Questions for the crowd:
* Is there ANY known route/firmware quirk to recover sub-second wake phase in ActiGraph GT3X+ devices?
* With 0.3–0.8s timing noise, which coupling metrics cleanly separate true lagged coordination from jittered simultaneous co-movement?
* Other non-circular anchor ideas?
* Is there ANY known route/firmware quirk to recover sub-second wake phase in ActiGraph GT3X+ devices?
* With 0.3–0.8s timing noise, which coupling metrics cleanly separate true lagged coordination from jittered simultaneous co-movement?
* Other non-circular anchor ideas?
October 22, 2026 at 3:31 PM
Questions for the crowd:
* Is there ANY known route/firmware quirk to recover sub-second wake phase in ActiGraph GT3X+ devices?
* With 0.3–0.8s timing noise, which coupling metrics cleanly separate true lagged coordination from jittered simultaneous co-movement?
* Other non-circular anchor ideas?
* Is there ANY known route/firmware quirk to recover sub-second wake phase in ActiGraph GT3X+ devices?
* With 0.3–0.8s timing noise, which coupling metrics cleanly separate true lagged coordination from jittered simultaneous co-movement?
* Other non-circular anchor ideas?
New on CRAN: chms (7.1). View at https://CRAN.R-project.org/package=chms
chms: Accelerometer Processing Methods for Cycle 7 of the CHMS
'ActiGraph wGT3X-BT' accelerometer processing methods using the standardized workflow developed by Statistics Canada for cycle 7 of the Canadian Health Measures Survey (CHMS). The package promotes transparent and reproducible data processing while supporting the harmonization of analytical approaches among researchers wishing to align with Statistics Canada's methods. For general details about the processing methods, please consult Clarke J, Gribbon A, St-Laurent M, Ferrao T, Barnes J, Kuzik N, Colley R (2026) <<a href="https://doi.org/10.25318%2F82-003-x202600200001-eng" target="_top">doi:10.25318/82-003-x202600200001-eng</a>>.
CRAN.R-project.org
September 8, 2026 at 11:15 PM
New on CRAN: chms (7.1). View at https://CRAN.R-project.org/package=chms
Hi! Does anyone know of where I can buy Actigraph belts and belt clips within the UK? @exerciseworks
January 20, 2025 at 6:51 AM
Hi! Does anyone know of where I can buy Actigraph belts and belt clips within the UK? @exerciseworks
New CRAN package actiRhythm with initial version 0.1.0
#rstats
https://cran.r-project.org/package=actiRhythm
#rstats
https://cran.r-project.org/package=actiRhythm
CRAN: Package actiRhythm
Quantifies the circadian rest-activity rhythm, the roughly 24-hour cycle of activity and rest, from activity counts and raw accelerometer recordings. Analyses run on an activity-count vector and its timestamps, and a built-in reader loads 'ActiGraph' '.agd' files (an 'SQLite' database) directly. Computes nonparametric metrics (interdaily stability (IS) and intradaily variability (IV) following Witting et al. (1990) <<a href="https://doi.org/10.1016%2F0006-3223%2890%2990523-5" target="_top">doi:10.1016/0006-3223(90)90523-5</a>>; relative amplitude (RA), the least-active 5-hour window (L5), and the most-active 10-hour window (M10) following Van Someren et al. (1999) <<a