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title: "Published cut-points and how to use them in GGIR"
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**NOTE: If you are viewing this page via CRAN note that the main GGIR documentation has been migrated to the [GGIR GitHub pages](https://wadpac.github.io/GGIR/).**

# 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.
Please note that GGIR refers to cut-points as thresholds, but we are referring to the
same thing: A value or a set of values to help split levels of movement intensity.
As newer cut-points are frequently published the list below may not be up to date.
**Please let us know if you are aware of any published cut-points that 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://doi.org/10.1007%2Fs40279-017-0716-0)
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),
     [...])
```

As a side note - Although counts are epoch length specific it is possible to convert them into gravitational units by dividing the count value by the sampling rate as used by the count algorithm (30 Hertz for ActiGraph counts) multiplied by the number of seconds in the time window for which the count value was derived. For example, 100 counts per minute equates to an average acceleration per minute of 100 / (30 x 60) = 0.055 _g_, and 1950 counts per minute equals 1.0833 _g_. Note that the absolute acceleration values from different acceleration metrics are not comparable. However, expressing data in _g_-units can at least ease evaluating the validity of metrics relative to  criterion acceleration and removes the non-informative connection to sampling rate (van Hees et al. 2013 https://doi.org/10.1371/journal.pone.0061691). As this calculation shows, count-units and _g_-unit metrics only differ by a constant, by which there is no practical difference in their ability to explain variance in human behaviour. Similarly, if you would like to convert an average daily acceleration of 0.05 _g_ back to counts per day then you would have to multiply by (30 Hertz x 60 seconds x 1440 minutes), which gives 129600 counts per day.

# 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  |
+-------------------+--------------------+---------------+-------------------------+------------------+
| Mielke 2023       | GENEActiv\         | 35 (SD=11) yr | **Default values\       | Light: 36\       |
|                   | Non-dominant wrist |               | **`do.enmo = TRUE`\     | Moderate: 92\    |
|                   |                    |               | `acc.metric = "ENMO"`   | Vigorous: 283    |
+-------------------+--------------------+---------------+-------------------------+------------------+
| Mielke 2023       | ActiGraph\         | 35 (SD=11) yr | **Default values\       | Light: 25\       |
|                   | Non-dominant wrist |               | **`do.enmo = TRUE`\     | Moderate: 78\    |
|                   |                    |               | `acc.metric = "ENMO"`   | Vigorous: 249    |
+-------------------+--------------------+---------------+-------------------------+------------------+
| Mielke 2023       | GENEActiv\         | 35 (SD=11) yr | **Default values\       | Light: 30\       |
|                   | Dominant wrist     |               | **`do.enmo = TRUE`\     | Moderate: 85\    |
|                   |                    |               | `acc.metric = "ENMO"`   | Vigorous: 270    |
+-------------------+--------------------+---------------+-------------------------+------------------+
| Mielke 2023       | ActiGraph\         | 35 (SD=11) yr | **Default values\       | Light: 40\       |
|                   | Dominant wrist     |               | **`do.enmo = TRUE`\     | Moderate: 65\    |
|                   |                    |               | `acc.metric = "ENMO"`   | Vigorous: 190    |
+-------------------+--------------------+---------------+-------------------------+------------------+
| 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://doi.org/10.1371%2Fjournal.pone.0109913), 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   |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Bammann 2021     | ActiGraph\         | 62.9 (SD=3.6) yr  | **Default values\      | Moderate: 94\   |
|                  | Hip                |                   | **`do.enmo = TRUE`\    | Vigorous: 230   |
|                  |                    |                   | `acc.metric = "ENMO"`  |                 |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Bammann 2021     | ActiGraph\         | 62.9 (SD=3.6) yr  | **Default values\      | Moderate: 122\  |
|                  | Dominant Wrist     |                   | **`do.enmo = TRUE`\    | Vigorous: 234   |
|                  |                    |                   | `acc.metric = "ENMO"`  |                 |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Bammann 2021     | ActiGraph\         | 62.9 (SD=3.6) yr  | **Default values\      | Moderate: 100\  |
|                  | Non-dominant Wrist |                   | **`do.enmo = TRUE`\    | Vigorous: 245   |
|                  |                    |                   | `acc.metric = "ENMO"`  |                 |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Bammann 2021     | ActiGraph\         | 62.9 (SD=3.6) yr  | **Default values\      | Moderate: 342   |
|                  | Dominant Ankle     |                   | **`do.enmo = TRUE`\    |                 |
|                  |                    |                   | `acc.metric = "ENMO"`  |                 |
+------------------+--------------------+-------------------+------------------------+-----------------+
| Bammann 2021     | ActiGraph\         | 62.9 (SD=3.6) yr  | **Default values\      | Moderate: 331   |
|                  | Non-dominant Ankle |                   | **`do.enmo = TRUE`\    |                 |
|                  |                    |                   | `acc.metric = "ENMO"`  |                 |
+------------------+--------------------+-------------------+------------------------+-----------------+
| 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/why-does-ggir-facilitate-cut-points/>


# References

-   Aittasalo 2015: <https://doi.org/10.1186/s13102-015-0010-0>
-   Bammann 2021: <https://doi.org/10.1371/journal.pone.0252615>
-   Dibben 2020: <https://doi.org/10.1186/s13102-020-00196-7>
-   Dillon 2016: <https://doi.org/10.1371%2Fjournal.pone.0109913>
-   Esliger 2011: <https://doi.org/10.1249/mss.0b013e31820513be>
-   Fraysse 2020: <https://doi.org/10.3389%2Ffspor.2020.579278>
-   Hildebrand 2014: <https://doi.org/10.1249/mss.0000000000000289>
-   Hildebrand 2016: <https://doi.org/10.1111/sms.12795>
-   Mielke 2023: <https://doi.org/10.1111/sms.14416>
-   Migueles 2021: <https://doi.org/10.3390%2Fs21103326>
-   Phillips 2013: <https://doi.org/10.1016/j.jsams.2012.05.013>
-   Sanders 2018: <https://doi.org/10.1080/02640414.2018.1555904>
-   Schaefer 2014: <https://doi.org/10.1249%2FMSS.0000000000000150>
-   Roscoe 2017: <https://doi.org/10.1007/s00431-017-2948-2>
-   Vähä-Ypyä 2015: <https://doi.org/10.1371/journal.pone.0134813>





