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SOFTWARE (NON-INTERNET/MOBILE) | Healthcare Software
firstbeat.com

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Founded Year

2002

Stage

Acquired | Acquired

About Firstbeat

Firstbeat provides body analytics for sports and wellbeing. It transforms heartbeat data into personalized insights on stress, sleep, and exercise. It is available on many consumer devices.On June 30th, 2020 Firstbeat was acquired by Garmin, terms of the transaction were not disclosed.

Firstbeat Headquarter Location

Yiopistonkatu 28A 2nd Floor

Jyvaskyla, FI40100,

Finland

+358 20 7631 660

Latest Firstbeat News

Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study

Feb 3, 2021

JMIR mHealth and uHealth October 01, 2020 . Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study Heart Rate Variability and Firstbeat Method for Detecting Sleep Stages in Healthy Young Adults: Feasibility Study Authors of this article: Corresponding Author: Abstract Background: Polysomnography (PSG) is considered the only reliable way to distinguish between different sleep stages. Wearable devices provide objective markers of sleep; however, these devices often rely only on accelerometer data, which do not enable reliable sleep stage detection. The alteration between sleep stages correlates with changes in physiological measures such as heart rate variability (HRV). Utilizing HRV measures may thus increase accuracy in wearable algorithms. Objective: We examined the validity of the Firstbeat sleep analysis method, which is based on HRV and accelerometer measurements. The Firstbeat method was compared against PSG in a sample of healthy adults. Our aim was to evaluate how well Firstbeat distinguishes sleep stages, and which stages are most accurately detected with this method. Methods: Twenty healthy adults (mean age 24.5 years, SD 3.5, range 20-37 years; 50% women) wore a Firstbeat Bodyguard 2 measurement device and a Geneactiv actigraph, along with taking ambulatory SomnoMedics PSG measurements for two consecutive nights, resulting in 40 nights of sleep comparisons. We compared the measures of sleep onset, wake, combined stage 1 and stage 2 (light sleep), stage 3 (slow wave sleep), and rapid eye movement (REM) sleep between Firstbeat and PSG. We calculated the sensitivity, specificity, and accuracy from the 30-second epoch-by-epoch data. Results: In detecting wake, Firstbeat yielded good specificity (0.77), and excellent sensitivity (0.95) and accuracy (0.93) against PSG. Light sleep was detected with 0.69 specificity, 0.67 sensitivity, and 0.69 accuracy. Slow wave sleep was detected with 0.91 specificity, 0.72 sensitivity, and 0.87 accuracy. REM sleep was detected with 0.92 specificity, 0.60 sensitivity, and 0.84 accuracy. There were two measures that differed significantly between Firstbeat and PSG: Firstbeat underestimated REM sleep (mean 18 minutes, P=.03) and overestimated wake time (mean 14 minutes, P<.001). Conclusions: This study supports utilizing HRV alongside an accelerometer as a means for distinguishing sleep from wake and for identifying sleep stages. The Firstbeat method was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation. JMIR Mhealth Uhealth 2021;9(2):e24704 As a further sensitivity check, we compared the means of specificity, sensitivity, and accuracy to detect possible differences based on sex, measurement night, or the intervention reported previously [ 21 ]. Compared to females, there was a better specificity in REM sleep in male participants (0.95 vs 0.89, P=.004), but there were no other differences between sexes (P>.06). There was no first- or second-night effect in the specificity, sensitivity, and accuracy (all P>.38), nor regarding the presence of the previously reported music or slow-breathing intervention (all P>.13). Discussion Principal Findings Wearable devices have gained a significant share of the health and well-being consumer market, and new wearable devices and algorithms emerge frequently. Although a great majority of this research aims to detect sleep quality and duration based on data derived from accelerometer sensors [ 23 ], other measures such as respiratory signals have also been utilized [ 24 ]. Several reviews have evaluated the