href="https://doi.org/10.3109%2F07420529908998724" target="_top">doi:10.3109/07420529908998724</a>>), cosinor models with a rhythmicity test, confidence ellipses, population-mean cosinor, and two-group parameter comparison (Cornelissen (2014) <<a href="https://doi.org/10.1186%2F1742-4682-11-16" target="_top">doi:10.1186/1742-4682-11-16</a>>; Marler et al. (2006) <<a href="https://doi.org/10.1002%2Fsim.2466" target="_top">doi:10.1002/sim.2466</a>>), period estimation by Lomb-Scargle (Lomb (1976) <<a href="https://doi.org/10.1007%2FBF00648343" target="_top">doi:10.1007/BF00648343</a>>; Scargle (1982) <<a href="https://doi.org/10.1086%2F160554" target="_top">doi:10.1086/160554</a>>) and chi-square (Sokolove and Bushell (1978) <<a href="https://doi.org/10.1016%2F0022-5193%2878%2990022-X" target="_top">doi:10.1016/0022-5193(78)90022-X</a>>) periodograms with bootstrap confidence intervals and a sliding-window spectrogram, fractal and nonlinear measures (Peng et al. (1994) <<a href="https://doi.org/10.1103%2FPhysRevE.49.1685" target="_top">doi:10.1103/PhysRevE.49.1685</a>>; Kantelhardt et al. (2002) <<a href="https://doi.org/10.1016%2FS0378-4371%2802%2901383-3" target="_top">doi:10.1016/S0378-4371(02)01383-3</a>>), the Sleep Regularity Index (Phillips et al. (2017) <<a href="https://doi.org/10.1038%2Fs41598-017-03171-4" target="_top">doi:10.1038/s41598-017-03171-4</a>>), social jet lag, rest-activity state transitions, and locomotor inactivity during sleep (Winnebeck et al. (2018) <<a href="https://doi.org/10.1016%2Fj.cub.2017.11.063" target="_top">doi:10.1016/j.cub.2017.11.063</a>>). It also reads raw accelerometer files ('ActiGraph' '.gt3x', 'Axivity' '.cwa', 'GENEActiv' '.bin'), auto-calibrates them (van Hees et al. (2014) <<a href="https://doi.org/10.1152%2Fjapplphysiol.00421.2014" target="_top">doi:10.1152/japplphysiol.00421.2014</a>>), and derives the ENMO, MAD, and z-angle metrics with diary-free sleep-period detection (van Hees et al. (2018) <<a href="https://doi.org/10.1038%2Fs41598-018-31266-z" target="_top">doi:10.1038/s41598-018-31266-z</a>>), cross-checked against 'GGIR'. A batch mode runs the analysis over a folder of files and writes a multi-sheet 'Excel' workbook.
cran.r-project.org
July 29, 2026 at 5:02 PM
New CRAN package actiRhythm with initial version 0.1.0
#rstats
https://cran.r-project.org/package=actiRhythm
#rstats
https://cran.r-project.org/package=actiRhythm
New CRAN package actiread with initial version 0.2.0
#rstats
https://cran.r-project.org/package=actiread
#rstats
https://cran.r-project.org/package=actiread
CRAN: Package actiread
Provides baseline functions for reading actigraphy and activity data, relying on baseline functions from 'actibase'. Reads data from 'Axivity' 'CWA' <<a href="https://axivity.com/" target="_top">https://axivity.com/</a>> , ActiGraph 'GT3X' <<a href="https://ametris.com/actigraph-wgt3x-bt" target="_top">https://ametris.com/actigraph-wgt3x-bt</a>>, 'SensorLog' <<a href="https://sensorlog.berndthomas.net/" target="_top">https://sensorlog.berndthomas.net/</a>>, and 'SensorLogger' <<a href="https://www.tszheichoi.com/sensorlogger" target="_top">https://www.tszheichoi.com/sensorlogger</a>> zipped CSV files.
