5** is similar to data_masking_strategy =
    3, yet it selects X complete calendar days where X is specified by
    parameter `ndayswindow`.

Additionally, the maximum duration after the start of the recording that
the accelerometer is expected to be worn can also be set with parameter
`maxdur` for number of 24 hour blocks or parameter `max_calendar_days`
for number of calendar days.

## Key parameters

-   `data_masking_strategy`

-   `hrs.del.start`

-   `hrs.del.end`

-   `ndayswindow`

-   `maxdur`

-   `max_calendar_days`

## Related output

| (Part of) variable name                               | Description                                                                                                                                                                                     | Report(s)         |
|-------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------|
| data exclusion strategy                               | A log of the decision made when calling g.impute: value=1 mean ignore specific hours; value=2 mean ignore all data before the first midnight and after the last midnight                        | part2_summary.csv |
| n hours ignored at start of meas                      | Number of hours ignored at the end of the measurement (if data_masking_strategy = 1) or at the end of the ndayswindow (if data_masking_strategy = 3 or 5). A log of decision made in part2.R    | part2_summary.csv |
| n hours ignored at end of meas                        | Number of hours ignored at the start of the measurement (if data_masking_strategy = 1) or at the start of the ndayswindow (if data_masking_strategy = 3 or 5) A log of decision made in part2.R | part2_summary.csv |
| n days of measurement after which all data is ignored | Number of days of measurement after which all data is ignored (if data_masking_strategy = 1, 3 or 5) A log of decision made in part2.R                                                          | part2_summary.csv |
