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Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children
Open accessAbstract
PURPOSE: This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children. METHODS: 59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis. RESULTS: Fitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs. CONCLUSIONS: Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.
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“For the validation study, considering as a first main outcome HR, a sample size of n = 20 participants is deemed sufficient, as reported in the literature [50–52].”
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“There is limited evidence on whether long-term health monitoring using wearable devices will yield reliable and equitable real-world data18 across the diverse socioeconomic and demographic19,20 population spectrum,21,22 particularly among racial and ethnic minority groups.23”
From Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children (2023) · cites this paper as [20]
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“Rapid and ongoing developments in consumer wearable technology are enabling ever-richer measurements with finer temporal resolution for heart rate, activity, and sleep dynamics in free-living states [6,29,30].”
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“Such devices have recently been tested in children within our target age group providing relatively accurate performance for objective measures of physical activity intensity and volume across different activities [19].”
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“Prior studies have shown Fitbit devices to be a reliable and accurate tool for the estimation of adolescents’ daily step counts to measure the accumulated physical activity in adolescents over time [21, 34].”
MethodSocial epidemiology of Fitbit daily steps in early adolescence
Nagata, J. M., Alsamman, S., Smith, N., Yu, J., Ganson, K., Dooley, E. E., Wing, D., Baker, F., & Gabriel, K. P. (2023) · Pediatric Research · doi:10.1038/s41390-023-02700-4 · Cited by 22
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity
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“Physical activity—steps per day Prior research has demonstrated that Fitbit devices offer an accurate and reliable estimation of daily step count as a measure of accumulated physical activity in adolescents over time 35,36 .”
BackgroundMethodPerformance of Fitbit Devices as Tools for Assessing Sleep Patterns and Associated Factors
Park, J., Ahn, E. K., Yoon, K., & Kim, J. (2024) · Journal of Sleep Medicine · doi:10.13078/jsm.240010 · Cited by 21
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“One study of children aged 9–11 showed accuracy of the Fitbit Charge HR for measuring moderate to vigorous physical activity (MVPA) and sleep and inaccuracy for steps, heart rate, and energy expenditure (52).”
BackgroundThe impact of social networks on sleep among a cohort of college students
Wang, C., Mattingly, S., Payne, J., Lizardo, O., & Hachen, D. S. (2021) · SSM - Population Health · doi:10.1016/j.ssmph.2021.100937 · Cited by 18
“…is related to low conscientiousness and high neuroticism (Duggan et al., 2014), and conscientious individuals have earlier bedtime and wake time (Gray & Watson, 2002), as well as less frequent emotional napping (to improve their mood) and slightly more frequent mindful napping (to refocus)…”
BackgroundLatent Profiles of Sleep Patterns in Early Adolescence: Associations With Behavioral Health Risk
Zhang, L., Sasser, J., Doane, L. D., Peltz, J. S., & Oshri, A. (2023) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2023.08.021 · Cited by 16
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“SettingAuthor, year Research-gradeConsumergrade Pearson product moment correlaPate cutoff: <37.5 counts ActiGraph GT3X+ (right hip) Fitbit Flex (nondominant wrist) 2 consecutive days (24-hour period) 4.9 (1.0)27 (11)Free-living Byun et al [20], 2018 tion coefficients (r); MAPEa per 15 s; Evenson cutoff: ≤25 counts per…”
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“4% in distinguishing activity levels, specifically in distinguishing sedentary and light activity from moderate-to-vigorous activity [21].”
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“While in the studies carried out in controlled conditions individuals were constrained to predefined activities with stable gait patterns (25, 60), or at least most of them, the present study was carried out under a greater variability of motor patterns including a wide range of children’s daily life behaviors.”
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Kwon, S., Wan, N., Burns, R. D., Brusseau, T. A., Kim, Y., Kumar, S., Ertin, E., Wetter, D. W., Lam, C. Y., Wen, M., et al. (2021) · Sensors · doi:10.3390/s21041411 · Cited by 14
“Another significant enhancement that the MotionSense HRV has over other accelerometer-only devices is the availability of a PPG sensor, which can measure HR [5,71].”