cran.r-project.org
July 7, 2026 at 10:02 AM
New CRAN package actiread with initial version 0.2.0
#rstats
https://cran.r-project.org/package=actiread
#rstats
https://cran.r-project.org/package=actiread
New CRAN package actigraph.sleepr with initial version 0.3.1
#rstats
https://cran.r-project.org/package=actigraph.sleepr
#rstats
https://cran.r-project.org/package=actigraph.sleepr
CRAN: Package actigraph.sleepr
Reads *.agd files exported from 'ActiGraph' devices; implements the Troiano (2008) <<a href="https://doi.org/10.1249%2Fmss.0b013e31815a51b3" target="_top">doi:10.1249/mss.0b013e31815a51b3</a>> and Choi (2011) <<a href="https://doi.org/10.1249%2FMSS.0b013e3181ed61a3" target="_top">doi:10.1249/MSS.0b013e3181ed61a3</a>> algorithms for detecting periods on non-wear; implements the Sadeh (1994) <<a href="https://doi.org/10.1093%2Fsleep%2F17.3.201" target="_top">doi:10.1093/sleep/17.3.201</a>> and Cole-Kripke (1992) <<a href="https://doi.org/10.1093%2Fsleep%2F15.5.461" target="_top">doi:10.1093/sleep/15.5.461</a>> algorithms for detecting asleep/awake state and the Tudor-Locke (2014) <<a href="https://doi.org/10.1139%2Fapnm-2013-0173" target="_top">doi:10.1139/apnm-2013-0173</a>> algorithm to detect sleep periods from asleep/awake states.
cran.r-project.org
June 2, 2026 at 9:02 AM
New CRAN package actigraph.sleepr with initial version 0.3.1
#rstats
https://cran.r-project.org/package=actigraph.sleepr
#rstats
https://cran.r-project.org/package=actigraph.sleepr
New CRAN package stepmetrics with initial version 1.0.1
#rstats
https://cran.r-project.org/package=stepmetrics
#rstats
https://cran.r-project.org/package=stepmetrics
CRAN: Package stepmetrics
Provides functions to calculate step- and cadence-based metrics from timestamped accelerometer and wearable device data. Supports CSV and AGD files from 'ActiGraph' devices, CSV files from 'Fitbit' devices, and step counts derived with R package 'GGIR' <<a href="https://github.com/wadpac/GGIR" target="_top">https://github.com/wadpac/GGIR</a>>, with automatic handling of epoch lengths from 1 to 60 seconds. Metrics include total steps, cadence peaks, minutes and steps in predefined cadence bands, and time and steps in moderate-to-vigorous physical activity (MVPA). Methods and thresholds are informed by the literature, e.g., Tudor-Locke and Rowe (2012) <<a href="https://doi.org/10.2165%2F11599170-000000000-00000" target="_top">doi:10.2165/11599170-000000000-00000</a>>, Barreira et al. (2012) <<a href="https://doi.org/10.1249%2FMSS.0b013e318254f2a3" target="_top">doi:10.1249/MSS.0b013e318254f2a3</a>>, and Tudor-Locke et al. (2018) <<a href="https://doi.org/10.1136%2Fbjsports-2017-097628" target="_top">doi:10.1136/bjsports-2017-097628</a>>. The package record is also available on Zenodo (2023) <<a href="https://doi.org/10.5281%2Fzenodo.7858094" target="_top">doi:10.5281/zenodo.7858094</a>>.
cran.r-project.org
September 16, 2025 at 7:02 AM
New CRAN package stepmetrics with initial version 1.0.1
#rstats
https://cran.r-project.org/package=stepmetrics
#rstats
https://cran.r-project.org/package=stepmetrics
New CRAN package activityCounts with initial version 0.2.1
#rstats
https://cran.r-project.org/package=activityCounts
#rstats
https://cran.r-project.org/package=activityCounts
CRAN: Package activityCounts
ActiLife software generates activity counts from data collected by Actigraph accelerometers <<a href="https://s3.amazonaws.com/actigraphcorp.com/wp-content/uploads/2017/11/26205758/ActiGraph-White-Paper_What-is-a-Count_.pdf" target="_top">https://s3.amazonaws.com/actigraphcorp.com/wp-content/uploads/2017/11/26205758/ActiGraph-White-Paper_What-is-a-Count_.pdf</a>>. Actigraph is one of the most common research-grade accelerometers. There is considerable research validating and developing algorithms for human activity using ActiLife counts. Unfortunately, ActiLife counts are proprietary and difficult to implement if researchers use different accelerometer brands. The code creates ActiLife counts from raw acceleration data for different accelerometer brands and it is developed based on the study done by Brond and others (2017) <<a href="https://doi.org/10.1249%2FMSS.0000000000001344" target="_top">doi:10.1249/MSS.0000000000001344</a>>.
