---
title: "Published cut-points and how to use them in GGIR"
output:
   html_document:
    toc : true
    number_sections: true
    toc_depth: 3
    toc_float: true #by turning this on, the table of contents moves to the left of the page.
urlcolor: blue
vignette: >
  %\VignetteIndexEntry{Published cut-points and how to use them in GGIR}
  \usepackage[utf8]{inputenc}
  %\VignetteEngine{knitr::rmarkdown}
---

```{r, echo=FALSE, out.width = "60%", out.extra='style="border: 0; padding:20px"'}
knitr::include_graphics("GGIR-MASTERLOGO-RGB.png")
```

See also complementary vignettes on:
[General introduction to GGIR](https://cran.r-project.org/package=GGIR/vignettes/GGIR.html), [Day segment analyses](https://CRAN.R-project.org/package=GGIR/vignettes/TutorialDaySegmentAnalyses.html), [GGIR parameters](https://CRAN.R-project.org/package=GGIR/vignettes/GGIRParameters.html), [Embedding external functions (pdf)](https://CRAN.R-project.org/package=GGIR/vignettes/ExternalFunction.pdf), and [Reading ad-hoc csv file formats](https://CRAN.R-project.org/package=GGIR/vignettes/readmyacccsv.html).


# Considerations

The physical activity research field has used so called cut-points to segment
accelerometer time series based on level of intensity. In this vignette we have 
compiled a list of published cut-points with instructions on how to use them with GGIR.
As newer cut-points are frequently published the list below may not be up to date.
**Please let us know you if know of any cut-points we missed!**

## Cut-points expressed in gravitational units (this vignette)

This vignette focuses on cut-points for metrics that attempt to quantify average 
acceleration per epoch in gravitational units. The strength of these metrics is 
that their values are not affected by sampling rate and epoch length improving 
comparability across studies.

## Cut-points NOT expressed in gravitational units (not in this vignette)

However, GGIR also facilitates some metrics whose values are not expressed in 
gravitational units that were historically used. For example, the metric as 
described by Neishabouri (see GGIR argument `do.neishabouricounts`) which reflects
the indicator of accumulated body movement over time, referred to as counts, 
calculated by the ActiLife software from the ActiGraph accelerometer brand.
Cut-points for counts corresponding to the ActiGraph brand have been recurrently 
proposed in the literature, for example, see this [systematic review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231536/)
with a stratification by age group. Note that cut-points for ActiGraph counts proposed before
the introduction of multiday raw data collection are most likely
hardware-based calculations which may not perfectly align with ActiGraph
software-based (Actilife) calculations of counts that Neishabouri described.
As a result, older cut-points may need to be used with caution.

The cut-points you find in the literature for ActiGraph counts cannot be applied 
to Neishabouri counts directly because both are epoch length specific. The cut-points 
from the literature need to be corrected by a conversion factor. The conversion 
factor is calculated as the epoch length in the new study (e.g. 5 seconds) divided 
by the epoch length in the original study (e.g. 60 seconds). Note that no correction 
for differences in sampling rate is needed because Neishabouri counts already account 
for this via down-sampling.

If we would want to use cut-point "100 counts per minute" from the literature on 5
second epoch data, the GGIR function call would look like this:

```
GGIR([...],
     mode = 1:5,
     windowsizes = c(5, 900, 3600),
     do.neishabouricounts = TRUE,
     acc.metric = "NeishabouriCount_y",
     threshold.in = 100 * (5/60),
     [...])
```

# Relevant arguments to use cut-points in GGIR

The argument `mvpathreshold` is used in **part 2** to quantify the time
accumulated over a user-specified threshold over which the
moderate-to-vigorous intensity is expected to occur. The `mvpathreshold`
is applied over all the metrics extracted in part 1 with the arguments
*do.metric* (e.g., `do.enmo`, `do.mad`, `do.neishabouricounts`).