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“…and biosensors can collect continuous data on factors influencing neurobehavioral function, such as physical activity, sleep, heart rate variability, and environmental exposure, which are passively collected over extended periods (Diaz et al. 2016; Reid et al. 2017; Godino et al. 2020).”
Al-Shoaibi, A. A. A., Iyra, P., Raney, J. H., Ganson, K. T., Dooley, E. E., Testa, A. M., Jackson, D. B., Gabriel, K. P., Baker, F. C., & Nagata, J. M. (2023) · Academic Pediatrics · doi:10.1016/j.acap.2023.10.004 · Cited by 15
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Agreement between Fitbit and ActiGraph Estimates of Physical Activity in Young Children
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Effects of Sleep Restriction on Self-Reported Putting Performance in Golf
Nishida, M., Chiba, T., Murata, Y., & Shioda, K. (2022) · Perceptual and Motor Skills · doi:10.1177/00315125221087027 · Cited by 8
Impact of Environmental Noise and Sleep Health on Pediatric Hypertension Incidence: ABCD Study
Moraes, A. D. D., Ma, M. Y., Nascimento-Ferreira, M. V., Hunt, E. H., & Hoelscher, D. M. (2024) · Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease · doi:10.1161/jaha.124.037503 · Cited by 8
“19,22 We also adopted the data extraction, filtering, and processing protocols established by the ABCD study, including sleep thresholds.”
Biorhythms derived from consumer wearables predict postoperative complications in children
Hua, R., Carter, M. M., O’Brien, M. K., Pitt, J. B., Kwon, S., Manworren, R. C. B., Oscherwitz, G. L., Edobor, A., Chen, A., Ghomrawi, H., et al. (2025) · Science Advances · doi:10.1126/sciadv.adv2643 · Cited by 9
Why Don't We Sleep Enough? A Field Experiment Among College Students
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Consumer Wearables Define a Normative Recovery Trajectory Following the Modified Nuss Procedure
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Gender diversity and daily steps: Findings from the Adolescent Brain Cognitive Development Study
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“Several studies with children and adolescents have reported underestimation of TST by wearable devices (Godino et al. 2020; Menghini et al. 2021; Pesonen and Kuula 2018).”
BackgroundMediating social support through sensor-based technologies for children’s health behavior change
Baldwin, J. A., Lee, J., Tate, A. D., Okitondo, C. D., Johnsen, K., Schmidt, M. D., Rathbun, S. L., Novotny, E. R., & Ahn, S. J. (2023) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmad011 · Cited by 7
“…ongoing interactions with virtual agents that convey plausible feedback can increase rapport that is characterized by mutual trust and coordination, which is essential for psychological need satisfaction and motivation to achieve behavioral goals (Baker et al., 2020; Olafsson et al., 2020).”
Verloigne, M., Oeckel, V. V., Brondeel, R., & Poppe, L. (2021) · BMC Public Health · doi:10.1186/s12889-021-11694-9 · Cited by 6
“However, two studies measured the performance of the Fitbit Charge HR in children and adolescents [30, 31].”
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Zhang, Y., Bornkamp, N., Hivert, M., Oken, E., & James, P. (2024) · JMIR Pediatrics and Parenting · doi:10.2196/59159 · Cited by 6
“The wear habits might have influenced the accuracy of step counts and sleep stage recording [49-51].”
The Influence of Pubertal Development on Early Adolescent Sleep and Changes in Family Functioning
Peltz, J. S., Zhang, L., Sasser, J., Oshri, A., & Doane, L. D. (2023) · Journal of Youth and Adolescence · doi:10.1007/s10964-023-01882-8 · Cited by 6
“Fitbit devices have been validated in developmental samples of children and adolescents with comparisons to polysomnography, the gold-standard in sleep assessment (Godino et al., 2020).”
