-
Notifications
You must be signed in to change notification settings - Fork 60
Publication list
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Occasionally we come across publications where authors do not cite the GGIR software while we know from personal correspondence that GGIR was used.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/
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- Bachasson et al. Physical Activity Monitoring: A Promising Outcome Measure in Idiopathic Inflammatory Myopathies. (2017) doi: https://doi.org/10.1212/WNL.0000000000004061
- 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
- Bell et al. Healthy obesity and objective physical activity. (2015) doi: https://doi.org/10.3945/ajcn.115.110924
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Bradley et al. Sleep and circadian rhythm disturbance in bipolar disorder. (2017) doi: https://doi.org/10.1017/S0033291717000186
- Bradley et al. The association between sleep and cognitive abnormalities in bipolar disorder (2020) doi: https://doi.org/10.1017/S0033291718004038
- 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
- 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
- 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
- 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
- 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
- Cadenas-Sanchez et al. Fitness, physical activity and academic achievement in overweight/obese children (2020) doi: https://doi.org/10.1080/02640414.2020.1729516
- 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
- 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
- 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
- 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
- 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
- Chevance et al. Changing implicit attitudes for physical activity with associative learning. (2018) doi: https://doi.org/10.1007/s12662-018-0559-
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- Donnelly et al. Relationship Between Parent and Child Physical Activity Using Novel Acceleration Metrics (2020) doi: https://doi.org/10.1080/02701367.2020.1817295
- 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
- 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
- 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.
- 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
- Fairclough et al. Wear Compliance and Activity in Children Wearing Wrist- and Hip-Mounted Accelerometers. (2016) doi: https://doi.org/10.1249/MSS.0000000000000771
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Henson et al. Physical behaviors and chronotype in people with type 2 diabetes. (2020) doi: https://doi.org/10.1136/bmjdrc-2020-001375
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Jurado-Fasoli et al. Exercise training improves sleep quality: A randomized controlled trial (2020) doi: https://doi.org/10.1111/eci.13202
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Lane et al. Biological and clinical insights from genetics of insomnia symptoms (2019) doi: https://doi.org/10.1038/s41588-019-0361-7
- 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
- Leao et al. Longitudinal Associations Between Device-Measured Physical Activity and Early Childhood Neurodevelopment (2022) doi: https://doi.org/10.1123/jpah.2021-0587
- 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
- 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
- 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
- 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
- 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
- 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
- Longman et al. Time in Nature Associated with Decreased Fatigue in UK Truck Drivers (2021) doi: https://doi.org/10.3390/ijerph18063158
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Mora-Gonzalez et al. Sedentarism, Physical Activity, Steps, and Neurotrophic Factors in Obese Children. (2019) doi: https://doi.org/10.1249/MSS.0000000000002064
- 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
- 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
- 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
- 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
- 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
- Ocallaghan et al. Genetic and environmental influences on sleep-wake behaviors in adolescence (2021) doi: https://doi.org/10.1093/sleepadvances/zpab018
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Rowlands et al. Physical activity for bone health: How much and/or how hard? (2020) doi: https://doi.org/10.1249/MSS.0000000000002380
- 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
- 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
- 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
- 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
- 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
- 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
- Sayre et al. Ageing and physical function in East African foragers and pastoralists (2020) doi: https://doi.org/10.1098/rstb.2019.0608
- 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
- Shepherd et al. Physical activity, sleep, and fatigue in community dwelling Stroke Survivors (2018) doi: https://doi.org/10.1038/s41598-018-26279-7
- 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
- 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
- Smith et al. Predicting Adaptations to Resistance Training Plus Overfeeding Using Bayesian Regression: A Preliminary Investigation (2021) doi: https://doi.org/10.3390/jfmk6020036
- Smith et al. The relationship between autism spectrum and sleep–wake traits (2021) doi: https://doi.org/10.1002/aur.2660
- 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
- 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
- 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
- Stiles et al. Wrist-worn Accelerometry for Runners: Objective Quantification of Training Load. (2018) doi: https://doi.org/10.1249/MSS.0000000000001704
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zamora et al. Sleep Difficulties among Mexican Adolescents: Subjective and Objective Assessments of Sleep (2021) doi: https://doi.org/10.1080/15402002.2021.1916497
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Ellis et al. Hip and Wrist Accelerometer Algorithms for Free-Living Behavior Classification. (2017) doi: https://doi.org/10.1249/MSS.0000000000000840
- 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
- Hurter et al. Establishing Raw Acceleration Thresholds to Classify Sedentary and Stationary Behaviour in Children. (2018) doi: https://doi.org/10.3390/children5120172
- 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
- Kerr et al. Comparison of Accelerometry Methods for Estimating Physical Activity (2016) doi: https://doi.org/10.1249/MSS.0000000000001124
- 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
- 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
- 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
- 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
- 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
- Sundararajan et al. Sleep classification from wrist-worn accelerometer data using random forests (2021) doi: https://doi.org/10.1038/s41598-020-79217-x
- Suorsa et al. Comparison of Sedentary Time Between Thigh-Worn and Wrist-Worn Accelerometers (2020) doi: https://doi.org/10.1123/jmpb.2019-0052
- 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
- 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
- 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
- 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
- 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
- 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
- Rowlands AV et al. Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter? (2016) https://doi.org/10.1249/MSS.0000000000000978
- 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
- 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.
- 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
- 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