The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
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].”
In the citing paper
“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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Cites
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“[3,4] Although some of this heterogeneity may result from true biological mechanisms, the challenge of precisely measuring these complex habitual behaviors is likely a major contributor.”
BackgroundCalibration of two objective measures of physical activity for children
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“[34] It is important to highlight that aforementioned research-grade accelerometers must be hip-worn to measure physical activity intensity, whereas the Fitbit Charge HR is worn on the wrist.”
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“[3,4] Although some of this heterogeneity may result from true biological mechanisms, the challenge of precisely measuring these complex habitual behaviors is likely a major contributor.”
BackgroundAASM Scoring Manual Updates for 2017 (Version 2.4)
Berry, R. B., Brooks, R., Gamaldo, C. E., Harding, S. M., Lloyd, R. M., Quan, S. F., Troester, M. M., & Vaughn, B. V. (2017) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.6576 · Cited by 1,605 · cited 2×
“[27] Those who agreed had a trained technician arrive at their home approximately one hour before their usual bedtime to prepare for the recording.”
BackgroundMethodActivity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues
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“[28,29] The corresponding minute-level metrics from the Fitbit Charge HR were defined as ‘sleep’, ‘restless’, and ‘wake’.”
The role and validity of actigraphy in sleep medicine: an update
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“However, in concordance with research-grade actigraphy, [38] the Fitbit Charge HR had a lower specificity, accurately detecting wake epochs at a rate of 56%.”
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“[5,6] Tools for the objective assessment of the frequency, intensity (or quality), and duration of physical activity and sleep in adults and children have largely been developed for short-term use (i.”
Background
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Items 101–107 of 107
Dell’Acqua, C., Mastromatteo, L. Y., Degasperi, G., Cellini, N., Soliman, L., Oppioli, L., Palomba, D., Scrimin, S., & Benvenuti, S. M. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122148
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Precision Sleep Signatures to Predict Mental Health Outcomes in Youth
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“Given the strong agreement at the individual level, our data suggested that smartwatches can be used to reliably track heart rate across multiple exercise intensity thresholds [23-25].”
Self-citationAlexander, J. D., Mills, S., Sumpter, K., Smith, C. J., Das, B., Fan, R., Hunt, E. T., Brown, S. A., Curtis, B. L., Cummins, K. M., et al. (2026) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2026.101826
“…suggest that wearable and sensor based data collection are acceptable to participants, though additional strategies may still be needed to improve recruitment of participants from un-derrepresented sociodemographic backgrounds (Godino et al., 2020; E. H. Kim et al., 2023; Wing et al., 2022).”
InfluentialAssociations between paternal involvement and adolescent sleep, mental health, and brain development
Babaeeghazvini, P., Lee, J., & Lugo-Candelas, C. I. (2026) · Sleep Epidemiology · doi:10.1016/j.sleepe.2026.100173
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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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