ArticleChronobiology international2021
Performance of Fitbit Charge 3 against polysomnography in measuring sleep in adolescent boys and girls.
Article in Chronobiology international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed, 45 citations in OpenAlex.
- Effect of a nutraceutical combination on sleep quality among people with impaired sleep: a randomised, placebo-controlled trial.Scientific reports · 2024Trial
- Sleep Health Dimensions From Wearables and Transdiagnostic Mental Health in Young Adolescents.JAMA pediatrics · 2026Article
- Sleep Duration and Physical Activity as Predictors of Executive Function in Adolescents: A Longitudinal Study.Brain sciences · 2026Article
- Article
- ReSTech project on Xiaomi wearable devices for monitoring and detecting obstructive sleep apnoea: observational study protocol.BMJ open · 2025Article
- Evaluating Fitbits for Assessment of Physical Activity and Sleep in Pediatric Pain: Feasibility and Acceptability Pilot Study.JMIR formative research · 2025Article
- Enhancing artificial intelligence-driven sleep apnea diagnosis: The critical importance of input signal proficiency with a focus on mandibular jaw movements.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Quantification of Differences in Sleep Measurement by a Wrist-Worn Consumer Wearable Compared to Research-Grade Accelerometry and Sleep Diaries of Female Adults in Free-Living Conditions.Nature and science of sleep · 2025Article
- Sleep moderates how prenatal and childhood pollutant exposure impacts white matter microstructural integrity in adolescence.Npj biological timing and sleep · 2025Article
- Article
- Associations of seasonally available global positioning systems-derived walkability and objectively measured sleep in the Nurses' Health Study 3 Mobile Health Substudy.Environmental epidemiology (Philadelphia, Pa.) · 2024Article
- Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Evaluating reliability in wearable devices for sleep staging.NPJ digital medicine · 2024Review
- Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.JMIR mHealth and uHealth · 2023Article
- Resident Sleep During Traditional Home Call Compared to Night Float.Journal of academic ophthalmology (2017) · 2023Article
- Wearable and mobile technology to characterize daily patterns of sleep, stress, presleep worry, and mood in adolescent insomnia.Sleep health · 2023Article
- Performance of Four Commercial Wearable Sleep-Tracking Devices Tested Under Unrestricted Conditions at Home in Healthy Young Adults.Nature and science of sleep · 2022Article
- Article
- An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.Frontiers in neuroscience · 2022Article
- Integration of Sensor-Based and Self-Reported Metrics in a Sleep Diary: A Pilot Exploration.Behavioral sleep medicineArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 3 countries.
Funding
Abstract
We evaluated the performance of Fitbit Charge 3™ (FC3), a multi-sensor commercial sleep-tracker, for measuring sleep in adolescents against gold-standard laboratory polysomnography (PSG). Single-night PSG and FC3 sleep outcomes were compared in thirty-nine adolescents (22 girls; 16-19 years), 12 of whom presented with clinical/subclinical DSM-5 insomnia symptoms (7 girls). Discrepancy analysis, Bland-Altman plots, and epoch-by-epoch analyses were used to evaluate FC3 performance. The influence of several factors potentially affecting FC3 performance (e.g., sex, age, body mass index, firmware version, and magnitude of heart rate changes between consecutive PSG epochs) was also tested. In the sample of healthy adolescents, FC3 systematically underestimated PSG total sleep time by about 11 min and sleep efficiency by 2.5%, and overestimated wake after sleep onset by 9 min. Proportional biases were detected for "light" and "deep" sleep duration, resulting in significant underestimation of these parameters for those participants having longer PSG N1+ N2 and N3 durations, respectively. No significant systematic bias was detected for sleep efficiency and sleep onset latency. Epoch-by-epoch analysis showed sleep-stage sensitivity (average proportion of PSG epochs correctly classified by the device for a given sleep stage) of 68% for wake, 78% for "light" sleep, 59% for "deep" sleep, and 69% for rapid eye movement (REM) sleep in healthy sleepers. Similar results were found in the sample of adolescents with insomnia symptoms. Body mass index was positively associated with FC3-PSG discrepancies in wake after sleep onset (R
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.