accuracy of accelerometer-based sleep wearables [ 23 , 25 , 26 ], and a recent review summarized an overall evaluation of wearables utilizing other sensors [ 27 ]. They concluded that detecting sleep from wake is relatively successful in many devices, but when wearables aim to classify sleep stages as opposed to simply distinguish between sleep and wake, there is a challenge in distinguishing four choices (wake, light, deep, and REM sleep) [ 27 ], which makes the result more inaccurate. Commercial accelerometers typically yield accuracy between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52 in distinguishing sleep from wake [ 26 ]. However, when attempting to detect sleep stages, the results are less consistent. A recent review focusing on commercial accelerometers identifying sleep stages found great variation in accuracy depending on the study [ 26 ]. For instance, accuracy in detecting light sleep varied between 69% and 81%, accuracy in detecting SWS was between 36% and 89%, and that for REM sleep ranged between 62% and 89%. Such variation suggests that acceleration itself may not be sufficient in reliably identifying sleep stages. Previous studies have implied that HRV may be a useful marker for detecting sleep stages [ 10 , 11 ]. One study reported an accuracy of up to 89% in detecting SWS, but their method included respiratory signals alongside HRV [ 28 ]. When detecting sleep stages by utilizing both HRV and accelerometer data, one study managed to identify 75% of SWS correctly [ 29 ]. In that study, REM sleep was identified correctly in over 70% of epochs, whereas light sleep detection was the weakest with correct identification varying between 42% and 52%. Our findings are of similar accuracy, which further supports the notion of combining accelerometer and HRV-based measures for reproducible sleep staging. This study was performed to evaluate the ability of HRV- and acceleration-based Firstbeat sleep analysis methods to detect sleep and different sleep stages. In pairwise comparisons, the Firstbeat method detected light sleep and SWS with no statistically significant difference to the gold-standard PSG method. There were two measures that differed significantly between the Firstbeat method and PSG: Firstbeat underestimated REM sleep (mean 18 minutes) and overestimated wake (mean 14 minutes). Considering the number of minutes in the context of a typical night’s sleep, the differences are not alarmingly high in practice, especially when measuring sleep over repeated nights. Sleep onset detection was very accurate, which is in accordance with a review published earlier this year [ 30 ]. Sleep stages can only be detected using PSG, as the stages are, by definition, separated by different patterns in ECG, EOG, and ECM. REM sleep is particularly difficult to detect without measuring activity from EOG and EMG channels. Thus, relying on other physiological measures as a means for separating sleep stages is always based on secondhand information. Although HRV has both previously [ 10 , 11 ] and in this study reflected sleep stages relatively well, it cannot detect the immediate changes in EEG, EOG, and EMG. However, this study suggests that HRV-assisted sleep stage detection can serve as a good estimate of sleep architecture despite being less accurate in detecting specific sleep stages. When observing the comparisons in more analytical detail, we found that comparing the Firstbeat method against PSG yielded good specificity, and excellent sensitivity and accuracy in detecting wake. Regarding light sleep, the measures of specificity, sensitivity, and accuracy were less convincing. SWS detection had excellent specificity, adequate sensitivity, and good accuracy, while REM sleep was detected with similarly excellent specificity, adequate sensitivity, and good accuracy. These results suggest that the Firstbeat method is best at detecting sleep stages that have strong parasympathetic cardiac markers; however, light sleep is typically not significantly differentiated based its physiological fingerprint [ 12 , 14 ]. Strengths and Limitations Our study was fully balanced in sex distribution and we were able to evaluate the Firstbeat method across two different nights in two different settings in the