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Vincent van Hees edited this page Feb 17, 2022 · 122 revisions

This is a non-exhaustive list of peer-reviewed academic publications that used GGIR. The list is limited to publications for which we could check that GGIR was used.

Occasionally we come across publications where authors do not cite the GGIR software while we know from personal correspondence that GGIR was used. Research software citation is important for making the research reproducible and to give credit to the efforts that goes into the development and maintenance of Open Source software. Please cite the following paper in your work if using GGIR:

  • Migueles, J.H., Rowlands, A.V., Huber, F., Sabia, S. and van Hees, V.T., 2019. GGIR: a research community–driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. Journal for the Measurement of Physical Behaviour, 2(3), pp.188-196.

If you think a publication is missing from the list, please let me know: https://www.accelting.com/contact/

1. Original research publications that used GGIR (non-methodological)

  1. Acosta et al. Association of objectively measured physical activity with brown adipose tissue volume and activity in young adults. (2018) doi: https://doi.org/10.1210/jc.2018-01312
  2. Acosta et al. Sleep duration and quality are not associated with brown adipose tissue volume or activity - as determined by 18F-FDG uptake, in young, sedentary adults (2019) doi: https://doi.org/10.1093/sleep/zsz177
  3. Afshar et al. Changes in physical activity after bariatric surgery: using objective and self-reported measures. (2016) doi: https://doi.org/10.1016/j.soard.2016.09.012
  4. Aguayo et al. Objective and subjective sleep measures are associated with HbA1c and insulin sensitivity in the general population: Findings from the ORISCAV-LUX-2 study (2022) doi: https://doi.org/10.1016/j.diabet.2021.101263
  5. Albalak et al. Timing of objectively-collected physical activity in relation to body weight and metabolic health in sedentary older people: a cross-sectional and prospective analysis (2021) doi: https://doi.org/10.1038/s41366-021-01018-7
  6. Alonso Martinez et al. Physical Activity, Sedentary Behavior, Sleep and Self-Regulation in Spanish Preschoolers during the COVID-19 Lockdown (2021) doi: https://doi.org/10.3390/ijerph18020693
  7. Amaro-Gahete et al. Association of physical activity and fitness with S-Klotho plasma levels in middle-aged sedentary adults: The FIT-AGEING study (2019) doi: https://doi.org/10.1016/j.maturitas.2019.02.001
  8. Amaro-Gahete et al. Effects of different exercise training programs on body composition: A randomized control trial (2019) doi: https://doi.org/10.1111/sms.13414
  9. Amaro-Gahete et al. Association of Basal Metabolic Rate and Nutrients Oxidation with Cardiometabolic Risk Factors and Insulin Sensitivity in Sedentary Middle-Aged Adults (2020) doi: https://doi.org/10.3390/nu12041186
  10. Amaro-Gahete et al. Association of sedentary and physical activity time with maximal fat oxidation during exercise in sedentary adults (2020) doi: https://doi.org/10.1111/sms.13696
  11. Antczak et al. Day-to-day and longer-term longitudinal associations between physical activity, sedentary behavior, and sleep in children (2021) doi: https://doi.org/10.1093/sleep/zsaa219
  12. Argyridou et al. Evaluation of an 8-Week Vegan Diet on Plasma Trimethylamine-N-Oxide and Postchallenge Glucose in Adults with Dysglycemia or Obesity (2021) doi: https://doi.org/10.1093/jn/nxab046.
  13. Atkins et al. Measuring sedentary behaviors in patients with idiopathic pulmonary fibrosis using wrist-worn accelerometers. (2016) doi: https://doi.org/10.1111/crj.12589
  14. Atkins et al. Measuring sedentary behaviours in patients with idiopathic pulmonary fibrosis using wrist-worn accelerometers (2018) doi: https://doi.org/10.1111/crj.12589
  15. Bachasson et al. Physical Activity Monitoring: A Promising Outcome Measure in Idiopathic Inflammatory Myopathies. (2017) doi: https://doi.org/10.1212/WNL.0000000000004061
  16. Behravesh et al. A prospective study of the relationships between movement and glycemic control during day and night in pregnancy (2021) doi: https://doi.org/10.1038/s41598-021-03257-0
  17. Bell et al. Healthy obesity and objective physical activity. (2015) doi: https://doi.org/10.3945/ajcn.115.110924
  18. Benadjaoud et al. The association between accelerometer-assessed physical activity and respiratory function in older adults differs between smokers and non-smokers (2019) doi: https://doi.org/10.1038/s41598-019-46771-y
  19. Bielemann et al. Are consumption of dairy products and physical activity independently related to bone mineral density of 6-year-old children? Longitudinal and cross-sectional analyses in a birth cohort from Brazil (2018) doi: https://doi.org/10.1017/S1368980018001258
  20. Bielemann et al. Is vigorous-intensity physical activity required for improving bone mass in adolescence? Findings from a Brazilian birth cohort (2019) doi: https://doi.org/10.1007/s00198-019-04862-6
  21. Bielemann et al. Objectively Measured Physical Activity Reduces the Risk of Mortality among Brazilian Older Adults. (2019) doi: https://doi.org/10.1111/jgs.16180
  22. Boddy et al. Comparability of children's sedentary time estimates derived from wrist worn GENEActiv and hip worn ActiGraph accelerometer thresholds (2018) doi: https://doi.org/10.1016/j.jsams.2018.03.015
  23. Boddy et al. The backwards comparability of wrist worn GENEActiv and waist worn ActiGraph accelerometer estimates of sedentary time in children (2019) doi: https://doi.org/10.1016/j.jsams.2019.02.001
  24. Bortone et al. Activity Energy Expenditure Predicts Clinical Average Levels of Physical Activity in Older Population: Results from Salus in Apulia Study. (2020) doi: https://doi.org/ 10.3390/s20164585
