Camilla S Powierza1, Michael D Clark2, Jaime M Hughes3, Kevin A Carneiro4, Jason P Mihalik5. 1. School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC(∗). 2. School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC; the Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC; Human Movement Science Curriculum, Department of Allied Health Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, NC(†). 3. Program on Integrative Medicine and Department of Physical Medicine & Rehabilitation, The University of North Carolina at Chapel Hill, Chapel Hill, NC(‡). 4. Department of Physical Medicine & Rehabilitation, The University of North Carolina at Chapel Hill, Chapel Hill, NC(§). 5. Matthew Gfeller Sport-Related Traumatic Brain Injury Research Center, Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC; Human Movement Science Curriculum, Department of Allied Health Sciences, The University of North Carolina at Chapel Hill, 2201 Stallings-Evans Sports Medicine Center, Campus Box 8700, Chapel Hill, NC 27599(¶). Electronic address: jmihalik@email.unc.edu.
Abstract
BACKGROUND: Aerobic exercise at a subsymptom heart rate has been recommended as therapy for postconcussion syndrome. Assessing adherence with an accurate heart rate-monitoring instrument is difficult, limiting the proliferation of large-scale randomized controlled trials. OBJECTIVE: To evaluate the validity of the Fitbit Charge HR compared with electrocardiogram (EKG) to monitor heart rate during a treadmill-based exercise protocol. DESIGN: A methods comparison study. SETTING: Sports medicine research center within a tertiary care institution. PARTICIPANTS: A convenience sample of 22 healthy participants (12 female) aged 18-26 years (mean age: 22 ± 2 years). METHODS: Fitbit Charge HR heart rate measurements were compared with EKG data concurrently collected while participants completed the Buffalo Concussion Treadmill Test. MAIN OUTCOME MEASURES: Agreement between Fitbit Charge HR and EKG was assessed by intraclass correlation coefficients (ICC3,1), Bland-Altman limits of agreement, and percent error. RESULTS: We observed a strong single-measure absolute agreement between Fitbit Charge HR and EKG (intraclass correlation coefficient = 0.83; 95% confidence interval 0.67-0.90). Fitbit Charge HR underestimated heart rate compared with EKG (mean difference = -6.04 bpm; standard deviation = 10.40 bpm; Bland-Altman 95% limits of agreement = -26.42 to 14.35 bpm). A total of 69.9% of Fitbit heart rate measurements were within 10% error compared with EKG, and 91.5% of all heart rate measurements were within 20% error. CONCLUSIONS: Although the mean bias in measuring heart rate was relatively small, the limits of agreement between the Fitbit Charge HR and EKG were broad. Thus, the Fitbit Charge HR would not be a suitable option for monitoring heart rate within a narrow range. For the purposes of postconcussion exercise therapy, the relatively inexpensive cost, easy implementation, and low maintenance make Fitbit Charge HR a viable option for assessing adherence to an exercise program when expensive clinical equipment is unavailable. LEVEL OF EVIDENCE: II.
BACKGROUND: Aerobic exercise at a subsymptom heart rate has been recommended as therapy for postconcussion syndrome. Assessing adherence with an accurate heart rate-monitoring instrument is difficult, limiting the proliferation of large-scale randomized controlled trials. OBJECTIVE: To evaluate the validity of the Fitbit Charge HR compared with electrocardiogram (EKG) to monitor heart rate during a treadmill-based exercise protocol. DESIGN: A methods comparison study. SETTING: Sports medicine research center within a tertiary care institution. PARTICIPANTS: A convenience sample of 22 healthy participants (12 female) aged 18-26 years (mean age: 22 ± 2 years). METHODS:Fitbit Charge HR heart rate measurements were compared with EKG data concurrently collected while participants completed the Buffalo Concussion Treadmill Test. MAIN OUTCOME MEASURES: Agreement between Fitbit Charge HR and EKG was assessed by intraclass correlation coefficients (ICC3,1), Bland-Altman limits of agreement, and percent error. RESULTS: We observed a strong single-measure absolute agreement between Fitbit Charge HR and EKG (intraclass correlation coefficient = 0.83; 95% confidence interval 0.67-0.90). Fitbit Charge HR underestimated heart rate compared with EKG (mean difference = -6.04 bpm; standard deviation = 10.40 bpm; Bland-Altman 95% limits of agreement = -26.42 to 14.35 bpm). A total of 69.9% of Fitbit heart rate measurements were within 10% error compared with EKG, and 91.5% of all heart rate measurements were within 20% error. CONCLUSIONS: Although the mean bias in measuring heart rate was relatively small, the limits of agreement between the Fitbit Charge HR and EKG were broad. Thus, the Fitbit Charge HR would not be a suitable option for monitoring heart rate within a narrow range. For the purposes of postconcussion exercise therapy, the relatively inexpensive cost, easy implementation, and low maintenance make Fitbit Charge HR a viable option for assessing adherence to an exercise program when expensive clinical equipment is unavailable. LEVEL OF EVIDENCE: II.
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