Oeckel, V. V., Poppe, L., Deforche, B. I., Brondeel, R., Miatton, M., & Verloigne, M. (2024) · BMC Public Health · doi:10.1186/s12889-024-18014-x · Cited by 9
“However, two studies measured the performance of the Fitbit Charge HR, which is an older model compared to the Fitbit Charge 3, in children and adolescents [37, 38].”
ResultTagliaferri, S. D., Nguyen, J., Han, L. K. M., Cotton, S., Menssink, J. M., Ratheesh, A., Noel, M. E., & Schmaal, L. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.12.025 · Cited by 8
Hunt, E. T., Brazendale, K., Moraes, A. C. D. D., Malkani, R., Heredia, N. I., Pfledderer, C. D., Brown, D. M. Y., Hoelscher, D. M., Beets, M., & Weaver, R. (2024) · AJPM Focus · doi:10.1016/j.focus.2024.100253 · Cited by 6
“First, the large population-based sample size recruiting children from across the U.S. allows for strong inference when examining the impacts of COVID-19 on PA.”
MethodInfluentialGhanim, F., Harkness, K., Wiley, B., Guadagni, V., & Murias, K. (2025) · European Journal of Pediatrics · doi:10.1007/s00431-025-06209-2 · Cited by 6
“The accuracy, reliability, and relevance of wearable devices in clinical settings have yet to be fully elucidated, especially in children and individuals with neurodevelopmental conditions [22].”
Carter, M. M., Zeineddin, S. A., Bai, I., Pitt, J. B., Hua, R., Kwon, S., Ghomrawi, H., & Abdullah, F. (2024) · Surgery · doi:10.1016/j.surg.2023.12.009 · Cited by 5
Junghans-Rutelonis, A. N., Sim, L. A., Harbeck‐Weber, C., Dresher, E. R., Timm, W., & Weiss, K. E. (2024) · Frontiers in Pain Research · doi:10.3389/fpain.2023.1325270 · Cited by 4
Reference Values for Wristband Accelerometry Data in Children Aged 6–11 Years of Age
Lammers, A. E., Romanowski, A. L., Baumgartner, H., Diller, G., & Uebing, A. S. (2022) · Frontiers in Pediatrics · doi:10.3389/fped.2022.808372 · Cited by 5
“While a modest underestimation of step counts (∼160 steps/day) was reported when comparing a commercial waist worn Fitbit product against a more expensive research product in 59 healthy children, the results of the two products were found to be comparable (17).”
ResultMayorga-Vega, D., Casado-Robles, C., Guijarro-Romero, S., & Viciana, J. (2024) · Journal of Sports Science and Medicine · doi:10.52082/jssm.2024.79 · Cited by 7
“For instance, Godino et al. (2020) studied the criterion-related validity of the Fitbit Charge HR (non-dominant wrist) in primary schoolchildren (mean = 9.9, 9 - 11 years) under controlled and semi free-living conditions (14 structured activities, including sitting, stationary cycling, treadmill…”
BackgroundResultInfluentialMichael, C., Taxali, A., Angstadt, M., McCurry, K. L., Weigard, A. S., Kardan, O., Molloy, M. F., Toda-Thorne, K., Burchell, L., Dziubinski, M., et al. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100522 · Cited by 5
“Fitbit measures of sleep have been examined in other studies using ABCD data and have demonstrated adequate sensitivity (55).”
Lindholm, A., Jarbin, H., Aili, K., Nygren, J., Svedberg, P., & Larsson, I. (2024) · Children · doi:10.3390/children11060636 · Cited by 5
“Funding: This research received external funding from The Knowledge Foundation, no. 20200012 and Swedish Research Council for Health Working Life and Welfare (Forte), no. 2021-00664.”