  25. Bradley et al. Sleep and circadian rhythm disturbance in bipolar disorder. (2017) doi: https://doi.org/10.1017/S0033291717000186
  26. Bradley et al. The association between sleep and cognitive abnormalities in bipolar disorder (2020) doi: https://doi.org/10.1017/S0033291718004038
  27. Buchan et al. A comparison of physical activity from Actigraph GT3X+ accelerometers worn on the dominant and non‐dominant wrist. (2018) doi: https://doi.org/10.1111/cpf.12538
  28. Buchan et al. Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts based processing methods. (2018) doi: https://doi.org/10.1080/02640414.2018.1527198
  29. Buchan et al. The use of the intensity gradient and average acceleration metrics to explore associations with BMI z-score in children. (2019) doi: https://doi.org/10.1080/02640414.2019.1664536
  30. Buchan et al. Comparison of Free-Living and Laboratory Activity Outcomes from ActiGraph Accelerometers Worn on the Dominant and Non-Dominant Wrists (2020) doi: https://doi.org/10.1080/1091367X.2020.1801441
  31. Cabanas-Sánchez et al. Twenty four-hour activity cycle in older adults using wrist-worn accelerometers: The seniors-ENRICA-2 study (2020) doi: https://doi.org/10.1111/sms.13612
  32. Cadenas-Sanchez et al. Fitness, physical activity and academic achievement in overweight/obese children (2020) doi: https://doi.org/10.1080/02640414.2020.1729516
  33. Cassidy et al. Accelerometer-derived physical activity in those with cardiometabolic disease compared to healthy adults: a UK Biobank study of 52,556 participants (2018) doi: https://doi.org/10.1007/s00592-018-1161-8
  34. Charman et al. The effect of percutaneous coronary intervention on habitual physical activity in older patients. (2016) doi: https://doi.org/0.1186/s12872-016-0428-7
  35. Chen et al. Socio-demographic and maternal predictors of adherence to 24-hour movement guidelines in Singaporean children (2019) doi: https://doi.org/10.1186/s12966-019-0834-1
  36. Chen et al. Associations between early-life screen viewing and 24 hour movement behaviours: findings from a longitudinal birth cohort study (2020) doi: https://doi.org/10.1016/S2352-4642(19)30424-9
  37. Chevance et al. Do implicit attitudes toward physical activity and sedentary behavior prospectively predict objective physical activity among persons with obesity? (2017) doi: https://doi.org/10.1007/s10865-017-9881-8
  38. Chevance et al. Changing implicit attitudes for physical activity with associative learning. (2018) doi: https://doi.org/10.1007/s12662-018-0559-
  39. Chong et al. Changes in 24-hour movement behaviours during the transition from primary to secondary school among Australian children (2021) doi: https://doi.org/10.1080/17461391.2021.1903562
  40. Coyle-Asbil et al. Comparison of Different Signal Processing Methodologies and Their Impact on the Range of Acceleration Amplitudes Experienced by Preschool-Aged Children (2021) doi: https://doi.org/10.1080/1091367X.2021.2009836
  41. Crotti et al. Development of raw acceleration cut-points for wrist and hip accelerometers to assess sedentary behaviour and physical activity in 5–7-year-old children. (2020) doi: https://doi.org/10.1080/02640414.2020.1740469
  42. Cruz et al. The effects of the Australian bushfires on physical activity in children (2021) doi: https://doi.org/10.1016/j.envint.2020.106214
  43. Cumming et al. Maturational timing, physical self-perceptions and physical activity in UK adolescent females: investigation of a mediated effects model (2020) doi: https://doi.org/10.1080/03014460.2020.1784277
  44. da Costa et al. Prevalence and sociodemographic factors associated with meeting the 24-hour movement guidelines in a sample of Brazilian adolescents (2020) doi: https://doi.org/10.1371/journal.pone.0239833
  45. da Costa et al. Association between sociodemographic, dietary, and substance use factors and accelerometer-measured 24-hour movement behaviours in Brazilian adolescents (2021) doi: https://doi.org/10.1007/s00431-021-04112-0
  46. da Silva et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry (2014) doi: https://doi.org/10.1093/ije/dyu203
  47. da Silva et al. Built environment and physical activity: domain- and activity-specific associations among Brazilian adolescents. (2017) doi: https://doi.org/10.1186/s12889-017-4538-7
  48. da Silva et al. Correlates of accelerometer-assessed physical activity in pregnancy—The 2015 Pelotas (Brazil) Birth Cohort Study (2018) doi: https://doi.org/10.1111/sms.13083
  49. da Silva et al. Associations of physical activity and sedentary time with body composition in Brazilian young adults (2019) doi: https://doi.org/10.1038/s41598-019-41935-2
  50. da Silva et al. How many days are needed to estimate wrist-worn accelerometry-assessed physical activity during the second trimester in pregnancy? (2019) doi: https://doi.org/10.1371/journal.pone.0211442
  51. da Silva et al. Correlates of accelerometer‐assessed physical activity in pregnancy:The 2015 Pelotas (Brazil) Birth Cohort Study. (2018) doi: https://doi.org/10.1111/sms.13083
  52. Dawkins et al. Comparing 24 h physical activity profiles: Office workers, women with a history of gestational diabetes and people with chronic disease condition(s) (2020) doi: https://doi.org/10.1080/02640414.2020.1812202
  53. Dawkins et al. Normative wrist-worn accelerometer values for self-paced walking and running: a walk in the park (2021) doi: https://doi.org/10.1080/02640414.2021.1976491
  54. Dibben et al. Factors Associated with Objectively Assessed Physical Activity Levels of Heart Failure Patients (2020) doi: https://doi.org/10. 35248/2155-9880. 20. 11. 655.
