A validation study of Fitbit Charge 2™ compared with polysomnography in adults

被引:270
作者
de Zambotti, Massimiliano [1 ]
Goldstone, Aimee [1 ]
Claudatos, Stephanie [1 ]
Colrain, Ian M. [1 ]
Baker, Fiona C. [1 ]
机构
[1] SRI Int, Ctr Hlth Sci, Menlo Pk, CA 94025 USA
关键词
Wearables; validation; multisensory; actigraphy; HEART-RATE-VARIABILITY; SLEEP; ACTIGRAPHY; ADOLESCENTS; WRIST; CLASSIFICATION; POPULATION; ACTIVATION; MOVEMENTS; DISORDER;
D O I
10.1080/07420528.2017.1413578
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2 (TM)), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2 (TM) data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19-61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS,> 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2 (TM) sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2 (TM) showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep ("light sleep"), 0.49 accuracy in detecting N3 sleep ("deep sleep"), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2 (TM) significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2 (TM) outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland-Altman agreement limits for all sleep measures. Fitbit Charge 2 (TM) correctly identified 82% of PSG-defined non-REM-REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2 (TM) shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2 (TM) accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).
引用
收藏
页码:465 / 476
页数:12
相关论文
共 35 条
  • [1] Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification
    Aktaruzzaman, Md
    Rivolta, Massimo Walter
    Karmacharya, Ruby
    Scarabottolo, Nello
    Pugnetti, Luigi
    Garegnani, Massimo
    Bovi, Gabriele
    Scalera, Giovanni
    Ferrarin, Maurizio
    Sassi, Roberto
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 89 : 212 - 221
  • [2] [Anonymous], 2014, INT CLASSIFICATION S
  • [3] FLUCTUATIONS IN AUTONOMIC NERVOUS ACTIVITY DURING SLEEP DISPLAYED BY POWER SPECTRUM ANALYSIS OF HEART-RATE-VARIABILITY
    BAHARAV, A
    KOTAGAL, S
    GIBBONS, V
    RUBIN, BK
    PRATT, G
    KARIN, J
    AKSELROD, S
    [J]. NEUROLOGY, 1995, 45 (06) : 1183 - 1187
  • [4] Fitbit Flex: an unreliable device for longitudinal sleep measures in a non-clinical population
    Baroni, Argelinda
    Bruzzese, Jean-Marie
    Di Bartolo, Christina A.
    Shatkin, Jess P.
    [J]. SLEEP AND BREATHING, 2016, 20 (02) : 853 - 854
  • [5] Validation of the Insomnia Severity Index as an outcome measure for insomnia research
    Bastien, Celyne H.
    Vallieres, Annie
    Morin, Charles M.
    [J]. SLEEP MEDICINE, 2001, 2 (04) : 297 - 307
  • [6] ESTIMATION OF SLEEP STAGES USING CARDIAC AND ACCELEROMETER DATA FROM A WRIST-WORN DEVICE
    Beattie, Z.
    Pantelopoulos, A.
    Ghoreyshi, A.
    Oyang, Y.
    Statan, A.
    Heneghan, C.
    [J]. SLEEP, 2017, 40 : A26 - A26
  • [7] STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT
    BLAND, JM
    ALTMAN, DG
    [J]. LANCET, 1986, 1 (8476) : 307 - 310
  • [8] THE PITTSBURGH SLEEP QUALITY INDEX - A NEW INSTRUMENT FOR PSYCHIATRIC PRACTICE AND RESEARCH
    BUYSSE, DJ
    REYNOLDS, CF
    MONK, TH
    BERMAN, SR
    KUPFER, DJ
    [J]. PSYCHIATRY RESEARCH, 1989, 28 (02) : 193 - 213
  • [9] Utility of the Fitbit Flex to evaluate sleep in major depressive disorder: A comparison against polysomnography and wrist-worn actigraphy
    Cook, Jesse D.
    Prairie, Michael L.
    Plante, David T.
    [J]. JOURNAL OF AFFECTIVE DISORDERS, 2017, 217 : 299 - 305
  • [10] The Sleep of the Ring: Comparison of the OURA Sleep Tracker Against Polysomnography
    de Zambotti, Massimiliano
    Rosas, Leonardo
    Colrain, Ian M.
    Baker, Fiona C.
    [J]. BEHAVIORAL SLEEP MEDICINE, 2019, 17 (02) : 124 - 136