Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device

Kawasaki, Yu; Kasai, Takatoshi; Sakurama, Yuko; Sekiguchi, Akiko; Kitamura, Eri; Midorikawa, Itsuki; Shiroshita, Nanako; Kawana, Fusae; Ogasawara, Etsuko; Kitade, Mari; Koikawa, Natsue; Matsuda, Takao

doi:10.2147/nss.s351274

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…Finally, the top feature associated with sleep was the average length of deep sleep. A comparison of Fitbit’s deep sleep stage with EEG signals found that it aligns with the N3 stage of the NREM sleep stage [ 54 ]. There is interest around the role of N3 sleep in dementia given its pivotal role in memory consolidation, but evidence about this association is still limited [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Xu,

Kim,

Chung

et al. 2024

JMIR Form Res

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Background Early signs of Alzheimer disease (AD) are difficult to detect, causing diagnoses to be significantly delayed to time points when brain damage has already occurred and current experimental treatments have little effect on slowing disease progression. Tracking cognitive decline at early stages is critical for patients to make lifestyle changes and consider new and experimental therapies. Frequently studied biomarkers are invasive and costly and are limited for predicting conversion from normal to mild cognitive impairment (MCI). Objective This study aimed to use data collected from fitness trackers to predict MCI status. Methods In this pilot study, fitness trackers were worn by 20 participants: 12 patients with MCI and 8 age-matched controls. We collected physical activity, heart rate, and sleep data from each participant for up to 1 month and further developed a machine learning model to predict MCI status. Results Our machine learning model was able to perfectly separate between MCI and controls (area under the curve=1.0). The top predictive features from the model included peak, cardio, and fat burn heart rate zones; resting heart rate; average deep sleep time; and total light activity time. Conclusions Our results suggest that a longitudinal digital biomarker differentiates between controls and patients with MCI in a very cost-effective and noninvasive way and hence may be very useful for identifying patients with very early AD who can benefit from clinical trials and new, disease-modifying therapies.

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Section: Discussionmentioning

confidence: 99%

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Xu,

Kim,

Chung

et al. 2024

JMIR Form Res

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“…Therefore, a rapid growth trend may occur. 33 Fitbit devices can measure not only sleep, but also heart rate and physical activity. The validity of various Fitbit device models was evaluated through meta-analysis and discovered as an important factor of validity events in heart rate function and physical activity measurement function.…”

Section: Discussionmentioning

confidence: 99%

Validation of Fitbit Inspire 2TM Against Polysomnography in Adults Considering Adaptation for Use

Lim¹,

Kim²,

Lee³

et al. 2023

NSS

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Purpose The commercialization of sleep activity tracking devices has made it possible to manage sleep quality at home. However, it is necessary to verify the reliability and accuracy of wearable devices through comparison with polysomnography (PSG), which is the standard for tracking sleep activity. This study aimed to monitor overall sleep activity using Fitbit Inspire 2™ (FBI2) and to evaluate its performance and effectiveness through PSG under the same conditions. Patients and Methods We compared the FBI2 and PSG data of nine participants (four male and five female participants; average age, 39 years) without severe sleeping problems. The participants wore FBI2 continuously for 14 days, considering the period of adaptation to the device. FBI2 and PSG sleep data were compared using paired t -tests, Bland–Altman plots, and epoch-by-epoch analysis for 18 samples by pooling data from two replicates. Results The average values for each sleep stage obtained from FBI2 and PSG showed significant differences in the total sleep time (TST), deep sleep, and rapid eye motion (REM). In the Bland–Altman analysis, TST ( P = 0.02), deep sleep ( P = 0.05), and REM ( P = 0.03) were significantly overstated in FBI2 compared to PSG. In addition, time in bed, sleep efficiency, and wake after sleep onset were overestimated, while light sleep was underestimated. However, these differences were not statistically significant. FBI2 showed a high sensitivity (93.9%) and low specificity (13.1%), with an accuracy of 76%. The sensitivity and specificity of each sleep stage was 54.3% and 62.3%, respectively, for light sleep, 84.8% and 50.1%, respectively, for deep sleep, and 86.4% and 59.1%, respectively for REM sleep. Conclusion The use of FBI2 as an objective tool for measuring sleep in daily life can be considered appropriate. However, further research is warranted on its application in participants with sleep-wake problems.

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“… 4 Contrary to actigraphy-only devices, multi-sensor devices allow for the predictionof sleep stages above and beyond simple binary sleep/wake distinctions. 7 …”

Section: The Goodmentioning

confidence: 99%

The good, the bad, and the ugly of consumer sleep technologies use among athletes: A call for action

Trabelsi¹,

BaHammam²,

Chtourou³

et al. 2023

Journal of Sport and Health Science

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Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device

Cited by 8 publications

References 20 publications

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers

Validation of Fitbit Inspire 2TM Against Polysomnography in Adults Considering Adaptation for Use

The good, the bad, and the ugly of consumer sleep technologies use among athletes: A call for action

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