  55. Difrancesco et al. Sleep, circadian rhythm, and physical activity patterns in depressive and anxiety disorders: A 2‐week ambulatory assessment study. (2019) doi: https://doi.org/10.1002/da.2294
  56. Ding et al. Prenatal and birth predictors of objectively measured physical activity and sedentary time in three population-based birth cohorts in Brazil (2020) doi: https://doi.org/10.1038/s41598-019-57070-x
  57. Diniz-Sousa et al. Accelerometry calibration in people with class II-III obesity: Energy expenditure prediction and physical activity intensity identification. (2019) doi: https://doi.org/10.1016/j.gaitpost.2019.11.008
  58. Donnelly et al. Relationship Between Parent and Child Physical Activity Using Novel Acceleration Metrics (2020) doi: https://doi.org/10.1080/02701367.2020.1817295
  59. Edwardson et al. Effectiveness of the Stand More AT (SMArT) Work intervention: cluster randomised controlled trial. (2018) doi: https://doi.org/10.1136/bmj.k3870
  60. Esteban-Cornejo et al. Physical Activity throughout Adolescence and Cognitive Performance at 18 Years of Age. (2015) doi: https://doi.org/10.1249/MSS.0000000000000706
  61. Euler et al. Rural–Urban Differences in Baseline Dietary Intake and Physical Activity Levels of Adolescents (2019) doi: https://doi.org/10.5888/pcd16.180200.
  62. Exel et al. Physical activity and sedentary behavior in amateur sports: master athletes are not free from prolonged sedentary time (2019) doi: https://doi.org/10.1007/s11332-019-00527-3
  63. Fairclough et al. Wear Compliance and Activity in Children Wearing Wrist- and Hip-Mounted Accelerometers. (2016) doi: https://doi.org/10.1249/MSS.0000000000000771
  64. Fairclough et al. Fitness, fatness and the reallocation of time between children’s daily movement behaviours: an analysis of compositional data (2017) doi: https://doi.org/10.1186/s12966-017-0521-z
  65. Fairclough et al. Cross-sectional associations between 24-hour activity behaviours and mental health indicators in children and adolescents: A compositional data analysis (2021) doi: https://doi.org/10.1080/02640414.2021.1890351
  66. Farina et al. Acceptability and feasibility of wearing activity monitors in community‐dwelling older adults with dementia (2019) doi: https://doi.org/10.1002/gps.5064
  67. Fernández‑Verdejo et al. Deciphering the constrained total energy expenditure model in humans by associating accelerometer‑measured physical activity from wrist and hip (2021) doi: https://doi.org/10.1038/s41598-021-91750-x
  68. Flack et al. Building research in diet and cognition (BRIDGE): Baseline characteristics of older obese African American adults in a randomized controlled trial to examine the effect of the Mediterranean diet with and without weight loss on cognitive functioning (2021) doi: https://doi.org/10.1016/j.pmedr.2020.101302
  69. Florez et al. The Power of Social Networks and Social Support in Promotion of Physical Activity and Body Mass Index among African American Adults. (2018) doi: https://doi.org/10.1016/j.ssmph.2018.03.004
  70. Gaba et al. How do short sleepers use extra waking hours? A compositional analysis of 24-h time-use patterns among children and adolescents (2020) doi: https://doi.org/10.1186/s12966-020-01004-8
  71. Galmes-Panades et al. Isotemporal substitution of inactive time with physical activity and time in bed: cross-sectional associations with cardiometabolic health in the PREDIMED-Plus study. (2019) doi: https://doi.org/10.1186/s12966-019-0892-4
  72. Garcia-Hermoso et al. Exercise program and blood pressure in children: The moderating role of sedentary time (2020) doi: https://doi.org/10.1016/j.jsams.2020.02.012
  73. Gilson et al. How do short sleepers use extra waking hours? A compositional analysis of 24-h time-use patterns among children and adolescents (2020) doi: https://doi.org//10.1186/s12966-020-01004-8
  74. Gilson et al. VO2peak and 24-hour sleep, sedentary behavior, and physical activity in Australian truck drivers (2021) doi: https://doi.org/10.1111/sms.13965
  75. Gomez-Bruton et al. Associations of dietary energy density with body composition and cardiometabolic risk in children with overweight and obesity: role of energy density calculations, under-reporting energy intake and physical activity (2019) doi: https://doi.org/10.1017/S0007114519000278
  76. Grimes et al. Accelerometery as a measure of modifiable physical activity in high-risk elderly preoperative patients: a prospective observational pilot study (2019) doi: https://doi.org/10.1136/bmjopen-2019-032346
  77. Halonen et al. Cross-sectional associations of neighbourhood socioeconomic disadvantage and greenness with accelerometer-measured leisure-time physical activity in a cohort of ageing workers. (2020) doi: https://doi.org/10.1136/bmjopen-2020-038673
  78. Hamer et al. Change in device-measured physical activity assessed in childhood and adolescence in relation to depressive symptoms: a general population-based cohort study (2020) doi: https://doi.org/10.1136/jech-2019-213399
  79. Harper et al. Management of fatigue with physical activity and behavioural change support in vasculitis: a feasibility study (2020) doi: https://doi.org/10.1093/rheumatology/keaa890
  80. Harrington et al. Effectiveness of the ‘Girls Active’ school-based physical activity programme: A cluster randomised controlled trial. (2018) doi: https://doi.org/10.1186/s12966-018-0664-6
  81. Harrington et al. Concurrent screen use and cross-sectional association with lifestyle behaviours and psychosocial health in adolescent females (2021) doi: https://doi.org/10.1111/apa.15806
  82. Hausler et al. Association between actigraphy-based sleep duration variability and cardiovascular risk factors - Results of a population-based study. (2019) doi: https://doi.org/10.1016/j.sleep.2019.02.008
  83. Henson et al. Physical behaviors and chronotype in people with type 2 diabetes. (2020) doi: https://doi.org/10.1136/bmjdrc-2020-001375
  84. Horne et al. An evaluation of sleep disturbance on in-patient psychiatric units in the UK (2018) doi: https://doi.org/10.1192/bjb.2018.42