In **part 5**, `threshold.lig`, `threshold.mod`, and `threshold.vig` are
used to indicate the thresholds to separate inactivity from light, light
from moderate, and moderate from vigorous, respectively.These thresholds
are applied over the metric defined with `acc.metric` (default =
"ENMO"). Here a summary table for the parameters definition to calculate
some of the acceleration metrics that has been previously used for the
calibration of cut-points and how to define them to be used in the physical 
activity intensity classification with cut-points.

+--------------+-------------------------------+---------------------------------------+
| Metric       | To derive metric              | Define metric for cut-points          |
+==============+:==============================+:======================================+
| ENMO         | `do.enmo = TRUE`              | `acc.metric = "ENMO"`                 |
+--------------+-------------------------------+---------------------------------------+
| ENMOa        | `do.enmoa = TRUE`             | `acc.metric = "ENMOa"`                |
+--------------+-------------------------------+---------------------------------------+
| LFENMO       | `do.lfenmo = TRUE`            | `acc.metric = "LFENMO"`               |
+--------------+-------------------------------+---------------------------------------+
| MAD          | `do.mad = TRUE`               | `acc.metric = "MAD"`                  |
+--------------+-------------------------------+---------------------------------------+
| Neishabouri\ | `do.neishabouricounts = TRUE` | `acc.metric = "NeishabouriCount_x"`\  |
| counts       |                               | `acc.metric = "NeishabouriCount_y"`\  |
|              |                               | `acc.metric = "NeishabouriCount_z"`\  |
|              |                               | `acc.metric = "NeishabouriCount_vm"`  |
+--------------+-------------------------------+---------------------------------------+


# Summary of published cut-points

## Cut-points for preschoolers

+---------------+--------------------+-------------+------------------------+------------------+
| Cut-points    | Device\            | Age         | Relevant arguments     | thresholds       |
|               | Attachment site    |             |                        |                  |
+===============+:===================+:============+:=======================+:=================+
| Roscoe 2017\* | GENEActiv\         | 4-5 yr      | `do.enmoa = TRUE`\     | Light: 61.8\     |
|               | Non-dominant wrist |             | `do.enmo = FALSE`\     | Moderate: 100.4\ |
|               |                    |             | `acc.metric = "ENMOa"` | Vigorous: N/A    |
+---------------+--------------------+-------------+------------------------+------------------+
| Roscoe 2017\* | GENEActiv\         | 4-5 yr      | `do.enmoa = TRUE`\     | Light: 94.5\     |
|               | Dominant wrist     |             | `do.enmo = FALSE`\     | Moderate: 108.5\ |
|               |                    |             | `acc.metric = "ENMOa"` | Vigorous: N/A    |
+---------------+--------------------+-------------+------------------------+------------------+

\*These publications used acceleration metrics that sum their values per epoch rather than average them per epoch like GGIR does. So, to use their cut-point in GGIR, we provide a scaled version of the cut-points presented in the paper as: `(CutPointFromPaper_in_gsecs/85.7) * 1000`. Note that sample frequency of 87.5 as reported in the publication was incorrect and based on correspondence with authors we replaced this by 85.7.