BackgroundZhang, Y., & Halgunseth, L. C. (2024) · Mental Health Science · doi:10.1002/mhs2.98 · Cited by 4
Mendt, S., Zout, G., Rabuffetti, M., Gunga, H., Bunker, A., Barteit, S., & Maggioni, M. A. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1491401 · Cited by 5
Predicting long-term sleep deprivation using wearable sensors and health surveys
Trujillo, R., Zhang, E., Templeton, J. M., & Poellabauer, C. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108749 · Cited by 3
Evaluation of the Garmin Vivofit 4 for assessing sleep in youth experiencing sleep disturbances
Hibbing, P. R., Pilla, M., Birmingham, L., Byrd, A., Ndagijimana, T., Sadeghi, S., Seigfreid, N., Farr, D., Al-Shawwa, B. A., Ingram, D. G., et al. (2024) · Digital Health · doi:10.1177/20552076241277150 · Cited by 3
“18,19 The results of Meltzer et al. 17 showed generally poor performance of the Fitbit Ultra compared to PSG, and they also observed a tendency for systematic error along lines of age and condition severity.”
MethodResultInfluentialYang, S. H., Kim, H. I., Riper, M. L. V., & Choi, E. K. (2024) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2024.10.001 · Cited by 4
[An overview on sleep research based on functional near infrared spectroscopy].
Huang, M., Jiao, X., Jiang, J., Yang, J., Chu, H., Pan, J., & Cao, Y. (2021) · PubMed Central · doi:10.7507/1001-5515.202102003 · Cited by 3
Ayán, C., González-Devesa, D., Diz, J. C., & Varela, S. (2024) · Children · doi:10.3390/children11010125 · Cited by 3
“The energy expended during recess was measured using a Fitbit wristband, a device which has been previously used in this type of study [27].”
MethodLiang, K., Le, F., Chi, P., Chen, S., Huang, L., & Chi, X. (2024) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2024.102758 · Cited by 3
Martinot, J., Lê-Dông, N.-N., Malhotra, A., & Pépin, J. (2024) · Journal of Prosthodontics · doi:10.1111/jopr.14003 · Cited by 3
Zhang, L., Geier, C. F., House, E. M., & Oshri, A. (2025) · Brain and Behavior · doi:10.1002/brb3.70579 · Cited by 3
Carson, J. R., Wing, D., Godino, J., Higgins, M., & Larsen, B. A. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22111663 · Cited by 2
“Compared to laboratory research grade measures (electrocardiogram and indirect calorimetry), Fitbit devices showed 85.4% accuracy in classifying Moderate-Vigorous Physical Activity (MVPA) among boys and girls ages 9–11 [9].”
InfluentialSelf-citationNagata, J. M., Nguyen, N. D., Frimpong, I., Heuer, A., Helmer, C. K., Al-Shoaibi, A. A. A., Ganson, K. T., Testa, A. M., Dooley, E. E., Le, T. P., et al. (2026) · BMC Public Health · doi:10.1186/s12889-026-26756-z · Cited by 4
“Data were retrieved and interpreted using the ABCD Study guidelines [31, 32].”
Nagata, J. M., Frimpong, I., Nguyen, N. D., Heuer, A., Helmer, C. K., Al-Shoaibi, A. A. A., Ganson, K. T., Testa, A. M., Dooley, E. E., Gabriel, K. P., et al. (2026) · Obesity · doi:10.1002/oby.70181 · Cited by 2
Ehlers, C. L., Wills, D. N., Benedict, J., & Amodeo, L. R. (2023) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.15079 · Cited by 4
Autocorrelation of daily resting heart rate: A novel metric of postoperative recovery
Carter, M. M., Hua, R., O’Brien, M. K., Pitt, J. B., Kwon, S., Jayaraman, A., Ghomrawi, H., & Abdullah, F. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105655 · Cited by 2
Yamakita, M., Ando, D., Sugita, H., Akiyama, Y., Sato, M., Yokomichi, H., Yamaguchi, K., & Yamagata, Z. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18073351 · Cited by 2
“, San Francisco, CA, USA) also has adequate sensitivity in classifying moderate and vigorous activity and sleep [26].”