  85. Horta et al. Objectively measured physical activity and sedentary-time are associated with arterial stiffness in Brazilian young adults (2015) doi: https://doi.org/10.1016/j.atherosclerosis.2015.09.005
  86. Hurter et al. Back to school after lockdown: The effect of COVID-19 restrictions on children’s device-based physical activity metrics (2022) doi: https://doi.org/10.1016/j.jshs.2022.01.009
  87. Innerd et al. Using open source accelerometer analysis to assess physical activity and sedentary behaviour in overweight and obese adults. (2018) doi: https://doi.org/10.1186/s12889-018-5215-1
  88. Jakubec et al. Is adherence to the 24-hour movement guidelines associated with a reduced risk of adiposity among children and adolescents? (2020) doi: https://doi.org/10.1186/s12889-020-09213-3
  89. Jiminez-Moreno et al. Analyzing walking speeds with ankle and wrist worn accelerometers in a cohort with myotonic dystrophy (2019) doi: https://doi.org/10.1080/09638288.2018.1482376
  90. Johson et al. Measures Derived from Panoramic Ultrasonography and Animal-Based Protein Intake Are Related to Muscular Performance in Middle-Aged Adults (2021) doi: https://doi.org/10.3390/jcm10050988
  91. Jones et al. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour. (2019) doi: https://doi.org/10.1038/s41467-019-09576-1
  92. Jones et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms (2019) doi: https://doi.org/10.1038/s41467-018-08259-7
  93. Jurado-Fasoli et al. Exercise training improves sleep quality: A randomized controlled trial (2020) doi: https://doi.org/10.1111/eci.13202
  94. Khan et al. Effects of a School Based Intervention on Children’s Physical Activity and Healthy Eating: A Mixed-Methods Study. (2019) doi: https://doi.org/10.3390/ijerph16224320
  95. Khanna et al. Rituximab for the treatment of fatigue in primary biliary cholangitis (formerly primary biliary cirrhosis): a randomised controlled trial. (2018) doi: https://doi.org/10.3310/eme05020
  96. Khunti et al. Promoting physical activity with self-management support for those with multimorbidity: a randomised controlled trial (2021) doi: https://doi.org/10.3399/BJGP.2021.0172
  97. Kim et al. Surveillance of Youth Physical Activity and Sedentary Behavior With Wrist Accelerometry. (2016) doi: https://doi.org/10.1016/j.amepre.2017.01.012
  98. Knuth et al. Objectively-measured physical activity in children is influenced by social indicators rather than biological lifecourse factors: Evidence from a Brazilian cohort. (2016) doi: https://doi.org/10.1016/j.ypmed.2016.12.051
  99. Kolle et al. Does objectively measured physical activity modify the association between early weight gain and fat mass in young adulthood? (2017) doi: https://doi.org/10.1186/s12889-017-4924-1
  100. Koolhaas et al. Objective Measures of Activity in the Elderly: Distribution and Associations With Demographic and Health Factors. (2017) doi: https://doi.org/10.1016/j.jamda.2017.04.017
  101. Koopman-Verhoef et al. Objective measures of activity in the elderly: Distribution and associations with demographic and health factors (2017) doi: https://doi.org/10.1016/j.jamda.2017.04.017
  102. Koopman-Verhoef et al. Preschool family irregularity and the development of sleep problems in childhood: a longitudinal study (2019) doi: https://doi.org/10.1111/jcpp.13060
  103. Lacoste et al. A quasi-experimental study of the effects of an outdoor learning program on physical activity patterns of children with a migrant background: the PASE Study (2021) doi: https://doi.org/10.5334/paah.133
  104. Lambert et al. Web-Based Intervention Using Behavioral Activation and Physical Activity for Adults With Depression (The eMotion Study): Pilot Randomized Controlled Trial. (2018) doi: https://doi.org/10.2196/10112
  105. Landon-Cardinal et al. Relationship between change in physical activity and in clinical status in patients with idiopathic inflammatory myopathy: a prospective cohort study. (2020) doi: https://doi.org/10.1016/j.semarthrit.2020.06.014
  106. Lane et al. Biological and clinical insights from genetics of insomnia symptoms (2019) doi: https://doi.org/10.1038/s41588-019-0361-7
  107. Lean et al. Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. (2018) doi: https://doi.org/10.1016/S0140-6736(17)33102-1
  108. Leao et al. Longitudinal Associations Between Device-Measured Physical Activity and Early Childhood Neurodevelopment (2022) doi: https://doi.org/10.1123/jpah.2021-0587
  109. Lee et al. A population-based prospective study on rest-activity rhythm and mild cognitive impairment among Hong Kong healthy community-dwelling older adults (2021) doi: https://doi.org/10.1016/j.nbscr.2021.100065
  110. Leppanen et al. Hip and wrist accelerometers showed consistent associations with fitness and fatness in children aged 8‐12 years (2020) doi: https://doi.org/10.1111/apa.15043
  111. Li et al. Mediating Effect of Motor Competence on the Relationship between Physical Activity and Quality of Life in Children with Attention Deficit Hyperactivity DisorderChildren with Attention Deficit Hyperactivity Disorder (2021) doi: https://doi.org/10.1155/2021/4814250
  112. Lim et al. Physical activity among hospitalised older people: insights from upper and lower limb accelerometry (2018) doi: https://doi.org/10.1007/s40520-018-0930-0
  113. Lloyd et al. Trial baseline characteristics of a cluster randomised controlled trial of a school-located obesity prevention programme; the Healthy Lifestyles Programme (HeLP) trial. (2017) doi: https://doi.org/10.1186/s12889-017-4196-9
  114. Lloyd et al. Effectiveness of the Healthy Lifestyles Programme (HeLP) to prevent obesity in UK primary-school children: a cluster randomised controlled trial. (2018) doi: https://doi.org/10.1016/S2352-4642(17)30151-7
  115. Longman et al. Time in Nature Associated with Decreased Fatigue in UK Truck Drivers (2021) doi: https://doi.org/10.3390/ijerph18063158
  116. Malheiros et al. School schedule affects sleep, but not physical activity, screen time and diet behaviors (2021) doi: https://doi.org/10.1016/j.sleep.2021.06.025