## Cut-points for children/adolescents

+------------------+--------------------+----------+-----------------------------------+------------------+
| Cut-points       | Device\            | Age      | Relevant arguments                | thresholds       |
|                  | Attachment site    |          |                                   |                  |
+:=================+:===================+:=========+:==================================+:=================+
| Phillips 2013\*  | GENEA\             | 8-14 yr  | `do.enmoa = TRUE`\                | Light: 87.5\     |
|                  | Left wrist         |          | `do.enmo = FALSE`\                | Moderate: 250\   |
|                  |                    |          | `acc.metric = "ENMOa"`            | Vigorous: 750    |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Phillips 2013\*  | GENEA\             | 8-14 yr  | `do.enmoa = TRUE`\                | Light: 75\       |
|                  | Right wrist        |          | `do.enmo = FALSE`\                | Moderate: 275\   |
|                  |                    |          | `acc.metric = "ENMOa"`            | Vigorous: 700    |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Phillips 2013\*  | GENEA\             | 8-14 yr  | `do.enmoa = TRUE`\                | Light: 37.5\     |
|                  | Hip                |          | `do.enmo = FALSE`\                | Moderate: 212.5\ |
|                  |                    |          | `acc.metric = "ENMOa"`            | Vigorous: 637.5  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Schaefer 2014\*  | GENEActiv\         | 6-11 yr  | `do.bfen = TRUE`\                 | Light: 190\      |
|                  | Non-dominant wrist |          | `lb = 0.2`\                       | Moderate: 314\   |
|                  |                    |          | `hb = 15 `\                       | Vigorous: 998    |
|                  |                    |          | `do.enmo = FALSE`\                |                  |
|                  |                    |          | `acc.metric = "BFEN"`             |                  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Hildebrand 2014\ | ActiGraph\         | 7-11 yr  | **Default values\                 | Light: 35.6\     |
| Hildebrand 2016  | Non-dominant wrist |          | **`do.enmo = TRUE`\               | Moderate: 201.4\ |
|                  |                    |          | `acc.metric = "ENMO"`             | Vigorous: 707.0  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Hildebrand 2014\ | GENEActiv\         | 7-11 yr  | **Default values\                 | Light: 56.3\     |
| Hildebrand 2016  | Non-dominant wrist |          | **`do.enmo = TRUE`\               | Moderate: 191.6\ |
|                  |                    |          | `acc.metric = "ENMO"`             | Vigorous: 695.8  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Hildebrand 2014\ | ActiGraph\         | 7-11 yr  | **Default values\                 | Light: 63.3\     |
| Hildebrand 2016  | Hip                |          | **`do.enmo = TRUE`\               | Moderate: 142.6\ |
|                  |                    |          | `acc.metric = "ENMO"`             | Vigorous: 464.6  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Hildebrand 2014\ | GENEActiv\         | 7-11 yr  | **Default values\                 | Light: 64.1\     |
| Hildebrand 2016  | Hip                |          | **`do.enmo = TRUE`\               | Moderate: 152.8\ |
|                  |                    |          | `acc.metric = "ENMO"`             | Vigorous: 514.3  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Aittasalo 2015   | ActiGraph\         | 13-15 yr | **Default values\                 | Light: 26.9\     |
|                  | Hip                |          | **`do.mad = TRUE`\                | Moderate: 332\   |
|                  |                    |          | `do.enmo = FALSE`\                | Vigorous: 558.3  |
|                  |                    |          | `acc.metric = "MAD"`              |                  |
+------------------+--------------------+----------+-----------------------------------+------------------+
| Aittasalo 2015   | Hookie AM20\       | 13-15 yr | **Default values\                 | Light: 28.7\     |
|                  | Hip                |          | **`do.mad = TRUE`\                | Moderate: 338\   |
|                  |                    |          | `do.enmo = FALSE`\                | Vigorous: 558.3  |
|                  |                    |          | `acc.metric = "MAD"`              |                  |
+------------------+--------------------+----------+-----------------------------------+------------------+

\*These publications used acceleration metrics that sum their values per epoch rather than average them per epoch like GGIR does. So, to use their cut-point in GGIR, we provide a scaled version of the cut-points presented in the paper as: `(CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000`
\*\* This  publication used acceleration metrics that expressed their cut-points in _g_ units. So, to use their cut-point in GGIR, we provide a cut-point  multiplied by 1000.

