Step count accuracy of StepWatch and FitBit One™ among individuals with a unilateral transtibial amputation

Arch, Elisa S.; Sions, Jaclyn Megan; Horne, John Robert; Bodt, Barry A.

doi:10.1177/0309364618767138

Cited by 20 publications

(31 citation statements)

References 30 publications

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“…A total of 42 studies (84 comparisons) examined wearable device step count measurements compared with the reference standard criterion measure of accelerometry [ 33 , 45 , 48 , 49 , 56 , 59 , 60 , 64 , 74 - 76 , 89 , 96 , 101 , 106 - 112 , 120 , 131 - 136 , 149 , 154 - 156 , 159 , 167 , 168 , 172 - 175 ] in free-living settings ( Multimedia Appendix 6 ). Of these, 28 studies recruited healthy adults (mean age 33.7 years, SD 13.9 years), nine studies recruited adults living with limited mobility/chronic diseases (mean age 60.1 years, SD 11.2 years), four studies recruited healthy children (mean age 12.5 years, SD 2.6 years), and one study recruited children living with cardiac diseases (mean age 13 years, SD 2.2 years).…”

Section: Resultsmentioning

confidence: 99%

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Fuller

Colwell

Low

et al. 2020

JMIR Mhealth Uhealth

337

278

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Background Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures. Objective The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure. Methods We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability. Results We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand. Conclusions Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

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Section: Resultsmentioning

confidence: 99%

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Fuller

Colwell

Low

et al. 2020

JMIR Mhealth Uhealth

337

278

View full text Add to dashboard Cite

show abstract

“… This method of quantifying cadence variability can differentiate between K2 and K3 groups. Arch (2018) [ 31 ] Step count accuracy of StepWatch and FitBit One among individuals with a unilateral transtibial amputation 50 participants (All BK) Fitbit One, StepWatch (P. Ankle) Activity level – steps (7 days) Compare step count accuracy of Fitbit One and StepWatch monitors during tasks and free-living. Both monitors were accurate during forward-linear walking, but the StepWatch was more accurate during the Four Square Step Test and the Figure-of-8 Walk Test.…”

Section: Resultsmentioning

confidence: 99%

“…The 13 papers on developing or validating actimeters, algorithms or scores for activity classification for lower-limb prosthesis users mainly focused on development of sensors for monitoring activity [ 34 , 35 , 37 , 39 , 42 ]. However, they also included development of smart-phone software to monitor falls [ 40 ] or visualise gait [ 97 ] and papers on comparing sensors [ 31 , 98 ], validating sensors [ 38 ] and validating classification methods [ 30 ]. In the case of upper-limb studies, three papers related to the development of algorithms for the assessment of activity [ 32 , 33 , 41 ].…”

Section: Resultsmentioning

confidence: 99%

Technology for monitoring everyday prosthesis use: a systematic review

Chadwell

Diment

Micó-Amigo

et al. 2020

J NeuroEngineering Rehabil

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Background: Understanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper-and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring, with particular focus on low-resource settings. Methods: A systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis users during daily-living. Results: Sixty lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lowerlimb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm. Conclusions: Activity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user's requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.

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“…multidirectional stepping, rapid gait initiation and termination, and turning; ICC = 0.88-0.90). 25 Furthermore, the FitBit® One TM has been shown to accurately assess step counts in other mobility-impaired patient populations (e.g. older adults, 26 patients who have experienced a stroke 27 ).…”

Section: Methodsmentioning

confidence: 99%

Performance-based outcome measures are associated with cadence variability during community ambulation among individuals with a transtibial amputation

Beisheim

Arch

Horne³

et al. 2020

Prosthetics &Amp; Orthotics International

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Background: In the United States, Medicare Functional Classification Level (K-level) guidelines require demonstration of cadence variability to justify higher-level prosthetic componentry prescription; however, clinical assessment of cadence variability is subjective. Currently, no clinical outcome measures are associated with cadence variability during community ambulation. Objectives: Evaluate whether physical performance, i.e. 10-meter Walk Test (10mWT)-based walking speeds, L-Test, and Figure-of-8 Walk Test scores, is associated with community-based cadence variability among individuals with a transtibial amputation. Study design: Cross-sectional. Methods: Forty-nine participants, aged 18–85 years, with a unilateral transtibial amputation were included. Linear regression models were conducted to determine whether physical performance was associated with cadence variability (a unitless calculation from FitBit® OneTM minute-by-minute step counts), while controlling for sex, age, and time since amputation ( p ⩽ .013). Results: Beyond covariates, self-selected gait speed explained the greatest amount of variance in cadence variability (19.2%, p < .001). Other outcome measures explained smaller, but significant, amounts of the variance (11.1–17.1%, p = .001–.008). For each 0.1 m/s-increase in self-selected and fast gait speeds, or each 1-s decrease in L-Test and F8WT time, community-based cadence variability increased by 1.76, 1.07, 0.39, and 0.79, respectively ( p < .013). Conclusions: In clinical settings, faster self-selected gait speed best predicted increased cadence variability during community ambulation. Clinical relevance The 10-meter Walk Test may be prioritized during prosthetic evaluations to provide objective self-selected walking speed data, which informs the assessment of cadence variability potential outside of clinical settings.

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Step count accuracy of StepWatch and FitBit One™ among individuals with a unilateral transtibial amputation

Cited by 20 publications

References 30 publications

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Technology for monitoring everyday prosthesis use: a systematic review

Performance-based outcome measures are associated with cadence variability during community ambulation among individuals with a transtibial amputation

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