cran.r-project.org
April 7, 2025 at 5:02 PM
New CRAN package activityCounts with initial version 0.2.1
#rstats
https://cran.r-project.org/package=activityCounts
#rstats
https://cran.r-project.org/package=activityCounts
#Sleephealth is essential for one's wellbeing, but few studies show how socioeconomic differences can impact #objectivesleepmeasures. In this study, actigraph data from 442 young adults was analyzed to highlight key characteristics of sleep across a diverse sample.
academic.oup.com/sleepadvance...
academic.oup.com/sleepadvance...
June 2, 2026 at 1:09 PM
#Sleephealth is essential for one's wellbeing, but few studies show how socioeconomic differences can impact #objectivesleepmeasures. In this study, actigraph data from 442 young adults was analyzed to highlight key characteristics of sleep across a diverse sample.
academic.oup.com/sleepadvance...
academic.oup.com/sleepadvance...
The #CoronaryArteryRiskinYoungAdults (CARDIA) study analyzed 7 days of wearable #actigraph data and #questionnaires from 899 #middle-aged participants. The goal was to uncover which clinical, psychosocial, and lifestyle factors are most closely linked to #racialdisparities in #sleephealth.
Association of clinical, psychological, and lifestyle factors with disparities in sleep: the CARDIA sleep study
AbstractStudy Objectives. To determine the degree to which clinical, psychosocial, and lifestyle factors are associated with racial disparities in sleep he
academic.oup.com
April 13, 2026 at 12:54 PM
The #CoronaryArteryRiskinYoungAdults (CARDIA) study analyzed 7 days of wearable #actigraph data and #questionnaires from 899 #middle-aged participants. The goal was to uncover which clinical, psychosocial, and lifestyle factors are most closely linked to #racialdisparities in #sleephealth.
Solidot: "每周一到两天步行逾 4 千步与老年女性更低的心脏病和死亡风险相关"
每周一到两天步行逾 4 千步与老年女性更低的心脏病和死亡风险相关
根据发表在《The British Journal of Sports Medicine》期刊上的一项研究，Mass General Brigham 的研究人员分析了 13,547 名年长女性一周的步行数和十年内的死亡率和心血管疾病发病率，发现每周一到两天步行数达到 4,000 步与更低的死亡和心血管疾病风险相关——步数越多，益处越大，直至风险降低至一定水平。这些女性的平均年龄是 71.8 岁，她们在 2011 年至 2015 年间佩戴 ActiGraph GT3X+ 加速度计追踪一周的步数。根据每周步行数她们被分成 4,000、5,000、6,000 或 7,000 步组。结果显示，相比每周步数不足 4000 的人，每周有一到两天达到 4,000 步数的人死亡风险降低 26%，心血管疾病风险降低 27%。每周三天或三天以上达到 4,000 步数的人死亡风险进一步降低 40%。达到更高步数的女性心血管疾病风险则趋于平稳。
www.solidot.org
October 23, 2025 at 2:37 AM
Solidot: "每周一到两天步行逾 4 千步与老年女性更低的心脏病和死亡风险相关"
每周一到两天步行逾 4 千步与老年女性更低的心脏病和死亡风险相关
根据发表在《The British Journal of Sports Medicine》期刊上的一项研究，Mass General Brigham 的研究人员分析了 13,547 名年长女性一周的步行数和十年内的死亡率和心血管疾病发病率，发现每周一到两天步行数达到 4,000 步与更低的死亡和心血管疾病风险相关——步数越多，益处越大，直至风险降低至一定水平。这些女性的平均年龄是 71.8 岁，她们在 2011 年至 2015 年间佩戴 ActiGraph GT3X+ 加速度计追踪一周的步数。根据每周步行数她们被分成 4,00
根据发表在《The British Journal of Sports Medicine》期刊上的一项研究，Mass General Brigham 的研究人员分析了 13,547 名年长女性一周的步行数和十年内的死亡率和心血管疾病发病率，发现每周一到两天步行数达到 4,000 步与更低的死亡和心血管疾病风险相关——步数越多，益处越大，直至风险降低至一定水平。这些女性的平均年龄是 71.8 岁，她们在 2011 年至 2015 年间佩戴 ActiGraph GT3X+ 加速度计追踪一周的步数。根据每周步行数她们被分成 4,00
October 23, 2025 at 2:31 AM
每周一到两天步行逾 4 千步与老年女性更低的心脏病和死亡风险相关
