---
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

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!**

It is important to highlight that some of the presented cut-points were originally 
based on acceleration metrics that are not available in GGIR. In particular 
acceleration metrics that sum their values per epoch rather than average them
per epoch like GGIR does. However, we can use them in GGIR by multiplying the 
cut-point by the sample frequency as used in the study that proposed it.
For each of the studies this is detailed in the footnotes. In the tables below these 
conversion are already performed. Thus, some of the cut-points as shown differ with the
values reported in the publications.

Note that GGIR intentionally does not sum values per epoch because that approach
makes the cut-point sample frequency and epoch length dependent, which complicates
comparisons and harmonisation of literature. The explained variance and accuracy
remains identical because we are only multiplying with a constant, so no 
information will be lost.

# 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 | **Default values\                 | Light: N/A\      |
|                   | Hip                |               | **`do.mad = TRUE`\                | Moderate: 91\    |
|                   |                    |               | `do.enmo = FALSE`\                | Vigorous: 414    |
|                   |                    |               | `acc.metric = "MAD"`              |                  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+
| 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  |
+-------------------+--------------------+---------------+-----------------------------------+------------------+

\*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://www.jsams.org/article/S1440-2440(12)00112-0/fulltext>
-   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://onlinelibrary.wiley.com/doi/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/>