participants’ own homes. Thus, the ecological validity in this study can be considered excellent. As a limitation, even though our sample had some variation in PSQI-measured sleep quality, this study did not include any participants with diagnosed sleep disorders. Our study included only healthy participants, and the results are likely to be different if any health issues, particularly cardiovascular, or any sleep disorders are present. This is a question to solve before utilizing the Firstbeat method in clinical contexts. Conclusion Combining HRV with accelerometer measurements can be considered a feasible method with sufficient validity to measure nocturnal sleep stage variation. We found that the specificity, sensitivity, and accuracy were the weakest in detecting light sleep. Nevertheless, considering its availability, affordability, and ease of administration, Firstbeat may be a useful tool in various contexts, particularly in consumer-based sleep-measuring environments to produce an overview of sleep structures. Acknowledgments Conflicts of Interest References Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn BV. The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications. Illinois: American Academy of Sleep Medicine; 2012. Carskadon M, Dement WC, Kryger M, Roth T, Roehrs T. Normal human sleep: an overview. In: Dement WC, Kryger MH, Roth T, editors. Principles and practice of sleep medicine. Philadelphia: Elsevier Saunders; 2005:4. Bliwise D, Coleman R, Bergmann B, Wincor MZ, Pivik RT, Rechtschaffen A. Facial muscle tonus during REM and NREM sleep. Psychophysiology 1974 Jul;11(4):497-508. [ CrossRef ] [ Medline ] Brunner DP, Dijk DJ, Borbély AA. A quantitative analysis of phasic and tonic submental EMG activity in human sleep. Physiol Behav 1990 Nov;48(5):741-748. [ CrossRef ] [ Medline ] Parmeggiani PL. Physiologic regulation in sleep. In: Dement WC, Kryger MH, Roth T, editors. Principles and Practice of Sleep Medicine. Philadelphia: Elsevier Saunders; 2005:185. Van de Borne P, Nguyen H, Biston P, Linkowski P, Degaute JP. Effects of wake and sleep stages on the 24-h autonomic control of blood pressure and heart rate in recumbent men. Am J Physiol 1994 Feb;266(2 Pt 2):H548-H554. [ CrossRef ] [ Medline ] Somers VK, Dyken ME, Mark AL, Abboud FM. Sympathetic-nerve activity during sleep in normal subjects. N Engl J Med 1993 Feb 04;328(5):303-307. [ CrossRef ] [ Medline ] Szymusiak R. Body temperature and sleep. In: Romanovsky AA, editor. Handbook of Clinical Neurology. Philadelphia: Elsevier; 2018:341-351. Bach V, Telliez F, Libert JP. The interaction between sleep and thermoregulation in adults and neonates. Sleep Med Rev 2002 Dec;6(6):481-492. [ CrossRef ] [ Medline ] Herzig D, Eser P, Omlin X, Riener R, Wilhelm M, Achermann P. Reproducibility of Heart Rate Variability Is Parameter and Sleep Stage Dependent. Front Physiol 2017;8:1100. [ CrossRef ] [ Medline ] Chouchou F, Desseilles M. Heart rate variability: a tool to explore the sleeping brain? Front Neurosci 2014;8:402. [ CrossRef ] [ Medline ] Berlad I, Shlitner A, Ben-Haim S, Lavie P. Power spectrum analysis and heart rate variability in Stage 4 and REM sleep: evidence for state-specific changes in autonomic dominance. J Sleep Res 1993 Jun;2(2):88-90 [ FREE Full text ] [ CrossRef ] [ Medline ] Boudreau P, Yeh W, Dumont GA, Boivin DB. Circadian variation of heart rate variability across sleep stages. Sleep 2013 Dec 01;36(12):1919-1928 [ FREE Full text ] [ CrossRef ] [ Medline ] Penzel T, Kantelhardt JW, Lo C, Voigt K, Vogelmeier C. Dynamics of heart rate and sleep stages in normals and patients with sleep apnea. Neuropsychopharmacology 2003 Jul;28(Suppl 1):S48-S53. [ CrossRef ] [ Medline ] Acquavella J, Mehra R, Bron M, Suomi JM, Hess GP. Prevalence of narcolepsy and other sleep disorders and frequency of diagnostic tests from 2013-2016 in insured patients actively seeking care. J Clin Sleep Med 2020 Aug 15;16(8):1255-1263. [ CrossRef ] [ Medline ] Boe AJ, McGee Koch LL, O'Brien MK, Shawen N, Rogers JA, Lieber RL, et al. Automating sleep stage classification using wireless, wearable sensors. NPJ Digit Med 2019 Dec 20;2(1):131. [ CrossRef ] [ Medline ] Crespo-Ruiz B, Rivas-Galan S, Fernandez-Vega C, Crespo-Ruiz C, Maicas-Perez L. Executive Stress