## Cut-points for adults

+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Cut-points        | Device\            | Age           | Relevant arguments                | thresholds       |
|                   | Attachment site    |               |                                   |                  |
+:==================+:===================+:==============+:==================================+:=================+
| Esliger 2011\*    | Left wrist         | 40-65 yr      | `do.enmoa = TRUE`\                | Light: 45\       |
|                   |                    |               | `do.enmo = FALSE`\                | Moderate: 134\   |
|                   |                    |               | `acc.metric = "ENMOa"`            | Vigorous: 377    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Esliger 2011\*    | Right wrist        | 40-65 yr      | `do.enmoa = TRUE`\                | Light: 80\       |
|                   |                    |               | `do.enmo = FALSE`\                | Moderate: 92\    |
|                   |                    |               | `acc.metric = "ENMOa"`            | Vigorous: 437    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Esliger 2011\*    | Waist              | 40-65 yr      | `do.enmoa = TRUE`\                | Light: 16\       |
|                   |                    |               | `do.enmo = FALSE`\                | Moderate: 46\    |
|                   |                    |               | `acc.metric = "ENMOa"`            | Vigorous: 428    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Hildebrand 2014\  | ActiGraph\         | 21-61 yr      | **Default values\                 | Light: 44.8\     |
| Hildebrand 2016   | Non-dominant wrist |               | **`do.enmo = TRUE`\               | Moderate: 100.6\ |
|                   |                    |               | `acc.metric = "ENMO"`             | Vigorous: 428.8  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Hildebrand 2014\  | GENEActiv\         | 21-61 yr      | **Default values\                 | Light: 45.8\     |
| Hildebrand 2016   | Non-dominant wrist |               | **`do.enmo = TRUE`\               | Moderate: 93.2\  |
|                   |                    |               | `acc.metric = "ENMO"`             | Vigorous: 418.3  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Hildebrand 2014\  | ActiGraph\         | 21-61 yr      | **Default values\                 | Light: 47.4\     |
| Hildebrand 2016   | Hip                |               | **`do.enmo = TRUE`\               | Moderate: 69.1\  |
|                   |                    |               | `acc.metric = "ENMO"`             | Vigorous: 258.7  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Hildebrand 2014\  | GENEActiv\         | 21-61 yr      | **Default values\                 | Light: 46.9\     |
| Hildebrand 2016   | Hip                |               | **`do.enmo = TRUE`\               | Moderate: 68.7\  |
|                   |                    |               | `acc.metric = "ENMO"`             | Vigorous: 266.8  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Vähä-Ypyä 2015    | Hookie AM20\       | 35 (SD=11) yr | `do.mad = TRUE`\                  | Light: N/A\      |
|                   | Hip                |               | `do.enmo = FALSE`\                | Moderate: 91\    |
|                   |                    |               | `acc.metric = "MAD"`              | Vigorous: 414    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Dillon 2016\*^,†^ | GENEActiv\         | 50-69 yr      | `do.enmoa = TRUE`\                | Light: 105.6\    |
|                   | Non-dominant wrist |               | `do.enmo = FALSE`\                | Moderate: 174.2\ |
|                   |                    |               | `acc.metric = "ENMOa"`            | Vigorous: 330    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Dillon 2016\*^,†^ | GENEActiv\         | 50-69 yr      | `do.enmoa = TRUE`\                | Light: 127.8\    |
|                   | Dominant wrist     |               | `do.enmo = FALSE`\                | Moderate: 187.6\ |
|                   |                    |               | `acc.metric = "ENMOa"`            | Vigorous: 396.4  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Buchan 2023\*^,†^ | activPAL\          | 23 (SD=4) yr  | **Default values\                 | Light: 26.4\     |
|                   | Right thigh        |               | `do.enmo = TRUE`\                 | Moderate: N/A\   |
|                   |                    |               | `acc.metric = "ENMO"`             | Vigorous: N/A    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| Buchan 2023\*^,†^ | activPAL\          | 23 (SD=4) yr  | `do.mad = TRUE`\                  | Light: 30.1\     |
|                   | Right thigh        |               | `do.enmo = FALSE`\                | Moderate: N/A\   |
|                   |                    |               | `acc.metric = "MAD"`              | Vigorous: N/A    |
+-------------------+--------------------+---------------+-----------------------------------+------------------+

\*These publications used acceleration metrics that sum their values per epoch rather than average them per epoch like GGIR does. So, to use their cut-point in GGIR, we provide a scaled version of the cut-points presented in the paper as: `(CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000`
^†^ In this [publication](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858250/), there are cut-point based on data sampled at 30 Hz and 100 Hz. When scaling the cut-points as specified in (\*), the resulting thresholds are virtually the same (the ones presented in this table).