根据发表在《The British Journal of Sports Medicine》期刊上的一项研究，Mass General Brigham 的研究人员分析了 13,547 名年长女性一周的步行数和十年内的死亡率和心血管疾病发病率，发现每周一到两天步行数达到 4,000 步与更低的死亡和心血管疾病风险相关——步数越多，益处越大，直至风险降低至一定水平。这些女性的平均年龄是 71.8 岁，她们在 2011 年至 2015 年间佩戴 ActiGraph GT3X+ 加速度计追踪一周的步数。根据每周步行数她们被分成 4,00
根据发表在《The British Journal of Sports Medicine》期刊上的一项研究，Mass General Brigham 的研究人员分析了 13,547 名年长女性一周的步行数和十年内的死亡率和心血管疾病发病率，发现每周一到两天步行数达到 4,000 步与更低的死亡和心血管疾病风险相关——步数越多，益处越大，直至风险降低至一定水平。这些女性的平均年龄是 71.8 岁，她们在 2011 年至 2015 年间佩戴 ActiGraph GT3X+ 加速度计追踪一周的步数。根据每周步行数她们被分成 4,00
🚶♂️The way you walk offers clues to your health
📉 IMI project MOBILISE-D's algorithms translate walking patterns into indicators to track changes in disease status
✅ Companies are using the public algorithms in their products
👉 europa.eu/!GFFHYt
#IHITransformingHealth #IHISummerOfScience #HorizonEU
📉 IMI project MOBILISE-D's algorithms translate walking patterns into indicators to track changes in disease status
✅ Companies are using the public algorithms in their products
👉 europa.eu/!GFFHYt
#IHITransformingHealth #IHISummerOfScience #HorizonEU
Real-world results: Mobilise-D algorithms enter the market
The algorithms that translate walking patterns into indicators of health have been integrated by private companies EMPATICA, Clario, McRoberts and Actigraph into their products.
europa.eu
August 19, 2025 at 9:00 AM
🚶♂️The way you walk offers clues to your health
📉 IMI project MOBILISE-D's algorithms translate walking patterns into indicators to track changes in disease status
✅ Companies are using the public algorithms in their products
👉 europa.eu/!GFFHYt
#IHITransformingHealth #IHISummerOfScience #HorizonEU
📉 IMI project MOBILISE-D's algorithms translate walking patterns into indicators to track changes in disease status
✅ Companies are using the public algorithms in their products
👉 europa.eu/!GFFHYt
#IHITransformingHealth #IHISummerOfScience #HorizonEU
the neurologist says i have to wear this cute beacon while i sleep. it smells like niacinimide & cigarettes, i guess to encourage me to dream of beautiful women. or maybe because they forgot to wash it
May 15, 2026 at 12:57 AM
the neurologist says i have to wear this cute beacon while i sleep. it smells like niacinimide & cigarettes, i guess to encourage me to dream of beautiful women. or maybe because they forgot to wash it
[Last 3 sentences]: Participants who withdrew had worse baseline scores across domains. Seven microbiome and six ActiGraph® samples were collected. DISCUSSION: This is one of the first PT + PMR supportive care interventions using virtual health among older adults with
4/5
4/5
November 29, 2024 at 3:07 AM
[Last 3 sentences]: Participants who withdrew had worse baseline scores across domains. Seven microbiome and six ActiGraph® samples were collected. DISCUSSION: This is one of the first PT + PMR supportive care interventions using virtual health among older adults with
4/5
4/5
Love seeing moves to accelerate life-changing research with scalable #healthtech!