  117. McDevitt et al. Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring (2021) doi: https://doi.org/10.3390/s21062034
  118. McDonough et al. Effects of a remote, YouTube-delivered exercise intervention on young adults’ physical activity, sedentary behavior, and sleep during the COVID-19 pandemic: Randomized controlled trial (2022) doi: https://doi.org/10.1016/j.jshs.2021.07.009
  119. McGowan et al. Actigraphic patterns, impulsivity and mood instability in bipolar disorder, borderline personality disorder and healthy controls. (2020) doi: https://doi.org/10.1111/acps.13148
  120. McLellan et al. Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X+ accelerometers among children 7–12 years old (2020) doi: https://doi.org/10.1016/j.jshs.2019.02.005
  121. Menai et al. Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study. (2017) doi: https://doi.org/10.1038/srep45772
  122. Mickute et al. Device‐measured physical activity and its association with physical function in adults with type 2 diabetes mellitus. (2020) doi: https://doi.org/10.1111/dme.14393
  123. Mielke et al. Associations between Device-measured Physical Activity and Cardiometabolic Health in the Transition to Early Adulthood (2021) doi: https://doi.org/10.1249/MSS.0000000000002696
  124. Migueles et al. Comparability of published cut‐points for the assessment of physical activity: Implications for data harmonization. (2018) doi: https://doi.org/10.1111/sms.13356
  125. Migueles et al. Comparability of accelerometer signal aggregation metrics across placements and dominant wrist cut points for the assessment of physical activity in adults. (2019) doi: https://doi.org/10.1038/s41598-019-54267-y
  126. Migueles et al. Associations of Objectively-Assessed Physical Activity and Sedentary Time with Hippocampal Gray Matter Volume in Children with Overweight/Obesity. (2020) doi: https://doi.org/10.3390/jcm9041080
  127. Migueles et al. Associations of sleep with gray matter volume and their implications for academic achievement, executive function and intelligence in children with overweight/obesity. (2020) doi: https://doi.org/10.1111/ijpo.12707
  128. Migueles et al. Step-Based Metrics and Overall Physical Activity in Children With Overweight or Obesity: Cross-Sectional Study (2020) doi: https://doi.org/10.2196/14841
  129. Miller et al. Associations of object control motor skill proficiency, game play competence, physical activity and cardiorespiratory fitness among primary school children. (2018) doi: https://doi.org/10.1080/02640414.2018.1488384
  130. Mora-Gonzalez et al. Sedentarism, Physical Activity, Steps, and Neurotrophic Factors in Obese Children. (2019) doi: https://doi.org/10.1249/MSS.0000000000002064
  131. Mora-Gonzalez et al. Fitness, physical activity, sedentary time, inhibitory control, and neuroelectric activity in children with overweight or obesity: The ActiveBrains project. (2020) doi: https://doi.org/10.1111/psyp.13579
  132. Nakamura et al. Physical Activity Throughout Adolescence and Hba1c in Early Adulthood: Birth Cohort Study. (2017) doi: https://doi.org/10.1123/jpah.2016-0245
  133. Noonan et al. Comparison of children’s free-living physical activity derived from wrist and hip raw accelerations during the segmented week. (2017) doi: https://doi.org/10.1080/02640414.2016.1255347
  134. Noonan et al. Context matters! sources of variability in weekend physical activity among families: a repeated measures study, (2017) doi: https://doi.org/10.1186/s12889-017-4232-9
  135. Novak et al. Do we have to reduce the recall period? Validity of a daily physical activity questionnaire (PAQ24) in young active adults (2020) doi: https://doi.org/10.1186/s12889-020-8165-3
  136. Ocallaghan et al. Genetic and environmental influences on sleep-wake behaviors in adolescence (2021) doi: https://doi.org/10.1093/sleepadvances/zpab018
  137. Okkersen et al. Cognitive behavioural therapy with optional graded exercise therapy in patients with severe fatigue with myotonic dystrophy type 1: a multicentre, single-blind, randomised trial, (2018) doi: https://doi.org/10.1016/S1474-4422(18)30203-5
  138. Ormel et al. Effects of supervised exercise during adjuvant endocrine therapy in overweight or obese patients with breast cancer: The I-MOVE study (2021) doi: https://doi.org/10.1016/j.breast.2021.05.004
  139. Owen et al. The Feasibility of a Novel School Peer-Led Mentoring Model to Improve the Physical Activity Levels and Sedentary Time of Adolescent Girls: The Girls Peer Activity (G-PACT) Project. (2019) doi: https://doi.org/10.3390/children5060067
  140. Panandreou et al. Long Daytime Napping Is Associated with Increased Adiposity and Type 2 Diabetes in an Elderly Population with Metabolic Syndrome. (2019) doi: https://doi.org/10.3390/jcm8071053
  141. Papandreou et al. High sleep variability predicts a blunted weight loss response and short sleep duration a reduced decrease in waist circumference in the PREDIMED-Plus Trial (2020) doi: https://doi.org/10.1038/s41366-019-0401-5
  142. Park et al. Diet and Physical Activity as Determinants of Continuously Measured Glucose Levels in Persons at High Risk of Type 2 Diabetes (2022) doi: https://doi.org/10.3390/nu14020366
  143. Phelan et al. Randomized controlled clinical trial of behavioral lifestyle intervention with partial meal replacement to reduce excessive gestational weight gain. (2018) doi: https://doi.org/10.1093/ajcn/nqx043
  144. Plaza-Florido et al. Heart Rate Is a Better Predictor of Cardiorespiratory Fitness Than Heart Rate Variability in Overweight/Obese Children: The ActiveBrains Project (2019) doi: https://doi.org/10.3389/fphys.2019.00510
  145. Ramirez et al. Physical activity levels objectively measured among older adults: a population-based study in a Southern city of Brazil. (2017) doi: https://doi.org/10.1186/s12966-017-0465-3
  146. Ratcliffe et al. Patient‐centred measurement of recovery from day‐case surgery using wrist worn accelerometers: a pilot and feasibility study (2021) doi: https://doi.org/10.1111/anae.15267
