Sensors Capabilities, Performance, and Use of Consumer Sleep Technology

Zambotti, Massimiliano de; Cellini, Nicola; Menghini, Luca; Sarlo, Michela; Baker, Fiona C.

doi:10.1016/j.jsmc.2019.11.003

Cited by 69 publications

(56 citation statements)

References 175 publications

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“…[29,42] However, an ongoing and unsolved issue in consumer wearable technology validation is that AASM visual sleep scoring rules are subjected to human interpretation, challenging the concept of PSG sleep as the true reference for comparison. [29] Given the frequently observed relationship between sleep disruption and consumer sleep-tracking technology performance (greater PSG-device biases with greater amount of PSG wake), [43] our study results can only be extended to healthy children. In addition, a methodological limitation needs to be considered.…”

Section: Plos Onementioning

confidence: 97%

“…This is possible due to the measure and use of cardiac function data and other features, in addition to motion, showing sleep stage differentiation. [43] This implementation is particularly relevant, allowing for the tracking of sleep composition across periods of maturation such as during childhood and adolescence.…”

Section: Plos Onementioning

confidence: 99%

“…However, despite the theoretical advantages in combining cardiac function data and motion for sleep/wake assessment (particularly in the classification of motionless wake), low specificity seems to still be a limitation of multi-sensor sleep trackers (see for review). [29,42,43] The level of accuracy that we will be able to reach by using peripheral physiology and motion and advancing signal processing, to estimate EEG-based sleep macrostructure, is still unknown. Currently, the performance of standard actigraphy and commercial wearable technology is not dissimilar when both devices are evaluated in the same study and compared against PSG.…”

Section: Plos Onementioning

confidence: 99%

“…Currently, the performance of standard actigraphy and commercial wearable technology is not dissimilar when both devices are evaluated in the same study and compared against PSG. [43] Considering factors like devices cost, implementation, data accessibility, multi-sensors capability, and multi-systems integration, consumer wearable technology may offer an appealing solution for measuring sleep on a large scale.…”

Section: Plos Onementioning

confidence: 99%

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Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children

et al. 2020

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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.

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Section: Plos Onementioning

confidence: 97%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children

et al. 2020

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“…These additional sensors may include ankle accelerometers to estimate leg movements during sleep [8], voltage sensors to measure the electrocardiogram, optic sensors to measure haemoglobin oxygen saturation, and the smartphone microphone to measure respiratory sounds such as snoring. The low-cost and open-source characteristics of our system may also allow integration with third-party mobile health applications such as the recently designed Appnea-Q mobile app [13] as well as with third-party wearable monitors [18].…”

Section: Discussionmentioning

confidence: 99%

An Internet of Medical Things System to Increase Continuous Positive Airway Pressure Usage in Patients with Sleep-Disordered Breathing

et al. 2021

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Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with increased daytime sleepiness and cardiovascular risk. Continuous positive airway pressure (CPAP), requiring a pressure-generating device connected via tubing to a mask during sleep, is an effective treatment. However, patients’ adherence to CPAP is often suboptimal. Behavioral interventions are effective in improving adherence to CPAP. We aimed to provide proof of principle for the operation of a low-cost, self-standing, internet-based system to measure and promote adherence to CPAP. The system is composed of triaxial acceleration sensors attached to the CPAP mask and to the wrist, able to record CPAP usage information, and a mobile app that collects such information and, thorough a chatbot, feeds back to the patient to improve adherence to treatment. The mask subsystem identifies time periods when the mask is put on based on relatively high values of the ratio between acceleration spectral power at frequencies $$< 0.35$$ < 0.35 Hz vs. 0.35–2 Hz over 1-min windows. Accuracy in identification may be increased taking account of the surges in the standard deviation of wrist accelerations over 1-min windows that accompany putting on and taking off the mask. The whole system can represent a unique tool capable of monitoring and improving patients’ adherence to CPAP treatment. Its main strength lies in its simplicity, low cost, and independence from the specific CPAP device and mask employed.

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Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children

et al. 2023

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ImportanceThe use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people’s understanding of and willingness to join and remain engaged in remote health studies.ObjectiveTo examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children.Design, Setting, and ParticipantsThis cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022.Main Outcomes and MeasuresThe 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined.ResultsThe mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (−59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P &lt; .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P &lt; .001). Children from low-income households (&lt;$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P &lt; .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P &lt; .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (β = −43.00 hours; 95% CI, −55.11 to −30.88 hours; P &lt; .001).Conclusions and RelevanceIn this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals’ health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.

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Sensors Capabilities, Performance, and Use of Consumer Sleep Technology

Cited by 69 publications

References 175 publications

Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children

Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children

An Internet of Medical Things System to Increase Continuous Positive Airway Pressure Usage in Patients with Sleep-Disordered Breathing

Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children

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