GGIR Part 1 | GGIR Part 2 | Person level summary (csv) | Letter codes to indicate aggregation type | Distribution of acceleration | Circadian rhythm: MXLX, IV, and IS | Circadian rhythm: Cosinor | Day level summary (csv) | Data_quality_report (csv) | GGIR Part 3 | GGIR Part 4 | Night level summaries (csv) | Person level summaries (csv) | Visualisation (pdf) | GGIR Part 5 | Fragmentation | GGIR Part 6 | MXLX output variables | Cosinor output variables | Other output variables in part 6

Input arguments/parameters overview | Arguments/parameters description | GGIR function input arguments | mode | datadir | outputdir | studyname | f0 | f1 | do.report | configfile | myfun | General Parameters | Raw Data Parameters | Metrics Parameters | Cleaning Parameters | Sleep Parameters | Physical activity Parameters | 24/7 Parameters | Output Parameters

Introduction | Example with external R function | Write external function | Provide external function to GGIR | Example with external Python function | Integration in GGIR output | Part 1 | Part 2 | External functions released by GGIR collaborators:

Introduction | The read.myacc.csv function | Input arguments | General arguments | Arguments for files containing a header | Arguments for files including timestamps | Arguments for files with acceleration stored in bits | Arguments for files including temperature | Arguments for files including wear time information | Arguments to find time gaps and resampling | Usage of the read.myacc.csv function | Example using the shell function

Verisense algorithm | How to use the Verisense algorithm? | Time resolution for deriving statistics | Per day or groups of days | Per day segment | Step count and cadence summary statistics | Stratified by acceleration and/or cadence level | Per acceleration level | Per cadence level with absolute thresholds | Per cadence level with relative thresholds | Per most and least active consecutive cadence time window | Walking bouts

Considerations | Cut-points expressed in gravitational units (this vignette) | Cut-points NOT expressed in gravitational units (not in this vignette) | Relevant arguments to use cut-points in GGIR | Summary of published cut-points | Cut-points for preschoolers | Cut-points for children/adolescents | Cut-points for adults | Cut-points for older adults | Notes on cut-point validity | References

Algorithm description | Output related to nap detection | Disclaimer

Introduction | What is GGIR? | Contributing, Support, and Keeping up to date | Setting up your work environment | Install R and RStudio | Prepare folder structure | GGIR shell function | Key general arguments | Key arguments related to sleep analysis | Basic sleep log | Advanced sleep log | Key arguments related to time use analysis | Published cut-points and how to use them | Example call | Configuration file | Time for action: How to run your analysis? | From the R console on your own desktop/laptop | In a cluster | Processing time | Inspecting the results | Output part 2 | Person level summary | Day level summary | Data_quality_report | Output part 4 | Night level summaries | Non-default variables in part 4 csv report | visualisation_sleep.pdf | Output part 5 | Day level summary | Output part 6 | Motivation and clarification | Reproducibilty of GGIR analyses | Auto-calibration | Non-wear detection | Clipping score | Why collapse information to epoch level? | Why does the first epoch not allign with the original start of the recording | Sleep analysis | Notes on sleep classification algorithms designed for count data | Replication of the movement counts needed | Missing information for replicating movement counts | An educated guess and how you can to help optimise the implementation | Guiders | Daysleepers (nights workers) | Cleaningcode | Difference between cleaned and full output | Data cleaning file | Waking-waking or 24 hour time-use analysis | Time series output files | Day inclusion criteria | Fragmentation metrics | Why use data metric ENMO as default? | What does GGIR stand for? | Circadian Rhythm analyses | ActiGraph's idle sleep mode | Time gap imputation | The importance of reporting idle.sleep.mode usage | MX metrics (minimum intensity of most active X minutes) | Minimum recording duration | LUX sensor data processing | Other Resources | Citing GGIR | Copyright for GGIR logo

Introduction | Clock hour-based segmentation | Segmentation guided by activity log | Implementation in GGIR | Examples | Clock-hour based segmentation: | Activity log based segmentation: | Cleaning parameters for day segments (in part 5): | Analyses performed per day segment