  147. Ricardo et al. Objectively measured physical activity in one-year-old children from a Brazilian cohort: levels, patterns and determinants. (2019) doi: https://doi.org/10.1186/s12966-019-0895-1
  148. Richardson et al. One size doesn’t fit all: cross-sectional associations between neighborhood walkability, crime and physical activity depends on age and sex of residents. (2017) doi: https://doi.org/10.1186/s12889-016-3959-z
  149. Richmond et al. Investigating causal relations between sleep traits and risk of breast cancer in women: mendelian randomisation study (2019) doi: https://doi.org/10.1136/bmj.l2327
  150. Rosique-Esteban et al. Cross-sectional associations of objectively-measured sleep characteristics with obesity and type 2 diabetes in the PREDIMED-Plus trial. (2018) doi: https://doi.org/10.1093/sleep/zsy190
  151. Rowlands et al. A data-driven, meaningful, easy to interpret, standardised accelerometer outcome variable for global surveillance (2019) doi: https://doi.org/10.1016/j.jsams.2019.06.016
  152. Rowlands et al. Physical activity for bone health: How much and/or how hard? (2020) doi: https://doi.org/10.1249/MSS.0000000000002380
  153. Rowlands et al. The impact of COVID-19 restrictions on accelerometer-assessed physical activity and sleep in individuals with type 2 diabetes (2021) doi: https://doi.org/10.1111/dme.14549
  154. Rowlands et al. Association of Timing and Balance of Physical Activity and Rest/Sleep With Risk ofCOVID-19: A UK Biobank Study (2021) doi: https://doi.org/10.1016/j.mayocp.2020.10.032
  155. Sabia et al. Association Between Questionnaire- and Accelerometer-Assessed Physical Activity: The Role of Sociodemographic Factors. (2014) doi: https://doi.org/10.1093/aje/kwt330
  156. Sabia et al. Physical Activity and Adiposity Markers at Older Ages: Accelerometer Vs Questionnaire Data. (2015) doi: https://doi.org/10.1016/j.jamda.2015.01.086
  157. Sandborg et al. Effectiveness of a Smartphone App to Promote Healthy Weight Gain, Diet, and Physical Activity During Pregnancy (HealthyMoms): Randomized Controlled Trial (2021) doi: https://doi.org/2021/3/e26091
  158. Sayre et al. High levels of objectively measured physical activity across adolescence and adulthood among the Pokot pastoralists of Kenya. (2018) doi: https://doi.org/10.1002/ajhb.23205
  159. Sayre et al. Ageing and physical function in East African foragers and pastoralists (2020) doi: https://doi.org/10.1098/rstb.2019.0608
  160. Shelley et al. A formative study exploring perceptions of physical activity and physical activity monitoring among children and young people with cystic fibrosis and health care professionals. (2018) doi: https://doi.org/10.1186/s12887-018-1301-x
  161. Shepherd et al. Physical activity, sleep, and fatigue in community dwelling Stroke Survivors (2018) doi: https://doi.org/10.1038/s41598-018-26279-7
  162. Sherry et al. Sleep duration and sleep efficiency in UK long-distance heavy goods vehicle drivers (2021) doi: https://doi.org/10.1136/oemed-2021-107643
  163. Smith et al. Physical behaviors and fundamental movement skills in British and Iranian children: An isotemporal substitution analysis (2020) doi: https://doi.org/10.1111/sms.13837
  164. Smith et al. Predicting Adaptations to Resistance Training Plus Overfeeding Using Bayesian Regression: A Preliminary Investigation (2021) doi: https://doi.org/10.3390/jfmk6020036
  165. Smith et al. The relationship between autism spectrum and sleep–wake traits (2021) doi: https://doi.org/10.1002/aur.2660
  166. Stewart et al. Exploring the Relationship Between Planned and Performed Physical Activity in University Students: the Utility of a Smartphone App (2019) doi: https://doi.org/10.2196/preprints.17581
  167. Stewart et al. Using a Mobile Phone App to Analyze the Relationship Between Planned and Performed Physical Activity in University Students: Observational Study (2021) doi: https://doi.org/0.2196/17581
  168. Stiles et al. A small amount of precisely measured high-intensity habitual physical activity predicts bone health in pre- and post-menopausal women in UK Biobank. (2017) doi: https://doi.org/10.1093/ije/dyx08
  169. Stiles et al. Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load. (2018) doi: https://doi.org/10.1249/MSS.0000000000001704
  170. Stone et al. In-person vs home schooling during the COVID-19 pandemic: Differences in sleep, circadian timing, and mood in early adolescence (2021) doi: https://doi.org/10.1111/jpi.12757
  171. Suorsa et al. The Effect of a Consumer-Based Activity Tracker Intervention on Accelerometer-Measured Sedentary Time Among Retirees: A Randomized Controlled REACT Trial (2021) doi: https://doi.org/10.1093/gerona/glab107
  172. Taylor et al. Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community. (2017) doi: https://doi.org/10.3390/ijerph14050534
  173. Taylor et al. Acceptability and Feasibility of Single-Component Primary School Physical Activity Interventions to Inform the AS:Sk Project. (2018) doi: https://doi.org/10.3390/children5120171
  174. Taylor et al. Evaluation of a Pilot School-Based Physical Activity Clustered Randomised Controlled Trial—Active Schools: Skelmersdale (2018) doi: https://doi.org/10.3390/ijerph15051011
  175. Taylor et al. Effect of High‐Intensity Interval Training on Visceral and Liver Fat in Cardiac Rehabilitation: A Randomized Controlled (2020) doi: https://doi.org/10.1002/oby.22833
  176. Teras et al. Associations of accelerometer-based sleep duration and self-reported sleep difficulties with cognitive function in late mid-life: The Finnish Retirement and Aging Study. (2019) doi: https://doi.org/10.1016/j.sleep.2019.08.024
  177. Thewlis et al. Objectively measured 24-hour activity profiles before and after total hip arthroplasty (2019) doi: https://doi.org/10.1302/0301-620X.101B4.BJJ-2018-1240.R1
  178. Tillin et al. Yoga and Cardiovascular Health Trial (YACHT): a UK-based randomised mechanistic study of a yoga intervention plus usual care versus usual care alone following an acute coronary event. (2019) doi: https://doi.org/10.1136/bmjopen-2019-030119