## Cut-points for older adults

+------------------+--------------------+-------------------+------------------------+-----------------+
| Cut-points       | Device\            | Age               | Relevant arguments     | thresholds      |
|                  | Attachment site    |                   |                        |                 |
+:=================+:===================+:==================+:=======================+:================+
| Sanders 2019\*   | GENEActiv\         | 60-86 yr          | **Default values\      | Light: 20\      |
|                  | Non-dominant wrist |                   | **`do.enmo = TRUE`\    | Moderate: 32\   |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Sanders 2019\*\* | GENEActiv\         | 60-86 yr          | **Default values\      | Light: 57\      |
|                  | Non-dominant wrist |                   | **`do.enmo = TRUE`\    | Moderate: 104\  |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Sanders 2019\*   | ActiGraph\         | 60-86 yr          | **Default values\      | Light: 6\       |
|                  | Hip                |                   | **`do.enmo = TRUE`\    | Moderate: 19\   |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Sanders 2019\*\* | ActiGraph\         | 60-86 yr          | **Default values\      | Light: 15\      |
|                  | Hip                |                   | **`do.enmo = TRUE`\    | Moderate: 69\   |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Migueles 2021    | ActiGraph\         | ≥70 yr \          | **Default values\      | Light: 18\      |
|                  | Non-dominant wrist | (mean: 78.7 yr)   | **`do.enmo = TRUE`\    | Moderate: 60\   |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Migueles 2021    | ActiGraph\         | ≥70 yr \          | **Default values\      | Light: 22\      |
|                  | Dominant wrist     | (mean: 78.7 yr)   | **`do.enmo = TRUE`\    | Moderate: 64\   |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Migueles 2021    | ActiGraph\         | ≥70 yr \          | **Default values\      | Light: 7\       |
|                  | Hip                | (mean: 78.7 yr)   | **`do.enmo = TRUE`\    | Moderate: 14\   |
|                  |                    |                   | `acc.metric = "ENMO"`  | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Fraysse 2020^†^  | GENEActiv\        | ≥70 yr \          | `do.enmoa = TRUE`\     | Light: 42.5\    |
|                  | Non-dominant wrist | (mean: 77 yr)     | `do.enmo = FALSE`\     | Moderate: 98\   |
|                  |                    |                   | `acc.metric = "ENMOa"` | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Fraysse 2020^†^  | GENEActiv\         | ≥70 yr \          | `do.enmoa = TRUE`\     | Light: 62.5\    |
|                  | Dominant wrist     | (mean: 77 yr)     | `do.enmo = FALSE`\     | Moderate: 92.5\ |
|                  |                    |                   | `acc.metric = "ENMOa"` | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Dibben 2020^‡^   | GENEActiv\         | 70.7 (SD=14.1) yr | `do.enmoa = TRUE`\     | Light: 18.6\    |
|                  | Right wrist        |                   | `do.enmo = FALSE`\     | Moderate: 45.5\ |
|                  |                    |                   | `acc.metric = "ENMOa"` | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Dibben 2020^‡^   | GENEActiv\         | 70.7 (SD=14.1) yr | `do.mad = TRUE`\       | Light: 18.3\    |
|                  | Right wrist        |                   | `do.enmo = FALSE`\     | Moderate: 26.2\ |
|                  |                    |                   | `acc.metric = "MAD"`   | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Dibben 2020^‡^   | GENEActiv\         | 70.7 (SD=14.1) yr | `do.enmoa = TRUE`\     | Light: 16.7\    |
|                  | Left wrist         |                   | `do.enmo = FALSE`\     | Moderate: 43.6\ |
|                  |                    |                   | `acc.metric = "ENMOa"` | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Dibben 2020^‡^   | GENEActiv\         | 70.7 (SD=14.1) yr | `do.mad = TRUE`\       | Light: 18.7\    |
|                  | Left wrist         |                   | `do.enmo = FALSE`\     | Moderate: 22.8\ |
|                  |                    |                   | `acc.metric = "MAD"`   | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Dibben 2020^‡^   | GENEActiv\         | 70.7 (SD=14.1) yr | `do.enmoa = TRUE`\     | Light: 7.6\     |
|                  | Hip                |                   | `do.enmo = FALSE`\     | Moderate: 40.6\ |
|                  |                    |                   | `acc.metric = "ENMOa"` | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Dibben 2020^‡^   | GENEActiv\         | 70.7 (SD=14.1) yr | `do.mad = TRUE`\       | Light: 1\       |
|                  | Hip                |                   | `do.enmo = FALSE`\     | Moderate: 2.4\  |
|                  |                    |                   | `acc.metric = "MAD"`   | Vigorous: N/A   |
+------------------+--------------------+-------------------+------------------------+-----------------+