ActiGraph’s recent acquisition is a big step for innovators leveraging #digitalhealth tools to make #clinicaltrials more patient-centric and accessible: bit.ly/40pR7VK
#futureofhealthcare #wearables #rpm
ActiGraph’s recent acquisition is a big step for innovators leveraging #digitalhealth tools to make #clinicaltrials more patient-centric and accessible: bit.ly/40pR7VK
#futureofhealthcare #wearables #rpm
Wearable maker ActiGraph acquires assets from Biofourmis to boost clinical trial offerings
ActiGraph, which makes digital tools used in clinical trials, acquired the life sciences business of Biofourmis, once considered a digital health unicorn.
bit.ly
January 7, 2025 at 8:36 PM
Love seeing moves to accelerate life-changing research with scalable #healthtech!
ActiGraph’s recent acquisition is a big step for innovators leveraging #digitalhealth tools to make #clinicaltrials more patient-centric and accessible: bit.ly/40pR7VK
#futureofhealthcare #wearables #rpm
ActiGraph’s recent acquisition is a big step for innovators leveraging #digitalhealth tools to make #clinicaltrials more patient-centric and accessible: bit.ly/40pR7VK
#futureofhealthcare #wearables #rpm
New on CRAN: actiRhythm (0.1.0). View at https://CRAN.R-project.org/package=actiRhythm
actiRhythm: Circadian Rest-Activity Rhythm Analysis
Quantifies the circadian rest-activity rhythm, the roughly 24-hour cycle of activity and rest, from activity counts and raw accelerometer recordings. Analyses run on an activity-count vector and its timestamps, and a built-in reader loads 'ActiGraph' '.agd' files (an 'SQLite' database) directly. Computes nonparametric metrics (interdaily stability (IS) and intradaily variability (IV) following Witting et al. (1990) <<a href="https://doi.org/10.1016%2F0006-3223%2890%2990523-5" target="_top">doi:10.1016/0006-3223(90)90523-5</a>>; relative amplitude (RA), the least-active 5-hour window (L5), and the most-active 10-hour window (M10) following Van Someren et al. (1999) <<a href="https://doi.org/10.3109%2F07420529908998724" target="_top">doi:10.3109/07420529908998724</a>>), cosinor models with a rhythmicity test, confidence ellipses, population-mean cosinor, and two-group parameter comparison (Cornelissen (2014) <<a href="https://doi.org/10.1186%2F1742-4682-11-16" target="_top">doi:10.1186/1742-4682-11-16</a>>; Marler et al. (2006) <<a href="https://doi.org/10.1002%2Fsim.2466" target="_top">doi:10.1002/sim.2466</a>>), period estimation by Lomb-Scargle (Lomb (1976) <<a href="https://doi.org/10.1007%2FBF00648343" target="_top">doi:10.1007/BF00648343</a>>; Scargle (1982) <<a href="https://doi.org/10.1086%2F160554" target="_top">doi:10.1086/160554</a>>) and chi-square (Sokolove and Bushell (1978) <<a href="https://doi.org/10.1016%2F0022-5193%2878%2990022-X" target="_top">doi:10.1016/0022-5193(78)90022-X</a>>) periodograms with bootstrap confidence intervals and a sliding-window spectrogram, fractal and nonlinear measures (Peng et al. (1994) <<a href="https://doi.org/10.1103%2FPhysRevE.49.1685" target="_top">doi:10.1103/PhysRevE.49.1685</a>>; Kantelhardt et al. (2002) <<a href="https://doi.org/10.1016%2FS0378-4371%2802%2901383-3" target="_top">doi:10.1016/S0378-4371(02)01383-3</a>>), the