  179. Troxel et al. Broken Windows, Broken Zzs: Poor Housing and Neighborhood Conditions Are Associated with Objective Measures of Sleep Health (2020) doi: https://doi.org/10.1007/s11524-019-00418-5
  180. Tsereteli et al. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions (2022) doi: https://doi.org/10.1007/s00125-021-05608-y
  181. van de Langenberg et al. Diet, Physical Activity, and Daylight Exposure Patterns in Night-Shift Workers and Day Workers. (2018) doi: https://doi.org/10.1093/annweh/wxy097
  182. Vetrovsky et al. Morning fatigue and structured exercise interact to affect non-exercise physical activity of fit and healthy older adults (2021) doi: https://doi.org/10.1186/s12877-021-02131-y
  183. Wang et al. Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes (2019) doi: https://doi.org/10.1038/s41467-019-11456-7
  184. Warehime et al. Postpartum physical activity and sleep levels in overweight, obese and normal-weight mothers (2018) doi: https://doi.org/10.12968/bjom.2018.26.6.400
  185. Wendt et al. Sleep parameters measured by accelerometry: descriptive analyses from the 22-year follow-up of the Pelotas 1993 Birth Cohort. (2019) doi: https://doi.org/10.1016/j.sleep.2019.10.020
  186. Westbury et al. Associations Between Objectively Measured Physical Activity, Body Composition and Sarcopenia: Findings from the Hertfordshire Sarcopenia Study (HSS) (2018) doi: https://doi.org/10.1007/s00223-018-0413-5
  187. Wilkinson et al. The validity of the ‘General Practice Physical Activity Questionnaire’ against accelerometery in patients with chronic kidney disease (2020) doi: https://doi.org/10.1080/09593985.2020.1855684
  188. Williams et al. Physical fitness, physical activity and adiposity: associations with risk factors for cardiometabolic disease and cognitive function across adolescence (2022) doi: https://doi.org/10.1186/s12887-022-03118-3
  189. Windred et al. Objective assessment of sleep regularity in 60 000 UK Biobank participants using an opensource package (2021) doi: https://doi.org/10.1093/sleep/zsab254
  190. Wu et al. Association between sleep quality and physical activity in postpartum women. (2019) doi: https://doi.org/10.1016/j.sleh.2019.07.008
  191. Yerramalla et al. Objectively measured total sedentary time and pattern of sedentary accumulation in older adults: associations with incident cardiovascular disease and all-cause mortality (2022) doi: https://doi.org/0.1093/gerona/glac023
  192. Zalewski et al. No Effect of Glucomannan on Body Weight Reduction in Children and Adolescents with Overweight and Obesity: A Randomized Controlled Trial (2019) doi: https://doi.org/10.1016/j.jpeds.2019.03.044
  193. Zamora et al. Sleep Difficulties among Mexican Adolescents: Subjective and Objective Assessments of Sleep (2021) doi: https://doi.org/10.1080/15402002.2021.1916497
  194. Zhu et al. Objective sleep assessment in >80,000 UK mid-life adults: Associations with sociodemographic characteristics, physical activity and caffeine. (2019) doi: https://doi.org/0.1371/journal.pone.0226220

2. Study protocol publications that announced the use of GGIR

  1. Alley et al. Efficacy of a computer-tailored web-based physical activity intervention using Fitbits for older adults: a randomised controlled trial protocol. (2019) doi: https://doi.org/10.1136/bmjopen-2019-033305
  2. Chapman et al. Protocol for a randomised controlled trial of interventions to promote adoption and maintenance of physical activity in adults with mental illness. (2018) doi: https://doi.org/10.1136/bmjopen-2018-023460
  3. Dallosso et al. Movement through Active Personalised engagement (MAP) — a self-management programme designed to promote physical activity in people with multimorbidity: study protocol for a randomised controlled trial. (2018) doi: https://doi.org/10.1186/s13063-018-2939-2
  4. Duncan et al. Balanced: a randomised trial examining the efficacy of two self-monitoring methods for an app-based multi-behaviour intervention to improve physical activity, sitting and sleep in adults (2016) doi: https://doi.org/10.1186/s12889-016-3256-x
  5. Duncan et al. Examining the efficacy of a multicomponent m-Health physical activity, diet and sleep intervention for weight loss in overweight and obese adults: randomised controlled trial protocol. (2018) doi: https://doi.org/10.1136/bmjopen-2018-026179
  6. Gibson et al. Towards targeted dietary support for shift workers with type 2 diabetes (Shift-Diabetes study): A mixed-methods case study protocol (2021) doi: https://doi.org/10.1111/dme.14714
  7. Gilson et al. Effects of the Active Choices Program on Self-Managed Physical Activity and Social Connectedness in Australian Defence Force Veterans: Protocol for a Cluster-Randomized Trial (2021) doi: https://doi.org/10.2196/21911
  8. Herring et al. Physical Activity after Cardiac EventS (PACES) – a group education programme with subsequent text-message support designed to increase physical activity in individuals with diagnosed coronary heart disease: study protocol for a randomised controlled trial (2018) doi: https://doi.org/10.1186/s13063-018-2923-x
  9. Herring et al. Physical Activity after Cardiac EventS (PACES) – a group education programme with subsequent text-message support designed to increase physical activity in individuals with diagnosed coronary heart disease: study protocol for a randomised controlled trial. (2018) doi: https://doi.org/10.1186/s13063-018-2923-x
  10. Kuut et al. A randomised controlled trial testing the efficacy of Fit after COVID, a cognitive behavioural therapy targeting severe postinfectious fatigue following COVID-19 (ReCOVer): study protocol (2021) doi: https://doi.org/10.1186/s13063-021-05569-y
  11. Kwasnicka et al. Comparing motivational, self-regulatory and habitual processes in a computer-tailored physical activity intervention in hospital employees - protocol for the PATHS randomised controlled trial. (2017) doi: https://doi.org/10.1186/s12889-017-4415-4