\*Cut-points derived from applying the Youden index on ROC curves.\
\*\* Cut-points derived from increasing Sensitivity over Specificity for light and vice versa for moderate on ROC curves (see [paper](https://doi.org/10.1080/02640414.2018.1555904) for more details).\
^†^ These publications used acceleration metrics that sum their values per epoch rather than average them per epoch like GGIR does. So, to use their cut-point in GGIR, we provide a scaled version of the cut-points presented in the paper as: `(CutPointFromPaper_in_gmins/(sampleRateFromPaper * EpochLengthInSecondsPaper)) * 1000`
^‡^ More cut-points excluding data on aided walking and washing up activities can be found in the [publication](https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-020-00196-7).

# Notes on cut-point validity

**Sensor calibration**

In all of the studies above, excluding Hildebrand et al. 2016, no effort
was made to calibrate the acceleration sensors relative to gravitational
acceleration prior to cut-point development. Theoretically this can be
expected to cause a bias in the cut-point estimates proportional to the
calibration error in each device, especially for cut-points based on
acceleration metrics which rely on the assumption of accurate
calibration such as metrics: ENMO, EN, ENMOa, and by that also metric
SVMgs used by studies such as Esliger 2011, Phillips 2013, and Dibben
2020.

**Idle sleep mode and ActiGraph**

As discussed in the [main package vignette](https://cran.r-project.org/package=GGIR/vignettes/GGIR.html),
studies using the ActiGraph sensor often forget to clarify whether idle sleep
mode was used and if so, how it was accounted for in the data processing.

**How about all the criticism towards cut-point methods?**

For a more elaborate reflection on the limitations of cut-points and a motivation
why cut-points still have value in GGIR see:
<https://www.accelting.com/updates/why-does-ggir-facilitate-cut-points/>


# References

-   Esliger 2011: <https://journals.lww.com/acsm-msse/Fulltext/2011/06000/Validation_of_the_GENEA_Accelerometer.22.aspx>
-   Phillips 2013: <https://doi.org/10.1016/j.jsams.2012.05.013>
-   Schaefer 2014: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960318/>
-   Hildebrand 2014: <https://journals.lww.com/acsm-msse/Fulltext/2014/09000/Age_Group_Comparability_of_Raw_Accelerometer.17.aspx>
-   Hildebrand 2016: <https://doi.org/10.1111/sms.12795>
-   Vähä-Ypyä 2015: <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0134813>
-   Aittasalo 2015: <https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-015-0010-0>
-   Roscoe 2017: <https://link.springer.com/article/10.1007/s00431-017-2948-2>
-   Sanders 2018: <https://doi.org/10.1080/02640414.2018.1555904>
-   Migueles 2021: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150960/>
-   Fraysse 2020: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7843957/>
-   Dibben 2020: <https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102-020-00196-7>
-   Dillon 2016: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4858250/>