BackgroundWhy Don't We Sleep Enough? A Field Experiment Among College StudentsPreprint
Avery, M., Giuntella, O., & Jiao, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4196320 · Cited by 2
Mitchell, J. A., Morales, K. H., Williamson, A. A., Jawahar, A., Juste, L., Vajravelu, M. E., Zemel, B. S., Dinges, D. F., & Fiks, A. G. (2023) · medRxiv · doi:10.1101/2023.01.04.23284151 · Cited by 3
Molloy, M. F., Taxali, A., Angstadt, M., Toda-Thorne, K., McCurry, K. L., Weigard, A. S., Kardan, O., Lehrmann, C., Vens, J., Michael, C., et al. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101668 · Cited by 2
“Fitbit-based measures of sleep duration have been examined in other studies using the ABCD sample and have been shown to have adequate sensitivity in validation studies (Godino et al., 2020).”
Moraes, A. C. D. D., Hunt, E. T., Szeszulski, J., de Mello, G. D., Nascimento-Ferreira, M., & Madden, P. (2026) · Scandinavian Journal of Medicine & Science in Sports · doi:10.1111/sms.70346
Benefits of Daytime Naps on Consecutive Days for Motor Adaptation LearningPreprint
Murata, Y., Nishida, M., Ichinose, A., Suyama, S., Youn, S., & Shioda, K. (2022) · Preprints.org · doi:10.20944/preprints202205.0075.v1
Martinko, A., Karuc, J., Jurić, P., Podnar, H., & Sorić, M. (2022) · doi:10.2196/preprints.37547
Damme, K. S. F., Vargas, T., Walther, S., Shankman, S. A., & Mittal, V. A. (2023) · Research Square · doi:10.21203/rs.3.rs-3270112/v1
“4 Despite previous literature suggesting a general relationship between cardiovascular health and mental health across the psychosis spectrum 7,23,24,26,50,51 and internalizing, 2–4, 18,20,21,52 the current study found that internalizing alone was related when accounting for other symptom…”
BackgroundZhang, Y., Bornkamp, N., Hivert, M., Oken, E., & James, P. (2024) · doi:10.2196/preprints.59159
Intermonitor Variability of Garmin Vivofit® Jr. Wristband
Díaz-Quesada, G., Giménez‐Egido, J. M., Connor, J. D., Ortega, E., & Torres‐Luque, G. (2024) · Applied Sciences · doi:10.3390/app14093854
Tagliaferri, S. D., Nguyen, J., Han, L. K. M., Cotton, S., Menssink, J. M., Ratheesh, A., Noel, M. E., & Schmaal, L. (2024) · doi:10.31219/osf.io/f496x
Analysis of the Energy Expenditure of Sports School Activities in Children
González-Devesa, D., Sánchez-Lastra, M. A., Ayán, C., Blanco-Martínez, N., Rivas, M. S., Vidal-Mariño, M., & Varela, S. (2024) · Children · doi:10.3390/children11080946
“Energy expenditure during activities was measured using a Fitbit wristband, a device that has been utilized in previous research [30].”
MethodKwon, S., Bai, Y., Kim, Y., Burns, R. D., Brusseau, T. A., & Byun, W. (2024) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2024.2417793
“…least one weekend day, which is longer than previous studies that tested the validity of the WAMs in children with shorter study periods (1–5 days) (Godino et al., 2020; Kang et al., 2019; Kwon & Kim et al., 2021; Kim & Lochbaum, 2018; Mooses et al., 2018); thus, our data should reflect a more…”
Carter, M. M., Chen, A. R., Pitt, J. B., Hua, R., Edobor, A., Kwon, S., Goldstein, S. D., Ghomrawi, H., & Abdullah, F. (2024) · Pediatric Surgery International · doi:10.1007/s00383-024-05889-5
Towards a Multimodal Approach for Assessing ADHD Hyperactivity Behaviors
Cibrian, F. L., Min, L., & Arnold, V. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-77571-0_1
Suggestions for APA Research and Translational Perspective
Cascella, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-78833-8_5
Hu, C., Delaney, C. M., Chavarro, J. E., Laden, F., Librett, R., Katuska, L. M., Kaplan, E. R., Li, Y., Rueschman, M., Kossowsky, J., et al. (2025) · Nature and Science of Sleep · doi:10.2147/nss.s530812
“18,20,22,32,33 Several previous studies with models of this consumer wearable were in laboratory settings, and our findings from a field-based study design provide the insight that the magnitude of differences observed is like that of lab studies.”