  12. Mavilidi et al. Integrating physical activity into the primary school curriculum: rationale and study protocol for the “Thinking while Moving in English” cluster randomized controlled trial (2019) doi: https://doi.org/10.1186/s12889-019-6635-2
  13. Robinson et al. Protocol for a two-cohort randomized cluster clinical trial of a motor skills intervention: The Promoting Activity and Trajectories of Health (PATH) Study (2020) doi: https://doi.org/10.1136/bmjopen-2020-037497
  14. Taylor et al. Study protocol for the FITR Heart Study: Feasibility, safety, adherence, and efficacy of high intensity interval training in a hospital-initiated rehabilitation program for coronary heart disease. (2017) doi: https://doi.org/10.1016/j.conctc.2017.10.002
  15. Watson et al. Life on holidays: study protocol for a 3-year longitudinal study tracking changes in children’s fitness and fatness during the in-school versus summer holiday period. (2019) doi: https://doi.org/10.1186/s12889-019-7671-7

3. Original research publications (Methodological) involving the use of GGIR

  1. Bakrania et al. Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches (2016) doi: https://doi.org/10.1371/journal.pone.0164045
  2. Bammann et al. Generation and validation of ActiGraph GT3X+ accelerometer cut-points for assessing physical activity intensity in older adults. The OUTDOOR ACTIVE validation study. (2021) doi: https://doi.org/10.1371/journal.pone.0252615
  3. Birnbaumer et al. Absolute Accelerometer-Based Intensity Prescription Compared to Physiological Variables in Pregnant and Nonpregnant Women (2020) doi: https://doi.org/10.3390/ijerph17165651
  4. Buchan et al. Equivalence of activity outcomes derived from three research grade accelerometers worn simultaneously on each wrist (2021) doi: https://doi.org/10.1080/02640414.2021.2019429
  5. Duncan et al. Using accelerometry to classify physical activity intensity in older adults: What is the optimal wear-site? (2019) doi: https://doi.org/10.1080/17461391.2019.1694078
  6. Edwardson et al. Comparability of Postural and Physical Activity Metrics from Different Accelerometer Brands Worn on the Thigh: Data Harmonization Possibilities (2022) doi: https://doi.org//10.1080/1091367X.2021.1944154
  7. Ellis et al. Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification. (2017) doi: https://doi.org/10.1249/MSS.0000000000000840
  8. Femiano et al. Validation of open-source step-counting algorithms for wrist-worn tri-axial accelerometers in cardiovascular patients (2022) doi: https://doi.org/10.1016/j.gaitpost.2021.11.035
  9. Hurter et al. Establishing Raw Acceleration Thresholds to Classify Sedentary and Stationary Behaviour in Children. (2018) doi: https://doi.org/10.3390/children5120172
  10. Jimenez-Moreno et al. Analyzing walking speeds with ankle and wrist worn accelerometers in a cohort with myotonic dystrophy. (2018) doi: https://doi.org/10.1080/09638288.2018.1482376
  11. Kerr et al. Comparison of Accelerometry Methods for Estimating Physical Activity (2016) doi: https://doi.org/10.1249/MSS.0000000000001124
  12. McLellan et al. Wear compliance, sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers (2018) doi: https://doi.org/10.1080/02640414.2018.1461322
  13. Montoye et al. Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn. (2018) doi: https://doi.org/10.1152/japplphysiol.00760.2017
  14. Montoye et al. Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults (2020) doi: https://doi.org/https://doi.org/10.1080/02640414.2020.1794244
  15. Rowlands et al. Providing a Basis for Harmonization of Accelerometer-Assessed Physical Activity Outcomes Across Epidemiological Datasets (2019) doi: https://doi.org/10.1123/jmpb.2018-0073
  16. Sanders et al. Evaluation of wrist and hip sedentary behaviour and moderate-to-vigorous physical activity raw acceleration cutpoints in older adults. (2018) doi: https://doi.org/10.1080/02640414.2018.1555904
  17. Sundararajan et al. Sleep classification from wrist-worn accelerometer data using random forests (2021) doi: https://doi.org/10.1038/s41598-020-79217-x
  18. Suorsa et al. Comparison of Sedentary Time Between Thigh-Worn and Wrist-Worn Accelerometers (2020) doi: https://doi.org/10.1123/jmpb.2019-0052

4. Other methodological publications relating to GGIR

  1. van Hees et al. Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. 2011 ;6(7):e22922. doi: https://doi.org/10.1371/journal.pone.0022922
  2. van Hees et al. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013 Apr 23;8(4):e61691. doi: https://doi.org/10.1371/journal.pone.0061691
  3. Hildebrand M et al. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014 Sep;46(9):1816–24. doi: https://doi.org/10.1249/MSS.0000000000000289
  4. van Hees et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. (2014) https://doi.org/10.1152/japplphysiol.00421.2014
  5. van Hees et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. PLoS One. 2015 Nov 16;10(11):e0142533. doi: https://doi.org/10.1371/journal.pone.0142533
  6. Hildebrand M et al. Evaluation of raw acceleration sedentary thresholds in children and adults. Scand J Med Sci Sports. 2016 Nov 22. doi: https://doi.org/10.1111/sms.12795
  7. Rowlands AV et al. Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter? (2016) https://doi.org/10.1249/MSS.0000000000000978
  8. Rowlands, A.V. et al (2016). Moving forward with backwards compatibility: Translating wrist accelerometer data. Medicine and Science in Sport and Exercise doi: https://doi.org/10.1249/MSS.0000000000001015
  9. van Hees VT, Sabia S, et al. Estimating sleep parameters using an accelerometer without sleep diary. (2018) doi: https://doi.org/10.1038/s41598-018-31266-z.
  10. van Kuppevelt D, Heywood J, et al. Segmenting accelerometer data from daily life with unsupervised machine learning. PLoSONE, (2019) doi: https://doi.org/10.1371/journal.pone.0208692
  11. Ahmadi MN, Nathan N, et al. Non-wear or sleep? Evaluation of five non-wear detection algorithms for raw accelerometer data. (2020) doi: https://doi.org/10.1080/02640414.2019.1703301
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