Management: Physiological Load of Stress and Recovery in Executives on Workdays. Int J Environ Res Public Health 2018 Dec 13;15(12):2847 [ FREE Full text ] [ CrossRef ] [ Medline ] Mutikainen S, Helander E, Pietilä J, Korhonen I, Kujala UM. Objectively measured physical activity in Finnish employees: a cross-sectional study. BMJ Open 2014 Dec 10;4(12):e005927 [ FREE Full text ] [ CrossRef ] [ Medline ] Smolander J, Ajoviita M, Juuti T, Nummela A, Rusko H. Estimating oxygen consumption from heart rate and heart rate variability without individual calibration. Clin Physiol Funct Imaging 2011 Jul;31(4):266-271. [ CrossRef ] [ Medline ] Lown M, Yue AM, Shah BN, Corbett SJ, Lewith G, Stuart B, et al. Screening for Atrial Fibrillation Using Economical and Accurate Technology (From the SAFETY Study). Am J Cardiol 2018 Oct 15;122(8):1339-1344. [ CrossRef ] [ Medline ] Kuula L, Halonen R, Kajanto K, Lipsanen J, Makkonen T, Peltonen M, et al. The Effects of Presleep Slow Breathing and Music Listening on Polysomnographic Sleep Measures - a pilot trial. Sci Rep 2020 May 04;10(1):7427. [ CrossRef ] [ Medline ] Kahawage P, Jumabhoy R, Hamill K, de Zambotti M, Drummond SPA. Validity, potential clinical utility, and comparison of consumer and research-grade activity trackers in Insomnia Disorder I: In-lab validation against polysomnography. J Sleep Res 2020 Feb;29(1):e12931. [ CrossRef ] [ Medline ] Baron KG, Duffecy J, Berendsen MA, Cheung Mason I, Lattie EG, Manalo NC. Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Med Rev 2018 Aug;40:151-159 [ FREE Full text ] [ CrossRef ] [ Medline ] Dunn J, Runge R, Snyder M. Wearables and the medical revolution. Per Med 2018 Sep;15(5):429-448 [ FREE Full text ] [ CrossRef ] [ Medline ] Danzig R, Wang M, Shah A, Trotti LM. The wrist is not the brain: Estimation of sleep by clinical and consumer wearable actigraphy devices is impacted by multiple patient- and device-specific factors. J Sleep Res 2020 Feb;29(1):e12926. [ CrossRef ] [ Medline ] Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Accuracy of wristband Fitbit models in assessing sleep: systematic review and meta-analysis. J Med Internet Res 2019 Nov 28;21(11):e16273 [ FREE Full text ] [ CrossRef ] [ Medline ] de Zambotti M, Cellini N, Goldstone A, Colrain IM, Baker FC. Wearable sleep technology in clinical and research settings. Med Sci Sports Exerc 2019 Jul;51(7):1538-1557 [ FREE Full text ] [ CrossRef ] [ Medline ] Long X, Fonseca P, Aarts RM, Haakma R, Rolink J, Leonhardt S. Detection of Nocturnal Slow Wave Sleep Based on Cardiorespiratory Activity in Healthy Adults. IEEE J Biomed Health Inform 2017 Jan;21(1):123-133. [ CrossRef ] [ Medline ] Muzet A, Werner S, Fuchs G, Roth T, Saoud JB, Viola AU, et al. Assessing sleep architecture and continuity measures through the analysis of heart rate and wrist movement recordings in healthy subjects: comparison with results based on polysomnography. Sleep Med 2016 May;21:47-56 [ FREE Full text ] [ CrossRef ] [ Medline ] Scott H, Lack L, Lovato N. A systematic review of the accuracy of sleep wearable devices for estimating sleep onset. Sleep Med Rev 2020 Feb;49:101227. [ CrossRef ] [ Medline ] ‎

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Expert Collections containing Firstbeat

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

Firstbeat is included in 3 Expert Collections, including Fitness Tech.

F

Fitness Tech

1,260 items

This Collection includes startups developing software and technology to augment approaches to developing or maintaining physical fitness, including workout apps, wearables, and connected fitness equipment.

C

Conference Exhibitors

6,062 items

Companies that will be exhibiting at CES 2018

S

Sleep Health & Wellness

796 items

These companies aim to assess or improve the quantity/quality of sleep, or use sleep data in the monitoring or diagnosis of other health conditions.

Firstbeat Patents

Firstbeat has filed 33 patents.

The 3 most popular patent topics include:

  • Physical exercise
  • Exercise physiology
  • Aerobic exercise
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Physical exercise, Aerobic exercise, Exercise physiology, Software testing, Software engineering

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