Sleep Regularity Index (Phillips et al. (2017) <<a href="https://doi.org/10.1038%2Fs41598-017-03171-4" target="_top">doi:10.1038/s41598-017-03171-4</a>>), social jet lag, rest-activity state transitions, and locomotor inactivity during sleep (Winnebeck et al. (2018) <<a href="https://doi.org/10.1016%2Fj.cub.2017.11.063" target="_top">doi:10.1016/j.cub.2017.11.063</a>>). It also reads raw accelerometer files ('ActiGraph' '.gt3x', 'Axivity' '.cwa', 'GENEActiv' '.bin'), auto-calibrates them (van Hees et al. (2014) <<a href="https://doi.org/10.1152%2Fjapplphysiol.00421.2014" target="_top">doi:10.1152/japplphysiol.00421.2014</a>>), and derives the ENMO, MAD, and z-angle metrics with diary-free sleep-period detection (van Hees et al. (2018) <<a href="https://doi.org/10.1038%2Fs41598-018-31266-z" target="_top">doi:10.1038/s41598-018-31266-z</a>>), cross-checked against 'GGIR'. A batch mode runs the analysis over a folder of files and writes a multi-sheet 'Excel' workbook.
CRAN.R-project.org
July 29, 2026 at 9:55 PM
New on CRAN: actiRhythm (0.1.0). View at https://CRAN.R-project.org/package=actiRhythm
New on CRAN: actiread (0.2.0). View at https://CRAN.R-project.org/package=actiread
actiread: Baseline Package for Reading Actigraphy and Activity Data
Provides baseline functions for reading actigraphy and activity data, relying on baseline functions from 'actibase'. Reads data from 'Axivity' 'CWA' <<a href="https://axivity.com/" target="_top">https://axivity.com/</a>> , ActiGraph 'GT3X' <<a href="https://ametris.com/actigraph-wgt3x-bt" target="_top">https://ametris.com/actigraph-wgt3x-bt</a>>, 'SensorLog' <<a href="https://sensorlog.berndthomas.net/" target="_top">https://sensorlog.berndthomas.net/</a>>, and 'SensorLogger' <<a href="https://www.tszheichoi.com/sensorlogger" target="_top">https://www.tszheichoi.com/sensorlogger</a>> zipped CSV files.
CRAN.R-project.org
July 7, 2026 at 11:41 AM
New on CRAN: actiread (0.2.0). View at https://CRAN.R-project.org/package=actiread
New on CRAN: actigraph.sleepr (0.3.1). View at https://CRAN.R-project.org/package=actigraph.sleepr
actigraph.sleepr: Detect Periods of Sleep and Non-Wear in 'ActiGraph' Data
Reads *.agd files exported from 'ActiGraph' devices; implements the Troiano (2008) <<a href="https://doi.org/10.1249%2Fmss.0b013e31815a51b3" target="_top">doi:10.1249/mss.0b013e31815a51b3</a>> and Choi (2011) <<a href="https://doi.org/10.1249%2FMSS.0b013e3181ed61a3" target="_top">doi:10.1249/MSS.0b013e3181ed61a3</a>> algorithms for detecting periods on non-wear; implements the Sadeh (1994) <<a href="https://doi.org/10.1093%2Fsleep%2F17.3.201" target="_top">doi:10.1093/sleep/17.3.201</a>> and Cole-Kripke (1992) <<a href="https://doi.org/10.1093%2Fsleep%2F15.5.461" target="_top">doi:10.1093/sleep/15.5.461</a>> algorithms for detecting asleep/awake state and the Tudor-Locke (2014) <<a href="https://doi.org/10.1139%2Fapnm-2013-0173" target="_top">doi:10.1139/apnm-2013-0173</a>> algorithm to detect sleep periods from asleep/awake states.
CRAN.R-project.org
June 2, 2026 at 12:42 PM
New on CRAN: actigraph.sleepr (0.3.1). View at https://CRAN.R-project.org/package=actigraph.sleepr