Mummaneni, A., Amir, C., Allen, N. B., & Ho, T. C. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.10.002
“The estimation of sleep duration by the Fitbit device was previously validated against polysomnography and research actigraphy (37-39), including in adolescents (40), although it is notable that accelerometers tend to overestimate sleep duration in individuals with insomnia (41).”
Brown, D. M. Y., Pfledderer, C. D., Chen, S., Stoepker, P., Hamilton, C. N. B., Besenyi, G. M., & Mailey, E. L. (2025) · Journal of Physical Activity and Health · doi:10.1123/jpah.2025-0317 · Cited by 1
Verloigne, M., Oeckel, V. V., Brondeel, R., & Poppe, L. (2021) · Figshare · doi:10.6084/m9.figshare.16620652
Verloigne, M., Oeckel, V. V., Brondeel, R., & Poppe, L. (2021) · Figshare · doi:10.6084/m9.figshare.16620658
Verloigne, M., Oeckel, V. V., Brondeel, R., & Poppe, L. (2021) · Figshare · doi:10.6084/m9.figshare.16620655
Oeckel, V. V., Poppe, L., Deforche, B., Brondeel, R., Miatton, M., & Verloigne, M. (2024) · Figshare · doi:10.6084/m9.figshare.25237347
Noggle, N., Wing, D., Godino, J., Higgins, M., Moreno, D., Lawton, D., Dowling, A. V., Imsirovic, J., & Moran, R. J. (2025) · doi:10.2196/preprints.90009
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Laws, M. G., Brinkman, C., Baez, S., & Marmura, H. (2026) · International Journal of Athletic Therapy & Training · doi:10.1123/ijatt.2025-0100
Feldman, E. L., Bates, A., Souhala, J., & Slavish, D. C. (2026) · Journal of Clinical Sleep Medicine · doi:10.1007/s44470-026-00042-6
“Participants were instructed to wear the device at bedtime and to try to obtain sufficient sleep for data inclusion [6 ].”
InfluentialPatel, B., Tyrka, A. R., & Angeles, W. W. L.-D. L. (2026) · Journal of Attention Disorders · doi:10.1177/10870547261421667
“This study’s use of multi-night actigraphy data supplements the subjective reports with robust objective data on sleep duration and REM sleep duration, allowing for comparisons between subjective and objective sleep measures on the same set of patients.”
Influential
Metadata
- Authors
- Godino, J. G., Wing, D., de Zambotti, M., Baker, F. C., Bagot, K., Inkelis, S., Pautz, C., Higgins, M., Nichols, J., Brumback, T., Chevance, G., Colrain, I. M., Patrick, K., & Tapert, S. F.
- Affiliations
- University of California San Diego (US)
- SRI International (US)
- Northern Kentucky University (US)
- Corresponding author
- Godino, Job G.
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-09-04
- Volume
- 15
- Issue
- 9
- Pages
- e0237719
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7473549
- OpenAlex
- W3083535211
- Cited by
- 107
- References
- 45
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 35 · 2023: 17 · 2022: 16 · 2021: 11
- FWCI
- 6.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- National Institute on Drug Abuse (U01DA041089)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wearable activity trackers, physical activity measurement, sleep assessment, children, step count accuracy, heart rate monitoring, energy expenditure, moderate-to-vigorous physical activity, sedentary behavior, device validation
- MeSH terms
- Fitness Trackers, Child, Energy Metabolism, Female, Heart Rate, Humans, Male, Sleep, Biosensing Techniques, Exercise, Polysomnography
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